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1. Sequence-based feature prediction and annotation of proteins

Author

Juncker, A.S., Jensen, L.J., Pierleoni, A., Bernsel, A., Tress, M.L., Bork, P., von Heijne, G., Valencia, A., Ouzounis, C.A., Casadio, R., and Brunak, S.

Subjects
ComputingMethodologies_PATTERNRECOGNITION, Cardiovascular and Metabolic Diseases
Abstract

A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations, for example, the entire repertoire of kinase-binding motifs in the human proteome.

Published
2009

5. The Cafa Challenge Reports Improved Protein Function Prediction And New Functional Annotations For Hundreds Of Genes Through Experimental Screens

Author

Heiko Schoof, Ahmet Sureyya Rifaioglu, Ian Sillitoe, Shanfeng Zhu, Marco Carraro, Naihui Zhou, Asa Ben-Hur, Rui Fa, Alice C. McHardy, David W. Ritchie, George Georghiou, Filip Ginter, Haixuan Yang, Alex A. Freitas, Constance J. Jeffery, Tapio Salakoski, Radoslav Davidovic, Huy N Nguyen, Devon Johnson, Yotam Frank, Alexandra J. Lee, Sean D. Mooney, Marco Falda, Marie-Dominique Devignes, Gianfranco Politano, David T. Jones, Silvio C. E. Tosatto, Renzhi Cao, Zihan Zhang, Sabeur Aridhi, Stefano Pascarelli, Vedrana Vidulin, Qizhong Mao, Balint Z. Kacsoh, Patricia C. Babbitt, Giovanni Bosco, Farrokh Mehryary, Florian Boecker, Alfonso E. Romero, Angela D. Wilkins, Saso Dzeroski, Richard Bonneau, Hans Moen, Chengxin Zhang, Prajwal Bhat, Giuliano Grossi, Martti Tolvanen, Matteo Re, Meet Barot, Mohammad R. K. Mofrad, Predrag Radivojac, Stefano Di Carlo, Tatyana Goldberg, Branislava Gemovic, Suyang Dai, Pier Luigi Martelli, Giorgio Valentini, Maxat Kulmanov, Maria Jesus Martin, Claire O'Donovan, Dallas J. Larsen, Alexandre Renaux, Alan Medlar, Jeffrey M. Yunes, Erica Suh, Volkan Atalay, Vladimir Gligorijević, Fran Supek, Elaine Zosa, Wei-Cheng Tseng, Nafiz Hamid, Marco Mesiti, Tunca Doğan, Petri Törönen, Hafeez Ur Rehman, Jose Manuel Rodriguez, Alessandro Petrini, Sayoni Das, Burkhard Rost, Miguel Amezola, Mateo Torres, Jianlin Cheng, Daisuke Kihara, Liisa Holm, Marco Frasca, Steven E. Brenner, Stefano Toppo, Adrian M. Altenhoff, Chenguang Zhao, Daniel B. Roche, Alperen Dalkiran, Alex W. Crocker, Marco Notaro, Iddo Friedberg, Michal Linial, Julian Gough, Damiano Piovesan, Slobodan Vucetic, Natalie Thurlby, Olivier Lichtarge, Jari Björne, Jonas Reeb, Rabie Saidi, Yuxiang Jiang, Christophe Dessimoz, Jie Hou, Ronghui You, Tomislav Šmuc, Paolo Fontana, Michele Berselli, Jia-Ming Chang, Deborah A. Hogan, Larry Davis, Ehsaneddin Asgari, Shuwei Yao, Zheng Wang, Fabio Fabris, Michael L. Tress, Caleb Chandler, Christine A. Orengo, Rengul Cetin Atalay, Castrense Savojardo, Danielle A Brackenridge, Peter W. Rose, Yang Zhang, Dane Jo, Gage S. Black, Shanshan Zhang, Aashish Jain, Liam J. McGuffin, Timothy Bergquist, Peter L. Freddolino, Robert Hoehndorf, Rita Casadio, Da Chen Emily Koo, Mark N. Wass, Hai Fang, Casey S. Greene, Suwisa Kaewphan, Magdalena Antczak, Wen-Hung Liao, Enrico Lavezzo, Neven Sumonja, Ashton Omdahl, José M. Fernández, Ilya Novikov, Jonathan B. Dayton, Feng Zhang, Vladimir Perovic, Cen Wan, Jonathan G. Lees, Kai Hakala, Weidong Tian, Alex Warwick Vesztrocy, Domenico Cozzetto, Nevena Veljkovic, Yi-Wei Liu, Imane Boudellioua, Po-Han Chi, Kimberley A. Lewis, Seyed Ziaeddin Alborzi, Giuseppe Profiti, Alberto Paccanaro, Itamar Borukhov, Alfredo Benso, Indika Kahanda, Rebecca L. Hurto, Bilgisayar Mühendisliği, National Science Foundation (United States), Gordon and Betty Moore Foundation, United States of Department of Health & Human Services, Cystic Fibrosis Foundation, Consejo Nacional de Ciencia y Tecnología (México), Deutsche Forschungsgemeinschaft (Alemania), European Research Council, Ministerio de Ciencia e Innovación (España), Unión Europea, University of Turku (Finlandia), Finlands Akademi (Finlandia), National Natural Science Foundation of China, Nanjing Agricultural University. The Academy of Science. National Key Research & Development Program of China, Ministero dell Istruzione, dell Universita e della Ricerca (Italia), Shanghai Municipal Science and Technology Major Project, Biotechnology and Biological Sciences Research Council (Reino Unido), Extreme Science and Engineering Discovery Environment, Ministry of