Gemović, Branislava S.

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Authority KeyName Variants
orcid::0000-0002-8987-020X
  • Gemović, Branislava S. (14)
Projects
Application of the EIIP/ISM bioinformatics platform in discovery of novel therapeutic targets and potential therapeutic molecules TRANSPLANT - Trans-national Infrastructure for Plant Genomic Science
MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data Synthesis, Quantitative Structure and Activity Relationship, Physico-Chemical Characterisation and Analysis of Pharmacologically Active Substances
Pharmacodynamic and pharmacogenomic research of new drugs in the treatment of solid tumors National Science Foundation [DBI-1458477, DBI-1458443, DBI-1458390, DBI-1458359, IIS-1319551, DBI-1262189, DBI-1149224], National Institutes of Health [R01GM093123, R01GM097528, R01GM076990, R01GM071749, R01LM009722, UL1TR000423], National Natural Science Foundation of China [3147124, 91231116], National Basic Research Program of China [2012CB316505], NSERC [RGPIN 371348-11], Microsoft Research/FAPESP grant [2009/53161-6], FAPESP [2010/50491-1], Biotechnology and Biological Sciences Research Council [BB/L020505/1, BB/F020481/1, BB/K004131/1, BB/F00964X/1, BB/L018241/1], Spanish Ministry of Economics and Competitiveness [BIO2012-40205], KU Leuven [CoE PFV/10/016 SymBioSys], Newton International Fellowship Scheme of the Royal Society grant [NF080750], Gordon and Betty Moore Foundations Data-Driven Discovery Initiative grant [GBMF4552], Academy of Finland, British Heart Foundation [RG/13/5/30112], Parkinsons UK [G-1307], Alexander von Humboldt Foundation through the German Federal Ministry for Education and Research, Ernst Ludwig Ehrlich Studienwerk, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research grant [DE-AC02-05CH11231], Australian Research Council grant [DP150101550], NIH [T15 LM00945102], FP7 REGPOT grant InnoMol, University of Padova [CPDA138081/13, GRIC13AAI9], Swiss National Science Foundation [150654], UK BBSRC grant [BB/M015009/1], ICREA

Author's Bibliography

Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies

Gemović, Branislava S.; Perović, Vladimir R.; Davidović, Radoslav S.; Drljača, Tamara; Veljković, Nevena V.

(2021)

TY  - JOUR
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Davidović, Radoslav S.
AU  - Drljača, Tamara
AU  - Veljković, Nevena V.
PY  - 2021
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8894
AB  - For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm–Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.
T2  - PLOS One
T1  - Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies
VL  - 16
IS  - 1
SP  - e0244948
DO  - 10.1371/journal.pone.0244948
ER  - 
@article{
author = "Gemović, Branislava S. and Perović, Vladimir R. and Davidović, Radoslav S. and Drljača, Tamara and Veljković, Nevena V.",
year = "2021",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/8894",
abstract = "For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm–Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.",
journal = "PLOS One",
title = "Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies",
volume = "16",
number = "1",
pages = "e0244948",
doi = "10.1371/journal.pone.0244948"
}
Gemović, B. S., Perović, V. R., Davidović, R. S., Drljača, T.,& Veljković, N. V. (2021). Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies.
PLOS One, 16(1), e0244948.
https://doi.org/10.1371/journal.pone.0244948
Gemović BS, Perović VR, Davidović RS, Drljača T, Veljković NV. Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies. PLOS One. 2021;16(1):e0244948
Gemović Branislava S., Perović Vladimir R., Davidović Radoslav S., Drljača Tamara, Veljković Nevena V., "Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies" PLOS One, 16, no. 1 (2021):e0244948,
https://doi.org/10.1371/journal.pone.0244948 .
1

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Zhou, Naihui; Jiang, Yuxiang; Bergquist, Timothy R; Lee, Alexandra J; Kacsoh, Balint Z; Crocker, Alex W; Lewis, Kimberley A; Georghiou, George; Nguyen, Huy N; Hamid, Md Nafiz; Davis, Larry; Dogan, Tunca; Atalay, Volkan; Rifaioglu, Ahmet S; Dalkıran, Alperen; Cetin Atalay, Rengul; Zhang, Chengxin; Hurto, Rebecca L; Freddolino, Peter L; Zhang, Yang; Bhat, Prajwal; Supek, Fran; Fernández, José M; Gemović, Branislava S.; Perović, Vladimir R.; Davidović, Radoslav S.; Šumonja, Neven; Veljković, Nevena V.; Asgari, Ehsaneddin; Mofrad, Mohammad R.K.; Profiti, Giuseppe; Savojardo, Castrense; Martelli, Pier Luigi; Casadio, Rita; Boecker, Florian; Schoof, Heiko; Kahanda, Indika; Thurlby, Natalie; McHardy, Alice C; Renaux, Alexandre; Saidi, Rabie; Gough, Julian; Freitas, Alex A; Antczak, Magdalena; Fabris, Fabio; Wass, Mark N; Hou, Jie; Cheng, Jianlin; Wang, Zheng; Romero, Alfonso E; Paccanaro, Alberto; Yang, Haixuan; Goldberg, Tatyana; Zhao, Chenguang; Holm, Liisa; Törönen, Petri; Medlar, Alan J; Zosa, Elaine; Borukhov, Itamar; Novikov, Ilya; Wilkins, Angela; Lichtarge, Olivier; Chi, Po-Han; Tseng, Wei-Cheng; Linial, Michal; Rose, Peter W; Dessimoz, Christophe; Vidulin, Vedrana; Dzeroski, Saso; Sillitoe, Ian; Das, Sayoni; Lees, Jonathan Gill; Jones, David T; Wan, Cen; Cozzetto, Domenico; Fa, Rui; Torres, Mateo; Warwick Vesztrocy, Alex; Rodriguez, Jose Manuel; Tress, Michael L; Frasca, Marco; Notaro, Marco; Grossi, Giuliano; Petrini, Alessandro; Re, Matteo; Valentini, Giorgio; Mesiti, Marco; Roche, Daniel B; Reeb, Jonas; Ritchie, David W; Aridhi, Sabeur; Alborzi, Seyed Ziaeddin; Devignes, Marie-Dominique; Koo, Da Chen Emily; Bonneau, Richard; Gligorijević, Vladimir; Barot, Meet; Fang, Hai; Toppo, Stefano; Lavezzo, Enrico; Falda, Marco; Berselli, Michele; Tosatto, Silvio C.E.; Carraro, Marco; Piovesan, Damiano; Ur Rehman, Hafeez; Mao, Qizhong; Zhang, Shanshan; Vucetic, Slobodan; Black, Gage S; Jo, Dane; Suh, Erica; Dayton, Jonathan B; Larsen, Dallas J; Omdahl, Ashton R; McGuffin, Liam J; Brackenridge, Danielle A; Babbitt, Patricia C; Yunes, Jeffrey M; Fontana, Paolo; Zhang, Feng; Zhu, Shanfeng; You, Ronghui; Zhang, Zihan; Dai, Suyang; Yao, Shuwei; Tian, Weidong; Cao, Renzhi; Chandler, Caleb; Amezola, Miguel; Johnson, Devon; Chang, Jia-Ming; Liao, Wen-Hung; Liu, Yi-Wei; Pascarelli, Stefano; Frank, Yotam; Hoehndorf, Robert; Kulmanov, Maxat; Boudellioua, Imane; Politano, Gianfranco; Di Carlo, Stefano; Benso, Alfredo; Hakala, Kai; Ginter, Filip; Mehryary, Farrokh; Kaewphan, Suwisa; Björne, Jari; Moen, Hans; Tolvanen, Martti E.E.; Salakoski, Tapio; Kihara, Daisuke; Jain, Aashish; Šmuc, Tomislav; Altenhoff, Adrian; Ben-Hur, Asa; Rost, Burkhard; Brenner, Steven E; Orengo, Christine A; Jeffery, Constance J; Bosco, Giovanni; Hogan, Deborah A; Martin, Maria J; O’Donovan, Claire; Mooney, Sean D; Greene, Casey S; Radivojac, Predrag; Friedberg, Iddo

(2019)

