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Halder SK, Mim MM, Alif MMH, Shathi JF, Alam N, Shil A, Himel MK. Oxa-376 and Oxa-530 variants of β-lactamase: computational study uncovers potential therapeutic targets of Acinetobacter baumannii. RSC Adv 2022; 12:24319-24338. [PMID: 36128545 PMCID: PMC9412156 DOI: 10.1039/d2ra02939a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/17/2022] [Indexed: 11/21/2022] Open
Abstract
Antimicrobial resistance is a major global health crisis, resulting in thousands of deaths each year. Antibiotics' effectiveness against microorganisms deteriorates over time as multidrug resistance (MDR) develops, which is exacerbated by irregular antibiotic use, poor disease management, and the evasive nature of bacteria. The World Health Organization has recognized multidrug resistance as a critical public health concern, and Acinetobacter baumannii has been at the center of attention due to its ability to develop multidrug resistance (MDR). It generally produces carbapenem-hydrolyzing oxacillinase, which has been identified as the primary source of beta-lactam resistance in MDR bacteria. Recently, point mutations in A. baumannii have been identified as a key factor of multidrug resistance, making them a prime concern for researchers. The goal of the current work was to establish a unique way of finding multidrug-resistant variants and identify the most damaging mutations in the existing databases. We characterized the deleterious variants of oxacillinases using several computational tools. Following a thorough analysis, Oxa-376 and Oxa-530 were found to be more damaging when compared with the wild-type Oxa-51. The mutants' 3D structures were then prepared and refined with RaptorX, GalaxyRefine, and SAVES servers. Our research incorporates seven antimicrobial agents to illustrate the resistance capability of the variants of oxacillinase by evaluating binding affinity in Autodock-vina and Schrodinger software. RMSD, RMSF, Radius of gyration analysis, the solvent-accessible surface area (SASA), hydrogen bonding analysis and MM-GBSA from Molecular Dynamics Simulation revealed the dynamic nature and stability of wild-type and Oxa-376 and Oxa-530 variants. Our findings will benefit researchers looking for the deleterious mutations of Acinetobacter baumannii and new therapeutics to combat those variants. However, further studies are necessary to evaluate the mechanism of hydrolyzing activity and antibiotic resistance of these variants. Determining novel therapeutic targets of Acinetobacter baumannii. Deleterious variants, causing antibiotic resistance, were identified by molecular docking and molecular dynamics simulation suggesting new therapeutic targets Oxa-376 and Oxa-530.![]()
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Affiliation(s)
- Sajal Kumar Halder
- Department of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
- Research Assistant at Padma Bioresearch, Dhaka, Bangladesh
| | - Maria Mulla Mim
- Department of Pharmacy, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Md. Meharab Hassan Alif
- Department of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Jannatul Fardous Shathi
- Department of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Nuhu Alam
- Department of Botany, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Aparna Shil
- Department of Botany, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
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2
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Narwani TJ, Etchebest C, Craveur P, Léonard S, Rebehmed J, Srinivasan N, Bornot A, Gelly JC, de Brevern AG. In silico prediction of protein flexibility with local structure approach. Biochimie 2019; 165:150-155. [PMID: 31377194 DOI: 10.1016/j.biochi.2019.07.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 07/26/2019] [Indexed: 12/30/2022]
Abstract
Flexibility is an intrinsic essential feature of protein structures, directly linked to their functions. To this day, most of the prediction methods use the crystallographic data (namely B-factors) as the only indicator of protein's inner flexibility and predicts them as rigid or flexible. PredyFlexy stands differently from other approaches as it relies on the definition of protein flexibility (i) not only taken from crystallographic data, but also (ii) from Root Mean Square Fluctuation (RMSFs) observed in Molecular Dynamics simulations. It also uses a specific representation of protein structures, named Long Structural Prototypes (LSPs). From Position-Specific Scoring Matrix, the 120 LSPs are predicted with a good accuracy and directly used to predict (i) the protein flexibility in three categories (flexible, intermediate and rigid), (ii) the normalized B-factors, (iii) the normalized RMSFs, and (iv) a confidence index. Prediction accuracy among these three classes is equivalent to the best two class prediction methods, while the normalized B-factors and normalized RMSFs have a good correlation with experimental and in silico values. Thus, PredyFlexy is a unique approach, which is of major utility for the scientific community. It support parallelization features and can be run on a local cluster using multiple cores.
