1
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Han B, Ren C, Wang W, Li J, Gong X. Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions. Genes (Basel) 2023; 14:432. [PMID: 36833360 PMCID: PMC9956190 DOI: 10.3390/genes14020432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/02/2023] [Accepted: 02/05/2023] [Indexed: 02/11/2023] Open
Abstract
Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives.
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Affiliation(s)
- Bingqing Han
- Mathematical Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China
| | - Chongjiao Ren
- Mathematical Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China
| | - Wenda Wang
- Mathematical Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China
| | - Jiashan Li
- Mathematical Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China
| | - Xinqi Gong
- Mathematical Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China
- Beijing Academy of Intelligence, Beijing 100083, China
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2
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Maljković MM, Mitić NS, de Brevern AG. Prediction of structural alphabet protein blocks using data mining. Biochimie 2022; 197:74-85. [PMID: 35143919 DOI: 10.1016/j.biochi.2022.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/22/2022] [Accepted: 01/31/2022] [Indexed: 11/17/2022]
Abstract
3D protein structures determine proteins' biological functions. The 3D structure of the protein backbone can be approximated using the prototypes of local protein conformations. Sets of these prototypes are called structural alphabets (SAs). Amongst several approaches to the prediction of 3D structures from amino acid sequences, one approach is based on the prediction of SA prototypes for a given amino acid sequence. Protein Blocks (PBs) is the most known SA, and it is composed of 16 prototypes of five consecutive amino acids which were identified as optimal prototypes considering the ability to correctly approximate the local structure and the prediction accuracy of prototypes from an amino acid sequence. We developed models for PBs prediction from sequence information using different data mining approaches and machine learning algorithms. Besides the amino acid sequences, the results of the following tools were used to train the models: the Spider3 predictor of protein structure properties, several predictors of the protein's intrinsically disordered regions, and a tool for finding repeats in amino acid sequences. The highest accuracy of the constructed models is 80%, which is a significant improvement compared to the previous best available prediction, whose accuracy was 61%. Analyzing the models constructed by applying different algorithms, it was noticed that the significance of input attributes differs among the models constructed by algorithms. Using the information about amino acids belonging to intrinsically disordered regions and repeats improves the precision of prediction for some PBs using the CART classification algorithm, while this is not the case with the C5.0 classification algorithm. Improved prediction approaches can have interesting applications in protein structural model approaches or computational protein design.
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Affiliation(s)
- Mirjana M Maljković
- Faculty of Mathematics, University of Belgrade, Studentski Trg 16, 11000, Belgrade, Serbia.
| | - Nenad S Mitić
- Faculty of Mathematics, University of Belgrade, Studentski Trg 16, 11000, Belgrade, Serbia
| | - Alexandre G de Brevern
- Université de Paris, INSERM UMR_S 1134, DSIMB, Université de la Réunion, INTS6, Rue Alexandre Cabanel, 75015, Paris, France
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3
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Cretin G, Galochkina T, de Brevern AG, Gelly JC. PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction. Int J Mol Sci 2021; 22:ijms22168831. [PMID: 34445537 PMCID: PMC8396346 DOI: 10.3390/ijms22168831] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Protein Blocks (PBs) are a widely used structural alphabet describing local protein backbone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as 1D PB sequences was previously successfully applied to protein structure alignment and protein structure prediction. In the current study, we present a new model, PYTHIA (predicting any conformation at high accuracy), for the prediction of the protein local conformations in terms of PBs directly from the amino acid sequence. PYTHIA is based on a deep residual inception-inside-inception neural network with convolutional block attention modules, predicting 1 of 16 PB classes from evolutionary information combined to physicochemical properties of individual amino acids. PYTHIA clearly outperforms the LOCUSTRA reference method for all PB classes and demonstrates great performance for PB prediction on particularly challenging proteins from the CASP14 free modelling category.
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Affiliation(s)
- Gabriel Cretin
- Biologie Intégrée du Globule Rouge, Université de Paris, UMR_S1134, BIGR, INSERM, 75015 Paris, France; (G.C.); (T.G.); (A.G.d.B.)
