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Zhang C, Freddolino L. A large-scale assessment of sequence database search tools for homology-based protein function prediction. Brief Bioinform 2024; 25:bbae349. [PMID: 39038936 PMCID: PMC11262835 DOI: 10.1093/bib/bbae349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 06/03/2024] [Accepted: 07/05/2024] [Indexed: 07/24/2024] Open
Abstract
Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. These searches are also a critical component in most state-of-the-art machine learning and deep learning-based protein function predictors. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND-one of the most popular tools for function prediction-under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. Additionally, we developed a new scoring function to derive GO prediction from homologous hits that consistently outperform previously proposed scoring functions. These findings enable the improvement of almost all protein function prediction algorithms with a few easily implementable changes in their sequence homolog-based component. This study emphasizes the critical role of search parameter settings in homology-based function transfer and should have an important contribution to the development of future protein function prediction algorithms.
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Affiliation(s)
- Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, Department of Biological Chemistry, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, United States
| | - Lydia Freddolino
- Department of Computational Medicine and Bioinformatics, Department of Biological Chemistry, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, United States
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2
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Yu Y, Xu S, He R, Liang G. Application of Molecular Simulation Methods in Food Science: Status and Prospects. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:2684-2703. [PMID: 36719790 DOI: 10.1021/acs.jafc.2c06789] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.
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Affiliation(s)
- Yuandong Yu
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
| | - Shiqi Xu
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
| | - Ran He
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
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3
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McGreig JE, Uri H, Antczak M, Sternberg MJE, Michaelis M, Wass MN. 3DLigandSite: structure-based prediction of protein-ligand binding sites. Nucleic Acids Res 2022; 50:W13-W20. [PMID: 35412635 PMCID: PMC9252821 DOI: 10.1093/nar/gkac250] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/13/2022] [Accepted: 04/03/2022] [Indexed: 01/13/2023] Open
Abstract
3DLigandSite is a web tool for the prediction of ligand-binding sites in proteins. Here, we report a significant update since the first release of 3DLigandSite in 2010. The overall methodology remains the same, with candidate binding sites in proteins inferred using known binding sites in related protein structures as templates. However, the initial structural modelling step now uses the newly available structures from the AlphaFold database or alternatively Phyre2 when AlphaFold structures are not available. Further, a sequence-based search using HHSearch has been introduced to identify template structures with bound ligands that are used to infer the ligand-binding residues in the query protein. Finally, we introduced a machine learning element as the final prediction step, which improves the accuracy of predictions and provides a confidence score for each residue predicted to be part of a binding site. Validation of 3DLigandSite on a set of 6416 binding sites obtained 92% recall at 75% precision for non-metal binding sites and 52% recall at 75% precision for metal binding sites. 3DLigandSite is available at https://www.wass-michaelislab.org/3dligandsite. Users submit either a protein sequence or structure. Results are displayed in multiple formats including an interactive Mol* molecular visualization of the protein and the predicted binding sites.
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Affiliation(s)
- Jake E McGreig
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Hannah Uri
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Magdalena Antczak
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
| | - Martin Michaelis
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Mark N Wass
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
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4
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Vicedomini R, Bouly JP, Laine E, Falciatore A, Carbone A. Multiple profile models extract features from protein sequence data and resolve functional diversity of very different protein families. Mol Biol Evol 2022; 39:6556147. [PMID: 35353898 PMCID: PMC9016551 DOI: 10.1093/molbev/msac070] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Functional classification of proteins from sequences alone has become a critical bottleneck in understanding the myriad of protein sequences that accumulate in our databases. The great diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of the evolution of living organisms and for biotechnological applications. ProfileView is a sequence-based computational method, designed to functionally classify sets of homologous sequences. It relies on two main ideas: the use of multiple profile models whose construction explores evolutionary information in available databases, and a novel definition of a representation space in which to analyse sequences with multiple profile models combined together. ProfileView classifies protein families by enriching known functional groups with new sequences and discovering new groups and subgroups. We validate ProfileView on seven classes of widespread proteins involved in the interaction with nucleic acids, amino acids and small molecules, and in a large variety of functions and enzymatic reactions. Profile-View agrees with the large set of functional data collected for these proteins from the literature regarding the organisation into functional subgroups and residues that characterise the functions. In addition, ProfileView resolves undefined functional classifications and extracts the molecular determinants underlying protein functional diversity, showing its potential to select sequences towards accurate experimental design and discovery of novel biological functions. On protein families with complex domain architecture, ProfileView functional classification reconciles domain combinations, unlike phylogenetic reconstruction. ProfileView proves to outperform the functional classification approach PANTHER, the two k-mer based methods CUPP and eCAMI and a neural network approach based on Restricted Boltzmann Machines. It overcomes time complexity limitations of the latter.
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Affiliation(s)
- R Vicedomini
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 4 place Jussieu, 75005 Paris, France.,Sorbonne Université, Institut des Sciences du Calcul et des Données
| | - J P Bouly
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 4 place Jussieu, 75005 Paris, France.,CNRS, Sorbonne Université Institut de Biologie Physico-Chimique, Laboratory of Chloroplast Biology and Light Sensing in Microalgae - UMR7141, Paris, France
| | - E Laine
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 4 place Jussieu, 75005 Paris, France
| | - A Falciatore
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 4 place Jussieu, 75005 Paris, France.,CNRS, Sorbonne Université Institut de Biologie Physico-Chimique, Laboratory of Chloroplast Biology and Light Sensing in Microalgae - UMR7141, Paris, France
| | - A Carbone
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 4 place Jussieu, 75005 Paris, France.,Institut Universitaire de France, Paris 75005, France
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5
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Wang LR, Wong L, Goh WWB. How doppelgänger effects in biomedical data confound machine learning. Drug Discov Today 2021; 27:678-685. [PMID: 34743902 DOI: 10.1016/j.drudis.2021.10.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 09/22/2021] [Accepted: 10/22/2021] [Indexed: 12/26/2022]
Abstract
Machine learning (ML) models have been increasingly adopted in drug development for faster identification of potential targets. Cross-validation techniques are commonly used to evaluate these models. However, the reliability of such validation methods can be affected by the presence of data doppelgängers. Data doppelgängers occur when independently derived data are very similar to each other, causing models to perform well regardless of how they are trained (i.e., the doppelgänger effect). Despite the abundance of data doppelgängers in biomedical data and their inflationary effects, they remain uncharacterized. We show their prevalence in biomedical data, demonstrate how doppelgängers arise, and provide proof of their confounding effects. To mitigate the doppelgänger effect, we recommend identifying data doppelgängers before the training-validation split.
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Affiliation(s)
- Li Rong Wang
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore; Department of Pathology, National University of Singapore, Singapore
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore.
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6
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An integrated deep learning and dynamic programming method for predicting tumor suppressor genes, oncogenes, and fusion from PDB structures. Comput Biol Med 2021; 133:104323. [PMID: 33934067 DOI: 10.1016/j.compbiomed.2021.104323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 02/18/2021] [Accepted: 03/07/2021] [Indexed: 11/20/2022]
Abstract
Mutations in proto-oncogenes (ONGO) and the loss of regulatory function of tumor suppression genes (TSG) are the common underlying mechanism for uncontrolled tumor growth. While cancer is a heterogeneous complex of distinct diseases, finding the potentiality of the genes related functionality to ONGO or TSG through computational studies can help develop drugs that target the disease. This paper proposes a classification method that starts with a preprocessing stage to extract the feature map sets from the input 3D protein structural information. The next stage is a deep convolutional neural network stage (DCNN) that outputs the probability of functional classification of genes. We explored and tested two approaches: in Approach 1, all filtered and cleaned 3D-protein-structures (PDB) are pooled together, whereas in Approach 2, the primary structures and their corresponding PDBs are separated according to the genes' primary structural information. Following the DCNN stage, a dynamic programming-based method is used to determine the final prediction of the primary structures' functionality. We validated our proposed method using the COSMIC online database. For the ONGO vs TSG classification problem the AUROC of the DCNN stage for Approach 1 and Approach 2 DCNN are 0.978 and 0.765, respectively. The AUROCs of the final genes' primary structure functionality classification for Approach 1 and Approach 2 are 0.989, and 0.879, respectively. For comparison, the current state-of-the-art reported AUROC is 0.924. Our results warrant further study to apply the deep learning models to humans' (GRCh38) genes, for predicting their corresponding probabilities of functionality in the cancer drivers.