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Affiliation(s)
- Tarun J Narwani
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France
| | - Catherine Etchebest
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France
| | - Pierrick Craveur
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France; Molecular Graphics Laboratory, Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Sylvain Léonard
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France
| | - Joseph Rebehmed
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France; Department of Computer Science and Mathematics, Lebanese American University, Byblos 1h401 2010, Lebanon
| | | | - Aurélie Bornot
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France
| | - Jean-Christophe Gelly
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France
| | - Alexandre G de Brevern
- INSERM, U 1134, DSIMB, Univ Paris, Univ de La Réunion, Univ des Antilles, F-75739, Paris, France; Institut National de La Transfusion Sanguine (INTS), F-75739, Paris, France; Laboratoire D'Excellence GR-Ex, F-75739, Paris, France; Molecular Graphics Laboratory, Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
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3
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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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4
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Characterization and Prediction of Protein Flexibility Based on Structural Alphabets. BIOMED RESEARCH INTERNATIONAL 2016; 2016:4628025. [PMID: 27660756 PMCID: PMC5021887 DOI: 10.1155/2016/4628025] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 08/02/2016] [Indexed: 11/25/2022]
Abstract
Motivation. To assist efforts in determining and exploring the functional properties of proteins, it is desirable to characterize and predict protein flexibilities. Results. In this study, the conformational entropy is used as an indicator of the protein flexibility. We first explore whether the conformational change can capture the protein flexibility. The well-defined decoy structures are converted into one-dimensional series of letters from a structural alphabet. Four different structure alphabets, including the secondary structure in 3-class and 8-class, the PB structure alphabet (16-letter), and the DW structure alphabet (28-letter), are investigated. The conformational entropy is then calculated from the structure alphabet letters. Some of the proteins show high correlation between the conformation entropy and the protein flexibility. We then predict the protein flexibility from basic amino acid sequence. The local structures are predicted by the dual-layer model and the conformational entropy of the predicted class distribution is then calculated. The results show that the conformational entropy is a good indicator of the protein flexibility, but false positives remain a problem. The DW structure alphabet performs the best, which means that more subtle local structures can be captured by large number of structure alphabet letters. Overall this study provides a simple and efficient method for the characterization and prediction of the protein flexibility.
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Esque J, Urbain A, Etchebest C, de Brevern AG. Sequence-structure relationship study in all-α transmembrane proteins using an unsupervised learning approach. Amino Acids 2015; 47:2303-22. [PMID: 26043903 DOI: 10.1007/s00726-015-2010-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 05/15/2015] [Indexed: 01/28/2023]
Abstract
Transmembrane proteins (TMPs) are major drug targets, but the knowledge of their precise topology structure remains highly limited compared with globular proteins. In spite of the difficulties in obtaining their structures, an important effort has been made these last years to increase their number from an experimental and computational point of view. In view of this emerging challenge, the development of computational methods to extract knowledge from these data is crucial for the better understanding of their functions and in improving the quality of structural models. Here, we revisit an efficient unsupervised learning procedure, called Hybrid Protein Model (HPM), which is applied to the analysis of transmembrane proteins belonging to the all-α structural class. HPM method is an original classification procedure that efficiently combines sequence and structure learning. The procedure was initially applied to the analysis of globular proteins. In the present case, HPM classifies a set of overlapping protein fragments, extracted from a non-redundant databank of TMP 3D structure. After fine-tuning of the learning parameters, the optimal classification results in 65 clusters. They represent at best similar relationships between sequence and local structure properties of TMPs. Interestingly, HPM distinguishes among the resulting clusters two helical regions with distinct hydrophobic patterns. This underlines the complexity of the topology of these proteins. The HPM classification enlightens unusual relationship between amino acids in TMP fragments, which can be useful to elaborate new amino acids substitution matrices. Finally, two challenging applications are described: the first one aims at annotating protein functions (channel or not), the second one intends to assess the quality of the structures (X-ray or models) via a new scoring function deduced from the HPM classification.