- Laboratoire d’Excellence GR-Ex, 75015 Paris, France
| | - Tatiana Galochkina
- Biologie Intégrée du Globule Rouge, Université de Paris, UMR_S1134, BIGR, INSERM, 75015 Paris, France; (G.C.); (T.G.); (A.G.d.B.)
- Laboratoire d’Excellence GR-Ex, 75015 Paris, France
| | - Alexandre G. de Brevern
- Biologie Intégrée du Globule Rouge, Université de Paris, UMR_S1134, BIGR, INSERM, 75015 Paris, France; (G.C.); (T.G.); (A.G.d.B.)
- Laboratoire d’Excellence GR-Ex, 75015 Paris, France
| | - Jean-Christophe Gelly
- Biologie Intégrée du Globule Rouge, Université de Paris, UMR_S1134, BIGR, INSERM, 75015 Paris, France; (G.C.); (T.G.); (A.G.d.B.)
- Laboratoire d’Excellence GR-Ex, 75015 Paris, France
- Correspondence:
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4
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Narwani TJ, Craveur P, Shinada NK, Floch A, Santuz H, Vattekatte AM, Srinivasan N, Rebehmed J, Gelly JC, Etchebest C, de Brevern AG. Discrete analyses of protein dynamics. J Biomol Struct Dyn 2019; 38:2988-3002. [PMID: 31361191 DOI: 10.1080/07391102.2019.1650112] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Protein structures are highly dynamic macromolecules. This dynamics is often analysed through experimental and/or computational methods only for an isolated or a limited number of proteins. Here, we explore large-scale protein dynamics simulation to observe dynamics of local protein conformations using different perspectives. We analysed molecular dynamics to investigate protein flexibility locally, using classical approaches such as RMSf, solvent accessibility, but also innovative approaches such as local entropy. First, we focussed on classical secondary structures and analysed specifically how β-strand, β-turns, and bends evolve during molecular simulations. We underlined interesting specific bias between β-turns and bends, which are considered as the same category, while their dynamics show differences. Second, we used a structural alphabet that is able to approximate every part of the protein structures conformations, namely protein blocks (PBs) to analyse (i) how each initial local protein conformations evolve during dynamics and (ii) if some exchange can exist among these PBs. Interestingly, the results are largely complex than simple regular/rigid and coil/flexible exchange. AbbreviationsNeqnumber of equivalentPBProtein BlocksPDBProtein DataBankRMSfroot mean square fluctuationsCommunicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Tarun Jairaj Narwani
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France
| | - Pierrick Craveur
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nicolas K Shinada
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Discngine, SAS, Paris, France
| | - Aline Floch
- Laboratoire D'Excellence GR-Ex, Paris, France.,Etablissement Français du Sang Ile de France, Créteil, France.,IMRB - INSERM U955 Team 2 « Transfusion et Maladies du Globule Rouge », Paris Est- Créteil Univ, Créteil, France.,UPEC, Université Paris Est-Créteil, Créteil, France
| | - Hubert Santuz
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France
| | - Akhila Melarkode Vattekatte
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Faculté Des Sciences et Technologies, Saint Denis Messag, La Réunion, France
| | | | - Joseph Rebehmed
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Department of Computer Science and Mathematics, Lebanese American University, Byblos, Lebanon
| | - Jean-Christophe Gelly
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Faculté Des Sciences et Technologies, Saint Denis Messag, La Réunion, France.,IBL, Paris, France
| | - Catherine Etchebest
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Faculté Des Sciences et Technologies, Saint Denis Messag, La Réunion, France
| | - Alexandre G de Brevern
- Biologie Intégrée du Globule Rouge UMR_S1134, Inserm, Univ. Paris, Univ. de la Réunion, Univ. des Antilles, Paris, France.,Laboratoire D'Excellence GR-Ex, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Faculté Des Sciences et Technologies, Saint Denis Messag, La Réunion, France.,IBL, Paris, France