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7
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Khan IK, Jain A, Rawi R, Bensmail H, Kihara D. Prediction of protein group function by iterative classification on functional relevance network. Bioinformatics 2020; 35:1388-1394. [PMID: 30192921 DOI: 10.1093/bioinformatics/bty787] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 08/28/2018] [Accepted: 09/04/2018] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Biological experiments including proteomics and transcriptomics approaches often reveal sets of proteins that are most likely to be involved in a disease/disorder. To understand the functional nature of a set of proteins, it is important to capture the function of the proteins as a group, even in cases where function of individual proteins is not known. In this work, we propose a model that takes groups of proteins found to work together in a certain biological context, integrates them into functional relevance networks, and subsequently employs an iterative inference on graphical models to identify group functions of the proteins, which are then extended to predict function of individual proteins. RESULTS The proposed algorithm, iterative group function prediction (iGFP), depicts proteins as a graph that represents functional relevance of proteins considering their known functional, proteomics and transcriptional features. Proteins in the graph will be clustered into groups by their mutual functional relevance, which is iteratively updated using a probabilistic graphical model, the conditional random field. iGFP showed robust accuracy even when substantial amount of GO annotations were missing. The perspective of 'group' function annotation opens up novel approaches for understanding functional nature of proteins in biological systems.Availability and implementation: http://kiharalab.org/iGFP/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ishita K Khan
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.,eBay Search Science, San Jose, CA, USA
| | - Aashish Jain
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Reda Rawi
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.,Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Halima Bensmail
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.,Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
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8
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Makrodimitris S, van Ham RCHJ, Reinders MJT. Improving protein function prediction using protein sequence and GO-term similarities. Bioinformatics 2020; 35:1116-1124. [PMID: 30169569 PMCID: PMC6449755 DOI: 10.1093/bioinformatics/bty751] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2017] [Revised: 07/04/2018] [Accepted: 08/28/2018] [Indexed: 12/26/2022] Open
Abstract
MOTIVATION Most automatic functional annotation methods assign Gene Ontology (GO) terms to proteins based on annotations of highly similar proteins. We advocate that proteins that are less similar are still informative. Also, despite their simplicity and structure, GO terms seem to be hard for computers to learn, in particular the Biological Process ontology, which has the most terms (>29 000). We propose to use Label-Space Dimensionality Reduction (LSDR) techniques to exploit the redundancy of GO terms and transform them into a more compact latent representation that is easier to predict. RESULTS We compare proteins using a sequence similarity profile (SSP) to a set of annotated training proteins. We introduce two new LSDR methods, one based on the structure of the GO, and one based on semantic similarity of terms. We show that these LSDR methods, as well as three existing ones, improve the Critical Assessment of Functional Annotation performance of several function prediction algorithms. Cross-validation experiments on Arabidopsis thaliana proteins pinpoint the superiority of our GO-aware LSDR over generic LSDR. Our experiments on A.thaliana proteins show that the SSP representation in combination with a kNN classifier outperforms state-of-the-art and baseline methods in terms of cross-validated F-measure. AVAILABILITY AND IMPLEMENTATION Source code for the experiments is available at https://github.com/stamakro/SSP-LSDR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Stavros Makrodimitris
- Department of Intelligent Systems, Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.,Department of Bioinformatics, Keygene N.V., Wageningen, The Netherlands
| | - Roeland C H J van Ham
- Department of Intelligent Systems, Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.,Department of Bioinformatics, Keygene N.V., Wageningen, The Netherlands
| | - Marcel J T Reinders
- Department of Intelligent Systems, Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
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9
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Jain A, Kihara D. Phylo-PFP: improved automated protein function prediction using phylogenetic distance of distantly related sequences. Bioinformatics 2019; 35:753-759. [PMID: 30165572 DOI: 10.1093/bioinformatics/bty704] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 07/30/2018] [Accepted: 08/23/2018] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Function annotation of proteins is fundamental in contemporary biology across fields including genomics, molecular biology, biochemistry, systems biology and bioinformatics. Function prediction is indispensable in providing clues for interpreting omics-scale data as well as in assisting biologists to build hypotheses for designing experiments. As sequencing genomes is now routine due to the rapid advancement of sequencing technologies, computational protein function prediction methods have become increasingly important. A conventional method of annotating a protein sequence is to transfer functions from top hits of a homology search; however, this approach has substantial short comings including a low coverage in genome annotation. RESULTS Here we have developed Phylo-PFP, a new sequence-based protein function prediction method, which mines functional information from a broad range of similar sequences, including those with a low sequence similarity identified by a PSI-BLAST search. To evaluate functional similarity between identified sequences and the query protein more accurately, Phylo-PFP reranks retrieved sequences by considering their phylogenetic distance. Compared to the Phylo-PFP's predecessor, PFP, which was among the top ranked methods in the second round of the Critical Assessment of Functional Annotation (CAFA2), Phylo-PFP demonstrated substantial improvement in prediction accuracy. Phylo-PFP was further shown to outperform prediction programs to date that were ranked top in CAFA2. AVAILABILITY AND IMPLEMENTATION Phylo-PFP web server is available for at http://kiharalab.org/phylo_pfp.php. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Aashish Jain
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.,Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
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10
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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, Gemovic B, Perovic VR, Davidović RS, Sumonja N, Veljkovic N, Asgari E, Mofrad MRK, 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 PH, Tseng WC, 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 MD, Koo DCE, Bonneau R, Gligorijević V, Barot M, Fang H, Toppo S, Lavezzo E, Falda M, Berselli M, Tosatto SCE, 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 JM, Liao WH, Liu YW, 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 MEE, 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 Biol 2019; 20:244. [PMID: 31744546 PMCID: PMC6864930 DOI: 10.1186/s13059-019-1835-8] [Citation(s) in RCA: 202] [Impact Index Per Article: 40.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 09/24/2019] [Indexed: 12/23/2022] Open
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.
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Affiliation(s)
- Naihui Zhou
- Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA.,Program in Bioinformatics and Computational Biology, Ames, IA, USA
| | - Yuxiang Jiang
- Indiana University Bloomington, Bloomington, Indiana, USA
| | - Timothy R Bergquist
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Alexandra J Lee
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Balint Z Kacsoh
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA.,Department of Molecular and Systems Biology, Hanover, NH, USA
| | - Alex W Crocker
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Kimberley A Lewis
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - George Georghiou
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom
| | - Huy N Nguyen
- Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA.,Program in Computer Science, Ames, IA, USA
| | - Md Nafiz Hamid
- Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA.,Program in Bioinformatics and Computational Biology, Ames, IA, USA
| | - Larry Davis
- Program in Bioinformatics and Computational Biology, Ames, IA, USA
| | - Tunca Dogan
- Department of Computer Engineering, Hacettepe University, Ankara, Turkey.,European Molecular Biolo gy Labora tory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Volkan Atalay
- Department of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey
| | - Ahmet S Rifaioglu
- Department of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey.,Department of Computer Engineering, Iskenderun Technical University, Hatay, Turkey
| | - Alperen Dalkıran
- Department of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey
| | - Rengul Cetin Atalay
- CanSyL, Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Rebecca L Hurto
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Peter L Freddolino
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | | | - Fran Supek
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - José M Fernández
- INB Coordination Unit, Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Catalonia, Spain.,(former) INB GN2, Structural and Computational Biology Programme, Spanish National Cancer Research Centre, Barcelona, Catalonia, Spain
| | - Branislava Gemovic
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Vladimir R Perovic
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Radoslav S Davidović
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Neven Sumonja
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences VINCA, University of Belgrade, Belgrade, Serbia
| | - Ehsaneddin Asgari
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering, University of California Berkeley, Berkeley, CA, USA.,Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Berkeley, CA, USA
| | | | - Giuseppe Profiti
- Bologna Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.,National Research Council, IBIOM, Bologna, Italy
| | - Castrense Savojardo
- Bologna Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Pier Luigi Martelli
- Bologna Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Rita Casadio
- Bologna Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Florian Boecker
- University of Bonn: INRES Crop Bioinformatics, Bonn, North Rhine-Westphalia, Germany
| | - Heiko Schoof
- INRES Crop Bioinformatics, University of Bonn, Bonn, Germany
| | - Indika Kahanda
- Gianforte School of Computing, Montana State University, Bozeman, Montana, USA
| | - Natalie Thurlby
- University of Bristol, Computer Science, Bristol, Bristol, United Kingdom
| | - Alice C McHardy
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany.,RESIST, DFG Cluster of Excellence 2155, Brunswick, Germany
| | - Alexandre Renaux
- Interuniversity Institute of Bioinformatics in Brussels, Université libre de Bruxelles - Vrije Universiteit Brussel, Brussels, Belgium.,Machine Learning Group, Université libre de Bruxelles, Brussels, Belgium.,Artificial Intelligence lab, Vrije Universiteit Brussel, Brussels, Belgium
| | - Rabie Saidi
- European Molecular Biolo gy Labora tory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Julian Gough
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Alex A Freitas
- University of Kent, School of Computing, Canterbury, United Kingdom
| | - Magdalena Antczak
- School of Biosciences, University of Kent, Canterbury, Kent, United Kingdom
| | - Fabio Fabris
- University of Kent, School of Computing, Canterbury, United Kingdom
| | - Mark N Wass
- School of Biosciences, University of Kent, Canterbury, Kent, United Kingdom
| | - Jie Hou
- University of Missouri, Computer Science, Columbia, Missouri, USA.,Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
| | - Zheng Wang
- University of Miami, Coral Gables, Florida, USA
| | - Alfonso E Romero
- Centre for Systems and Synthetic Biology, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom
| | - Alberto Paccanaro
- Centre for Systems and Synthetic Biology, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom
| | - Haixuan Yang
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Galway, Ireland.,Technical University of Munich, Garching, Germany
| | - Tatyana Goldberg
- Department of Informatics, Bioinformatics & Computational Biology-i12, Technische Universitat Munchen, Munich, Germany
| | - Chenguang Zhao
- Faculty for Informatics, Garching, Germany.,Department for Bioinformatics and Computational Biology, Garching, Germany.,School of Computing Sciences and Computer Engineering, Hattiesburg, Mississippi, USA
| | - Liisa Holm
- Institute of Biotechnology, Helsinki Institute of Life Sciences, University of Helsinki, Finland, Helsinki, Finland
| | - Petri Törönen
- Institute of Biotechnology, Helsinki Institute of Life Sciences, University of Helsinki, Finland, Helsinki, Finland
| | - Alan J Medlar
- Institute of Biotechnology, Helsinki Institute of Life Sciences, University of Helsinki, Finland, Helsinki, Finland
| | - Elaine Zosa
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | | | - Ilya Novikov
- Baylor College of Medicine, Department of Biochemistry and Molecular Biology, Houston, TX, USA
| | - Angela Wilkins
- Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, TX, USA
| | - Olivier Lichtarge
- Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, TX, USA
| | - Po-Han Chi
- National TsingHua University, Hsinchu, Taiwan
| | - Wei-Cheng Tseng
- Department of Electrical Engineering in National Tsing Hua University, Hsinchu City, Taiwan
| | - Michal Linial
- The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Peter W Rose
- University of California San Diego, San Diego Supercomputer Center, La Jolla, California, USA
| | - Christophe Dessimoz
- Department of Computational Biology and Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Department of Genetics, Evolution & Environment, and Department of Computer Science, University College London, London, UK.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Vedrana Vidulin
- Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Saso Dzeroski
- Jozef Stefan Institute, Ljubljana, Slovenia.,Jozef Stefan International Postgraduate School, Ljubljana, Slovenia
| | - Ian Sillitoe
- Research Department of Structural and Molecular Biology, University College London, London, England
| | - Sayoni Das
- Research Department of Structural and Molecular Biology, University College London, London, United Kingdom
| | - Jonathan Gill Lees
- Research Department of Structural and Molecular Biology, University College London, London, United Kingdom.,Department of Health and Life Sciences, Oxford Brookes University, London, UK
| | - David T Jones
- The Francis Crick Institute, Biomedical Data Science Laboratory, London, United Kingdom.,Department of Genetics, Evolution and Environment, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Cen Wan
- Department of Computer Science, University College London, London, United Kingdom.,The Francis Crick Institute, Biomedical Data Science Laboratory, London, United Kingdom
| | - Domenico Cozzetto
- Department of Computer Science, University College London, London, United Kingdom.,The Francis Crick Institute, Biomedical Data Science Laboratory, London, United Kingdom
| | - Rui Fa
- Department of Computer Science, University College London, London, United Kingdom.,The Francis Crick Institute, Biomedical Data Science Laboratory, London, United Kingdom
| | - Mateo Torres
- Centre for Systems and Synthetic Biology, Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom
| | - Alex Warwick Vesztrocy
- Department of Genetics, Evolution and Environment, University College London, Gower Street, London, WC1E 6BT, United Kingdom.,SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - Jose Manuel Rodriguez
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain
| | - Michael L Tress
- Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Marco Frasca
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Marco Notaro
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Giuliano Grossi
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Alessandro Petrini
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Matteo Re
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Giorgio Valentini
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy
| | - Marco Mesiti
- Università degli Studi di Milano - Computer Science Department - AnacletoLab, Milan, Milan, Italy.,Institut de Biologie Computationnelle, LIRMM, CNRS-UMR 5506, Universite de Montpellier, Montpellier, France
| | - Daniel B Roche
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universitat Munchen, Munich, Germany
| | - Jonas Reeb
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universitat Munchen, Munich, Germany
| | - David W Ritchie
- University of Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France
| | - Sabeur Aridhi
- University of Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France
| | | | - Marie-Dominique Devignes
- University of Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.,University of Lorraine, Nancy, Lorraine, France.,Inria, Nancy, France
| | | | - Richard Bonneau
- NYU Center for Data Science, New York, 10010, NY, USA.,Flatiron Institute, CCB, New York, 10010, NY, USA
| | - Vladimir Gligorijević
- Center for Computational Biology (CCB), Flatiron Institute, Simons Foundation, New York, New York, USA
| | - Meet Barot
- Center for Data Science, New York University, New York, 10011, NY, USA
| | - Hai Fang
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Stefano Toppo
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Enrico Lavezzo
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Marco Falda
- Department of Biology, University of Padova, Padova, Italy
| | - Michele Berselli
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Silvio C E Tosatto
- CNR Institute of Neuroscience, Padova, Italy.,Department of Biomedical Sciences, University of Padua, Padova, Italy
| | - Marco Carraro
- Department of Biomedical Sciences, University of Padua, Padova, Italy
| | - Damiano Piovesan
- Department of Biomedical Sciences, University of Padua, Padova, Italy
| | - Hafeez Ur Rehman
- Department of Computer Science, National University of Computer and Emerging Sciences, Peshawar, Khyber Pakhtoonkhwa, Pakistan
| | - Qizhong Mao
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA.,University of California, Riverside, Philadelphia, PA, USA
| | - Shanshan Zhang
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Slobodan Vucetic
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Gage S Black
- Department of Biology, Brigham Young University, Provo, UT, USA.,Bioinformatics Research Group, Provo, UT, USA
| | - Dane Jo
- Department of Biology, Brigham Young University, Provo, UT, USA.,Bioinformatics Research Group, Provo, UT, USA
| | - Erica Suh
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Jonathan B Dayton
- Department of Biology, Brigham Young University, Provo, UT, USA.,Bioinformatics Research Group, Provo, UT, USA
| | - Dallas J Larsen
- Department of Biology, Brigham Young University, Provo, UT, USA.,Bioinformatics Research Group, Provo, UT, USA
| | - Ashton R Omdahl
- Department of Biology, Brigham Young University, Provo, UT, USA.,Bioinformatics Research Group, Provo, UT, USA
| | - Liam J McGuffin
- School of Biological Sciences, University of Reading, Reading, England, United Kingdom
| | | | - Patricia C Babbitt
- Department of Pharmaceutical Chemistry, San Francisco, CA, USA.,Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 94158, CA, USA
| | - Jeffrey M Yunes
- UC Berkeley - UCSF Graduate Program in Bioengineering, University of California, San Francisco, 94158, CA, USA.,Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 94158, CA, USA
| | - Paolo Fontana
- Research and Innovation Center, Edmund Mach Foundation, San Michele all'Adige, Italy
| | - Feng Zhang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, Shanghai, China.,Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, Shanghai, China
| | - Shanfeng Zhu
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.,Institute of Science and Technology for Brain-Inspired Intelligence and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Ronghui You
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.,Institute of Science and Technology for Brain-Inspired Intelligence and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Zihan Zhang
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Suyang Dai
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Shuwei Yao
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.,Institute of Science and Technology for Brain-Inspired Intelligence and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, China
| | - Weidong Tian
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, Shanghai, China.,Department of Pediatrics, Brain Tumor Center, Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA
| | - Caleb Chandler
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA
| | - Miguel Amezola
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA
| | - Devon Johnson
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA
| | - Jia-Ming Chang
- Department of Computer Science, National Chengchi University, Taipei, Taiwan
| | - Wen-Hung Liao
- Department of Computer Science, National Chengchi University, Taipei, Taiwan
| | - Yi-Wei Liu
- Department of Computer Science, National Chengchi University, Taipei, Taiwan
| | | | | | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Jeddah, Saudi Arabia
| | - Maxat Kulmanov
- Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Jeddah, Saudi Arabia
| | - Imane Boudellioua
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.,Computer, Electrical and Mathematical Sciences Engineering Division (CEMSE), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Gianfranco Politano
- Control and Computer Engineering Department, Politecnico di Torino, Torino, TO, Italy
| | - Stefano Di Carlo
- Control and Computer Engineering Department, Politecnico di Torino, Torino, TO, Italy
| | - Alfredo Benso
- Control and Computer Engineering Department, Politecnico di Torino, Torino, TO, Italy
| | - Kai Hakala
- Department of Future Technologies, Turku NLP Group, University of Turku, Turku, Finland.,University of Turku Graduate School (UTUGS), Turku, Finland
| | - Filip Ginter
- Department of Future Technologies, Turku NLP Group, University of Turku, Turku, Finland.,University of Turku, Turku, Finland
| | - Farrokh Mehryary
- Department of Future Technologies, Turku NLP Group, University of Turku, Turku, Finland.,University of Turku Graduate School (UTUGS), Turku, Finland
| | - Suwisa Kaewphan
- Department of Future Technologies, Turku NLP Group, University of Turku, Turku, Finland.,University of Turku Graduate School (UTUGS), Turku, Finland.,Turku Centre for Computer Science (TUCS), Turku, Finland
| | - Jari Björne
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Turku, FI-20014, Finland.,Turku Centre for Computer Science (TUCS), Agora, Vesilinnantie 3, Turku, FI-20500, Finland
| | | | | | - Tapio Salakoski
- Department of Future Technologies, Faculty of Science and Engineering, University of Turku, Turku, FI-20014, Finland.,Turku Centre for Computer Science (TUCS), Agora, Vesilinnantie 3, Turku, FI-20500, Finland
| | - Daisuke Kihara
- Department of Biological Sciences, Department of Computer Science, Purdue University, 47907, IN, USA.,Department of Pediatrics, University of Cincinnati, Cincinnati, 45229, OH, USA
| | - Aashish Jain
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Tomislav Šmuc
- Division of Electronics, Rudjer Boskovic Institute, Zagreb, Croatia
| | - Adrian Altenhoff
- Department of Computer Science, ETH Zurich, Zurich, Switzerland.,SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Asa Ben-Hur
- Department of Computer Science, Colorado State University, Fort Collins, CO, USA
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology-i12, Technische Universitat Munchen, Munich, Germany.,Institute for Food and Plant Sciences WZW, Technische Universität München, Freising, Germany
| | | | - Christine A Orengo
- Research Department of Structural and Molecular Biology, University College London, London, United Kingdom
| | - Constance J Jeffery
- Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Giovanni Bosco
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Deborah A Hogan
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA.,Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Maria J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Philadelphia, Pennsylvania, USA
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
| | - Iddo Friedberg
- Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA.
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11
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Environmental conditions shape the nature of a minimal bacterial genome. Nat Commun 2019; 10:3100. [PMID: 31308405 PMCID: PMC6629657 DOI: 10.1038/s41467-019-10837-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 06/04/2019] [Indexed: 12/16/2022] Open
Abstract
Of the 473 genes in the genome of the bacterium with the smallest genome generated to date, 149 genes have unknown function, emphasising a universal problem; less than 1% of proteins have experimentally determined annotations. Here, we combine the results from state-of-the-art in silico methods for functional annotation and assign functions to 66 of the 149 proteins. Proteins that are still not annotated lack orthologues, lack protein domains, and/ or are membrane proteins. Twenty-four likely transporter proteins are identified indicating the importance of nutrient uptake into and waste disposal out of the minimal bacterial cell in a nutrient-rich environment after removal of metabolic enzymes. Hence, the environment shapes the nature of a minimal genome. Our findings also show that the combination of multiple different state-of-the-art in silico methods for annotating proteins is able to predict functions, even for difficult to characterise proteins and identify crucial gaps for further development.
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12
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Fodeh SJ, Tiwari A. Exploiting MEDLINE for gene molecular function prediction via NMF based multi-label classification. J Biomed Inform 2018; 86:160-166. [PMID: 30130573 DOI: 10.1016/j.jbi.2018.08.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 08/13/2018] [Accepted: 08/17/2018] [Indexed: 11/25/2022]
Abstract
Gene ontology (GO) provides a representation of terms and categories used to describe genes and their molecular functions, cellular components and biological processes. GO has been the standard for describing the functions of specific genes in different model organisms. GO annotation, or the tagging of genes with GO terms, has mostly been a manual and time-consuming curation process. Although many automated approaches have been proposed for annotation, few have utilized knowledge available in the literature. In this manuscript, we describe the development and evaluation of an innovative predictive system to automatically assign molecular functions (GO terms) to genes using the biomedical literature. Because genes could be associated with multiple molecular functions, we posed the GO molecular function annotation as a multi-label classification problem with several classes. We used non-negative matrix factorization (NMF) for feature reduction and then classified the genes. To address the multi-label aspect of the data, we used the binary-relevance method. Although we experimented with several classifiers, the combination of binary-relevance and K-nearest neighbor (KNN) classifier performed best. Our evaluation on UniProtKB/Swiss-Prot dataset showed the best performance of 0.84 in terms of F1-measure.
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Affiliation(s)
- Samah Jamal Fodeh
- Yale Center for Medical Informatics, Yale University, 300 George st, Suite 501, New Haven, CT 06511, United States.
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13
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Vidulin V, Šmuc T, Džeroski S, Supek F. The evolutionary signal in metagenome phyletic profiles predicts many gene functions. MICROBIOME 2018; 6:129. [PMID: 29991352 PMCID: PMC6040064 DOI: 10.1186/s40168-018-0506-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 06/19/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND The function of many genes is still not known even in model organisms. An increasing availability of microbiome DNA sequencing data provides an opportunity to infer gene function in a systematic manner. RESULTS We evaluated if the evolutionary signal contained in metagenome phyletic profiles (MPP) is predictive of a broad array of gene functions. The MPPs are an encoding of environmental DNA sequencing data that consists of relative abundances of gene families across metagenomes. We find that such MPPs can accurately predict 826 Gene Ontology functional categories, while drawing on human gut microbiomes, ocean metagenomes, and DNA sequences from various other engineered and natural environments. Overall, in this task, the MPPs are highly accurate, and moreover they provide coverage for a set of Gene Ontology terms largely complementary to standard phylogenetic profiles, derived from fully sequenced genomes. We also find that metagenomes approximated from taxon relative abundance obtained via 16S rRNA gene sequencing may provide surprisingly useful predictive models. Crucially, the MPPs derived from different types of environments can infer distinct, non-overlapping sets of gene functions and therefore complement each other. Consistently, simulations on > 5000 metagenomes indicate that the amount of data is not in itself critical for maximizing predictive accuracy, while the diversity of sampled environments appears to be the critical factor for obtaining robust models. CONCLUSIONS In past work, metagenomics has provided invaluable insight into ecology of various habitats, into diversity of microbial life and also into human health and disease mechanisms. We propose that environmental DNA sequencing additionally constitutes a useful tool to predict biological roles of genes, yielding inferences out of reach for existing comparative genomics approaches.
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Affiliation(s)
- Vedrana Vidulin
- Faculty of Information Studies, 8000 Novo Mesto, Slovenia
- Division of Electronics, Rudjer Boskovic Institute, 10000 Zagreb, Croatia
- Department of Knowledge Technologies, Jozef Stefan Institute, 1000 Ljubljana, Slovenia
| | - Tomislav Šmuc
- Division of Electronics, Rudjer Boskovic Institute, 10000 Zagreb, Croatia
| | - Sašo Džeroski
- Department of Knowledge Technologies, Jozef Stefan Institute, 1000 Ljubljana, Slovenia
| | - Fran Supek
- Genome Data Science, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, 08028 Barcelona, Spain
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14
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Taha K. Inferring the Functions of Proteins from the Interrelationships between Functional Categories. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:157-167. [PMID: 27723600 DOI: 10.1109/tcbb.2016.2615608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This study proposes a new method to determine the functions of an unannotated protein. The proteins and amino acid residues mentioned in biomedical texts associated with an unannotated protein can be considered as characteristics terms for , which are highly predictive of the potential functions of . Similarly, proteins and amino acid residues mentioned in biomedical texts associated with proteins annotated with a functional category can be considered as characteristics terms of . We introduce in this paper an information extraction system called IFP_IFC that predicts the functions of an unannotated protein by representing and each functional category by a vector of weights. Each weight reflects the degree of association between a characteristic term and (or a characteristic term and ). First, IFP_IFC constructs a network, whose nodes represent the different functional categories, and its edges the interrelationships between the nodes. Then, it determines the functions of by employing random walks with restarts on the mentioned network. The walker is the vector of . Finally, is assigned to the functional categories of the nodes in the network that are visited most by the walker. We evaluated the quality of IFP_IFC by comparing it experimentally with two other systems. Results showed marked improvement.