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Affiliation(s)
- Jérémy Esque
- INSERM, U 1134, DSIMB, 75739, Paris, France.,Univ. Paris Diderot, Sorbonne Paris Cité UMR-S 1134, 75739, Paris, France.,Institut National de la Transfusion Sanguine (INTS), 75739, Paris, France.,Laboratoire d'Excellence GR-Ex, 75739, Paris, France.,Laboratoire d'Ingénierie des Fonctions Moléculaire (IFM), ISIS, UMR 7006, 67000, Strasbourg, France.,Department of Integrative Structural Biology, INSERM U964, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), 67404, Illkirch, France.,UMR7104, Centre National de la Recherche Scientifique (CNRS), 67404, Illkirch, France.,Université de Strasbourg, 67404, Illkirch, France
| | - Aurélie Urbain
- Institut Jean-Pierre Bourgin, INRA, UMR 1318, 78026, Versailles, France
| | - Catherine Etchebest
- INSERM, U 1134, DSIMB, 75739, Paris, France.,Univ. Paris Diderot, Sorbonne Paris Cité UMR-S 1134, 75739, Paris, France.,Institut National de la Transfusion Sanguine (INTS), 75739, Paris, France.,Laboratoire d'Excellence GR-Ex, 75739, Paris, France
| | - Alexandre G de Brevern
- INSERM, U 1134, DSIMB, 75739, Paris, France. .,Univ. Paris Diderot, Sorbonne Paris Cité UMR-S 1134, 75739, Paris, France. .,Institut National de la Transfusion Sanguine (INTS), 75739, Paris, France. .,Laboratoire d'Excellence GR-Ex, 75739, Paris, France.
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6
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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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7
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Schneider B, Černý J, Svozil D, Čech P, Gelly JC, de Brevern AG. Bioinformatic analysis of the protein/DNA interface. Nucleic Acids Res 2014; 42:3381-94. [PMID: 24335080 PMCID: PMC3950675 DOI: 10.1093/nar/gkt1273] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Revised: 11/14/2013] [Accepted: 11/14/2013] [Indexed: 01/04/2023] Open
Abstract
To investigate the principles driving recognition between proteins and DNA, we analyzed more than thousand crystal structures of protein/DNA complexes. We classified protein and DNA conformations by structural alphabets, protein blocks [de Brevern, Etchebest and Hazout (2000) (Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks. Prots. Struct. Funct. Genet., 41:271-287)] and dinucleotide conformers [Svozil, Kalina, Omelka and Schneider (2008) (DNA conformations and their sequence preferences. Nucleic Acids Res., 36:3690-3706)], respectively. Assembling the mutually interacting protein blocks and dinucleotide conformers into 'interaction matrices' revealed their correlations and conformer preferences at the interface relative to their occurrence outside the interface. The analyzed data demonstrated important differences between complexes of various types of proteins such as transcription factors and nucleases, distinct interaction patterns for the DNA minor groove relative to the major groove and phosphate and importance of water-mediated contacts. Water molecules mediate proportionally the largest number of contacts in the minor groove and form the largest proportion of contacts in complexes of transcription factors. The generally known induction of A-DNA forms by complexation was more accurately attributed to A-like and intermediate A/B conformers rare in naked DNA molecules.