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5
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Liu Y, Wang X, Liu B. A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction. Brief Bioinform 2019; 20:330-346. [PMID: 30657889 DOI: 10.1093/bib/bbx126] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Indexed: 01/06/2023] Open
Abstract
Intrinsically disordered proteins and regions are widely distributed in proteins, which are associated with many biological processes and diseases. Accurate prediction of intrinsically disordered proteins and regions is critical for both basic research (such as protein structure and function prediction) and practical applications (such as drug development). During the past decades, many computational approaches have been proposed, which have greatly facilitated the development of this important field. Therefore, a comprehensive and updated review is highly required. In this regard, we give a review on the computational methods for intrinsically disordered protein and region prediction, especially focusing on the recent development in this field. These computational approaches are divided into four categories based on their methodologies, including physicochemical-based method, machine-learning-based method, template-based method and meta method. Furthermore, their advantages and disadvantages are also discussed. The performance of 40 state-of-the-art predictors is directly compared on the target proteins in the task of disordered region prediction in the 10th Critical Assessment of protein Structure Prediction. A more comprehensive performance comparison of 45 different predictors is conducted based on seven widely used benchmark data sets. Finally, some open problems and perspectives are discussed.
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Affiliation(s)
- Yumeng Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China
| | - Xiaolong Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China
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6
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Craveur P, Narwani TJ, Rebehmed J, de Brevern AG. Investigation of the impact of PTMs on the protein backbone conformation. Amino Acids 2019; 51:1065-1079. [DOI: 10.1007/s00726-019-02747-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 05/18/2019] [Indexed: 12/17/2022]
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7
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Vetrivel I, Mahajan S, Tyagi M, Hoffmann L, Sanejouand YH, Srinivasan N, de Brevern AG, Cadet F, Offmann B. Knowledge-based prediction of protein backbone conformation using a structural alphabet. PLoS One 2017; 12:e0186215. [PMID: 29161266 PMCID: PMC5697859 DOI: 10.1371/journal.pone.0186215] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 09/27/2017] [Indexed: 01/19/2023] Open
Abstract
Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlight the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.
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Affiliation(s)
- Iyanar Vetrivel
- Université de Nantes, Unité Fonctionnalité et Ingénierie des Protéines (UFIP), UMR 6286 CNRS, UFR Sciences et Techniques, 2, chemin de la Houssinière, France
| | - Swapnil Mahajan
- Université de Nantes, Unité Fonctionnalité et Ingénierie des Protéines (UFIP), UMR 6286 CNRS, UFR Sciences et Techniques, 2, chemin de la Houssinière, France
- DSIMB, INSERM, UMR S-1134, Laboratory of Excellence, GR-Ex, Université de La Réunion, Faculty of Sciences and Technology, Saint Denis Cedex, La Réunion, France
| | - Manoj Tyagi
- Université de La Réunion, Saint Denis Cedex, La Réunion, France
| | - Lionel Hoffmann
- Université de Nantes, Unité Fonctionnalité et Ingénierie des Protéines (UFIP), UMR 6286 CNRS, UFR Sciences et Techniques, 2, chemin de la Houssinière, France
| | - Yves-Henri Sanejouand
- Université de Nantes, Unité Fonctionnalité et Ingénierie des Protéines (UFIP), UMR 6286 CNRS, UFR Sciences et Techniques, 2, chemin de la Houssinière, France
| | | | - Alexandre G. de Brevern
- INSERM UMR_S 1134, DSIMB team, Laboratory of Excellence, GR-Ex, Univ Paris Diderot, Univ Sorbonne Paris Cité, INTS, rue Alexandre Cabanel, Paris, France