TY  - JOUR
AU  - Zhou, Naihui
AU  - Jiang, Yuxiang
AU  - Bergquist, Timothy R
AU  - Lee, Alexandra J
AU  - Kacsoh, Balint Z
AU  - Crocker, Alex W
AU  - Lewis, Kimberley A
AU  - Georghiou, George
AU  - Nguyen, Huy N
AU  - Hamid, Md Nafiz
AU  - Davis, Larry
AU  - Dogan, Tunca
AU  - Atalay, Volkan
AU  - Rifaioglu, Ahmet S
AU  - Dalkıran, Alperen
AU  - Cetin Atalay, Rengul
AU  - Zhang, Chengxin
AU  - Hurto, Rebecca L
AU  - Freddolino, Peter L
AU  - Zhang, Yang
AU  - Bhat, Prajwal
AU  - Supek, Fran
AU  - Fernández, José M
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Davidović, Radoslav S.
AU  - Šumonja, Neven
AU  - Veljković, Nevena V.
AU  - Asgari, Ehsaneddin
AU  - Mofrad, Mohammad R.K.
AU  - Profiti, Giuseppe
AU  - Savojardo, Castrense
AU  - Martelli, Pier Luigi
AU  - Casadio, Rita
AU  - Boecker, Florian
AU  - Schoof, Heiko
AU  - Kahanda, Indika
AU  - Thurlby, Natalie
AU  - McHardy, Alice C
AU  - Renaux, Alexandre
AU  - Saidi, Rabie
AU  - Gough, Julian
AU  - Freitas, Alex A
AU  - Antczak, Magdalena
AU  - Fabris, Fabio
AU  - Wass, Mark N
AU  - Hou, Jie
AU  - Cheng, Jianlin
AU  - Wang, Zheng
AU  - Romero, Alfonso E
AU  - Paccanaro, Alberto
AU  - Yang, Haixuan
AU  - Goldberg, Tatyana
AU  - Zhao, Chenguang
AU  - Holm, Liisa
AU  - Törönen, Petri
AU  - Medlar, Alan J
AU  - Zosa, Elaine
AU  - Borukhov, Itamar
AU  - Novikov, Ilya
AU  - Wilkins, Angela
AU  - Lichtarge, Olivier
AU  - Chi, Po-Han
AU  - Tseng, Wei-Cheng
AU  - Linial, Michal
AU  - Rose, Peter W
AU  - Dessimoz, Christophe
AU  - Vidulin, Vedrana
AU  - Dzeroski, Saso
AU  - Sillitoe, Ian
AU  - Das, Sayoni
AU  - Lees, Jonathan Gill
AU  - Jones, David T
AU  - Wan, Cen
AU  - Cozzetto, Domenico
AU  - Fa, Rui
AU  - Torres, Mateo
AU  - Warwick Vesztrocy, Alex
AU  - Rodriguez, Jose Manuel
AU  - Tress, Michael L
AU  - Frasca, Marco
AU  - Notaro, Marco
AU  - Grossi, Giuliano
AU  - Petrini, Alessandro
AU  - Re, Matteo
AU  - Valentini, Giorgio
AU  - Mesiti, Marco
AU  - Roche, Daniel B
AU  - Reeb, Jonas
AU  - Ritchie, David W
AU  - Aridhi, Sabeur
AU  - Alborzi, Seyed Ziaeddin
AU  - Devignes, Marie-Dominique
AU  - Koo, Da Chen Emily
AU  - Bonneau, Richard
AU  - Gligorijević, Vladimir
AU  - Barot, Meet
AU  - Fang, Hai
AU  - Toppo, Stefano
AU  - Lavezzo, Enrico
AU  - Falda, Marco
AU  - Berselli, Michele
AU  - Tosatto, Silvio C.E.
AU  - Carraro, Marco
AU  - Piovesan, Damiano
AU  - Ur Rehman, Hafeez
AU  - Mao, Qizhong
AU  - Zhang, Shanshan
AU  - Vucetic, Slobodan
AU  - Black, Gage S
AU  - Jo, Dane
AU  - Suh, Erica
AU  - Dayton, Jonathan B
AU  - Larsen, Dallas J
AU  - Omdahl, Ashton R
AU  - McGuffin, Liam J
AU  - Brackenridge, Danielle A
AU  - Babbitt, Patricia C
AU  - Yunes, Jeffrey M
AU  - Fontana, Paolo
AU  - Zhang, Feng
AU  - Zhu, Shanfeng
AU  - You, Ronghui
AU  - Zhang, Zihan
AU  - Dai, Suyang
AU  - Yao, Shuwei
AU  - Tian, Weidong
AU  - Cao, Renzhi
AU  - Chandler, Caleb
AU  - Amezola, Miguel
AU  - Johnson, Devon
AU  - Chang, Jia-Ming
AU  - Liao, Wen-Hung
AU  - Liu, Yi-Wei
AU  - Pascarelli, Stefano
AU  - Frank, Yotam
AU  - Hoehndorf, Robert
AU  - Kulmanov, Maxat
AU  - Boudellioua, Imane
AU  - Politano, Gianfranco
AU  - Di Carlo, Stefano
AU  - Benso, Alfredo
AU  - Hakala, Kai
AU  - Ginter, Filip
AU  - Mehryary, Farrokh
AU  - Kaewphan, Suwisa
AU  - Björne, Jari
AU  - Moen, Hans
AU  - Tolvanen, Martti E.E.
AU  - Salakoski, Tapio
AU  - Kihara, Daisuke
AU  - Jain, Aashish
AU  - Šmuc, Tomislav
AU  - Altenhoff, Adrian
AU  - Ben-Hur, Asa
AU  - Rost, Burkhard
AU  - Brenner, Steven E
AU  - Orengo, Christine A
AU  - Jeffery, Constance J
AU  - Bosco, Giovanni
AU  - Hogan, Deborah A
AU  - Martin, Maria J
AU  - O’Donovan, Claire
AU  - Mooney, Sean D
AU  - Greene, Casey S
AU  - Radivojac, Predrag
AU  - Friedberg, Iddo
PY  - 2019
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/8655
AB  - Background: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-Term memory. Conclusion: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. © 2019 The Author(s).
T2  - Genome Biology
T1  - The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
VL  - 20
IS  - 1
SP  - 244
DO  - 10.1186/s13059-019-1835-8
ER  - 
@article{
author = "Zhou, Naihui and Jiang, Yuxiang and Bergquist, Timothy R and Lee, Alexandra J and Kacsoh, Balint Z and Crocker, Alex W and Lewis, Kimberley A and Georghiou, George and Nguyen, Huy N and Hamid, Md Nafiz and Davis, Larry and Dogan, Tunca and Atalay, Volkan and Rifaioglu, Ahmet S and Dalkıran, Alperen and Cetin Atalay, Rengul and Zhang, Chengxin and Hurto, Rebecca L and Freddolino, Peter L and Zhang, Yang and Bhat, Prajwal and Supek, Fran and Fernández, José M and Gemović, Branislava S. and Perović, Vladimir R. and Davidović, Radoslav S. and Šumonja, Neven and Veljković, Nevena V. and Asgari, Ehsaneddin and Mofrad, Mohammad R.K. and Profiti, Giuseppe and Savojardo, Castrense and Martelli, Pier Luigi and Casadio, Rita and Boecker, Florian and Schoof, Heiko and Kahanda, Indika and Thurlby, Natalie and McHardy, Alice C and Renaux, Alexandre and Saidi, Rabie and Gough, Julian and Freitas, Alex A and Antczak, Magdalena and Fabris, Fabio and Wass, Mark N and Hou, Jie and Cheng, Jianlin and Wang, Zheng and Romero, Alfonso E and Paccanaro, Alberto and Yang, Haixuan and Goldberg, Tatyana and Zhao, Chenguang and Holm, Liisa and Törönen, Petri and Medlar, Alan J and Zosa, Elaine and Borukhov, Itamar and Novikov, Ilya and Wilkins, Angela and Lichtarge, Olivier and Chi, Po-Han and Tseng, Wei-Cheng and Linial, Michal and Rose, Peter W and Dessimoz, Christophe and Vidulin, Vedrana and Dzeroski, Saso and Sillitoe, Ian and Das, Sayoni and Lees, Jonathan Gill and Jones, David T and Wan, Cen and Cozzetto, Domenico and Fa, Rui and Torres, Mateo and Warwick Vesztrocy, Alex and Rodriguez, Jose Manuel and Tress, Michael L and Frasca, Marco and Notaro, Marco and Grossi, Giuliano and Petrini, Alessandro and Re, Matteo and Valentini, Giorgio and Mesiti, Marco and Roche, Daniel B and Reeb, Jonas and Ritchie, David W and Aridhi, Sabeur and Alborzi, Seyed Ziaeddin and Devignes, Marie-Dominique and Koo, Da Chen Emily and Bonneau, Richard and Gligorijević, Vladimir and Barot, Meet and Fang, Hai and Toppo, Stefano and Lavezzo, Enrico and Falda, Marco and Berselli, Michele and Tosatto, Silvio C.E. and Carraro, Marco and Piovesan, Damiano and Ur Rehman, Hafeez and Mao, Qizhong and Zhang, Shanshan and Vucetic, Slobodan and Black, Gage S and Jo, Dane and Suh, Erica and Dayton, Jonathan B and Larsen, Dallas J and Omdahl, Ashton R and McGuffin, Liam J and Brackenridge, Danielle A and Babbitt, Patricia C and Yunes, Jeffrey M and Fontana, Paolo and Zhang, Feng and Zhu, Shanfeng and You, Ronghui and Zhang, Zihan and Dai, Suyang and Yao, Shuwei and Tian, Weidong and Cao, Renzhi and Chandler, Caleb and Amezola, Miguel and Johnson, Devon and Chang, Jia-Ming and Liao, Wen-Hung and Liu, Yi-Wei and Pascarelli, Stefano and Frank, Yotam and Hoehndorf, Robert and Kulmanov, Maxat and Boudellioua, Imane and Politano, Gianfranco and Di Carlo, Stefano and Benso, Alfredo and Hakala, Kai and Ginter, Filip and Mehryary, Farrokh and Kaewphan, Suwisa and Björne, Jari and Moen, Hans and Tolvanen, Martti E.E. and Salakoski, Tapio and Kihara, Daisuke and Jain, Aashish and Šmuc, Tomislav and Altenhoff, Adrian and Ben-Hur, Asa and Rost, Burkhard and Brenner, Steven E and Orengo, Christine A and Jeffery, Constance J and Bosco, Giovanni and Hogan, Deborah A and Martin, Maria J and O’Donovan, Claire and Mooney, Sean D and Greene, Casey S and Radivojac, Predrag and Friedberg, Iddo",
year = "2019",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/8655",