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Affiliation(s)
- Bohdan Schneider
- Institute of Biotechnology AS CR, Videnska 1083, CZ-142 20 Prague, Czech Republic, Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, Institute of Chemical Technology Prague, Technická 5, CZ-166 28 Prague, Czech Republic, INSERM, U665, DSIMB, F-75739 Paris, France, University of Paris Diderot, Sorbonne Paris Cité, UMR_S 665, 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
| | - Jiří Černý
- Institute of Biotechnology AS CR, Videnska 1083, CZ-142 20 Prague, Czech Republic, Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, Institute of Chemical Technology Prague, Technická 5, CZ-166 28 Prague, Czech Republic, INSERM, U665, DSIMB, F-75739 Paris, France, University of Paris Diderot, Sorbonne Paris Cité, UMR_S 665, 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
| | - Daniel Svozil
- Institute of Biotechnology AS CR, Videnska 1083, CZ-142 20 Prague, Czech Republic, Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, Institute of Chemical Technology Prague, Technická 5, CZ-166 28 Prague, Czech Republic, INSERM, U665, DSIMB, F-75739 Paris, France, University of Paris Diderot, Sorbonne Paris Cité, UMR_S 665, 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
| | - Petr Čech
- Institute of Biotechnology AS CR, Videnska 1083, CZ-142 20 Prague, Czech Republic, Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, Institute of Chemical Technology Prague, Technická 5, CZ-166 28 Prague, Czech Republic, INSERM, U665, DSIMB, F-75739 Paris, France, University of Paris Diderot, Sorbonne Paris Cité, UMR_S 665, 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
| | - Jean-Christophe Gelly
- Institute of Biotechnology AS CR, Videnska 1083, CZ-142 20 Prague, Czech Republic, Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, Institute of Chemical Technology Prague, Technická 5, CZ-166 28 Prague, Czech Republic, INSERM, U665, DSIMB, F-75739 Paris, France, University of Paris Diderot, Sorbonne Paris Cité, UMR_S 665, 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
- Institute of Biotechnology AS CR, Videnska 1083, CZ-142 20 Prague, Czech Republic, Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, Institute of Chemical Technology Prague, Technická 5, CZ-166 28 Prague, Czech Republic, INSERM, U665, DSIMB, F-75739 Paris, France, University of Paris Diderot, Sorbonne Paris Cité, UMR_S 665, 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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de Brevern AG, Bornot A, Craveur P, Etchebest C, Gelly JC. PredyFlexy: flexibility and local structure prediction from sequence. Nucleic Acids Res 2012; 40:W317-22. [PMID: 22689641 PMCID: PMC3394303 DOI: 10.1093/nar/gks482] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Protein structures are necessary for understanding protein function at a molecular level. Dynamics and flexibility of protein structures are also key elements of protein function. So, we have proposed to look at protein flexibility using novel methods: (i) using a structural alphabet and (ii) combining classical X-ray B-factor data and molecular dynamics simulations. First, we established a library composed of structural prototypes (LSPs) to describe protein structure by a limited set of recurring local structures. We developed a prediction method that proposes structural candidates in terms of LSPs and predict protein flexibility along a given sequence. Second, we examine flexibility according to two different descriptors: X-ray B-factors considered as good indicators of flexibility and the root mean square fluctuations, based on molecular dynamics simulations. We then define three flexibility classes and propose a method based on the LSP prediction method for predicting flexibility along the sequence. This method does not resort to sophisticate learning of flexibility but predicts flexibility from average flexibility of predicted local structures. The method is implemented in PredyFlexy web server. Results are similar to those obtained with the most recent, cutting-edge methods based on direct learning of flexibility data conducted with sophisticated algorithms. PredyFlexy can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/predyflexy/.