| | - Frédéric Cadet
- DSIMB, INSERM, UMR S-1134, Laboratory of Excellence, GR-Ex, Université de La Réunion, Faculty of Sciences and Technology, Saint Denis Cedex, La Réunion, France
- PEACCEL SAS, Paris, France
| | - Bernard Offmann
- Université de Nantes, Unité Fonctionnalité et Ingénierie des Protéines (UFIP), UMR 6286 CNRS, UFR Sciences et Techniques, 2, chemin de la Houssinière, France
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8
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Barnoud J, Santuz H, Craveur P, Joseph AP, Jallu V, de Brevern AG, Poulain P. PBxplore: a tool to analyze local protein structure and deformability with Protein Blocks. PeerJ 2017; 5:e4013. [PMID: 29177113 PMCID: PMC5700758 DOI: 10.7717/peerj.4013] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Accepted: 10/19/2017] [Indexed: 11/20/2022] Open
Abstract
This paper describes the development and application of a suite of tools, called PBxplore, to analyze the dynamics and deformability of protein structures using Protein Blocks (PBs). Proteins are highly dynamic macromolecules, and a classical way to analyze their inherent flexibility is to perform molecular dynamics simulations. The advantage of using small structural prototypes such as PBs is to give a good approximation of the local structure of the protein backbone. More importantly, by reducing the conformational complexity of protein structures, PBs allow analysis of local protein deformability which cannot be done with other methods and had been used efficiently in different applications. PBxplore is able to process large amounts of data such as those produced by molecular dynamics simulations. It produces frequencies, entropy and information logo outputs as text and graphics. PBxplore is available at https://github.com/pierrepo/PBxplore and is released under the open-source MIT license.
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Affiliation(s)
- Jonathan Barnoud
- INSERM, U 1134, DSIMB, Paris, France.,Univ. Paris Diderot, Sorbonne Paris Cité, Univ de la Réunion, Univ des Antilles, UMR-S 1134, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France.,Current affiliation: Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Groningen, The Netherlands
| | - Hubert Santuz
- INSERM, U 1134, DSIMB, Paris, France.,Univ. Paris Diderot, Sorbonne Paris Cité, Univ de la Réunion, Univ des Antilles, UMR-S 1134, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France.,Current affiliation: Laboratoire de Biochimie Théorique, CNRS UPR 9080, Institut de Biologie Physico-Chimique, Paris, France
| | - Pierrick Craveur
- INSERM, U 1134, DSIMB, Paris, France.,Univ. Paris Diderot, Sorbonne Paris Cité, Univ de la Réunion, Univ des Antilles, UMR-S 1134, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France.,Current affiliation: Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States of America
| | - Agnel Praveen Joseph
- INSERM, U 1134, DSIMB, Paris, France.,Univ. Paris Diderot, Sorbonne Paris Cité, Univ de la Réunion, Univ des Antilles, UMR-S 1134, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France.,Current affiliation: Birkbeck College, University of London, London, UK
| | | | - Alexandre G de Brevern
- INSERM, U 1134, DSIMB, Paris, France.,Univ. Paris Diderot, Sorbonne Paris Cité, Univ de la Réunion, Univ des Antilles, UMR-S 1134, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France
| | - Pierre Poulain
- INSERM, U 1134, DSIMB, Paris, France.,Univ. Paris Diderot, Sorbonne Paris Cité, Univ de la Réunion, Univ des Antilles, UMR-S 1134, Paris, France.,Institut National de la Transfusion Sanguine (INTS), Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France.,Current affiliation: Mitochondria, Metals and Oxidative Stress Group, Institut Jacques Monod, UMR 7592, Univ. Paris Diderot, CNRS, Sorbonne Paris Cité, Paris, France
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9