abstract = "Background: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-Term memory. Conclusion: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. © 2019 The Author(s).",
journal = "Genome Biology",
title = "The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens",
volume = "20",
number = "1",
pages = "244",
doi = "10.1186/s13059-019-1835-8"
}
Zhou, N., Jiang, Y., Bergquist, T. R., Lee, A. J., Kacsoh, B. Z., Crocker, A. W., Lewis, K. A., Georghiou, G., Nguyen, H. N., Hamid, M. N., Davis, L., Dogan, T., Atalay, V., Rifaioglu, A. S., Dalkıran, A., Cetin Atalay, R., Zhang, C., Hurto, R. L., Freddolino, P. L., Zhang, Y., Bhat, P., Supek, F., Fernández, J. M., Gemović, B. S., Perović, V. R., Davidović, R. S., Šumonja, N., Veljković, N. V., Asgari, E., Mofrad, M. R.K., Profiti, G., Savojardo, C., Martelli, P. L., Casadio, R., Boecker, F., Schoof, H., Kahanda, I., Thurlby, N., McHardy, A. C., Renaux, A., Saidi, R., Gough, J., Freitas, A. A., Antczak, M., Fabris, F., Wass, M. N., Hou, J., Cheng, J., Wang, Z., Romero, A. E., Paccanaro, A., Yang, H., Goldberg, T., Zhao, C., Holm, L., Törönen, P., Medlar, A. J., Zosa, E., Borukhov, I., Novikov, I., Wilkins, A., Lichtarge, O., Chi, P., Tseng, W., Linial, M., Rose, P. W., Dessimoz, C., Vidulin, V., Dzeroski, S., Sillitoe, I., Das, S., Lees, J. G., Jones, D. T., Wan, C., Cozzetto, D., Fa, R., Torres, M., Warwick Vesztrocy, A., Rodriguez, J. M., Tress, M. L., Frasca, M., Notaro, M., Grossi, G., Petrini, A., Re, M., Valentini, G., Mesiti, M., Roche, D. B., Reeb, J., Ritchie, D. W., Aridhi, S., Alborzi, S. Z., Devignes, M., Koo, D. C. E., Bonneau, R., Gligorijević, V., Barot, M., Fang, H., Toppo, S., Lavezzo, E., Falda, M., Berselli, M., Tosatto, S. C.E., Carraro, M., Piovesan, D., Ur Rehman, H., Mao, Q., Zhang, S., Vucetic, S., Black, G. S., Jo, D., Suh, E., Dayton, J. B., Larsen, D. J., Omdahl, A. R., McGuffin, L. J., Brackenridge, D. A., Babbitt, P. C., Yunes, J. M., Fontana, P., Zhang, F., Zhu, S., You, R., Zhang, Z., Dai, S., Yao, S., Tian, W., Cao, R., Chandler, C., Amezola, M., Johnson, D., Chang, J., Liao, W., Liu, Y., Pascarelli, S., Frank, Y., Hoehndorf, R., Kulmanov, M., Boudellioua, I., Politano, G., Di Carlo, S., Benso, A., Hakala, K., Ginter, F., Mehryary, F., Kaewphan, S., Björne, J., Moen, H., Tolvanen, M. E.E., Salakoski, T., Kihara, D., Jain, A., Šmuc, T., Altenhoff, A., Ben-Hur, A., Rost, B., Brenner, S. E., Orengo, C. A., Jeffery, C. J., Bosco, G., Hogan, D. A., Martin, M. J., O’Donovan, C., Mooney, S. D., Greene, C. S., Radivojac, P.,& Friedberg, I. (2019). The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.
Genome Biology, 20(1), 244.
https://doi.org/10.1186/s13059-019-1835-8
Zhou N, Jiang Y, Bergquist TR, Lee AJ, Kacsoh BZ, Crocker AW, Lewis KA, Georghiou G, Nguyen HN, Hamid MN, Davis L, Dogan T, Atalay V, Rifaioglu AS, Dalkıran A, Cetin Atalay R, Zhang C, Hurto RL, Freddolino PL, Zhang Y, Bhat P, Supek F, Fernández JM, Gemović BS, Perović VR, Davidović RS, Šumonja N, Veljković NV, Asgari E, Mofrad MR, Profiti G, Savojardo C, Martelli PL, Casadio R, Boecker F, Schoof H, Kahanda I, Thurlby N, McHardy AC, Renaux A, Saidi R, Gough J, Freitas AA, Antczak M, Fabris F, Wass MN, Hou J, Cheng J, Wang Z, Romero AE, Paccanaro A, Yang H, Goldberg T, Zhao C, Holm L, Törönen P, Medlar AJ, Zosa E, Borukhov I, Novikov I, Wilkins A, Lichtarge O, Chi P, Tseng W, Linial M, Rose PW, Dessimoz C, Vidulin V, Dzeroski S, Sillitoe I, Das S, Lees JG, Jones DT, Wan C, Cozzetto D, Fa R, Torres M, Warwick Vesztrocy A, Rodriguez JM, Tress ML, Frasca M, Notaro M, Grossi G, Petrini A, Re M, Valentini G, Mesiti M, Roche DB, Reeb J, Ritchie DW, Aridhi S, Alborzi SZ, Devignes M, Koo DCE, Bonneau R, Gligorijević V, Barot M, Fang H, Toppo S, Lavezzo E, Falda M, Berselli M, Tosatto SC, Carraro M, Piovesan D, Ur Rehman H, Mao Q, Zhang S, Vucetic S, Black GS, Jo D, Suh E, Dayton JB, Larsen DJ, Omdahl AR, McGuffin LJ, Brackenridge DA, Babbitt PC, Yunes JM, Fontana P, Zhang F, Zhu S, You R, Zhang Z, Dai S, Yao S, Tian W, Cao R, Chandler C, Amezola M, Johnson D, Chang J, Liao W, Liu Y, Pascarelli S, Frank Y, Hoehndorf R, Kulmanov M, Boudellioua I, Politano G, Di Carlo S, Benso A, Hakala K, Ginter F, Mehryary F, Kaewphan S, Björne J, Moen H, Tolvanen ME, Salakoski T, Kihara D, Jain A, Šmuc T, Altenhoff A, Ben-Hur A, Rost B, Brenner SE, Orengo CA, Jeffery CJ, Bosco G, Hogan DA, Martin MJ, O’Donovan C, Mooney SD, Greene CS, Radivojac P, Friedberg I. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome Biology. 2019;20(1):244
Zhou Naihui, Jiang Yuxiang, Bergquist Timothy R, Lee Alexandra J, Kacsoh Balint Z, Crocker Alex W, Lewis Kimberley A, Georghiou George, Nguyen Huy N, Hamid Md Nafiz, Davis Larry, Dogan Tunca, Atalay Volkan, Rifaioglu Ahmet S, Dalkıran Alperen, Cetin Atalay Rengul, Zhang Chengxin, Hurto Rebecca L, Freddolino Peter L, Zhang Yang, Bhat Prajwal, Supek Fran, Fernández José M, Gemović Branislava S., Perović Vladimir R., Davidović Radoslav S., Šumonja Neven, Veljković Nevena V., Asgari Ehsaneddin, Mofrad Mohammad R.K., Profiti Giuseppe, Savojardo Castrense, Martelli Pier Luigi, Casadio Rita, Boecker Florian, Schoof Heiko, Kahanda Indika, Thurlby Natalie, McHardy Alice C, Renaux Alexandre, Saidi Rabie, Gough Julian, Freitas Alex A, Antczak Magdalena, Fabris Fabio, Wass Mark N, Hou Jie, Cheng Jianlin, Wang Zheng, Romero Alfonso E, Paccanaro Alberto, Yang Haixuan, Goldberg Tatyana, Zhao Chenguang, Holm Liisa, Törönen Petri, Medlar Alan J, Zosa Elaine, Borukhov Itamar, Novikov Ilya, Wilkins Angela, Lichtarge Olivier, Chi Po-Han, Tseng Wei-Cheng, Linial Michal, Rose Peter W, Dessimoz Christophe, Vidulin Vedrana, Dzeroski Saso, Sillitoe Ian, Das Sayoni, Lees Jonathan Gill, Jones David T, Wan Cen, Cozzetto Domenico, Fa Rui, Torres Mateo, Warwick Vesztrocy Alex, Rodriguez Jose Manuel, Tress Michael L, Frasca Marco, Notaro Marco, Grossi Giuliano, Petrini Alessandro, Re Matteo, Valentini Giorgio, Mesiti Marco, Roche Daniel B, Reeb Jonas, Ritchie David W, Aridhi Sabeur, Alborzi Seyed Ziaeddin, Devignes Marie-Dominique, Koo Da Chen Emily, Bonneau Richard, Gligorijević Vladimir, Barot Meet, Fang Hai, Toppo Stefano, Lavezzo Enrico, Falda Marco, Berselli Michele, Tosatto Silvio C.E., Carraro Marco, Piovesan Damiano, Ur Rehman Hafeez, Mao Qizhong, Zhang Shanshan, Vucetic Slobodan, Black Gage S, Jo Dane, Suh Erica, Dayton Jonathan B, Larsen Dallas J, Omdahl Ashton R, McGuffin Liam J, Brackenridge Danielle A, Babbitt Patricia C, Yunes Jeffrey M, Fontana Paolo, Zhang Feng, Zhu Shanfeng, You Ronghui, Zhang Zihan, Dai Suyang, Yao Shuwei, Tian Weidong, Cao Renzhi, Chandler Caleb, Amezola Miguel, Johnson Devon, Chang Jia-Ming, Liao Wen-Hung, Liu Yi-Wei, Pascarelli Stefano, Frank Yotam, Hoehndorf Robert, Kulmanov Maxat, Boudellioua Imane, Politano Gianfranco, Di Carlo Stefano, Benso Alfredo, Hakala Kai, Ginter Filip, Mehryary Farrokh, Kaewphan Suwisa, Björne Jari, Moen Hans, Tolvanen Martti E.E., Salakoski Tapio, Kihara Daisuke, Jain Aashish, Šmuc Tomislav, Altenhoff Adrian, Ben-Hur Asa, Rost Burkhard, Brenner Steven E, Orengo Christine A, Jeffery Constance J, Bosco Giovanni, Hogan Deborah A, Martin Maria J, O’Donovan Claire, Mooney Sean D, Greene Casey S, Radivojac Predrag, Friedberg Iddo, "The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens" Genome Biology, 20, no. 1 (2019):244,
https://doi.org/10.1186/s13059-019-1835-8 .
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Automated feature engineering improves prediction of protein–protein interactions