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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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Bornot A, Etchebest C, de Brevern AG. Predicting protein flexibility through the prediction of local structures. Proteins 2010; 79:839-52. [PMID: 21287616 DOI: 10.1002/prot.22922] [Citation(s) in RCA: 94] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2010] [Revised: 09/28/2010] [Accepted: 09/29/2010] [Indexed: 11/06/2022]
Abstract
Protein structures are valuable tools for understanding protein function. However, protein dynamics is also considered a key element in protein function. Therefore, in addition to structural analysis, fully understanding protein function at the molecular level now requires accounting for flexibility. However, experimental techniques that produce both types of information simultaneously are still limited. Prediction approaches are useful alternative tools for obtaining otherwise unavailable data. It has been shown that protein structure can be described by a limited set of recurring local structures. In this context, we previously established a library composed of 120 overlapping long structural prototypes (LSPs) representing fragments of 11 residues in length and covering all known local protein structures. On the basis of the close sequence-structure relationship observed in LSPs, we developed a novel prediction method that proposes structural candidates in terms of LSPs along a given sequence. The prediction accuracy rate was high given the number of structural classes. In this study, we use this methodology to predict protein flexibility. We first examine flexibility according to two different descriptors, the B-factor and root mean square fluctuations from molecular dynamics simulations. We then show the relevance of using both descriptors together. We define three flexibility classes and propose a method based on the LSP prediction method for predicting flexibility along the sequence. The prediction rate reaches 49.6%. This method competes rather efficiently with the most recent, cutting-edge methods based on true flexibility data learning with sophisticated algorithms. Accordingly, flexibility information should be taken into account in structural prediction assessments.
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Affiliation(s)
- Aurélie Bornot
- INSERM UMR-S 665, Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), University Paris-Diderot, Institut National de Transfusion Sanguine, INTS, 6, rue Alexandre Cabanel, 75739 Paris cedex 15, France
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Ken-Li Lin, Chin-Teng Lin, Pal NR. Incremental Mountain Clustering Method to Find Building Blocks for Constructing Structures of Proteins. IEEE Trans Nanobioscience 2010; 9:278-88. [DOI: 10.1109/tnb.2010.2095467] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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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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Tyagi M, Bornot A, Offmann B, de Brevern AG. Analysis of loop boundaries using different local structure assignment methods. Protein Sci 2009; 18:1869-81. [PMID: 19606500 DOI: 10.1002/pro.198] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Loops connect regular secondary structures. In many instances, they are known to play important biological roles. Analysis and prediction of loop conformations depend directly on the definition of repetitive structures. Nonetheless, the secondary structure assignment methods (SSAMs) often lead to divergent assignments. In this study, we analyzed, both structure and sequence point of views, how the divergence between different SSAMs affect boundary definitions of loops connecting regular secondary structures. The analysis of SSAMs underlines that no clear consensus between the different SSAMs can be easily found. Because these latter greatly influence the loop boundary definitions, important variations are indeed observed, that is, capping positions are shifted between different SSAMs. On the other hand, our results show that the sequence information in these capping regions are more stable than expected, and, classical and equivalent sequence patterns were found for most of the SSAMs. This is, to our knowledge, the most exhaustive survey in this field as (i) various databank have been used leading to similar results without implication of protein redundancy and (ii) the first time various SSAMs have been used. This work hence gives new insights into the difficult question of assignment of repetitive structures and addresses the issue of loop boundaries definition. Although SSAMs give very different local structure assignments capping sequence patterns remain efficiently stable.