Education, Science and Technological Development (Serbia), Ministry of Science and Technology, Ministry for Education (Baviera) (Alemania), Yad Hanadiv, University of Milan (Italia), Swiss National Science Foundation, Unión Europea. European Cooperation in Science and Technology (COST), Plataforma ISCIII de Bioinformática (España), Scientific and Technological Research Council of Turkey, Ministry of Education (China), University of Padua (Italia), Mühendislik ve Doğa Bilimleri Fakültesi -- Bilgisayar Mühendisliği Bölümü, Rifaioğlu, Ahmet Süreyya, 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., Dalklran A., Cetin Atalay R., Zhang C., Hurto R.L., Freddolino P.L., Zhang Y., Bhat P., Supek F., Fernandez J.M., Gemovic B., Perovic V.R., Davidovic R.S., Sumonja N., Veljkovic N., 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., Toronen P., Medlar A.J., Zosa E., Borukhov I., Novikov I., Wilkins A., Lichtarge O., Chi P.-H., Tseng W.-C., 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.-D., Koo D.C.E., Bonneau R., Gligorijevic 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.-M., Liao W.-H., Liu Y.-W., 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., Bjorne J., Moen H., Tolvanen M.E.E., Salakoski T., Kihara D., Jain A., Smuc 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., Faculty of Economic and Social Sciences and Solvay Business School, Faculty of Sciences and Bioengineering Sciences, Faculty of Engineering, Computational genomics, Institute of Biotechnology, Bioinformatics, Genetics, Helsinki Institute of Life Science HiLIFE, Discovery Research Group/Prof. Hannu Toivonen, Iowa State University (ISU), European Bioinformatics Institute, École Polytechnique de Montréal (EPM), Vinča Institute of Nuclear Sciences, University of Belgrade [Belgrade], University of Bologna, Max Planck Institute for Plant Breeding Research (MPIPZ), European Virus Bioinformatics Center [Jena], Université libre de Bruxelles (ULB), Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes (LIMOS), SIGMA Clermont (SIGMA Clermont)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Department of Computer Science, University of Bristol [Bristol], Department of Computer Science [Columbia], University of Missouri [Columbia] (Mizzou), University of Missouri System-University of Missouri System, Yale School of Public Health (YSPH), Departamento de Geometría y Topología, Universidad de Granada (UGR), Tumor Biology Center, Centre for Nephrology [London, UK], University College of London [London] (UCL), Baylor College of Medicine (BCM), Baylor University, Department of Knowledge Technologies, Structural and Molecular Biology Department, University College London, Queen Mary University of London (QMUL), Spanish National Cancer Research Center (CNIO), Dipartimento di Informatica, Università degli Studi di Milano [Milano] (UNIMI), Dipartimento di Scienze dell'Informazione [Milano], United States Naval Academy, Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Department of Molecular Medicine, Universita degli Studi di Padova, Centro de Regulación Genómica (CRG), Universitat Pompeu Fabra [Barcelona] (UPF), Physics Department, National Tsing Hua University [Hsinchu] (NTHU), Dipartimento di Automatica e Informatica [Torino] (DAUIN), Politecnico di Torino = Polytechnic of Turin (Polito), University of Turku, Bioinformatics Laboratory, University of Turku-Turku Center for Computer Science, Toyota Technological Institute at Chicago [Chicago] (TTIC), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne (UNIL), Department of Computer Science [Colorado State University], Colorado State University [Fort Collins] (CSU), Centre for Plant Integrative Biology [Nothingham] (CPIB), University of Nottingham, UK (UON), BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany., University of Bologna/Università di Bologna, Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Université d'Auvergne - Clermont-Ferrand I (UdA)-SIGMA Clermont (SIGMA Clermont)-Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS), Universidad de Granada = University of Granada (UGR), Università degli Studi di Milano = University of Milan (UNIMI), Università degli Studi di Padova = University of Padua (Unipd), and Université de Lausanne = University of Lausanne (UNIL)