Šumonja, Neven; Gemović, Branislava S.; Veljković, Nevena V.; Perović, Vladimir R.

(2019)

TY  - JOUR
AU  - Šumonja, Neven
AU  - Gemović, Branislava S.
AU  - Veljković, Nevena V.
AU  - Perović, Vladimir R.
PY  - 2019
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/8395
AB  - Over the last decade, various machine learning (ML) and statistical approaches for protein–protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php. © 2019, Springer-Verlag GmbH Austria, part of Springer Nature.
T2  - Amino Acids
T1  - Automated feature engineering improves prediction of protein–protein interactions
VL  - 51
IS  - 8
SP  - 1187
EP  - 1200
DO  - 10.1007/s00726-019-02756-9
ER  - 
@article{
author = "Šumonja, Neven and Gemović, Branislava S. and Veljković, Nevena V. and Perović, Vladimir R.",
year = "2019",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/8395",
abstract = "Over the last decade, various machine learning (ML) and statistical approaches for protein–protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php. © 2019, Springer-Verlag GmbH Austria, part of Springer Nature.",
journal = "Amino Acids",
title = "Automated feature engineering improves prediction of protein–protein interactions",
volume = "51",
number = "8",
pages = "1187-1200",
doi = "10.1007/s00726-019-02756-9"
}
Šumonja, N., Gemović, B. S., Veljković, N. V.,& Perović, V. R. (2019). Automated feature engineering improves prediction of protein–protein interactions.
Amino Acids, 51(8), 1187-1200.
https://doi.org/10.1007/s00726-019-02756-9
Šumonja N, Gemović BS, Veljković NV, Perović VR. Automated feature engineering improves prediction of protein–protein interactions. Amino Acids. 2019;51(8):1187-1200
Šumonja Neven, Gemović Branislava S., Veljković Nevena V., Perović Vladimir R., "Automated feature engineering improves prediction of protein–protein interactions" Amino Acids, 51, no. 8 (2019):1187-1200,
https://doi.org/10.1007/s00726-019-02756-9 .
1
8
2
6

TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation

Perović, Vladimir R.; Šumonja, Neven; Gemović, Branislava S.; Toska, Eneda; Roberts, Stefan G. E.; Veljković, Nevena V.

(2017)

TY  - JOUR
AU  - Perović, Vladimir R.
AU  - Šumonja, Neven
AU  - Gemović, Branislava S.
AU  - Toska, Eneda
AU  - Roberts, Stefan G. E.
AU  - Veljković, Nevena V.
PY  - 2017
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/1468
AB  - The TRI_tool, a sequence-based web tool for prediction of protein interactions in the human transcriptional regulation, is intended for biomedical investigators who work on understanding the regulation of gene expression. It has an improved predictive performance due to the training on updated, human specific, experimentally validated datasets. The TRI_tool is designed to test up to 100 potential interactions with no time delay and to report both probabilities and binarized predictions.
T2  - Bioinformatics
T1  - TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation
VL  - 33
IS  - 2
SP  - 289
EP  - 291
DO  - 10.1093/bioinformatics/btw590
ER  - 
@article{
author = "Perović, Vladimir R. and Šumonja, Neven and Gemović, Branislava S. and Toska, Eneda and Roberts, Stefan G. E. and Veljković, Nevena V.",
year = "2017",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/1468",
abstract = "The TRI_tool, a sequence-based web tool for prediction of protein interactions in the human transcriptional regulation, is intended for biomedical investigators who work on understanding the regulation of gene expression. It has an improved predictive performance due to the training on updated, human specific, experimentally validated datasets. The TRI_tool is designed to test up to 100 potential interactions with no time delay and to report both probabilities and binarized predictions.",
journal = "Bioinformatics",
title = "TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation",
volume = "33",
number = "2",
pages = "289-291",
doi = "10.1093/bioinformatics/btw590"
}
Perović, V. R., Šumonja, N., Gemović, B. S., Toska, E., Roberts, S. G. E.,& Veljković, N. V. (2017). TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation.
Bioinformatics, 33(2), 289-291.
https://doi.org/10.1093/bioinformatics/btw590
Perović VR, Šumonja N, Gemović BS, Toska E, Roberts SGE, Veljković NV. TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation. Bioinformatics. 2017;33(2):289-291
Perović Vladimir R., Šumonja Neven, Gemović Branislava S., Toska Eneda, Roberts Stefan G. E., Veljković Nevena V., "TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation" Bioinformatics, 33, no. 2 (2017):289-291,
https://doi.org/10.1093/bioinformatics/btw590 .
1
7
5
6

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Jiang, Yuxiang; Gemović, Branislava S.; Glišić, Sanja; Perović, Vladimir R.; Veljković, Veljko; Veljković, Nevena V.

(2016)

TY  - JOUR
AU  - Jiang, Yuxiang
AU  - Gemović, Branislava S.
AU  - Glišić, Sanja
AU  - Perović, Vladimir R.
AU  - Veljković, Veljko
AU  - Veljković, Nevena V.
PY  - 2016
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/1244
AB  - Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.
T2  - Genome Biology
T1  - An expanded evaluation of protein function prediction methods shows an improvement in accuracy
VL  - 17
DO  - 10.1186/s13059-016-1037-6
ER  - 
@article{
author = "Jiang, Yuxiang and Gemović, Branislava S. and Glišić, Sanja and Perović, Vladimir R. and Veljković, Veljko and Veljković, Nevena V.",
year = "2016",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/1244",
abstract = "Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.",
journal = "Genome Biology",
title = "An expanded evaluation of protein function prediction methods shows an improvement in accuracy",
volume = "17",
doi = "10.1186/s13059-016-1037-6"
}
Jiang, Y., Gemović, B. S., Glišić, S., Perović, V. R., Veljković, V.,& Veljković, N. V. (2016). An expanded evaluation of protein function prediction methods shows an improvement in accuracy.
Genome Biology, 17.
https://doi.org/10.1186/s13059-016-1037-6
Jiang Y, Gemović BS, Glišić S, Perović VR, Veljković V, Veljković NV. An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biology. 2016;17
Jiang Yuxiang, Gemović Branislava S., Glišić Sanja, Perović Vladimir R., Veljković Veljko, Veljković Nevena V., "An expanded evaluation of protein function prediction methods shows an improvement in accuracy" Genome Biology, 17 (2016),
https://doi.org/10.1186/s13059-016-1037-6 .
32
238
143
169

Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells

Srdić-Rajić, Tatjana; Nikolic, Katarina; Cavic, Milena; Đokić, Ivana; Gemović, Branislava S.; Perović, Vladimir R.; Veljković, Nevena V.