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Affiliation(s)
- Manoj Tyagi
- Laboratoire de Biochimie et Génétique Moléculaire, Université de La Réunion, BP 7151, 15 avenue René Cassin, 97715 Saint Denis Messag Cedex 09, La Réunion, France
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Bornot A, Etchebest C, de Brevern AG. A new prediction strategy for long local protein structures using an original description. Proteins 2009; 76:570-87. [PMID: 19241475 DOI: 10.1002/prot.22370] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
A relevant and accurate description of three-dimensional (3D) protein structures can be achieved by characterizing recurrent local structures. In a previous study, we developed a library of 120 3D structural prototypes encompassing all known 11-residues long local protein structures and ensuring a good quality of structural approximation. A local structure prediction method was also proposed. Here, overlapping properties of local protein structures in global ones are taken into account to characterize frequent local networks. At the same time, we propose a new long local structure prediction strategy which involves the use of evolutionary information coupled with Support Vector Machines (SVMs). Our prediction is evaluated by a stringent geometrical assessment. Every local structure prediction with a Calpha RMSD less than 2.5 A from the true local structure is considered as correct. A global prediction rate of 63.1% is then reached, corresponding to an improvement of 7.7 points compared with the previous strategy. In the same way, the prediction of 88.33% of the 120 structural classes is improved with 8.65% mean gain. 85.33% of proteins have better prediction results with a 9.43% average gain. An analysis of prediction rate per local network also supports the global improvement and gives insights into the potential of our method for predicting super local structures. Moreover, a confidence index for the direct estimation of prediction quality is proposed. Finally, our method is proved to be very competitive with cutting-edge strategies encompassing three categories of local structure predictions.
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Affiliation(s)
- Aurélie Bornot
- INSERM UMR-S, Université Paris Diderot, Institut National de la Transfusion Sanguine, France.
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Tyagi M, Bornot A, Offmann B, de Brevern AG. Protein short loop prediction in terms of a structural alphabet. Comput Biol Chem 2009; 33:329-33. [PMID: 19625218 DOI: 10.1016/j.compbiolchem.2009.06.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2008] [Revised: 06/17/2009] [Accepted: 06/17/2009] [Indexed: 11/20/2022]
Abstract
Loops connect regular secondary structures. In many instances, they are known to play crucial biological roles. To bypass the limitation of secondary structure description, we previously defined a structural alphabet composed of 16 structural prototypes, called Protein Blocks (PBs). It leads to an accurate description of every region of 3D protein backbones and has been used in local structure prediction. In the present study, we used our structural alphabet to predict the loops connecting two repetitive structures. Thus, we showed interest to take into account the flanking regions, leading to prediction rate improvement up to 19.8%, but we also underline the sensitivity of such an approach. This research can be used to propose different structures for the loops and to probe and sample their flexibility. It is a useful tool for ab initio loop prediction and leads to insights into flexible docking approach.
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Affiliation(s)
- Manoj Tyagi
- Laboratoire de Biochimie et Génétique Moléculaire, Université de La Réunion, BP 7151, 15 avenue René Cassin, 97715 Saint Denis Messag Cedex 09, La Réunion, France
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Faure G, Bornot A, de Brevern AG. Analysis of protein contacts into Protein Units. Biochimie 2009; 91:876-87. [PMID: 19383526 DOI: 10.1016/j.biochi.2009.04.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2008] [Accepted: 04/13/2009] [Indexed: 11/18/2022]
Abstract
Three-dimensional structures of proteins are the support of their biological functions. Their folds are maintained by inter-residue interactions which are one of the main focuses to understand the mechanisms of protein folding and stability. Furthermore, protein structures can be composed of single or multiple functional domains that can fold and function independently. Hence, dividing a protein into domains is useful for obtaining an accurate structure and function determination. In previous studies, we enlightened protein contact properties according to different definitions and developed a novel methodology named Protein Peeling. Within protein structures, Protein Peeling characterizes small successive compact units along the sequence called protein units (PUs). The cutting done by Protein Peeling maximizes the number of contacts within the PUs and minimizes the number of contacts between them. This method is so a relevant tool in the context of the protein folding research and particularly regarding the hierarchical model proposed by George Rose. Here, we accurately analyze the PUs at different levels of cutting, using a non-redundant protein databank. Distribution of PU sizes, number of PUs or their accessibility are screened to determine their common and different features. Moreover, we highlight the preferential amino acid interactions inside and between PUs. Our results show that PUs are clearly an intermediate level between secondary structures and protein structural domains.