Subjects
Library, Male, Identification, Candida-albicans, Protein function prediction, Long-term memory, Biofilm, Critical assessment, Community challenge, Procedures, Genome, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0302 clinical medicine, Candida albicans, Molecular genetics, lcsh:QH301-705.5, ComputingMilieux_MISCELLANEOUS, Biological ontology, Settore BIO/11 - BIOLOGIA MOLECOLARE, 0303 health sciences, 318 Medical biotechnology, Biotechnology & applied microbiology, Ontology, Expectation, Genetics & heredity, Plant leaf, ddc, 3. Good health, Drosophila melanogaster, Human experiment, Fungal genome, Pseudomonas aeruginosa, Female, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Genome, Fungal, BIOINFORMATICS, Long-Term memory, Locomotion, Human, Adult, Memory, Long-Term, lcsh:QH426-470, Bioinformatics, Long term memory, Generation, Bacterial genome, Computational biology, Biology, Article, 03 medical and health sciences, Annotation, Big data, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Pseudomonas, Genetics, Animals, Humans, Gene, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Animal, Research, Experimental data, Molecular Sequence Annotation, Cell Biology, Nonhuman, Human genetics, lcsh:Genetics, lcsh:Biology (General), Biofilms, Proteins | Genes | Protein functions, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 030217 neurology & neurosurgery, Function (biology), Genome, Bacterial
Abstract