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15
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Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier. J Theor Biol 2017; 418:105-110. [DOI: 10.1016/j.jtbi.2017.01.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2016] [Revised: 09/24/2016] [Accepted: 01/04/2017] [Indexed: 12/13/2022]
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16
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Rubel ET, Raittz RT, Coimbra NADR, Gehlen MAC, Pedrosa FDO. ProClaT, a new bioinformatics tool for in silico protein reclassification: case study of DraB, a protein coded from the draTGB operon in Azospirillum brasilense. BMC Bioinformatics 2016; 17:455. [PMID: 28105917 PMCID: PMC5249018 DOI: 10.1186/s12859-016-1338-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background Azopirillum brasilense is a plant-growth promoting nitrogen-fixing bacteria that is used as bio-fertilizer in agriculture. Since nitrogen fixation has a high-energy demand, the reduction of N2 to NH4+ by nitrogenase occurs only under limiting conditions of NH4+ and O2. Moreover, the synthesis and activity of nitrogenase is highly regulated to prevent energy waste. In A. brasilense nitrogenase activity is regulated by the products of draG and draT. The product of the draB gene, located downstream in the draTGB operon, may be involved in the regulation of nitrogenase activity by an, as yet, unknown mechanism. Results A deep in silico analysis of the product of draB was undertaken aiming at suggesting its possible function and involvement with DraT and DraG in the regulation of nitrogenase activity in A. brasilense. In this work, we present a new artificial intelligence strategy for protein classification, named ProClaT. The features used by the pattern recognition model were derived from the primary structure of the DraB homologous proteins, calculated by a ProClaT internal algorithm. ProClaT was applied to this case study and the results revealed that the A. brasilense draB gene codes for a protein highly similar to the nitrogenase associated NifO protein of Azotobacter vinelandii. Conclusions This tool allowed the reclassification of DraB/NifO homologous proteins, hypothetical, conserved hypothetical and those annotated as putative arsenate reductase, ArsC, as NifO-like. An analysis of co-occurrence of draB, draT, draG and of other nif genes was performed, suggesting the involvement of draB (nifO) in nitrogen fixation, however, without the definition of a specific function. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1338-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Elisa Terumi Rubel
- Laboratory of Bioinformatics, Professional and Technological Education Sector, Federal University of Paraná, Curitiba, PR, Brazil.,, Rua Dr. Alcides Vieira Arcoverde 1225, Curitiba, Paraná, Brazil
| | - Roberto Tadeu Raittz
- Laboratory of Bioinformatics, Professional and Technological Education Sector, Federal University of Paraná, Curitiba, PR, Brazil.,, Rua Dr. Alcides Vieira Arcoverde 1225, Curitiba, Paraná, Brazil
| | - Nilson Antonio da Rocha Coimbra
- Laboratory of Bioinformatics, Professional and Technological Education Sector, Federal University of Paraná, Curitiba, PR, Brazil.,, Rua Dr. Alcides Vieira Arcoverde 1225, Curitiba, Paraná, Brazil
| | - Michelly Alves Coutinho Gehlen
- Laboratory of Bioinformatics, Professional and Technological Education Sector, Federal University of Paraná, Curitiba, PR, Brazil.,, Rua Dr. Alcides Vieira Arcoverde 1225, Curitiba, Paraná, Brazil
| | - Fábio de Oliveira Pedrosa
- Department of Biochemistry and Molecular Biology, Federal University of Paraná, Curitiba, PR, Brazil. .,, Av. Cel. Francisco H. dos Santos, s/n, Curitiba, Paraná, Brazil.
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17
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Huang G, Chu C, Huang T, Kong X, Zhang Y, Zhang N, Cai YD. Exploring Mouse Protein Function via Multiple Approaches. PLoS One 2016; 11:e0166580. [PMID: 27846315 PMCID: PMC5112993 DOI: 10.1371/journal.pone.0166580] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2016] [Accepted: 10/31/2016] [Indexed: 01/16/2023] Open
Abstract
Although the number of available protein sequences is growing exponentially, functional protein annotations lag far behind. Therefore, accurate identification of protein functions remains one of the major challenges in molecular biology. In this study, we presented a novel approach to predict mouse protein functions. The approach was a sequential combination of a similarity-based approach, an interaction-based approach and a pseudo amino acid composition-based approach. The method achieved an accuracy of about 0.8450 for the 1st-order predictions in the leave-one-out and ten-fold cross-validations. For the results yielded by the leave-one-out cross-validation, although the similarity-based approach alone achieved an accuracy of 0.8756, it was unable to predict the functions of proteins with no homologues. Comparatively, the pseudo amino acid composition-based approach alone reached an accuracy of 0.6786. Although the accuracy was lower than that of the previous approach, it could predict the functions of almost all proteins, even proteins with no homologues. Therefore, the combined method balanced the advantages and disadvantages of both approaches to achieve efficient performance. Furthermore, the results yielded by the ten-fold cross-validation indicate that the combined method is still effective and stable when there are no close homologs are available. However, the accuracy of the predicted functions can only be determined according to known protein functions based on current knowledge. Many protein functions remain unknown. By exploring the functions of proteins for which the 1st-order predicted functions are wrong but the 2nd-order predicted functions are correct, the 1st-order wrongly predicted functions were shown to be closely associated with the genes encoding the proteins. The so-called wrongly predicted functions could also potentially be correct upon future experimental verification. Therefore, the accuracy of the presented method may be much higher in reality.
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Affiliation(s)
- Guohua Huang
- Department of Mathematics, Shaoyang University, Shaoyang, Hunan, 422000, China
| | - Chen Chu
- Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xiangyin Kong
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yunhua Zhang
- College of Life Science, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Ning Zhang
- Department of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, Tianjin, China
- * E-mail: (NZ); (Y-DC)
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
- * E-mail: (NZ); (Y-DC)
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18
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Making sense of genomes of parasitic worms: Tackling bioinformatic challenges. Biotechnol Adv 2016; 34:663-686. [DOI: 10.1016/j.biotechadv.2016.03.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Revised: 02/25/2016] [Accepted: 03/01/2016] [Indexed: 01/25/2023]
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19
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Vidulin V, Šmuc T, Supek F. Extensive complementarity between gene function prediction methods. Bioinformatics 2016; 32:3645-3653. [PMID: 27522084 DOI: 10.1093/bioinformatics/btw532] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Revised: 07/11/2016] [Accepted: 08/09/2016] [Indexed: 12/22/2022] Open
Abstract
MOTIVATION The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesized that AFP approaches that draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions. RESULTS Our pipeline amalgamates 5 133 543 genes from 2071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1227 Gene Ontology (GO) terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse genomic AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to rely on a single most-confident prediction per gene/function, rather than enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known Escherichia coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the Critical Assessment of Function Annotation 2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them. AVAILABILITY AND IMPLEMENTATION The individual and integrated GO predictions for the complete set of genes are available from http://gorbi.irb.hr/ CONTACT: fran.supek@irb.hrSupplementary information: Supplementary materials are available at Bioinformatics online.
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Affiliation(s)
- Vedrana Vidulin
- Division of Electronics, Ruđer Bošković Institute, Bijenička cesta 54, Zagreb 10000, Croatia
| | - Tomislav Šmuc
- Division of Electronics, Ruđer Bošković Institute, Bijenička cesta 54, Zagreb 10000, Croatia
| | - Fran Supek
- Division of Electronics, Ruđer Bošković Institute, Bijenička cesta 54, Zagreb 10000, Croatia.,EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology and UPF, Dr. Aiguader 88, Barcelona 08003, Spain
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20
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Singh H, Raghava GPS. BLAST-based structural annotation of protein residues using Protein Data Bank. Biol Direct 2016; 11:4. [PMID: 26810894 PMCID: PMC4727276 DOI: 10.1186/s13062-016-0106-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Accepted: 01/18/2016] [Indexed: 11/10/2022] Open
Abstract
Background In the era of next-generation sequencing where thousands of genomes have been already sequenced; size of protein databases is growing with exponential rate. Structural annotation of these proteins is one of the biggest challenges for the computational biologist. Although, it is easy to perform BLAST search against Protein Data Bank (PDB) but it is difficult for a biologist to annotate protein residues from BLAST search. Results A web-server StarPDB has been developed for structural annotation of a protein based on its similarity with known protein structures. It uses standard BLAST software for performing similarity search of a query protein against protein structures in PDB. This server integrates wide range modules for assigning different types of annotation that includes, Secondary-structure, Accessible surface area, Tight-turns, DNA-RNA and Ligand modules. Secondary structure module allows users to predict regular secondary structure states to each residue in a protein. Accessible surface area predict the exposed or buried residues in a protein. Tight-turns module is designed to predict tight turns like beta-turns in a protein. DNA-RNA module developed for predicting DNA and RNA interacting residues in a protein. Similarly, Ligand module of server allows one to predicted ligands, metal and nucleotides ligand interacting residues in a protein. Conclusions In summary, this manuscript presents a web server for comprehensive annotation of a protein based on similarity search. It integrates number of visualization tools that facilitate users to understand structure and function of protein residues. This web server is available freely for scientific community from URL http://crdd.osdd.net/raghava/starpdb. Reviewers This article was reviewed by Prof Michael Gromiha, Prof. Thomas Dandekar and Dr. I. King Jordan. Electronic supplementary material The online version of this article (doi:10.1186/s13062-016-0106-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Harinder Singh
- Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
| | - Gajendra P S Raghava
- Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India.
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21
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Cao R, Cheng J. Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks. Methods 2016; 93:84-91. [PMID: 26370280 PMCID: PMC4894840 DOI: 10.1016/j.ymeth.2015.09.011] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 09/03/2015] [Accepted: 09/10/2015] [Indexed: 11/30/2022] Open
Abstract
MOTIVATIONS Protein function prediction is an important and challenging problem in bioinformatics and computational biology. Functionally relevant biological information such as protein sequences, gene expression, and protein-protein interactions has been used mostly separately for protein function prediction. One of the major challenges is how to effectively integrate multiple sources of both traditional and new information such as spatial gene-gene interaction networks generated from chromosomal conformation data together to improve protein function prediction. RESULTS In this work, we developed three different probabilistic scores (MIS, SEQ, and NET score) to combine protein sequence, function associations, and protein-protein interaction and spatial gene-gene interaction networks for protein function prediction. The MIS score is mainly generated from homologous proteins found by PSI-BLAST search, and also association rules between Gene Ontology terms, which are learned by mining the Swiss-Prot database. The SEQ score is generated from protein sequences. The NET score is generated from protein-protein interaction and spatial gene-gene interaction networks. These three scores were combined in a new Statistical Multiple Integrative Scoring System (SMISS) to predict protein function. We tested SMISS on the data set of 2011 Critical Assessment of Function Annotation (CAFA). The method performed substantially better than three base-line methods and an advanced method based on protein profile-sequence comparison, profile-profile comparison, and domain co-occurrence networks according to the maximum F-measure.