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Affiliation(s)
- Guilhem Faure
- INSERM UMR-S 726, Equipe de Bioinformatique Génomique et Moléculaire (EBGM), DSIMB, Université Paris Diderot - Paris 7, case 7113, 2 place Jussieu, 75251 Paris, France
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Li Q, Zhou C, Liu H. Fragment-based local statistical potentials derived by combining an alphabet of protein local structures with secondary structures and solvent accessibilities. Proteins 2009; 74:820-36. [PMID: 18704928 DOI: 10.1002/prot.22191] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
General and transferable statistical potentials to quantify the compatibility between local structures and local sequences of peptide fragments in proteins were derived. In the derivation, structure clusters of fragments are obtained by clustering five-residue fragments in native proteins based on their conformations represented by a local structure alphabet (de Brevern et al., Proteins 2000;41:271-287), secondary structure states, and solvent accessibilities. On the basis of the native sequences of the structurally clustered fragments, the probabilities of different amino acid sequences were estimated for each structure cluster. From the sequence probabilities, statistical energies as a function of sequence for a given structure were directly derived. The same sequence probabilities were employed in a database-matching approach to derive statistical energies as a function of local structure for a given sequence. Compared with prior models of local statistical potentials, we provided an integrated approach in which local conformations and local environments are treated jointly, structures are treated in units of fragments instead of individual residues so that coupling between the conformations of adjacent residues is included, and strong interdependences between the conformations of overlapping or neighboring fragment units are also considered. In tests including fragment threading, pseudosequence design, and local structure predictions, the potentials performed at least comparably and, in most cases, better than a number of existing models applicable to the same contexts indicating the advantages of such an integrated approach for deriving local potentials and suggesting applicability of the statistical potentials derived here in sequence designs and structure predictions.
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Affiliation(s)
- Quan Li
- School of Life Sciences, and Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, Anhui 230027, China
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Benros C, de Brevern AG, Hazout S. Analyzing the sequence–structure relationship of a library of local structural prototypes. J Theor Biol 2009; 256:215-26. [DOI: 10.1016/j.jtbi.2008.08.032] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2008] [Revised: 08/23/2008] [Accepted: 08/31/2008] [Indexed: 10/21/2022]
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Ku SY, Hu YJ. Protein structure search and local structure characterization. BMC Bioinformatics 2008; 9:349. [PMID: 18721472 PMCID: PMC2529324 DOI: 10.1186/1471-2105-9-349] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2008] [Accepted: 08/22/2008] [Indexed: 11/10/2022] Open
Abstract
Background Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA. Results We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at . Conclusion The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.
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Affiliation(s)
- Shih-Yen Ku
- Department of Computer Science, National Chiao Tung University, 1001 University Rd. Hsinchu, Taiwan.
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Dong Q, Wang X, Lin L. Prediction of protein local structures and folding fragments based on building-block library. Proteins 2008; 72:353-66. [PMID: 18214964 DOI: 10.1002/prot.21931] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In recent years, protein structure prediction using local structure information has made great progress. In this study, a novel and effective method is developed to predict the local structure and the folding fragments of proteins. First, the proteins with known structures are split into fragments. Second, these fragments, represented by dihedrals, are clustered to produce the building blocks (BBs). Third, an efficient machine learning method is used to predict the local structures of proteins from sequence profiles. Finally, a bi-gram model, trained by an iterated algorithm, is introduced to simulate the interactions of these BBs. For test proteins, the building-block lattice is constructed, which contains all the folding fragments of the proteins. The local structures and the optimal fragments are then obtained by the dynamic programming algorithm. The experiment is performed on a subset of the PDB database with sequence identity less than 25%. The results show that the performance of the method is better than the method that uses only sequence information. When multiple paths are returned, the average classification accuracy of local structures is 72.27% and the average prediction accuracy of local structures is 67.72%, which is a significant improvement in comparison with previous studies. The method can predict not only the local structures but also the folding fragments of proteins. This work is helpful for the ab initio protein structure prediction and especially, the understanding of the folding process of proteins.