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Ghouzam Y, Postic G, de Brevern AG, Gelly JC. Improving protein fold recognition with hybrid profiles combining sequence and structure evolution. Bioinformatics 2015; 31:3782-9. [PMID: 26254434 DOI: 10.1093/bioinformatics/btv462] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 08/02/2015] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Template-based modeling, the most successful approach for predicting protein 3D structure, often requires detecting distant evolutionary relationships between the target sequence and proteins of known structure. Developed for this purpose, fold recognition methods use elaborate strategies to exploit evolutionary information, mainly by encoding amino acid sequence into profiles. Since protein structure is more conserved than sequence, the inclusion of structural information can improve the detection of remote homology. RESULTS Here, we present ORION, a new fold recognition method based on the pairwise comparison of hybrid profiles that contain evolutionary information from both protein sequence and structure. Our method uses the 16-state structural alphabet Protein Blocks, which provides an accurate 1D description of protein structure local conformations. ORION systematically outperforms PSI-BLAST and HHsearch on several benchmarks, including target sequences from the modeling competitions CASP8, 9 and 10, and detects ∼10% more templates at fold and superfamily SCOP levels. AVAILABILITY Software freely available for download at http://www.dsimb.inserm.fr/orion/. CONTACT jean-christophe.gelly@univ-paris-diderot.fr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yassine Ghouzam
- Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France
| | - Guillaume Postic
- Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France
| | - Alexandre G de Brevern
- Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France
| | - Jean-Christophe Gelly
- Inserm U1134, Paris, France, Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, Paris, France, Institut National de la Transfusion Sanguine, Paris, France and Laboratory of Excellence GR-Ex, Paris, France
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10
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Craveur P, Joseph AP, Esque J, Narwani TJ, Noël F, Shinada N, Goguet M, Leonard S, Poulain P, Bertrand O, Faure G, Rebehmed J, Ghozlane A, Swapna LS, Bhaskara RM, Barnoud J, Téletchéa S, Jallu V, Cerny J, Schneider B, Etchebest C, Srinivasan N, Gelly JC, de Brevern AG. Protein flexibility in the light of structural alphabets. Front Mol Biosci 2015; 2:20. [PMID: 26075209 PMCID: PMC4445325 DOI: 10.3389/fmolb.2015.00020] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Accepted: 04/30/2015] [Indexed: 01/01/2023] Open
Abstract
Protein structures are valuable tools to understand protein function. Nonetheless, proteins are often considered as rigid macromolecules while their structures exhibit specific flexibility, which is essential to complete their functions. Analyses of protein structures and dynamics are often performed with a simplified three-state description, i.e., the classical secondary structures. More precise and complete description of protein backbone conformation can be obtained using libraries of small protein fragments that are able to approximate every part of protein structures. These libraries, called structural alphabets (SAs), have been widely used in structure analysis field, from definition of ligand binding sites to superimposition of protein structures. SAs are also well suited to analyze the dynamics of protein structures. Here, we review innovative approaches that investigate protein flexibility based on SAs description. Coupled to various sources of experimental data (e.g., B-factor) and computational methodology (e.g., Molecular Dynamic simulation), SAs turn out to be powerful tools to analyze protein dynamics, e.g., to examine allosteric mechanisms in large set of structures in complexes, to identify order/disorder transition. SAs were also shown to be quite efficient to predict protein flexibility from amino-acid sequence. Finally, in this review, we exemplify the interest of SAs for studying flexibility with different cases of proteins implicated in pathologies and diseases.