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Affiliation(s)
- Renzhi Cao
- Computer Science Department, Informatics Institute, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Computer Science Department, Informatics Institute, University of Missouri, Columbia, MO 65211, USA.
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22
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Shin WH, Bures MG, Kihara D. PatchSurfers: Two methods for local molecular property-based binding ligand prediction. Methods 2016; 93:41-50. [PMID: 26427548 PMCID: PMC4718779 DOI: 10.1016/j.ymeth.2015.09.026] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2015] [Revised: 09/27/2015] [Accepted: 09/28/2015] [Indexed: 01/09/2023] Open
Abstract
Protein function prediction is an active area of research in computational biology. Function prediction can help biologists make hypotheses for characterization of genes and help interpret biological assays, and thus is a productive area for collaboration between experimental and computational biologists. Among various function prediction methods, predicting binding ligand molecules for a target protein is an important class because ligand binding events for a protein are usually closely intertwined with the proteins' biological function, and also because predicted binding ligands can often be directly tested by biochemical assays. Binding ligand prediction methods can be classified into two types: those which are based on protein-protein (or pocket-pocket) comparison, and those that compare a target pocket directly to ligands. Recently, our group proposed two computational binding ligand prediction methods, Patch-Surfer, which is a pocket-pocket comparison method, and PL-PatchSurfer, which compares a pocket to ligand molecules. The two programs apply surface patch-based descriptions to calculate similarity or complementarity between molecules. A surface patch is characterized by physicochemical properties such as shape, hydrophobicity, and electrostatic potentials. These properties on the surface are represented using three-dimensional Zernike descriptors (3DZD), which are based on a series expansion of a 3 dimensional function. Utilizing 3DZD for describing the physicochemical properties has two main advantages: (1) rotational invariance and (2) fast comparison. Here, we introduce Patch-Surfer and PL-PatchSurfer with an emphasis on PL-PatchSurfer, which is more recently developed. Illustrative examples of PL-PatchSurfer performance on binding ligand prediction as well as virtual drug screening are also provided.
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Affiliation(s)
- Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Mark Gregory Bures
- Discovery Chemistry Research and Technologies, Eli Lilly and Company, Indianapolis, IN 46285, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
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23
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Abstract
Protein-ligand binding site prediction methods aim to predict, from amino acid sequence, protein-ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein-ligand interactions has become extremely important to help determine a protein's functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein-ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein-ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein-ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.
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24
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GoFDR: A sequence alignment based method for predicting protein functions. Methods 2016; 93:3-14. [DOI: 10.1016/j.ymeth.2015.08.009] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 07/27/2015] [Accepted: 08/11/2015] [Indexed: 01/01/2023] Open
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Roche DB, Brackenridge DA, McGuffin LJ. Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods. Int J Mol Sci 2015; 16:29829-42. [PMID: 26694353 PMCID: PMC4691145 DOI: 10.3390/ijms161226202] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2015] [Revised: 12/02/2015] [Accepted: 12/10/2015] [Indexed: 01/14/2023] Open
Abstract
Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein-ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein-ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein-ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.
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Affiliation(s)
- Daniel Barry Roche
- Institut de Biologie Computationnelle, LIRMM, CNRS, Université de Montpellier, Montpellier 34095, France.
- Centre de Recherche de Biochimie Macromoléculaire, CNRS-UMR 5237, Montpellier 34293, France.
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26
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Khan IK, Wei Q, Chapman S, KC DB, Kihara D. The PFP and ESG protein function prediction methods in 2014: effect of database updates and ensemble approaches. Gigascience 2015; 4:43. [PMID: 26380077 PMCID: PMC4570625 DOI: 10.1186/s13742-015-0083-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Accepted: 08/27/2015] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Functional annotation of novel proteins is one of the central problems in bioinformatics. With the ever-increasing development of genome sequencing technologies, more and more sequence information is becoming available to analyze and annotate. To achieve fast and automatic function annotation, many computational (automated) function prediction (AFP) methods have been developed. To objectively evaluate the performance of such methods on a large scale, community-wide assessment experiments have been conducted. The second round of the Critical Assessment of Function Annotation (CAFA) experiment was held in 2013-2014. Evaluation of participating groups was reported in a special interest group meeting at the Intelligent Systems in Molecular Biology (ISMB) conference in Boston in 2014. Our group participated in both CAFA1 and CAFA2 using multiple, in-house AFP methods. Here, we report benchmark results of our methods obtained in the course of preparation for CAFA2 prior to submitting function predictions for CAFA2 targets. RESULTS For CAFA2, we updated the annotation databases used by our methods, protein function prediction (PFP) and extended similarity group (ESG), and benchmarked their function prediction performances using the original (older) and updated databases. Performance evaluation for PFP with different settings and ESG are discussed. We also developed two ensemble methods that combine function predictions from six independent, sequence-based AFP methods. We further analyzed the performances of our prediction methods by enriching the predictions with prior distribution of gene ontology (GO) terms. Examples of predictions by the ensemble methods are discussed. CONCLUSIONS Updating the annotation database was successful, improving the Fmax prediction accuracy score for both PFP and ESG. Adding the prior distribution of GO terms did not make much improvement. Both of the ensemble methods we developed improved the average Fmax score over all individual component methods except for ESG. Our benchmark results will not only complement the overall assessment that will be done by the CAFA organizers, but also help elucidate the predictive powers of sequence-based function prediction methods in general.
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Affiliation(s)
- Ishita K. Khan
- Department of Computer Sciences, Purdue University, West Lafayette, IN 47907 USA
| | - Qing Wei
- Department of Computer Sciences, Purdue University, West Lafayette, IN 47907 USA
| | - Samuel Chapman
- Department of Computational Science and Engineering, North Carolina A & T State University, Greensboro, NC 27411 USA
| | - Dukka B. KC
- Department of Computational Science and Engineering, North Carolina A & T State University, Greensboro, NC 27411 USA
| | - Daisuke Kihara
- Department of Computer Sciences, Purdue University, West Lafayette, IN 47907 USA
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907 USA
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27
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Taha K, Yoo PD, Alzaabi M. iPFPi: A System for Improving Protein Function Prediction through Cumulative Iterations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:825-836. [PMID: 26357323 DOI: 10.1109/tcbb.2014.2344681] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We propose a classifier system called iPFPi that predicts the functions of un-annotated proteins. iPFPi assigns an un-annotated protein P the functions of GO annotation terms that are semantically similar to P. An un-annotated protein P and a GO annotation term T are represented by their characteristics. The characteristics of P are GO terms found within the abstracts of biomedical literature associated with P. The characteristics of Tare GO terms found within the abstracts of biomedical literature associated with the proteins annotated with the function of T. Let F and F/ be the important (dominant) sets of characteristic terms representing T and P, respectively. iPFPi would annotate P with the function of T, if F and F/ are semantically similar. We constructed a novel semantic similarity measure that takes into consideration several factors, such as the dominance degree of each characteristic term t in set F based on its score, which is a value that reflects the dominance status of t relative to other characteristic terms, using pairwise beats and looses procedure. Every time a protein P is annotated with the function of T, iPFPi updates and optimizes the current scores of the characteristic terms for T based on the weights of the characteristic terms for P. Set F will be updated accordingly. Thus, the accuracy of predicting the function of T as the function of subsequent proteins improves. This prediction accuracy keeps improving over time iteratively through the cumulative weights of the characteristic terms representing proteins that are successively annotated with the function of T. We evaluated the quality of iPFPi by comparing it experimentally with two recent protein function prediction systems. Results showed marked improvement.
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28
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El Hadrami A, Islam MR, Adam LR, Daayf F. A cupin domain-containing protein with a quercetinase activity (VdQase) regulates Verticillium dahliae's pathogenicity and contributes to counteracting host defenses. FRONTIERS IN PLANT SCIENCE 2015; 6:440. [PMID: 26113857 PMCID: PMC4462102 DOI: 10.3389/fpls.2015.00440] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Accepted: 05/29/2015] [Indexed: 05/11/2023]
Abstract
We previously identified rutin as part of potato root responses to its pathogen Verticillium dahliae. Rutin was directly toxic to the pathogen at doses greater than 160 μM, a threshold below which many V. dahliae pathogenicity-related genes were up-regulated. We identified and characterized a cupin domain-containing protein (VdQase) with a dioxygenase activity and a potential role in V. dahliae-potato interactions. The pathogenicity of VdQase knock-out mutants generated through Agrobacterium tumefasciens-mediated transformation was significantly reduced on susceptible potato cultivar Kennebec compared to wild type isolates. Fluorescence microscopy revealed a higher accumulation of flavonols in the stems of infected potatoes and a higher concentration of rutin in the leaves in response to the VdQase mutants as compared to wild type isolates. This, along with the HPLC characterization of high residual and non-utilized quercetin in presence of the knockout mutants, indicates the involvement of VdQase in the catabolism of quercetin and possibly other flavonols in planta. Quantification of Salicylic and Jasmonic Acids (SA, JA) in response to the mutants vs. wild type isolates revealed involvement of VdQase in the interference with signaling, suggesting a role in pathogenicity. It is hypothesized that the by-product of dioxygenation 2-protocatechuoylphloroglucinolcarboxylic acid, after dissociating into phloroglucinol and protocatechuoyl moieties, becomes a starting point for benzoic acid and SA, thereby interfering with the JA pathway and affecting the interaction outcome. These events may be key factors for V. dahliae in countering potato defenses and becoming notorious in the rhizosphere.
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Affiliation(s)
- Abdelbasset El Hadrami
- Department of Plant Science, University of ManitobaWinnipeg, MB, Canada
- OMEX Agriculture Inc., Oak BluffMB, Canada
| | - Md. Rashidul Islam
- Department of Plant Science, University of ManitobaWinnipeg, MB, Canada
- Department of Plant Pathology, Bangladesh Agricultural UniversityMymensingh, Bangladesh
| | - Lorne R. Adam
- Department of Plant Science, University of ManitobaWinnipeg, MB, Canada
| | - Fouad Daayf
- Department of Plant Science, University of ManitobaWinnipeg, MB, Canada
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29
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Sefid F, Rasooli I, Jahangiri A, Bazmara H. Functional Exposed Amino Acids of BauA as Potential Immunogen Against Acinetobacter baumannii. Acta Biotheor 2015; 63:129-49. [PMID: 25840681 DOI: 10.1007/s10441-015-9251-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2014] [Accepted: 03/31/2015] [Indexed: 12/12/2022]
Abstract
Multidrug-resistant Acinetobacter baumannii is recognized to be among the most difficult antimicrobial-resistant gram negative bacilli to control and treat. One of the major challenges that the pathogenic bacteria face in their host is the scarcity of freely available iron. To survive under such conditions, bacteria express new proteins on their outer membrane and also secrete iron chelators called siderophores. Antibodies directed against these proteins associated with iron uptake exert a bacteriostatic or bactericidal effect against A. baumanii in vitro, by blocking siderophore mediated iron uptake pathways. Attempts should be made to discover peptides that could mimic protein epitopes and possess the same immunogenicity as the whole protein. Subsequently, theoretical methods for epitope prediction have been developed leading to synthesis of such peptides that are important for development of immunodiagnostic tests and vaccines. The present study was designed to in silico resolving the major obstacles in the control or in prevention of the diseases caused by A. baumannii. We exploited bioinformatic tools to better understand and characterize the Baumannii acinetobactin utilization structure of A. baumannii and select appropriate regions as effective B cell epitopes. In conclusion, amino acids 26-191 of cork domain and 321-635 of part of the barrel domain including L4-L9, were selected as vaccine candidates. These two regions contain functional exposed amino acids with higher score of B cell epitopes properties. Majority of amino acids are hydrophilic, flexible, accessible, and favorable for B cells from secondary structure point of view.