(2016)

TY  - JOUR
AU  - Srdić-Rajić, Tatjana
AU  - Nikolic, Katarina
AU  - Cavic, Milena
AU  - Đokić, Ivana
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Veljković, Nevena V.
PY  - 2016
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/884
AB  - Imidazoline I1 receptor signaling is associated with pathways that regulate cell viability leading to varied cell-type specific phenotypes. We demonstrated that the antihypertensive drug rilmenidine, a selective imidazoline I1 receptor agonist, modulates proliferation and stimulates the proapoptotic protein Bax thus inducing the perturbation of the mitochondrial pathway and apoptosis in human leukemic K562 cells. Rilmenidine acts through a mechanism which involves deactivation of Ras/MAP kinases ERK, p38 and JNK. Moreover, rilmenidine renders K562 cells, which are particularly resistant to chemotherapeutic agents, susceptible to the DNA damaging drug doxorubicin. The rilmenidine co-treatment with doxorubicin reverses G2/M arrest and triggers apoptotic response to DNA damage. Our data offer new insights into the pathways associated with imidazoline I1 receptor activation in K562 cells suggesting rilmenidine as a valuable tool to deepen our understanding of imidazoline I1 receptor signaling in hematologic malignancies and to search for medicinally active agents. (C) 2015 Elsevier B.V. All rights reserved.
T2  - European Journal of Pharmaceutical Sciences
T1  - Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells
VL  - 81
SP  - 172
EP  - 180
DO  - 10.1016/j.ejps.2015.10.017
ER  - 
@article{
author = "Srdić-Rajić, Tatjana and Nikolic, Katarina and Cavic, Milena and Đokić, Ivana and Gemović, Branislava S. and Perović, Vladimir R. and Veljković, Nevena V.",
year = "2016",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/884",
abstract = "Imidazoline I1 receptor signaling is associated with pathways that regulate cell viability leading to varied cell-type specific phenotypes. We demonstrated that the antihypertensive drug rilmenidine, a selective imidazoline I1 receptor agonist, modulates proliferation and stimulates the proapoptotic protein Bax thus inducing the perturbation of the mitochondrial pathway and apoptosis in human leukemic K562 cells. Rilmenidine acts through a mechanism which involves deactivation of Ras/MAP kinases ERK, p38 and JNK. Moreover, rilmenidine renders K562 cells, which are particularly resistant to chemotherapeutic agents, susceptible to the DNA damaging drug doxorubicin. The rilmenidine co-treatment with doxorubicin reverses G2/M arrest and triggers apoptotic response to DNA damage. Our data offer new insights into the pathways associated with imidazoline I1 receptor activation in K562 cells suggesting rilmenidine as a valuable tool to deepen our understanding of imidazoline I1 receptor signaling in hematologic malignancies and to search for medicinally active agents. (C) 2015 Elsevier B.V. All rights reserved.",
journal = "European Journal of Pharmaceutical Sciences",
title = "Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells",
volume = "81",
pages = "172-180",
doi = "10.1016/j.ejps.2015.10.017"
}
Srdić-Rajić, T., Nikolic, K., Cavic, M., Đokić, I., Gemović, B. S., Perović, V. R.,& Veljković, N. V. (2016). Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells.
European Journal of Pharmaceutical Sciences, 81, 172-180.
https://doi.org/10.1016/j.ejps.2015.10.017
Srdić-Rajić T, Nikolic K, Cavic M, Đokić I, Gemović BS, Perović VR, Veljković NV. Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells. European Journal of Pharmaceutical Sciences. 2016;81:172-180
Srdić-Rajić Tatjana, Nikolic Katarina, Cavic Milena, Đokić Ivana, Gemović Branislava S., Perović Vladimir R., Veljković Nevena V., "Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells" European Journal of Pharmaceutical Sciences, 81 (2016):172-180,
https://doi.org/10.1016/j.ejps.2015.10.017 .
7
9
7
10

Bioinformatička analiza proteina uključenih u patogenezu mijeloidnih maligniteta

Gemović, Branislava S.

(Универзитет у Београду, Биолошки факултет, 2015)

TY  - BOOK
AU  - Gemović, Branislava S.
PY  - 2015
UR  - http://eteze.bg.ac.rs/application/showtheses?thesesId=2622
UR  - https://fedorabg.bg.ac.rs/fedora/get/o:10712/bdef:Content/download
UR  - http://vbs.rs/scripts/cobiss?command=DISPLAY&base=70036&RID=1024898994
UR  - http://nardus.mpn.gov.rs/123456789/4929
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/7276
AB  - Mijeloidni maligniteti su klonalne bolesti ćelija mijeloidne krvne loze, koje su, pre svega, posledica poremećaja samoobnavljanja i diferencijacije hematopoetskih matičnih ćelija. Godišnja incidenca u Evropi je 7,5-8,6/100 000, a prosečna petogodišnja stopa preživljavanja pacijenata, uz primenu standardne terapije, je 37%. S obzirom da maligniteti najčešće nose više od jedne vodeće – ‘driver’ mutacije, pri čemu je velika intra- i interkancerska genetička heterogenost, postoji potreba za individualizovanim terapeutskim pristupom svakom pacijentu. Personalizovana medicina označava princip postizanja maksimalne terapijske efikasnosti kroz upotrebu ciljanih terapeutika kod biološki okarakterisanih pacijenata. Bioinformatika ima značajnu ulogu u identifikaciji ciljnih molekula za razvoj terapije, racionalnom dizajnu lekova i identifikaciji informativnih biomarkera za prateću dijagnostiku.Predmet istraživanja u ovom radu bili su geni i njihovi proteinski produkti koji imaju važnu ulogu u patogenezi mijeloidnih maligniteta. WT1 je transkripcioni faktor, koji kontroliše ekspresiju gena uključenih u apoptozu, proliferaciju i diferencijaciju. Ovaj gen ima povećanu ekspresiju u 70-90%, a mutiran je u ~10% akutnih mijeloidnih leukemija (AML) i učestvuje u preko 40 protein-protein interakcija, koje su vremenski i kontekstno zavisne. Mapiranje celokupne mreže WT1 kofaktora doprineće identifikaciji novih molekula i interakcija, koje su potencijalni targeti za ciljanu terapiju AML.Nedavno otkriven tumor supresor u karcinomu dojke, NISCH, ispoljava efekat preko RAC signalnog puta, koji je ključan u regulaciji hematopoeze i uključen u patogenezu mijeloidnih maligniteta, i smatra se atraktivnim targetom za ciljanu terapiju. Poznavanjefunkcionalnih domena NISCH-a će omogućiti dizajn i optimizaciju jedinjenja sa antikancerskim delovanjem...
AB  - Myeloid malignancies are clonal diseases of myeloid cell line, which arise, primary, as a consequence of aberrant self-renewal and differentiation of hematopoietic stem cells. Annual incidence in Europe is 7.5-8.6/100 000 and the mean 5-year survival rate, with standard therapy, is 37%. Since malignancies often carry more than one driver mutation, with high intra- and inter-cancer genetic heterogeneity, there is a need for individualized therapeutic approach to each patient. Personalized medicine is based on the principle of achieving maximal therapeutic efficacy through the use of targeted drugs for biologically characterized patients. Bioinformatics has an important role in identification of molecules for targeted therapy, rational drug design and identification of informative biomarkers for companion diagnostics.The subject of this research included genes and their protein products that have important roles in the pathogenesis of myeloid malignancies. WT1 is a transcription factor that controls the expression of genes involved in apoptosis, proliferation and differentiation. This gene is overexpressed in 70-90%, mutated in ~10% of acute myeloid leukemia (AML) and it is engaged in more than 40 time- and context-dependant protein-protein interactions. Characterizing entire network of WT1 cofactors can improve identification of new molecules and their interactions that could be targets for therapy of AML.Recently identified tumor suppressor in breast cancer, NISCH, affects RAC signaling pathway, which is important in regulation of hematopoiesis and has a role in pathogenesis of myeloid malignancies, so it is considered an attractive target for targeted therapy.Characterization of functional domains of NISCH can enable design and optimization of compounds with anticancer effect...
PB  - Универзитет у Београду, Биолошки факултет
T2  - Универзитет у Београду
T1  - Bioinformatička analiza proteina uključenih u patogenezu mijeloidnih maligniteta
T1  - Bioinformatics analysis of proteins involved in pathogenesis of myeloid malignancies
ER  - 
@phdthesis{
author = "Gemović, Branislava S.",
year = "2015",
url = "http://eteze.bg.ac.rs/application/showtheses?thesesId=2622, https://fedorabg.bg.ac.rs/fedora/get/o:10712/bdef:Content/download, http://vbs.rs/scripts/cobiss?command=DISPLAY&base=70036&RID=1024898994, http://nardus.mpn.gov.rs/123456789/4929, http://vinar.vin.bg.ac.rs/handle/123456789/7276",
abstract = "Mijeloidni maligniteti su klonalne bolesti ćelija mijeloidne krvne loze, koje su, pre svega, posledica poremećaja samoobnavljanja i diferencijacije hematopoetskih matičnih ćelija. Godišnja incidenca u Evropi je 7,5-8,6/100 000, a prosečna petogodišnja stopa preživljavanja pacijenata, uz primenu standardne terapije, je 37%. S obzirom da maligniteti najčešće nose više od jedne vodeće – ‘driver’ mutacije, pri čemu je velika intra- i interkancerska genetička heterogenost, postoji potreba za individualizovanim terapeutskim pristupom svakom pacijentu. Personalizovana medicina označava princip postizanja maksimalne terapijske efikasnosti kroz upotrebu ciljanih terapeutika kod biološki okarakterisanih pacijenata. Bioinformatika ima značajnu ulogu u identifikaciji ciljnih molekula za razvoj terapije, racionalnom dizajnu lekova i identifikaciji informativnih biomarkera za prateću dijagnostiku.Predmet istraživanja u ovom radu bili su geni i njihovi proteinski produkti koji imaju važnu ulogu u patogenezi mijeloidnih maligniteta. WT1 je transkripcioni faktor, koji kontroliše ekspresiju gena uključenih u apoptozu, proliferaciju i diferencijaciju. Ovaj gen ima povećanu ekspresiju u 70-90%, a mutiran je u ~10% akutnih mijeloidnih leukemija (AML) i učestvuje u preko 40 protein-protein interakcija, koje su vremenski i kontekstno zavisne. Mapiranje celokupne mreže WT1 kofaktora doprineće identifikaciji novih molekula i interakcija, koje su potencijalni targeti za ciljanu terapiju AML.Nedavno otkriven tumor supresor u karcinomu dojke, NISCH, ispoljava efekat preko RAC signalnog puta, koji je ključan u regulaciji hematopoeze i uključen u patogenezu mijeloidnih maligniteta, i smatra se atraktivnim targetom za ciljanu terapiju. Poznavanjefunkcionalnih domena NISCH-a će omogućiti dizajn i optimizaciju jedinjenja sa antikancerskim delovanjem..., Myeloid malignancies are clonal diseases of myeloid cell line, which arise, primary, as a consequence of aberrant self-renewal and differentiation of hematopoietic stem cells. Annual incidence in Europe is 7.5-8.6/100 000 and the mean 5-year survival rate, with standard therapy, is 37%. Since malignancies often carry more than one driver mutation, with high intra- and inter-cancer genetic heterogeneity, there is a need for individualized therapeutic approach to each patient. Personalized medicine is based on the principle of achieving maximal therapeutic efficacy through the use of targeted drugs for biologically characterized patients. Bioinformatics has an important role in identification of molecules for targeted therapy, rational drug design and identification of informative biomarkers for companion diagnostics.The subject of this research included genes and their protein products that have important roles in the pathogenesis of myeloid malignancies. WT1 is a transcription factor that controls the expression of genes involved in apoptosis, proliferation and differentiation. This gene is overexpressed in 70-90%, mutated in ~10% of acute myeloid leukemia (AML) and it is engaged in more than 40 time- and context-dependant protein-protein interactions. Characterizing entire network of WT1 cofactors can improve identification of new molecules and their interactions that could be targets for therapy of AML.Recently identified tumor suppressor in breast cancer, NISCH, affects RAC signaling pathway, which is important in regulation of hematopoiesis and has a role in pathogenesis of myeloid malignancies, so it is considered an attractive target for targeted therapy.Characterization of functional domains of NISCH can enable design and optimization of compounds with anticancer effect...",
publisher = "Универзитет у Београду, Биолошки факултет",
journal = "Универзитет у Београду",
title = "Bioinformatička analiza proteina uključenih u patogenezu mijeloidnih maligniteta, Bioinformatics analysis of proteins involved in pathogenesis of myeloid malignancies"
}
Gemović, B. S. (2015). Bioinformatics analysis of proteins involved in pathogenesis of myeloid malignancies.
Универзитет у Београду
Универзитет у Београду, Биолошки факултет..
Gemović BS. Bioinformatics analysis of proteins involved in pathogenesis of myeloid malignancies. Универзитет у Београду. 2015;
Gemović Branislava S., "Bioinformatics analysis of proteins involved in pathogenesis of myeloid malignancies" Универзитет у Београду (2015)

