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Karuppasamy MP, Venkateswaran S, Subbiah P. PDB-2-PBv3.0: An updated protein block database. J Bioinform Comput Biol 2021; 18:2050009. [PMID: 32404014 DOI: 10.1142/s0219720020500092] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Our protein block (PB) sequence database PDB-2-PBv1.0 provides PB sequences and dihedral angles for 74,297 protein structures comprising of 103,252 protein chains of Protein Data Bank (PDB) as on 2011. Since there are a lot of practical applications of PB and also as the size of PDB database increases, it becomes necessary to provide the PB sequences for all PDB protein structures. The current updated PDB-2-PBv3.0 contains PB sequences for 147,602 PDB structures comprising of 400,355 protein chains as on October 2019. When compared to our previous version PDB-2-PBv1.0, the current PDB-2-PBv3.0 contains 2- and 4-fold increase in the number of protein structures and chains, respectively. Notably, it provides PB information for any protein chain, regardless of the missing atom records of protein structure data in PDB. It includes protein interaction information with DNA and RNA along with their corresponding functional classes from Nucleic Acid Database (NDB) and PDB. Now, the updated version allows the user to download multiple PB records by parameter search and/or by a given list. This database is freely accessible at http://bioinfo.bdu.ac.in/pb3.
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Affiliation(s)
- Muthuvel Prasath Karuppasamy
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620 024, Tamil Nadu, India
| | - Suresh Venkateswaran
- Department of Paediatrics, Emory University School of Medicine & Children's Healthcare of Atlanta, GA, USA
| | - Parthasarathy Subbiah
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620 024, Tamil Nadu, India
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3
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Vetrivel I, de Brevern AG, Cadet F, Srinivasan N, Offmann B. Structural variations within proteins can be as large as variations observed across their homologues. Biochimie 2019; 167:162-170. [PMID: 31560932 DOI: 10.1016/j.biochi.2019.09.013] [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: 01/25/2019] [Accepted: 09/18/2019] [Indexed: 10/26/2022]
Abstract
Understanding the structural plasticity of proteins is key to understanding the intricacies of their functions and mechanistic basis. In the current study, we analyzed the available multiple crystal structures of the same protein for the structural differences. For this purpose we used an abstraction of protein structures referred as Protein Blocks (PBs) that was previously established. We also characterized the nature of the structural variations for a few proteins using molecular dynamics simulations. In both the cases, the structural variations were summarized in the form of substitution matrices of PBs. We show that certain conformational states are preferably replaced by other specific conformational states. Interestingly, these structural variations are highly similar to those previously observed across structures of homologous proteins (r2 = 0.923) or across the ensemble of conformations from NMR data (r2 = 0.919). Thus our study quantitatively shows that overall trends of structural changes in a given protein are nearly identical to the trends of structural differences that occur in the topologically equivalent positions in homologous proteins. Specific case studies are used to illustrate the nature of these structural variations.
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Affiliation(s)
- Iyanar Vetrivel
- Université de Nantes, UFIP UMR 6286 CNRS, UFR Sciences et Techniques, 2 Chemin de La Houssinière, Nantes, France
| | - Alexandre G de Brevern
- INSERM UMR_S 1134, DSIMB Team, Laboratory of Excellence, GR-Ex, Univ Paris Diderot, Univ Sorbonne Paris Cité, INTS, 6 Rue Alexandre Cabanel, Paris, France
| | - Frédéric Cadet
- University of Paris, UMR_S1134, BIGR, Inserm, F-75015, Paris, France; DSIMB, UMR_S1134, BIGR, Inserm, Laboratory of Excellence GR-Ex, Faculty of Sciences and Technology, University of La Reunion, F-97715, Saint-Denis, France; PEACCEL, Protein Engineering Accelerator, 6 Square Albin Cachot, Box 42, 75013, Paris, France
| | | | - Bernard Offmann
- Université de Nantes, UFIP UMR 6286 CNRS, UFR Sciences et Techniques, 2 Chemin de La Houssinière, Nantes, France.
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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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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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6
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Abstract
The limitation of most HMMs is their inherent high dimensionality. Therefore we developed several variations of low complexity models that can be applied even to protein families with a few members. In this chapter we present these variations. All of them include the use of a hidden Markov model (HMM), with a small number of states (called reduced state-space HMM), which is trained with both amino acid sequence and secondary structure of proteins whose 3D structure is known and it is used for protein fold classification. We used data from Protein Data Bank and annotation from SCOP database for training and evaluation of the proposed HMM variations for a number of protein folds that belong to major structural classes. Results indicate that the variations have similar performance, or even better in some cases, on classifying proteins than SAM, which is a widely used HMM-based method for protein classification. The major advantage of the proposed variations is that we employed a small number of states and the algorithms used for training and scoring are of low complexity and thus relatively fast. The main variations examined include a version of the reduced state-space HMM with seven states (7-HMM), a version of the reduced state-space HMM with three states (3-HMM) and an optimized version of the reduced state-space HMM with three states, where an optimization process is applied to its scores (optimized 3-HMM).
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Affiliation(s)
- Christos Lampros
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, University Campus of Ioannina, GR45110, Ioannina, Greece
| | - Costas Papaloukas
- Department of Biological Applications and Technology, University of Ioannina, Ioannina, Greece
| | - Themis Exarchos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, University Campus of Ioannina, GR45110, Ioannina, Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, University Campus of Ioannina, GR45110, Ioannina, Greece.
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