Tosatto, Silvio/0000-0003-4525-7793; Zhang, Feng/0000-0003-3447-897X; Gonzalez, Jose Maria Fernandez/0000-0002-4806-5140; Devignes, Marie-Dominique/0000-0002-0399-8713; Wass, Mark/0000-0001-5428-6479; Falda, Marco/0000-0003-2642-519X; Thurlby, Natalie/0000-0002-1007-0286; Zosa, Elaine/0000-0003-2482-0663; Dessimoz, Christophe/0000-0002-2170-853X; Yunes, Jeffrey/0000-0003-1869-3231; Hamid, Md Nafiz/0000-0001-8681-6526; Hoehndorf, Robert/0000-0001-8149-5890; Dogan, Tunca/0000-0002-1298-9763; NOTARO, MARCO/0000-0003-4309-2200; Cozzetto, Domenico/0000-0001-6752-5432; Lewis, Kimberley/0000-0003-3010-8453; Roche, Daniel/0000-0002-9204-1840; Martin, Maria-Jesus/0000-0001-5454-2815; Tress, Michael/0000-0001-9046-6370; Tolvanen, Martti/0000-0003-3434-7646; Cheng, Jianlin/0000-0003-0305-2853; Rose, Peter/0000-0001-9981-9750; Renaux, Alexandre/0000-0002-4339-2791; Kacsoh, Balint/0000-0001-9171-0611; O'Donovan, Claire/0000-0001-8051-7429; Kulmanov, Maxat/0000-0003-1710-1820; Friedberg, Iddo/0000-0002-1789-8000; Zhou, Naihui/0000-0001-6268-6149, WOS: 000498615000001, PubMed ID: 31744546, 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., National Science FoundationNational Science Foundation (NSF) [DBI1564756, DBI-1458359, DBI-1458390, DMS1614777, CMMI1825941, NSF 1458390]; Gordon and Betty Moore FoundationGordon and Betty Moore Foundation [GBMF 4552]; National Institutes of Health NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [P20 GM113132]; Cystic Fibrosis Foundation [CFRDP STANTO19R0]; BBSRCBiotechnology and Biological Sciences Research Council (BBSRC) [BB/K004131/1, BB/F00964X/1, BB/M025047/1, BB/M015009/1]; Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT)Consejo Nacional de Ciencia y Tecnologia (CONACyT) [14-INV-088, PINV15-315]; NSFNational Science Foundation (NSF) [1660648, DBI 1759934, IIS1763246, DBI-1458477, 0965768, DMR-1420073, DBI-1458443]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM093123, DP1MH110234, UL1 TR002319, U24 TR002306]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2155 "RESIST"German Research Foundation (DFG) [39087428]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM123055, R01GM60595, R15GM120650, GM083107, GM116960, AI134678, NIH R35-GM128637, R00-GM097033]; ERCEuropean Research Council (ERC) [StG 757700]; Spanish Ministry of Science, Innovation and Universities [BFU2017-89833-P]; Severo Ochoa award; Centre of Excellence project "BioProspecting of Adriatic Sea"; Croatian Government; European Regional Development FundEuropean Union (EU) [KK.01.1.1.01.0002]; ATT Tieto kayttoon grant; Academy of FinlandAcademy of Finland; University of Turku; CSC-IT Center for Science Ltd.; University of Miami; National Cancer Institute of the National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI) [U01CA198942]; Helsinki Institute for Life Sciences; Academy of FinlandAcademy of Finland [292589]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [31671367, 31471245, 91631301, 61872094, 61572139]; National Key Research and Development Program of China [2016YFC1000505, 2017YFC0908402]; Italian Ministry of Education, University and Research (MIUR) PRIN 2017 projectMinistry of Education, Universities and Research (MIUR) [2017483NH8]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01, 2018SHZDZX01]; UK Biotechnology and Biological Sciences Research CouncilBiotechnology and Biological Sciences Research Council (BBSRC) [BB/N019431/1, BB/L020505/1, BB/L002817/1]; Elsevier; Extreme Science and Engineering Discovery Environment (XSEDE) award [MCB160101, MCB160124]; Ministry of Education, Science and Technological Development of the Republic of Serbia [173001]; Taiwan Ministry of Science and Technology [106-2221-E-004-011-MY2]; Montana State University; Bavarian Ministry for Education; Simons Foundation; NIH NINDSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [1R21NS103831-01]; University of Illinois at Chicago (UIC) Cancer Center award; UIC College of Liberal Arts and Sciences Faculty Award; UIC International Development Award; Yad Hanadiv [9660/2019]; National Institute of General Medical Science of the National Institute of Health [GM066099, GM079656]; Research Supporting Plan (PSR) of University of Milan [PSR2018-DIP-010-MFRAS]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [150654]; EMBL-European Bioinformatics Institute core funds; CAFA BBSRC [BB/N004876/1]; European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grantEuropean Union (EU) [778247]; COST ActionEuropean Cooperation in Science and Technology (COST) [BM1405]; NIH/NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R01 GM071749]; National Human Genome Research Institute of the National of Health [U41 HG007234]; INB Grant (ISCIII-SGEFI/ERDF) [PT17/0009/0001]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [EEEAG-116E930]; KanSil [2016K121540]; Universita degli Studi di Milano; 111 ProjectMinistry of Education, China - 111 Project [B18015]; key project of Shanghai Science Technology [16JC1420402]; ZJLab; project Ribes Network POR-FESR 3S4H [TOPP-ALFREVE18-01]; PRID/SID of University of Padova [TOPP-SID19-01]; NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R15GM120650]; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [URF/1/3454-01-01, URF/1/3790-01-01]; "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education; National Center for High-performance ComputingIstanbul Technical University, The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto kayttoon grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC -IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124.; The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660/2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Universita degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443.