Natural Products as Promising Therapeutics for Treatment of Influenza Disease

Senćanski, Milan V.; Radosevic, Draginja; Perović, Vladimir R.; Gemović, Branislava S.; Stanojevic, Maja; Veljković, Nevena V.; Glišić, Sanja

(2015)

TY  - JOUR
AU  - Senćanski, Milan V.
AU  - Radosevic, Draginja
AU  - Perović, Vladimir R.
AU  - Gemović, Branislava S.
AU  - Stanojevic, Maja
AU  - Veljković, Nevena V.
AU  - Glišić, Sanja
PY  - 2015
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/806
AB  - The influenza virus represents a permanent global health threat because it circulates not only within but also between numerous host populations, thereby frequently causing unexpected outbreaks in animals and humans with a generally unpredictable course of disease and epidemiology. Conventional influenza therapy is directed against the viral neuraminidase protein, which promotes virus release from infected cells, and the viral ion channel M2, which facilitates viral uncoating. However, these drugs, albeit effective, have a major drawback: their targets are of a highly variable sequence. As a consequence, the virus can readily acquire resistance by mutating the drug targets. Indeed, most seasonal A/H1N1 viruses and the 2009 H1N1 virus are resistant to M2 inhibitors, and a significant proportion of the seasonal A/H1N1 viruses are resistant to the neuraminidase inhibitor oseltamivir. Development of new effective drugs for treatment of disease during the regular influenza seasons and the possible influenza pandemic represents an important goal. The results presented here point out natural products as a promising source of low toxic and widely accessible drug candidates for treatment of the influenza disease. Natural products combined with new therapeutic targets and drug repurposing techniques, which accelerate development of new drugs, serve as an important platform for development of new influenza therapeutics.
T2  - Current Pharmaceutical Design
T1  - Natural Products as Promising Therapeutics for Treatment of Influenza Disease
VL  - 21
IS  - 38
SP  - 5573
EP  - 5588
DO  - 10.2174/1381612821666151002113426
ER  - 
@article{
author = "Senćanski, Milan V. and Radosevic, Draginja and Perović, Vladimir R. and Gemović, Branislava S. and Stanojevic, Maja and Veljković, Nevena V. and Glišić, Sanja",
year = "2015",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/806",
abstract = "The influenza virus represents a permanent global health threat because it circulates not only within but also between numerous host populations, thereby frequently causing unexpected outbreaks in animals and humans with a generally unpredictable course of disease and epidemiology. Conventional influenza therapy is directed against the viral neuraminidase protein, which promotes virus release from infected cells, and the viral ion channel M2, which facilitates viral uncoating. However, these drugs, albeit effective, have a major drawback: their targets are of a highly variable sequence. As a consequence, the virus can readily acquire resistance by mutating the drug targets. Indeed, most seasonal A/H1N1 viruses and the 2009 H1N1 virus are resistant to M2 inhibitors, and a significant proportion of the seasonal A/H1N1 viruses are resistant to the neuraminidase inhibitor oseltamivir. Development of new effective drugs for treatment of disease during the regular influenza seasons and the possible influenza pandemic represents an important goal. The results presented here point out natural products as a promising source of low toxic and widely accessible drug candidates for treatment of the influenza disease. Natural products combined with new therapeutic targets and drug repurposing techniques, which accelerate development of new drugs, serve as an important platform for development of new influenza therapeutics.",
journal = "Current Pharmaceutical Design",
title = "Natural Products as Promising Therapeutics for Treatment of Influenza Disease",
volume = "21",
number = "38",
pages = "5573-5588",
doi = "10.2174/1381612821666151002113426"
}
Senćanski, M. V., Radosevic, D., Perović, V. R., Gemović, B. S., Stanojevic, M., Veljković, N. V.,& Glišić, S. (2015). Natural Products as Promising Therapeutics for Treatment of Influenza Disease.
Current Pharmaceutical Design, 21(38), 5573-5588.
https://doi.org/10.2174/1381612821666151002113426
Senćanski MV, Radosevic D, Perović VR, Gemović BS, Stanojevic M, Veljković NV, Glišić S. Natural Products as Promising Therapeutics for Treatment of Influenza Disease. Current Pharmaceutical Design. 2015;21(38):5573-5588
Senćanski Milan V., Radosevic Draginja, Perović Vladimir R., Gemović Branislava S., Stanojevic Maja, Veljković Nevena V., Glišić Sanja, "Natural Products as Promising Therapeutics for Treatment of Influenza Disease" Current Pharmaceutical Design, 21, no. 38 (2015):5573-5588,
https://doi.org/10.2174/1381612821666151002113426 .
15
15
19

Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120

Vujicic, Ana Djordjevic; Gemović, Branislava S.; Veljković, Veljko; Glišić, Sanja; Veljković, Nevena V.