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Affiliation(s)
- Fatemeh Sefid
- Department of Biology, Shahed University, Tehran-Qom Express Way, Opposite Imam Khomeini's Shrine, 3319118651, Tehran, Iran
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30
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Sahraeian SM, Luo KR, Brenner SE. SIFTER search: a web server for accurate phylogeny-based protein function prediction. Nucleic Acids Res 2015; 43:W141-7. [PMID: 25979264 PMCID: PMC4489292 DOI: 10.1093/nar/gkv461] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2015] [Accepted: 04/27/2015] [Indexed: 12/26/2022] Open
Abstract
We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. The SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.
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Affiliation(s)
- Sayed M Sahraeian
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Kevin R Luo
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA
| | - Steven E Brenner
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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31
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Mills CL, Beuning PJ, Ondrechen MJ. Biochemical functional predictions for protein structures of unknown or uncertain function. Comput Struct Biotechnol J 2015; 13:182-91. [PMID: 25848497 PMCID: PMC4372640 DOI: 10.1016/j.csbj.2015.02.003] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Revised: 02/06/2015] [Accepted: 02/11/2015] [Indexed: 01/07/2023] Open
Abstract
With the exponential growth in the determination of protein sequences and structures via genome sequencing and structural genomics efforts, there is a growing need for reliable computational methods to determine the biochemical function of these proteins. This paper reviews the efforts to address the challenge of annotating the function at the molecular level of uncharacterized proteins. While sequence- and three-dimensional-structure-based methods for protein function prediction have been reviewed previously, the recent trends in local structure-based methods have received less attention. These local structure-based methods are the primary focus of this review. Computational methods have been developed to predict the residues important for catalysis and the local spatial arrangements of these residues can be used to identify protein function. In addition, the combination of different types of methods can help obtain more information and better predictions of function for proteins of unknown function. Global initiatives, including the Enzyme Function Initiative (EFI), COMputational BRidges to EXperiments (COMBREX), and the Critical Assessment of Function Annotation (CAFA), are evaluating and testing the different approaches to predicting the function of proteins of unknown function. These initiatives and global collaborations will increase the capability and reliability of methods to predict biochemical function computationally and will add substantial value to the current volume of structural genomics data by reducing the number of absent or inaccurate functional annotations.
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Affiliation(s)
- Caitlyn L Mills
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, United States
| | - Penny J Beuning
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, United States
| | - Mary Jo Ondrechen
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, United States
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Text as data: using text-based features for proteins representation and for computational prediction of their characteristics. Methods 2014; 74:54-64. [PMID: 25448299 DOI: 10.1016/j.ymeth.2014.10.027] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2014] [Revised: 09/21/2014] [Accepted: 10/21/2014] [Indexed: 11/21/2022] Open
Abstract
The current era of large-scale biology is characterized by a fast-paced growth in the number of sequenced genomes and, consequently, by a multitude of identified proteins whose function has yet to be determined. Simultaneously, any known or postulated information concerning genes and proteins is part of the ever-growing published scientific literature, which is expanding at a rate of over a million new publications per year. Computational tools that attempt to automatically predict and annotate protein characteristics, such as function and localization patterns, are being developed along with systems that aim to support the process via text mining. Most work on protein characterization focuses on features derived directly from protein sequence data. Protein-related work that does aim to utilize the literature typically concentrates on extracting specific facts (e.g., protein interactions) from text. In the past few years we have taken a different route, treating the literature as a source of text-based features, which can be employed just as sequence-based protein-features were used in earlier work, for predicting protein subcellular location and possibly also function. We discuss here in detail the overall approach, along with results from work we have done in this area demonstrating the value of this method and its potential use.
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Xu R, Zhou J, Liu B, He Y, Zou Q, Wang X, Chou KC. Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach. J Biomol Struct Dyn 2014; 33:1720-30. [PMID: 25252709 DOI: 10.1080/07391102.2014.968624] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
DNA-binding proteins are crucial for various cellular processes and hence have become an important target for both basic research and drug development. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to establish an automated method for rapidly and accurately identifying DNA-binding proteins based on their sequence information alone. Owing to the fact that all biological species have developed beginning from a very limited number of ancestral species, it is important to take into account the evolutionary information in developing such a high-throughput tool. In view of this, a new predictor was proposed by incorporating the evolutionary information into the general form of pseudo amino acid composition via the top-n-gram approach. It was observed by comparing the new predictor with the existing methods via both jackknife test and independent data-set test that the new predictor outperformed its counterparts. It is anticipated that the new predictor may become a useful vehicle for identifying DNA-binding proteins. It has not escaped our notice that the novel approach to extract evolutionary information into the formulation of statistical samples can be used to identify many other protein attributes as well.
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Affiliation(s)
- Ruifeng Xu
- a School of Computer Science and Technology , Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town , Xili, Shenzhen 518055 , Guangdong , China
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Hua YH, Wu CY, Sargsyan K, Lim C. Sequence-motif detection of NAD(P)-binding proteins: discovery of a unique antibacterial drug target. Sci Rep 2014; 4:6471. [PMID: 25253464 PMCID: PMC4174568 DOI: 10.1038/srep06471] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Accepted: 08/18/2014] [Indexed: 01/31/2023] Open
Abstract
Many enzymes use nicotinamide adenine dinucleotide or nicotinamide adenine dinucleotide phosphate (NAD(P)) as essential coenzymes. These enzymes often do not share significant sequence identity and cannot be easily detected by sequence homology. Previously, we determined all distinct locally conserved pyrophosphate-binding structures (3d motifs) from NAD(P)-bound protein structures, from which 1d sequence motifs were derived. Here, we aim to establish the precision of these 3d and 1d motifs to annotate NAD(P)-binding proteins. We show that the pyrophosphate-binding 3d motifs are characteristic of NAD(P)-binding proteins, as they are rarely found in nonNAD(P)-binding proteins. Furthermore, several 1d motifs could distinguish between proteins that bind only NAD and those that bind only NADP. They could also distinguish between NAD(P)-binding proteins from nonNAD(P)-binding ones. Interestingly, one of the pyrophosphate-binding 3d and corresponding 1d motifs was found only in enoyl-acyl carrier protein reductases, which are enzymes essential for bacterial fatty acid biosynthesis. This unique 3d motif serves as an attractive novel drug target, as it is conserved across many bacterial species and is not found in human proteins.
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Affiliation(s)
- Yun Hao Hua
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Chih Yuan Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Karen Sargsyan
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Carmay Lim
- 1] Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan [2] Department of Chemistry, National Tsing Hua University, Hsinchu 300, Taiwan
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EXIA2: web server of accurate and rapid protein catalytic residue prediction. BIOMED RESEARCH INTERNATIONAL 2014; 2014:807839. [PMID: 25295274 PMCID: PMC4177735 DOI: 10.1155/2014/807839] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Revised: 05/27/2014] [Accepted: 06/11/2014] [Indexed: 11/18/2022]
Abstract
We propose a method (EXIA2) of catalytic residue prediction based on protein structure without needing homology information. The method is based on the special side chain orientation of catalytic residues. We found that the side chain of catalytic residues usually points to the center of the catalytic site. The special orientation is usually observed in catalytic residues but not in noncatalytic residues, which usually have random side chain orientation. The method is shown to be the most accurate catalytic residue prediction method currently when combined with PSI-Blast sequence conservation. It performs better than other competing methods on several benchmark datasets that include over 1,200 enzyme structures. The areas under the ROC curve (AUC) on these benchmark datasets are in the range from 0.934 to 0.968.
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Moretti DM, Ahuja LG, Nunes RD, Cudischevitch CO, Daumas-Filho CRO, Medeiros-Castro P, Ventura-Martins G, Jablonka W, Gazos-Lopes F, Senna R, Sorgine MHF, Hartfelder K, Capurro M, Atella GC, Mesquita RD, Silva-Neto MAC. Molecular analysis of Aedes aegypti classical protein tyrosine phosphatases uncovers an ortholog of mammalian PTP-1B implicated in the control of egg production in mosquitoes. PLoS One 2014; 9:e104878. [PMID: 25137153 PMCID: PMC4138107 DOI: 10.1371/journal.pone.0104878] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2013] [Accepted: 07/18/2014] [Indexed: 01/26/2023] Open
Abstract
Background Protein Tyrosine Phosphatases (PTPs) are enzymes that catalyze phosphotyrosine dephosphorylation and modulate cell differentiation, growth and metabolism. In mammals, PTPs play a key role in the modulation of canonical pathways involved in metabolism and immunity. PTP1B is the prototype member of classical PTPs and a major target for treating human diseases, such as cancer, obesity and diabetes. These signaling enzymes are, hence, targets of a wide array of inhibitors. Anautogenous mosquitoes rely on blood meals to lay eggs and are vectors of the most prevalent human diseases. Identifying the mosquito ortholog of PTP1B and determining its involvement in egg production is, therefore, important in the search for a novel and crucial target for vector control. Methodology/Principal Findings We conducted an analysis to identify the ortholog of mammalian PTP1B in the Aedes aegypti genome. We identified eight genes coding for classical PTPs. In silico structural and functional analyses of proteins coded by such genes revealed that four of these code for catalytically active enzymes. Among the four genes coding for active PTPs, AAEL001919 exhibits the greatest degree of homology with the mammalian PTP1B. Next, we evaluated the role of this enzyme in egg formation. Blood feeding largely affects AAEL001919 expression, especially in the fat body and ovaries. These tissues are critically involved in the synthesis and storage of vitellogenin, the major yolk protein. Including the classical PTP inhibitor sodium orthovanadate or the PTP substrate DiFMUP in the blood meal decreased vitellogenin synthesis and egg production. Similarly, silencing AAEL001919 using RNA interference (RNAi) assays resulted in 30% suppression of egg production. Conclusions/Significance The data reported herein implicate, for the first time, a gene that codes for a classical PTP in mosquito egg formation. These findings raise the possibility that this class of enzymes may be used as novel targets to block egg formation in mosquitoes.