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Affiliation(s)
- Pierrick Craveur
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Agnel P Joseph
- Rutherford Appleton Laboratory, Science and Technology Facilities Council Didcot, UK
| | - Jeremy Esque
- Institut National de la Santé et de la Recherche Médicale U964,7 UMR Centre National de la Recherche Scientifique 7104, IGBMC, Université de Strasbourg Illkirch, France
| | - Tarun J Narwani
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Floriane Noël
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Nicolas Shinada
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Matthieu Goguet
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Sylvain Leonard
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Pierre Poulain
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Ets Poulain Pointe-Noire, Congo
| | - Olivier Bertrand
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Guilhem Faure
- National Library of Medicine, National Center for Biotechnology Information, National Institutes of Health Bethesda, MD, USA
| | - Joseph Rebehmed
- Centre National de la Recherche Scientifique UMR7590, Sorbonne Universités, Université Pierre et Marie Curie - MNHN - IRD - IUC Paris, France
| | | | - Lakshmipuram S Swapna
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore Bangalore, India ; Hospital for Sick Children, and Departments of Biochemistry and Molecular Genetics, University of Toronto Toronto, ON, Canada
| | - Ramachandra M Bhaskara
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore Bangalore, India ; Department of Theoretical Biophysics, Max Planck Institute of Biophysics Frankfurt, Germany
| | - Jonathan Barnoud
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Laboratoire de Physique, École Normale Supérieure de Lyon, Université de Lyon, Centre National de la Recherche Scientifique UMR 5672 Lyon, France
| | - Stéphane Téletchéa
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France ; Faculté des Sciences et Techniques, Université de Nantes, Unité Fonctionnalité et Ingénierie des Protéines, Centre National de la Recherche Scientifique UMR 6286, Université Nantes Nantes, France
| | - Vincent Jallu
- Platelet Unit, Institut National de la Transfusion Sanguine Paris, France
| | - Jiri Cerny
- Institute of Biotechnology, The Czech Academy of Sciences Prague, Czech Republic
| | - Bohdan Schneider
- Institute of Biotechnology, The Czech Academy of Sciences Prague, Czech Republic
| | - Catherine Etchebest
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | | | - Jean-Christophe Gelly
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
| | - Alexandre G de Brevern
- Institut National de la Santé et de la Recherche Médicale U 1134 Paris, France ; UMR_S 1134, DSIMB, Université Paris Diderot, Sorbonne Paris Cite Paris, France ; Institut National de la Transfusion Sanguine, DSIMB Paris, France ; UMR_S 1134, DSIMB, Laboratory of Excellence GR-Ex Paris, France
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11
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Computational and experimental approaches to reveal the effects of single nucleotide polymorphisms with respect to disease diagnostics. Int J Mol Sci 2014; 15:9670-717. [PMID: 24886813 PMCID: PMC4100115 DOI: 10.3390/ijms15069670] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 05/15/2014] [Accepted: 05/16/2014] [Indexed: 12/25/2022] Open
Abstract
DNA mutations are the cause of many human diseases and they are the reason for natural differences among individuals by affecting the structure, function, interactions, and other properties of DNA and expressed proteins. The ability to predict whether a given mutation is disease-causing or harmless is of great importance for the early detection of patients with a high risk of developing a particular disease and would pave the way for personalized medicine and diagnostics. Here we review existing methods and techniques to study and predict the effects of DNA mutations from three different perspectives: in silico, in vitro and in vivo. It is emphasized that the problem is complicated and successful detection of a pathogenic mutation frequently requires a combination of several methods and a knowledge of the biological phenomena associated with the corresponding macromolecules.
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12
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Craveur P, Rebehmed J, de Brevern AG. PTM-SD: a database of structurally resolved and annotated posttranslational modifications in proteins. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau041. [PMID: 24857970 PMCID: PMC4038255 DOI: 10.1093/database/bau041] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Posttranslational modifications (PTMs) define covalent and chemical modifications of protein residues. They play important roles in modulating various biological functions. Current PTM databases contain important sequence annotations but do not provide informative 3D structural resource about these modifications. Posttranslational modification structural database (PTM-SD) provides access to structurally solved modified residues, which are experimentally annotated as PTMs. It combines different PTM information and annotation gathered from other databases, e.g. Protein DataBank for the protein structures and dbPTM and PTMCuration for fine sequence annotation. PTM-SD gives an accurate detection of PTMs in structural data. PTM-SD can be browsed by PDB id, UniProt accession number, organism and classic PTM annotation. Advanced queries can also be performed, i.e. detailed PTM annotations, amino acid type, secondary structure, SCOP class classification, PDB chain length and number of PTMs by chain. Statistics and analyses can be computed on a selected dataset of PTMs. Each PTM entry is detailed in a dedicated page with information on the protein sequence, local conformation with secondary structure and Protein Blocks. PTM-SD gives valuable information on observed PTMs in protein 3D structure, which is of great interest for studying sequence-structure- function relationships at the light of PTMs, and could provide insights for comparative modeling and PTM predictions protocols. Database URL: PTM-SD can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/PTM-SD/.