(2014)

TY  - JOUR
AU  - Vujicic, Ana Djordjevic
AU  - Gemović, Branislava S.
AU  - Veljković, Veljko
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
PY  - 2014
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/5960
AB  - Background/Aim. High sera reactivity with a peptide derived from human immunodeficiency virus HIV-I envelope protein gp120, NMI., correlate with non-progressive HIV-1 infection and also may have protective role in breast and prostate cancer. We also detected a low NTM1 reactive antibodies titer in healthy HIV negative sera and showed that antibody levels can be significantly increased with vigorous physical activity. However, the immune system seems to be unresponsive or tolerant to this peptide, implicating that the NTM1 sequence encompasses or overlaps a certain innate immune epitope. The aim of this study was to present evidences that NTM1 binding antibodies are components of innate immune humoral response, by confirming their presence in sera of newborn babies. For this purpose we collected a set of 225 innate antigen sequences reported in the literature and screened it for candidate antigens with the highest sequence and spectral similarity to NTM1 derived from HIV-1 gp120. Methods. Sera from 18 newborns were tested using ELISA, with peptide NTM1. Sequences from innate antigen database were aligned by an EMBOSS Water bioinformatics tool. Results. We identified NTM1 reactive antibodies in sera of HIV negative newborn babies. Further, in order to identify which of already known innate antigens are the most similar to NTM1 peptide we screened innate immune antigen sequence database collected from the literature. This screening revealed that the most similar sequence are ribonucleoproteins RO60, in addition to previously identified N-terminus of vasoactive intestinal peptide. Conclusion. The results of this study confirm the hypothesis that NTM1 recognizing antibodies are a part of humoral innate immune response. Further, computational similarity screening revealed a vasoactive intestinal peptide and RO60 as the most similar sequences and the strongest candidate antigens. In the light of the presented results, it is appealing that testing blood reactivity at birth, with specific innate antigens, particularly a vasoactive intestinal peptide, can reveal the potential to develop- or boost protective immune response in breast and prostate cancer and HIV infection later in life.
T2  - Vojnosanitetski pregled
T1  - Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120
VL  - 71
IS  - 4
SP  - 352
EP  - 361
DO  - 10.2298/VSP1404352D
ER  - 
@article{
author = "Vujicic, Ana Djordjevic and Gemović, Branislava S. and Veljković, Veljko and Glišić, Sanja and Veljković, Nevena V.",
year = "2014",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/5960",
abstract = "Background/Aim. High sera reactivity with a peptide derived from human immunodeficiency virus HIV-I envelope protein gp120, NMI., correlate with non-progressive HIV-1 infection and also may have protective role in breast and prostate cancer. We also detected a low NTM1 reactive antibodies titer in healthy HIV negative sera and showed that antibody levels can be significantly increased with vigorous physical activity. However, the immune system seems to be unresponsive or tolerant to this peptide, implicating that the NTM1 sequence encompasses or overlaps a certain innate immune epitope. The aim of this study was to present evidences that NTM1 binding antibodies are components of innate immune humoral response, by confirming their presence in sera of newborn babies. For this purpose we collected a set of 225 innate antigen sequences reported in the literature and screened it for candidate antigens with the highest sequence and spectral similarity to NTM1 derived from HIV-1 gp120. Methods. Sera from 18 newborns were tested using ELISA, with peptide NTM1. Sequences from innate antigen database were aligned by an EMBOSS Water bioinformatics tool. Results. We identified NTM1 reactive antibodies in sera of HIV negative newborn babies. Further, in order to identify which of already known innate antigens are the most similar to NTM1 peptide we screened innate immune antigen sequence database collected from the literature. This screening revealed that the most similar sequence are ribonucleoproteins RO60, in addition to previously identified N-terminus of vasoactive intestinal peptide. Conclusion. The results of this study confirm the hypothesis that NTM1 recognizing antibodies are a part of humoral innate immune response. Further, computational similarity screening revealed a vasoactive intestinal peptide and RO60 as the most similar sequences and the strongest candidate antigens. In the light of the presented results, it is appealing that testing blood reactivity at birth, with specific innate antigens, particularly a vasoactive intestinal peptide, can reveal the potential to develop- or boost protective immune response in breast and prostate cancer and HIV infection later in life.",
journal = "Vojnosanitetski pregled",
title = "Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120",
volume = "71",
number = "4",
pages = "352-361",
doi = "10.2298/VSP1404352D"
}
Vujicic, A. D., Gemović, B. S., Veljković, V., Glišić, S.,& Veljković, N. V. (2014). Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120.
Vojnosanitetski pregled, 71(4), 352-361.
https://doi.org/10.2298/VSP1404352D
Vujicic AD, Gemović BS, Veljković V, Glišić S, Veljković NV. Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120. Vojnosanitetski pregled. 2014;71(4):352-361
Vujicic Ana Djordjevic, Gemović Branislava S., Veljković Veljko, Glišić Sanja, Veljković Nevena V., "Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120" Vojnosanitetski pregled, 71, no. 4 (2014):352-361,
https://doi.org/10.2298/VSP1404352D .
1
1

Predicting protein function from sequence and co-expression: preliminary results for breast cancer molecular markers

Gemović, Branislava S.; Perović, Vladimir R.; Glišić, Sanja; Veljković, Nevena V.

(2014)

TY  - CONF
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
PY  - 2014
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/7074
C3  - FEBS Journal
T1  - Predicting protein function from sequence and co-expression: preliminary results for breast cancer molecular markers
VL  - 281
SP  - 633
EP  - 634
ER  - 
@conference{
author = "Gemović, Branislava S. and Perović, Vladimir R. and Glišić, Sanja and Veljković, Nevena V.",
year = "2014",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/7074",
journal = "FEBS Journal",
title = "Predicting protein function from sequence and co-expression: preliminary results for breast cancer molecular markers",
volume = "281",
pages = "633-634"
}
Gemović, B. S., Perović, V. R., Glišić, S.,& Veljković, N. V. (2014). Predicting protein function from sequence and co-expression: preliminary results for breast cancer molecular markers.
FEBS Journal, 281, 633-634.
Gemović BS, Perović VR, Glišić S, Veljković NV. Predicting protein function from sequence and co-expression: preliminary results for breast cancer molecular markers. FEBS Journal. 2014;281:633-634
Gemović Branislava S., Perović Vladimir R., Glišić Sanja, Veljković Nevena V., "Predicting protein function from sequence and co-expression: preliminary results for breast cancer molecular markers" FEBS Journal, 281 (2014):633-634

Imidazoline-1 Receptor Ligands as Apoptotic Agents: Pharmacophore Modeling and Virtual Docking Study

Nikolic, Katarina; Veljković, Nevena V.; Gemović, Branislava S.; Srdić-Rajić, Tatjana; Agbaba, Danica

(2013)

TY  - JOUR
AU  - Nikolic, Katarina
AU  - Veljković, Nevena V.
AU  - Gemović, Branislava S.
AU  - Srdić-Rajić, Tatjana
AU  - Agbaba, Danica
PY  - 2013
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/5398
AB  - The group of imidazoline-1 receptors (I-1-IR) agonists encompasses drugs are currently used in treatment of high blood pressure and hyperglycemia. The I-1-IR protein structures have not been determined yet, but Nischarin protein that binds numerous imidazoline ligands inducing initiation of various cell-signaling cascades, including apoptosis, is identified as strong I-1-IR candidate. In this study we examined apoptotic activity of rilmenidine (potent I-1-IR agonist), moxonidine (moderate I-1-IR agonist), and efaroxan (I-1-IR partial agonist) on cancer cell line (K562) expressing Nischarin. The Nischarine domains mapping was performed by use of the Informational Spectrum Method (ISM). The 3D-Quantitative Structure-Activity Relationship (3D-QSAR) and virtual docking studies of 29 I-1-IR ligands (agonists, partial agonists, and antagonists) were carried out on I-1-IR receptors binding affinities. The 3D-QSAR study defined 3D-pharmacophore models for I-1-IR agonistic and I-1-IR antagonistic activity and created regression model for prediction of I-1-IR activity of novel compounds. The 3D-QSAR models were applied for design and evaluation of novel I-1-IR agonists and I-1-IR antagonists. The most promising I-1-IR ligands with enhanced activities than parent compounds were proposed for synthesis. The results of 3D-QSAR, ISM, and virtual docking studies were in perfect agreement and allowed precise definition of binding mode of I-1-IR agonists (Arg 758, Arg 866, Val 981, and Glu 1057) and significantly different binding modes of I-1-IR antagonists or partial I-1-IR agonists. The performed theoretical study provides reliable system for evaluation of I-1-IR agonistic and I-1-IR antagonistic activity of novel I-1-IR ligands, as drug candidates with anticancer activities.
T2  - Combinatorial Chemistry and High Throughput Screening
T1  - Imidazoline-1 Receptor Ligands as Apoptotic Agents: Pharmacophore Modeling and Virtual Docking Study
VL  - 16
IS  - 4
SP  - 298
EP  - 319
DO  - 10.2174/1386207311316040004
ER  - 
@article{
author = "Nikolic, Katarina and Veljković, Nevena V. and Gemović, Branislava S. and Srdić-Rajić, Tatjana and Agbaba, Danica",
year = "2013",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/5398",
abstract = "The group of imidazoline-1 receptors (I-1-IR) agonists encompasses drugs are currently used in treatment of high blood pressure and hyperglycemia. The I-1-IR protein structures have not been determined yet, but Nischarin protein that binds numerous imidazoline ligands inducing initiation of various cell-signaling cascades, including apoptosis, is identified as strong I-1-IR candidate. In this study we examined apoptotic activity of rilmenidine (potent I-1-IR agonist), moxonidine (moderate I-1-IR agonist), and efaroxan (I-1-IR partial agonist) on cancer cell line (K562) expressing Nischarin. The Nischarine domains mapping was performed by use of the Informational Spectrum Method (ISM). The 3D-Quantitative Structure-Activity Relationship (3D-QSAR) and virtual docking studies of 29 I-1-IR ligands (agonists, partial agonists, and antagonists) were carried out on I-1-IR receptors binding affinities. The 3D-QSAR study defined 3D-pharmacophore models for I-1-IR agonistic and I-1-IR antagonistic activity and created regression model for prediction of I-1-IR activity of novel compounds. The 3D-QSAR models were applied for design and evaluation of novel I-1-IR agonists and I-1-IR antagonists. The most promising I-1-IR ligands with enhanced activities than parent compounds were proposed for synthesis. The results of 3D-QSAR, ISM, and virtual docking studies were in perfect agreement and allowed precise definition of binding mode of I-1-IR agonists (Arg 758, Arg 866, Val 981, and Glu 1057) and significantly different binding modes of I-1-IR antagonists or partial I-1-IR agonists. The performed theoretical study provides reliable system for evaluation of I-1-IR agonistic and I-1-IR antagonistic activity of novel I-1-IR ligands, as drug candidates with anticancer activities.",
journal = "Combinatorial Chemistry and High Throughput Screening",
title = "Imidazoline-1 Receptor Ligands as Apoptotic Agents: Pharmacophore Modeling and Virtual Docking Study",
volume = "16",
number = "4",
pages = "298-319",
doi = "10.2174/1386207311316040004"
}
Nikolic, K., Veljković, N. V., Gemović, B. S., Srdić-Rajić, T.,& Agbaba, D. (2013). Imidazoline-1 Receptor Ligands as Apoptotic Agents: Pharmacophore Modeling and Virtual Docking Study.
Combinatorial Chemistry and High Throughput Screening, 16(4), 298-319.
https://doi.org/10.2174/1386207311316040004
Nikolic K, Veljković NV, Gemović BS, Srdić-Rajić T, Agbaba D. Imidazoline-1 Receptor Ligands as Apoptotic Agents: Pharmacophore Modeling and Virtual Docking Study. Combinatorial Chemistry and High Throughput Screening. 2013;16(4):298-319
Nikolic Katarina, Veljković Nevena V., Gemović Branislava S., Srdić-Rajić Tatjana, Agbaba Danica, "Imidazoline-1 Receptor Ligands as Apoptotic Agents: Pharmacophore Modeling and Virtual Docking Study" Combinatorial Chemistry and High Throughput Screening, 16, no. 4 (2013):298-319,
https://doi.org/10.2174/1386207311316040004 .
6
8
8

Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains

Gemović, Branislava S.; Perović, Vladimir R.; Glišić, Sanja; Veljković, Nevena V.

(2013)

TY  - JOUR
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
PY  - 2013
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/5770
AB  - There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.
T2  - Scientific World Journal
T1  - Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains
DO  - 10.1155/2013/948617
ER  - 
@article{
author = "Gemović, Branislava S. and Perović, Vladimir R. and Glišić, Sanja and Veljković, Nevena V.",
year = "2013",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/5770",
abstract = "There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.",
journal = "Scientific World Journal",
title = "Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains",
doi = "10.1155/2013/948617"
}
Gemović, B. S., Perović, V. R., Glišić, S.,& Veljković, N. V. (2013). Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains.
Scientific World Journal.
https://doi.org/10.1155/2013/948617
Gemović BS, Perović VR, Glišić S, Veljković NV. Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains. Scientific World Journal. 2013;
Gemović Branislava S., Perović Vladimir R., Glišić Sanja, Veljković Nevena V., "Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains" Scientific World Journal (2013),
https://doi.org/10.1155/2013/948617 .
8
5
5
4

Assessment of Hepatitis C Virus Protein Sequences with Regard to Interferon/Ribavirin Combination Therapy Response in Patients with HCV Genotype 1b

Glišić, Sanja; Veljković, Nevena V.; Jovanović-Ćupić, Snežana P.; Vasiljevic, Nada; Prljić, Jelena; Gemović, Branislava S.; Perović, Vladimir R.; Veljković, Veljko

(2012)

TY  - JOUR
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
AU  - Jovanović-Ćupić, Snežana P.
AU  - Vasiljevic, Nada
AU  - Prljić, Jelena
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Veljković, Veljko
PY  - 2012
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/4755
AB  - Hepatitis C virus (HCV) infection is a major and rising global health problem, affecting about 170 million people worldwide. The current standard of care treatment with interferon alpha and ribavirin in patients with the genotype 1 infection, the most frequent genotype in the USA and Western Europe, leads to a successful outcome in only about 50% of individuals. Accurate prediction of hepatitis C treatment response is of great benefit to patients and clinicians. The informational spectrum method, a virtual spectroscopy method for structure/function analysis of nucleotide and protein sequences, is applied here for the identification of the conserved information of the HCV proteins that correlate with the combination therapy outcome. Among the HCV proteins that we have analyzed the informational property of the p7 of HCV genotype 1b was best related to the therapy outcome. On the basis of these results, a simple bioinformatics criterion that could be useful in assessment of the response of HCV-infected patients to the combination therapy has been proposed.
T2  - Protein Journal
T1  - Assessment of Hepatitis C Virus Protein Sequences with Regard to Interferon/Ribavirin Combination Therapy Response in Patients with HCV Genotype 1b
VL  - 31
IS  - 2
SP  - 129
EP  - 136
DO  - 10.1007/s10930-011-9381-6
ER  - 
@article{
author = "Glišić, Sanja and Veljković, Nevena V. and Jovanović-Ćupić, Snežana P. and Vasiljevic, Nada and Prljić, Jelena and Gemović, Branislava S. and Perović, Vladimir R. and Veljković, Veljko",
year = "2012",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/4755",
abstract = "Hepatitis C virus (HCV) infection is a major and rising global health problem, affecting about 170 million people worldwide. The current standard of care treatment with interferon alpha and ribavirin in patients with the genotype 1 infection, the most frequent genotype in the USA and Western Europe, leads to a successful outcome in only about 50% of individuals. Accurate prediction of hepatitis C treatment response is of great benefit to patients and clinicians. The informational spectrum method, a virtual spectroscopy method for structure/function analysis of nucleotide and protein sequences, is applied here for the identification of the conserved information of the HCV proteins that correlate with the combination therapy outcome. Among the HCV proteins that we have analyzed the informational property of the p7 of HCV genotype 1b was best related to the therapy outcome. On the basis of these results, a simple bioinformatics criterion that could be useful in assessment of the response of HCV-infected patients to the combination therapy has been proposed.",
journal = "Protein Journal",
title = "Assessment of Hepatitis C Virus Protein Sequences with Regard to Interferon/Ribavirin Combination Therapy Response in Patients with HCV Genotype 1b",
volume = "31",
number = "2",
pages = "129-136",
doi = "10.1007/s10930-011-9381-6"
}
Glišić, S., Veljković, N. V., Jovanović-Ćupić, S. P., Vasiljevic, N., Prljić, J., Gemović, B. S., Perović, V. R.,& Veljković, V. (2012). Assessment of Hepatitis C Virus Protein Sequences with Regard to Interferon/Ribavirin Combination Therapy Response in Patients with HCV Genotype 1b.
Protein Journal, 31(2), 129-136.
https://doi.org/10.1007/s10930-011-9381-6
Glišić S, Veljković NV, Jovanović-Ćupić SP, Vasiljevic N, Prljić J, Gemović BS, Perović VR, Veljković V. Assessment of Hepatitis C Virus Protein Sequences with Regard to Interferon/Ribavirin Combination Therapy Response in Patients with HCV Genotype 1b. Protein Journal. 2012;31(2):129-136
Glišić Sanja, Veljković Nevena V., Jovanović-Ćupić Snežana P., Vasiljevic Nada, Prljić Jelena, Gemović Branislava S., Perović Vladimir R., Veljković Veljko, "Assessment of Hepatitis C Virus Protein Sequences with Regard to Interferon/Ribavirin Combination Therapy Response in Patients with HCV Genotype 1b" Protein Journal, 31, no. 2 (2012):129-136,
https://doi.org/10.1007/s10930-011-9381-6 .
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Bioinformatics approach to protein-protein interactions of WT1 isoforms

Gemović, Branislava S.; Perović, Vladimir R.; Glišić, Sanja; Veljković, Nevena V.

(2012)

TY  - CONF
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
PY  - 2012
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/6973
C3  - FEBS Journal
T1  - Bioinformatics approach to protein-protein interactions of WT1 isoforms
VL  - 279
SP  - 295
EP  - 295
ER  - 
@conference{
author = "Gemović, Branislava S. and Perović, Vladimir R. and Glišić, Sanja and Veljković, Nevena V.",
year = "2012",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/6973",
journal = "FEBS Journal",
title = "Bioinformatics approach to protein-protein interactions of WT1 isoforms",
volume = "279",
pages = "295-295"
}
Gemović, B. S., Perović, V. R., Glišić, S.,& Veljković, N. V. (2012). Bioinformatics approach to protein-protein interactions of WT1 isoforms.
FEBS Journal, 279, 295-295.
Gemović BS, Perović VR, Glišić S, Veljković NV. Bioinformatics approach to protein-protein interactions of WT1 isoforms. FEBS Journal. 2012;279:295-295
Gemović Branislava S., Perović Vladimir R., Glišić Sanja, Veljković Nevena V., "Bioinformatics approach to protein-protein interactions of WT1 isoforms" FEBS Journal, 279 (2012):295-295
1