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Affiliation(s)
- Debora Monteiro Moretti
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Lalima Gagan Ahuja
- Department of Pharmacology, University of California San Diego, San Diego, California, United States of America
| | - Rodrigo Dutra Nunes
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Cecília Oliveira Cudischevitch
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Carlos Renato Oliveira Daumas-Filho
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Priscilla Medeiros-Castro
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Guilherme Ventura-Martins
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Willy Jablonka
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Felipe Gazos-Lopes
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Raquel Senna
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Marcos Henrique Ferreira Sorgine
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Klaus Hartfelder
- Departamento de Biologia Celular e Molecular e Bioagentes Patogênicos, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil
| | - Margareth Capurro
- Departamento de Parasitologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, Brazil
| | - Georgia Correa Atella
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Rafael Dias Mesquita
- Departamento de Bioquímica, Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
| | - Mário Alberto Cardoso Silva-Neto
- Laboratório de Sinalização Celular (LabSiCel), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular (INCT-EM), Rio de Janeiro, RJ, Brazil
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Talman AM, Prieto JH, Marques S, Ubaida-Mohien C, Lawniczak M, Wass MN, Xu T, Frank R, Ecker A, Stanway RS, Krishna S, Sternberg MJE, Christophides GK, Graham DR, Dinglasan RR, Yates JR, Sinden RE. Proteomic analysis of the Plasmodium male gamete reveals the key role for glycolysis in flagellar motility. Malar J 2014; 13:315. [PMID: 25124718 PMCID: PMC4150949 DOI: 10.1186/1475-2875-13-315] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Accepted: 07/28/2014] [Indexed: 12/22/2022] Open
Abstract
Background Gametogenesis and fertilization play crucial roles in malaria transmission. While male gametes are thought to be amongst the simplest eukaryotic cells and are proven targets of transmission blocking immunity, little is known about their molecular organization. For example, the pathway of energy metabolism that power motility, a feature that facilitates gamete encounter and fertilization, is unknown. Methods Plasmodium berghei microgametes were purified and analysed by whole-cell proteomic analysis for the first time. Data are available via ProteomeXchange with identifier PXD001163. Results 615 proteins were recovered, they included all male gamete proteins described thus far. Amongst them were the 11 enzymes of the glycolytic pathway. The hexose transporter was localized to the gamete plasma membrane and it was shown that microgamete motility can be suppressed effectively by inhibitors of this transporter and of the glycolytic pathway. Conclusions This study describes the first whole-cell proteomic analysis of the malaria male gamete. It identifies glycolysis as the likely exclusive source of energy for flagellar beat, and provides new insights in original features of Plasmodium flagellar organization. Electronic supplementary material The online version of this article (doi:10.1186/1475-2875-13-315) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Arthur M Talman
- Division of Cell and Molecular Biology, Imperial College, London, UK.
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Computational prediction of protein function based on weighted mapping of domains and GO terms. BIOMED RESEARCH INTERNATIONAL 2014; 2014:641469. [PMID: 24868539 PMCID: PMC4017789 DOI: 10.1155/2014/641469] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2013] [Accepted: 03/12/2014] [Indexed: 11/17/2022]
Abstract
In this paper, we propose a novel method, SeekFun, to predict protein function based on weighted mapping of domains and GO terms. Firstly, a weighted mapping of domains and GO terms is constructed according to GO annotations and domain composition of the proteins. The association strength between domain and GO term is weighted by symmetrical conditional probability. Secondly, the mapping is extended along the true paths of the terms based on GO hierarchy. Finally, the terms associated with resident domains are transferred to host protein and real annotations of the host protein are determined by association strengths. Our careful comparisons demonstrate that SeekFun outperforms the concerned methods on most occasions. SeekFun provides a flexible and effective way for protein function prediction. It benefits from the well-constructed mapping of domains and GO terms, as well as the reasonable strategy for inferring annotations of protein from those of its domains.
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Nagao C, Nagano N, Mizuguchi K. Prediction of detailed enzyme functions and identification of specificity determining residues by random forests. PLoS One 2014; 9:e84623. [PMID: 24416252 PMCID: PMC3885575 DOI: 10.1371/journal.pone.0084623] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2013] [Accepted: 11/15/2013] [Indexed: 12/03/2022] Open
Abstract
Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily.
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Affiliation(s)
- Chioko Nagao
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
- * E-mail: (CN); (KM)
| | - Nozomi Nagano
- Computational Biology Research Center, AIST, Koto-ku, Tokyo, Japan
| | - Kenji Mizuguchi
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
- * E-mail: (CN); (KM)
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Exploring the adenylation domain repertoire of nonribosomal peptide synthetases using an ensemble of sequence-search methods. PLoS One 2013; 8:e65926. [PMID: 23874386 PMCID: PMC3712989 DOI: 10.1371/journal.pone.0065926] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2012] [Accepted: 05/01/2013] [Indexed: 11/24/2022] Open
Abstract
The introduction of two-dimension (2D) graphs and their numerical characterization for comparative analyses of DNA/RNA and protein sequences without the need of sequence alignments is an active yet recent research topic in bioinformatics. Here, we used a 2D artificial representation (four-color maps) with a simple numerical characterization through topological indices (TIs) to aid the discovering of remote homologous of Adenylation domains (A-domains) from the Nonribosomal Peptide Synthetases (NRPS) class in the proteome of the cyanobacteria Microcystis aeruginosa. Cyanobacteria are a rich source of structurally diverse oligopeptides that are predominantly synthesized by NPRS. Several A-domains share amino acid identities lower than 20 % being a possible source of remote homologous. Therefore, A-domains cannot be easily retrieved by BLASTp searches using a single template. To cope with the sequence diversity of the A-domains we have combined homology-search methods with an alignment-free tool that uses protein four-color-maps. TI2BioP (Topological Indices toBioPolymers) version 2.0, available at http://ti2biop.sourceforge.net/ allowed the calculation of simple TIs from the protein sequences (four-color maps). Such TIs were used as input predictors for the statistical estimations required to build the alignment-free models. We concluded that the use of graphical/numerical approaches in cooperation with other sequence search methods, like multi-templates BLASTp and profile HMM, can give the most complete exploration of the repertoire of highly diverse protein families.
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Vannucci FA, Foster DN, Gebhart CJ. Laser microdissection coupled with RNA-seq analysis of porcine enterocytes infected with an obligate intracellular pathogen (Lawsonia intracellularis). BMC Genomics 2013; 14:421. [PMID: 23800029 PMCID: PMC3718617 DOI: 10.1186/1471-2164-14-421] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2013] [Accepted: 06/18/2013] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Lawsonia intracellularis is an obligate intracellular bacterium and the etiologic agent of proliferative enteropathy. The disease is endemic in pigs, emerging in horses and has been described in various other species including nonhuman primates. Cell proliferation is associated with bacterial replication in enterocyte cytoplasm, but the molecular basis of the host-pathogen interaction is unknown. We used laser capture microdissection coupled with RNA-seq technology to characterize the transcriptional responses of infected enterocytes and the host-pathogen interaction. RESULTS Proliferative enterocytes was associated with activation of transcription, protein biosynthesis and genes acting on the G1 phase of the host cell cycle (Rho family). The lack of differentiation in infected enterocytes was demonstrated by the repression of membrane transporters related to nutrient acquisition. The activation of the copper uptake transporter by infected enterocytes was associated with high expression of the Zn/Cu superoxide dismutase by L. intracellularis. This suggests that the intracellular bacteria incorporate intracytoplasmic copper and express a sophisticated mechanism to cope with oxidative stress. CONCLUSIONS The feasibility of coupling microdissection and RNA-seq was demonstrated by characterizing the host-bacterial interactions from a specific cell type in a heterogeneous tissue. High expression of L. intracellularis genes encoding hypothetical proteins and activation of host Rho genes infers the role of unrecognized bacterial cyclomodulins in the pathogenesis of proliferative enteropathy.
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Affiliation(s)
- Fabio A Vannucci
- Department of Veterinary and Biomedical Science , College of Veterinary Medicine, University of Minnesota, St. Paul, MN 55108, USA
| | - Douglas N Foster
- Department of Animal Science, College of Food, Agricultural and Natural Resource Science, University of Minnesota, St. Paul, MN, USA
| | - Connie J Gebhart
- Department of Veterinary and Biomedical Science , College of Veterinary Medicine, University of Minnesota, St. Paul, MN 55108, USA
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Chitale M, Khan IK, Kihara D. In-depth performance evaluation of PFP and ESG sequence-based function prediction methods in CAFA 2011 experiment. BMC Bioinformatics 2013; 14 Suppl 3:S2. [PMID: 23514353 PMCID: PMC3584938 DOI: 10.1186/1471-2105-14-s3-s2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Many Automatic Function Prediction (AFP) methods were developed to cope with an increasing growth of the number of gene sequences that are available from high throughput sequencing experiments. To support the development of AFP methods, it is essential to have community wide experiments for evaluating performance of existing AFP methods. Critical Assessment of Function Annotation (CAFA) is one such community experiment. The meeting of CAFA was held as a Special Interest Group (SIG) meeting at the Intelligent Systems in Molecular Biology (ISMB) conference in 2011. Here, we perform a detailed analysis of two sequence-based function prediction methods, PFP and ESG, which were developed in our lab, using the predictions submitted to CAFA. RESULTS We evaluate PFP and ESG using four different measures in comparison with BLAST, Prior, and GOtcha. In addition to the predictions submitted to CAFA, we further investigate performance of a different scoring function to rank order predictions by PFP as well as PFP/ESG predictions enriched with Priors that simply adds frequently occurring Gene Ontology terms as a part of predictions. Prediction accuracies of each method were also evaluated separately for different functional categories. Successful and unsuccessful predictions by PFP and ESG are also discussed in comparison with BLAST. CONCLUSION The in-depth analysis discussed here will complement the overall assessment by the CAFA organizers. Since PFP and ESG are based on sequence database search results, our analyses are not only useful for PFP and ESG users but will also shed light on the relationship of the sequence similarity space and functions that can be inferred from the sequences.
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Affiliation(s)
- Meghana Chitale
- Department of Computer Science, Purdue University, 305 N, University Street, West Lafayette, Indiana 47907, USA
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Hamp T, Kassner R, Seemayer S, Vicedo E, Schaefer C, Achten D, Auer F, Boehm A, Braun T, Hecht M, Heron M, Hönigschmid P, Hopf TA, Kaufmann S, Kiening M, Krompass D, Landerer C, Mahlich Y, Roos M, Rost B. Homology-based inference sets the bar high for protein function prediction. BMC Bioinformatics 2013; 14 Suppl 3:S7. [PMID: 23514582 PMCID: PMC3584931 DOI: 10.1186/1471-2105-14-s3-s7] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Any method that de novo predicts protein function should do better than random. More challenging, it also ought to outperform simple homology-based inference. METHODS Here, we describe a few methods that predict protein function exclusively through homology. Together, they set the bar or lower limit for future improvements. RESULTS AND CONCLUSIONS During the development of these methods, we faced two surprises. Firstly, our most successful implementation for the baseline ranked very high at CAFA1. In fact, our best combination of homology-based methods fared only slightly worse than the top-of-the-line prediction method from the Jones group. Secondly, although the concept of homology-based inference is simple, this work revealed that the precise details of the implementation are crucial: not only did the methods span from top to bottom performers at CAFA, but also the reasons for these differences were unexpected. In this work, we also propose a new rigorous measure to compare predicted and experimental annotations. It puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users. Clearly, the definition of proper goals remains one major objective for CAFA.