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Affiliation(s)
- Pierrick Craveur
- INSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, FranceINSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, FranceINSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, FranceINSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, France
| | - Joseph Rebehmed
- INSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, FranceINSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, FranceINSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, FranceINSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, France
| | - Alexandre G de Brevern
- INSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, FranceINSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, FranceINSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, FranceINSERM, U 1134, DSIMB, F-75739 Paris, France, Univ Paris Diderot, Sorbonne Paris Cité, UMR-S 1134, F-75739 Paris, France, Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France and Laboratoire d'Excellence GR-Ex, F-75739 Paris, France
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13
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Joseph AP, de Brevern AG. From local structure to a global framework: recognition of protein folds. J R Soc Interface 2014; 11:20131147. [PMID: 24740960 DOI: 10.1098/rsif.2013.1147] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.
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Affiliation(s)
- Agnel Praveen Joseph
- Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Oxford, , Didcot OX11 0QX, UK
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14
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Léonard S, Joseph AP, Srinivasan N, Gelly JC, de Brevern AG. mulPBA: an efficient multiple protein structure alignment method based on a structural alphabet. J Biomol Struct Dyn 2013; 32:661-8. [PMID: 23659291 DOI: 10.1080/07391102.2013.787026] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
The increasing number of available protein structures requires efficient tools for multiple structure comparison. Indeed, multiple structural alignments are essential for the analysis of function, evolution and architecture of protein structures. For this purpose, we proposed a new web server called multiple Protein Block Alignment (mulPBA). This server implements a method based on a structural alphabet to describe the backbone conformation of a protein chain in terms of dihedral angles. This 'sequence-like' representation enables the use of powerful sequence alignment methods for primary structure comparison, followed by an iterative refinement of the structural superposition. This approach yields alignments superior to most of the rigid-body alignment methods and highly comparable with the flexible structure comparison approaches. We implement this method in a web server designed to do multiple structure superimpositions from a set of structures given by the user. Outputs are given as both sequence alignment and superposed 3D structures visualized directly by static images generated by PyMol or through a Jmol applet allowing dynamic interaction. Multiple global quality measures are given. Relatedness between structures is indicated by a distance dendogram. Superimposed structures in PDB format can be also downloaded, and the results are quickly obtained. mulPBA server can be accessed at www.dsimb.inserm.fr/dsimb_tools/mulpba/ .
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Affiliation(s)
- Sylvain Léonard
- a INSERM UMR-S 665, DSIMB , 6, rue Alexandre Cabanel, F-75739 , Paris , France
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15
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Panwar B, Gupta S, Raghava GPS. Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information. BMC Bioinformatics 2013; 14:44. [PMID: 23387468 PMCID: PMC3577447 DOI: 10.1186/1471-2105-14-44] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2012] [Accepted: 01/31/2013] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure. RESULTS In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0.81 for VIRs, VAIRs, VBIRs, PLPIRs respectively, using PSSM-based evolutionary information. All the modules developed in this study have been trained and tested on non-redundant datasets and evaluated using five-fold cross-validation technique. The performances were also evaluated on the balanced and different independent datasets. CONCLUSIONS This study demonstrates that it is possible to predict VIRs, VAIRs, VBIRs and PLPIRs from evolutionary information of protein sequence. In order to provide service to the scientific community, we have developed web-server and standalone software VitaPred (http://crdd.osdd.net/raghava/vitapred/).
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Affiliation(s)
- Bharat Panwar
- Bioinformatics Centre, Institute of Microbial Technology (CSIR), Chandigarh, India
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16
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Joseph AP, Srinivasan N, de Brevern AG. Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies. Biochimie 2012; 94:2025-34. [PMID: 22676903 DOI: 10.1016/j.biochi.2012.05.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2011] [Accepted: 05/21/2012] [Indexed: 12/30/2022]
Abstract
Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string. A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition. Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods.