Published
2019

6. Progress and challenges in predicting protein-protein interaction sites

Author

Alfonso Valencia, Piero Fariselli, Rita Casadio, Michael L. Tress, Iakes Ezkurdia, Lisa Bartoli, Ezkurdia I., Bartoli L., Fariselli P., Casadio R., Valencia A., and Tress M.L.

Subjects
Interface (Java), Computer science, PREDICTION, Protein Conformation, Surface Properties, Static Electricity, Feature selection, Machine learning, computer.software_genre, Field (computer science), Protein–protein interaction, PROTEIN COMPLEXES, Protein structure, Protein Interaction Mapping, Set (psychology), PROTEIN-PROTEIN INTERACTION, BINDING SITES, MACHINE LEARNING, Databases, Protein, Molecular Biology, Binding Sites, business.industry, Proteins, Identification (information), Multiprotein Complexes, Artificial intelligence, business, computer, Protein network, Algorithms, Information Systems
Abstract

The identification of protein-protein interaction sites is an essential intermediate step for mutant design and the prediction of protein networks. In recent years a significant number of methods have been developed to predict these interface residues and here we review the current status of the field. Progress in this area requires a clear view of the methodology applied, the data sets used for training and testing the systems, and the evaluation procedures. We have analysed the impact of a representative set of features and algorithms and highlighted the problems inherent in generating reliable protein data sets and in the posterior analysis of the results. Although it is clear that there have been some improvements in methods for predicting interacting sites, several major bottlenecks remain. Proteins in complexes are still under-represented in the structural databases and in particular many proteins involved in transient complexes are still to be crystallized. We provide suggestions for effective feature selection, and make it clear that community standards for testing, training and performance measures are necessary for progress in the field.