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Affiliation(s)
- Tobias Hamp
- TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany
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Wong A, Shatkay H. Protein function prediction using text-based features extracted from the biomedical literature: the CAFA challenge. BMC Bioinformatics 2013; 14 Suppl 3:S14. [PMID: 23514326 PMCID: PMC3584852 DOI: 10.1186/1471-2105-14-s3-s14] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Advances in sequencing technology over the past decade have resulted in an abundance of sequenced proteins whose function is yet unknown. As such, computational systems that can automatically predict and annotate protein function are in demand. Most computational systems use features derived from protein sequence or protein structure to predict function. In an earlier work, we demonstrated the utility of biomedical literature as a source of text features for predicting protein subcellular location. We have also shown that the combination of text-based and sequence-based prediction improves the performance of location predictors. Following up on this work, for the Critical Assessment of Function Annotations (CAFA) Challenge, we developed a text-based system that aims to predict molecular function and biological process (using Gene Ontology terms) for unannotated proteins. In this paper, we present the preliminary work and evaluation that we performed for our system, as part of the CAFA challenge. RESULTS We have developed a preliminary system that represents proteins using text-based features and predicts protein function using a k-nearest neighbour classifier (Text-KNN). We selected text features for our classifier by extracting key terms from biomedical abstracts based on their statistical properties. The system was trained and tested using 5-fold cross-validation over a dataset of 36,536 proteins. System performance was measured using the standard measures of precision, recall, F-measure and overall accuracy. The performance of our system was compared to two baseline classifiers: one that assigns function based solely on the prior distribution of protein function (Base-Prior) and one that assigns function based on sequence similarity (Base-Seq). The overall prediction accuracy of Text-KNN, Base-Prior, and Base-Seq for molecular function classes are 62%, 43%, and 58% while the overall accuracy for biological process classes are 17%, 11%, and 28% respectively. Results obtained as part of the CAFA evaluation itself on the CAFA dataset are reported as well. CONCLUSIONS Our evaluation shows that the text-based classifier consistently outperforms the baseline classifier that is based on prior distribution, and typically has comparable performance to the baseline classifier that uses sequence similarity. Moreover, the results suggest that combining text features with other types of features can potentially lead to improved prediction performance. The preliminary results also suggest that while our text-based classifier can be used to predict both molecular function and biological process in which a protein is involved, the classifier performs significantly better for predicting molecular function than for predicting biological process. A similar trend was observed for other classifiers participating in the CAFA challenge.
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Affiliation(s)
- Andrew Wong
- Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, ON, K7L 3N6, Canada
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Lopez D, Pazos F. Concomitant prediction of function and fold at the domain level with GO-based profiles. BMC Bioinformatics 2013; 14 Suppl 3:S12. [PMID: 23514233 PMCID: PMC3584904 DOI: 10.1186/1471-2105-14-s3-s12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Predicting the function of newly sequenced proteins is crucial due to the pace at which these raw sequences are being obtained. Almost all resources for predicting protein function assign functional terms to whole chains, and do not distinguish which particular domain is responsible for the allocated function. This is not a limitation of the methodologies themselves but it is due to the fact that in the databases of functional annotations these methods use for transferring functional terms to new proteins, these annotations are done on a whole-chain basis. Nevertheless, domains are the basic evolutionary and often functional units of proteins. In many cases, the domains of a protein chain have distinct molecular functions, independent from each other. For that reason resources with functional annotations at the domain level, as well as methodologies for predicting function for individual domains adapted to these resources are required. We present a methodology for predicting the molecular function of individual domains, based on a previously developed database of functional annotations at the domain level. The approach, which we show outperforms a standard method based on sequence searches in assigning function, concomitantly predicts the structural fold of the domains and can give hints on the functionally important residues associated to the predicted function.
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Affiliation(s)
- Daniel Lopez
- Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid, Spain
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Radivojac P, Clark WT, Oron TR, Schnoes AM, Wittkop T, Sokolov A, Graim K, Funk C, Verspoor K, Ben-Hur A, Pandey G, Yunes JM, Talwalkar AS, Repo S, Souza ML, Piovesan D, Casadio R, Wang Z, Cheng J, Fang H, Gough J, Koskinen P, Törönen P, Nokso-Koivisto J, Holm L, Cozzetto D, Buchan DWA, Bryson K, Jones DT, Limaye B, Inamdar H, Datta A, Manjari SK, Joshi R, Chitale M, Kihara D, Lisewski AM, Erdin S, Venner E, Lichtarge O, Rentzsch R, Yang H, Romero AE, Bhat P, Paccanaro A, Hamp T, Kaßner R, Seemayer S, Vicedo E, Schaefer C, Achten D, Auer F, Boehm A, Braun T, Hecht M, Heron M, Hönigschmid P, Hopf TA, Kaufmann S, Kiening M, Krompass D, Landerer C, Mahlich Y, Roos M, Björne J, Salakoski T, Wong A, Shatkay H, Gatzmann F, Sommer I, Wass MN, Sternberg MJE, Škunca N, Supek F, Bošnjak M, Panov P, Džeroski S, Šmuc T, Kourmpetis YAI, van Dijk ADJ, ter Braak CJF, Zhou Y, Gong Q, Dong X, Tian W, Falda M, Fontana P, Lavezzo E, Di Camillo B, Toppo S, Lan L, Djuric N, Guo Y, Vucetic S, Bairoch A, Linial M, Babbitt PC, Brenner SE, Orengo C, Rost B, Mooney SD, Friedberg I. A large-scale evaluation of computational protein function prediction. Nat Methods 2013; 10:221-7. [PMID: 23353650 PMCID: PMC3584181 DOI: 10.1038/nmeth.2340] [Citation(s) in RCA: 587] [Impact Index Per Article: 53.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2012] [Accepted: 12/10/2012] [Indexed: 01/03/2023]
Abstract
A report on the results of the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.
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Affiliation(s)
- Predrag Radivojac
- School of Informatics and Computing, Indiana University, Bloomington, Indiana, USA
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Volkamer A, Kuhn D, Rippmann F, Rarey M. Predicting enzymatic function from global binding site descriptors. Proteins 2012; 81:479-89. [DOI: 10.1002/prot.24205] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Revised: 09/21/2012] [Accepted: 10/11/2012] [Indexed: 11/09/2022]
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Khan I, Chitale M, Rayon C, Kihara D. Evaluation of function predictions by PFP, ESG,and PSI-BLAST for moonlighting proteins. BMC Proc 2012; 6 Suppl 7:S5. [PMID: 23173871 PMCID: PMC3504920 DOI: 10.1186/1753-6561-6-s7-s5] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Background Advancements in function prediction algorithms are enabling large scale computational annotation for newly sequenced genomes. With the increase in the number of functionally well characterized proteins it has been observed that there are many proteins involved in more than one function. These proteins characterized as moonlighting proteins show varied functional behavior depending on the cell type, localization in the cell, oligomerization, multiple binding sites, etc. The functional diversity shown by moonlighting proteins may have significant impact on the traditional sequence based function prediction methods. Here we investigate how well diverse functions of moonlighting proteins can be predicted by some existing function prediction methods. Results We have analyzed the performances of three major sequence based function prediction methods, PSI-BLAST, the Protein Function Prediction (PFP), and the Extended Similarity Group (ESG) on predicting diverse functions of moonlighting proteins. In predicting discrete functions of a set of 19 experimentally identified moonlighting proteins, PFP showed overall highest recall among the three methods. Although ESG showed the highest precision, its recall was lower than PSI-BLAST. Recall by PSI-BLAST greatly improved when BLOSUM45 was used instead of BLOSUM62. Conclusion We have analyzed the performances of PFP, ESG, and PSI-BLAST in predicting the functional diversity of moonlighting proteins. PFP shows overall better performance in predicting diverse moonlighting functions as compared with PSI-BLAST and ESG. Recall by PSI-BLAST greatly improved when BLOSUM45 was used. This analysis indicates that considering weakly similar sequences in prediction enhances the performance of sequence based AFP methods in predicting functional diversity of moonlighting proteins. The current study will also motivate development of novel computational frameworks for automatic identification of such proteins.
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Affiliation(s)
- Ishita Khan
- Department of Computer Science, College of Science, Purdue University, West Lafayette, IN 47907, USA.
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Ashkenazi S, Snir R, Ofran Y. Assessing the relationship between conservation of function and conservation of sequence using photosynthetic proteins. ACTA ACUST UNITED AC 2012; 28:3203-10. [PMID: 23080118 DOI: 10.1093/bioinformatics/bts608] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION Assessing the false positive rate of function prediction methods is difficult, as it is hard to establish that a protein does not have a certain function. To determine to what extent proteins with similar sequences have a common function, we focused on photosynthesis-related proteins. A protein that comes from a non-photosynthetic organism is, undoubtedly, not involved in photosynthesis. RESULTS We show that function diverges very rapidly: 70% of the close homologs of photosynthetic proteins come from non-photosynthetic organisms. Therefore, high sequence similarity, in most cases, is not tantamount to similar function. However, we found that many functionally similar proteins often share short sequence elements, which may correspond to a functional site and could reveal functional similarities more accurately than sequence similarity. CONCLUSIONS These results shed light on the way biological function is conserved in evolution and may help improve large-scale analysis of protein function.
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Affiliation(s)
- Shaul Ashkenazi
- The Goodman faculty of life sciences, Bar Ilan University, Ramat Gan 52900, Israel
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Hepowit NL, Uthandi S, Miranda HV, Toniutti M, Prunetti L, Olivarez O, De Vera IMS, Fanucci GE, Chen S, Maupin-Furlow JA. Archaeal JAB1/MPN/MOV34 metalloenzyme (HvJAMM1) cleaves ubiquitin-like small archaeal modifier proteins (SAMPs) from protein-conjugates. Mol Microbiol 2012; 86:971-87. [PMID: 22970855 DOI: 10.1111/mmi.12038] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/10/2012] [Indexed: 12/11/2022]
Abstract
Proteins with JAB1/MPN/MOV34 metalloenzyme (JAMM/MPN+) domains are widespread among all domains of life, yet poorly understood. Here we report the purification and characterization of an archaeal JAMM/MPN+ domain protein (HvJAMM1) from Haloferax volcanii that cleaves ubiquitin-like small archaeal modifier proteins (SAMP1/2) from protein conjugates. HvJAMM1 cleaved SAMP1/2 conjugates generated in H. volcanii as well as isopeptide- and linear-linked SAMP1-MoaE in purified form. Cleavage of linear linked SAMP1-MoaE was dependent on the presence of the SAMP domain and the C-terminal VSGG motif of this domain. While HvJAMM1 was inhibited by size exclusion chromatography and metal chelators, its activity could be restored by addition of excess ZnCl2 . HvJAMM1 residues (Glu31, His88, His90, Ser98 and Asp101) that were conserved with the JAMM/MPN+ active-site motif were required for enzyme activity. Together, these results provide the first example of a JAMM/MPN+ zinc metalloprotease that independently catalyses the cleavage of ubiquitin-like (isopeptide and linear) bonds from target proteins. In archaea, HvJAMM1 likely regulates sampylation and the pools of 'free' SAMP available for protein modification. HvJAMM1-type proteins are thought to release the SAMPs from proteins modified post-translationally as well as those synthesized as domain fusions.
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Affiliation(s)
- Nathaniel L Hepowit
- Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611-0700, USA
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