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Affiliation(s)
- Agnel Praveen Joseph
- INSERM, UMR-S 665, Dynamique des Structures et Interactions des Macromolécules Biologiques, 6, rue Alexandre Cabanel, 75739 Paris Cedex 15, France
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17
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Monastyrskyy B, Fidelis K, Moult J, Tramontano A, Kryshtafovych A. Evaluation of disorder predictions in CASP9. Proteins 2011; 79 Suppl 10:107-18. [PMID: 21928402 PMCID: PMC3212657 DOI: 10.1002/prot.23161] [Citation(s) in RCA: 105] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2011] [Revised: 07/11/2011] [Accepted: 07/15/2011] [Indexed: 11/10/2022]
Abstract
Lack of stable three-dimensional structure, or intrinsic disorder, is a common phenomenon in proteins. Naturally, unstructured regions are proven to be essential for carrying function by many proteins, and therefore identification of such regions is an important issue. CASP has been assessing the state of the art in predicting disorder regions from amino acid sequence since 2002. Here, we present the results of the evaluation of the disorder predictions submitted to CASP9. The assessment is based on the evaluation measures and procedures used in previous CASPs. The balanced accuracy and the Matthews correlation coefficient were chosen as basic measures for evaluating the correctness of binary classifications. The area under the receiver operating characteristic curve was the measure of choice for evaluating probability-based predictions of disorder. The CASP9 methods are shown to perform slightly better than the CASP7 methods but not better than the methods in CASP8. It was also shown that capability of most CASP9 methods to predict disorder decreases with increasing minimum disorder segment length.
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Affiliation(s)
- Bohdan Monastyrskyy
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis, CA 95616, USA
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis, CA 95616, USA
| | - John Moult
- Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, 9600 Gudelsky Drive, Rockville, MD 20850, USA
| | - Anna Tramontano
- Department of Physics, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Andriy Kryshtafovych
- Genome Center, University of California, Davis, 451 Health Sciences Dr., Davis, CA 95616, USA
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Species specific amino acid sequence–protein local structure relationships: An analysis in the light of a structural alphabet. J Theor Biol 2011; 276:209-17. [DOI: 10.1016/j.jtbi.2011.01.047] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2010] [Revised: 01/28/2011] [Accepted: 01/31/2011] [Indexed: 11/24/2022]
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Joseph AP, Agarwal G, Mahajan S, Gelly JC, Swapna LS, Offmann B, Cadet F, Bornot A, Tyagi M, Valadié H, Schneider B, Etchebest C, Srinivasan N, De Brevern AG. A short survey on protein blocks. Biophys Rev 2010; 2:137-147. [PMID: 21731588 DOI: 10.1007/s12551-010-0036-1] [Citation(s) in RCA: 97] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Protein structures are classically described in terms of secondary structures. Even if the regular secondary structures have relevant physical meaning, their recognition from atomic coordinates has some important limitations such as uncertainties in the assignment of boundaries of helical and β-strand regions. Further, on an average about 50% of all residues are assigned to an irregular state, i.e., the coil. Thus different research teams have focused on abstracting conformation of protein backbone in the localized short stretches. Using different geometric measures, local stretches in protein structures are clustered in a chosen number of states. A prototype representative of the local structures in each cluster is generally defined. These libraries of local structures prototypes are named as "structural alphabets". We have developed a structural alphabet, named Protein Blocks, not only to approximate the protein structure, but also to predict them from sequence. Since its development, we and other teams have explored numerous new research fields using this structural alphabet. We review here some of the most interesting applications.
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Affiliation(s)
- Agnel Praveen Joseph
- DSIMB, Dynamique des Structures et Interactions des Macromolécules Biologiques Université Paris-Diderot - Paris VII INTS INSERM : U665 INTS, 6 rue Alexandre Cabanel, 75739 Paris Cedex 15 FRANCE,FR
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Influence of assignment on the prediction of transmembrane helices in protein structures. Amino Acids 2010; 39:1241-54. [DOI: 10.1007/s00726-010-0559-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2009] [Accepted: 03/08/2010] [Indexed: 02/01/2023]
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