Published
2009

7. The implications of alternative splicing in the ENCODE protein complement

Author

Michael L. Tress, Pier Luigi Martelli, Adam Frankish, Gabrielle A. Reeves, Jan Jaap Wesselink, Corin Yeats, Páll ĺsólfur Ólason, Mario Albrecht, Hedi Hegyi, Alejandro Giorgetti, Domenico Raimondo, Julien Lagarde, Roman A. Laskowski, Gonzalo López, Michael I. Sadowski, James D. Watson, Piero Fariselli, Ivan Rossi, Alinda Nagy, Wang Kai, Zenia Størling, Massimiliano Orsini, Yassen Assenov, Hagen Blankenburg, Carola Huthmacher, Fidel Ramírez, Andreas Schlicker, France Denoeud, Phil Jones, Samuel Kerrien, Sandra Orchard, Stylianos E. Antonarakis, Alexandre Reymond, Ewan Birney, Søren Brunak, Rita Casadio, Roderic Guigo, Jennifer Harrow, Henning Hermjakob, David T. Jones, Thomas Lengauer, Christine A. Orengo, László Patthy, Janet M. Thornton, Anna Tramontano, Alfonso Valencia, Tress M.L., Martelli P.L., Frankish A., Reeves G.A., Wesselink J.J., Yeats C., Olason P.L., Albrecht M., Hegyi H., Giorgetti A., Raimondo D., Lagarde J., Laskowski R.A., Lopez G., Sadowski M.I., Watson J.D., Fariselli P., Rossi I., Nagy A., Kai W., Storling Z., Orsini M., Assenov Y., Blankenburg H., Huthmacher C., Ramirez F., Schlicker A., Denoeud F., Jones P., Kerrien S., Orchard S., Antonarakis S.E., Reymond A., Birney E., Brunak S., Casadio R., Guigo R., Harrow J., Hermjakob H., Jones D.T., Lengauer T., Orengo C.A., Patthy L., Thornton J.M., Tramontano A., and Valencia A.

Subjects
Protein isoform, Models, Molecular, SPLICE, STRUCTURE, Protein Conformation, RNA Splicing, FUNCTION, HUMAN, ISOFORMS, Biology, Protein Sorting Signals, Gene product, Protein structure, Databases, Genetic, RNA Precursors, Humans, Protein Isoforms, Gene, Genetics, Regulation of gene expression, ddc:616, The implications of alternative splicing in the ENCODE protein complement, Internet, Multidisciplinary, Genome, Human, Alternative splicing, function, human, human genome, isoforms, protein structure, splice, splicing, structure, Proteins, Biological Sciences, Protein Structure, Tertiary, Alternative Splicing, Gene Expression Regulation, RNA splicing, Human genome, Proteins/chemistry
Abstract

Alternative premessenger RNA splicing enables genes to generate more than one gene product. Splicing events that occur within protein coding regions have the potential to alter the biological function of the expressed protein and even to create new protein functions. Alternative splicing has been suggested as one explanation for the discrepancy between the number of human genes and functional complexity. Here, we carry out a detailed study of the alternatively spliced gene products annotated in the ENCODE pilot project. We find that alternative splicing in human genes is more frequent than has commonly been suggested, and we demonstrate that many of the potential alternative gene products will have markedly different structure and function from their constitutively spliced counterparts. For the vast majority of these alternative isoforms, little evidence exists to suggest they have a role as functional proteins, and it seems unlikely that the spectrum of conventional enzymatic or structural functions can be substantially extended through alternative splicing.

Published
2007

8. Sequence-based feature prediction and annotation of proteins

Author

Agnieszka S. Juncker, Christos A. Ouzounis, Søren Brunak, Andreas Bernsel, Gunnar von Heijne, Andrea Pierleoni, Michael L. Tress, Lars Juhl Jensen, Alfonso Valencia, Rita Casadio, Peer Bork, Juncker A.S., Jensen L.J., Pierleoni A., Bernsel A., Tress M.L., Bork P., von Heijne G., Valencia A., Ouzounis C.A., Casadio R., and Brunak S.

Subjects
Proteome, health care facilities, manpower, and services, education, Amino Acid Motifs, Review, Computational biology, Biology, Annotation, health services administration, Human proteome project, Humans, health care economics and organizations, Sequence (medicine), Protein function, Binding Sites, Repertoire, Computational Biology, Proteins, SEQUENCE ANALYSIS, ComputingMethodologies_PATTERNRECOGNITION, Workflow, Feature (computer vision), MACHINE LEARNING, GENOMICS, Protein Kinases, FUNCTIONAL ANNOTATION
Abstract

The combination of prediction tools in complex workflows and pipelines facilitates prediction of protein features from sequence., A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations, for example, the entire repertoire of kinase-binding motifs in the human proteome.

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