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Affiliation(s)
- Qiwen Dong
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
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Reconstruction of protein backbones from the BriX collection of canonical protein fragments. PLoS Comput Biol 2008; 4:e1000083. [PMID: 18483555 PMCID: PMC2367438 DOI: 10.1371/journal.pcbi.1000083] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2007] [Accepted: 04/07/2008] [Indexed: 12/23/2022] Open
Abstract
As modeling of changes in backbone conformation still lacks a computationally efficient solution, we developed a discretisation of the conformational states accessible to the protein backbone similar to the successful rotamer approach in side chains. The BriX fragment database, consisting of fragments from 4 to 14 residues long, was realized through identification of recurrent backbone fragments from a non-redundant set of high-resolution protein structures. BriX contains an alphabet of more than 1,000 frequently observed conformations per peptide length for 6 different variation levels. Analysis of the performance of BriX revealed an average structural coverage of protein structures of more than 99% within a root mean square distance (RMSD) of 1 Angstrom. Globally, we are able to reconstruct protein structures with an average accuracy of 0.48 Angstrom RMSD. As expected, regular structures are well covered, but, interestingly, many loop regions that appear irregular at first glance are also found to form a recurrent structural motif, albeit with lower frequency of occurrence than regular secondary structures. Larger loop regions could be completely reconstructed from smaller recurrent elements, between 4 and 8 residues long. Finally, we observed that a significant amount of short sequences tend to display strong structural ambiguity between alpha helix and extended conformations. When the sequence length increases, this so-called sequence plasticity is no longer observed, illustrating the context dependency of polypeptide structures. Large-scale DNA sequencing efforts produce large amounts of protein sequence data. However, in order to understand the function of a protein, its tertiary three-dimensional structure is required. Despite worldwide efforts in structural biology, experimental protein structures are determined at a significantly slower pace. As a result, computational methods for protein structure prediction receive significant attention. A large part of the structure prediction problem lies in the enormous size of the problem: proteins seem to occur in an infinite variety of shapes. Here, we propose that this huge complexity may be overcome by identifying recurrent protein fragments, which are frequently reused as building blocks to construct proteins that were hitherto thought to be unrelated. The BriX database is the outcome of identifying about 2,000 canonical shapes among 1,261 protein structures. We show any given protein can be reconstructed from this library of building blocks at a very high resolution, suggesting that the modelling of protein backbones may be greatly aided by our database.
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Dong Q, Wang X, Lin L, Wang Y. Analysis and prediction of protein local structure based on structure alphabets. Proteins 2008; 72:163-72. [DOI: 10.1002/prot.21904] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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De Brevern AG, Etchebest C, Benros C, Hazout S. "Pinning strategy": a novel approach for predicting the backbone structure in terms of protein blocks from sequence. J Biosci 2007; 32:51-70. [PMID: 17426380 DOI: 10.1007/s12038-007-0006-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The description of protein 3D structures can be performed through a library of 3D fragments, named a structural alphabet. Our structural alphabet is composed of 16 small protein fragments of 5 C alpha in length, called protein blocks (PBs). It allows an efficient approximation of the 3D protein structures and a correct prediction of the local structure. The 72 most frequent series of 5 consecutive PBs, called structural words (SWs)are able to cover more than 90% of the 3D structures. PBs are highly conditioned by the presence of a limited number of transitions between them. In this study, we propose a new method called "pinning strategy" that used this specific feature to predict long protein fragments. Its goal is to define highly probable successions of PBs. It starts from the most probable SW and is then extended with overlapping SWs. Starting from an initial prediction rate of 34.4%, the use of the SWs instead of the PBs allows a gain of 4.5%. The pinning strategy simply applied to the SWs increases the prediction accuracy to 39.9%. In a second step, the sequence-structure relationship is optimized, the prediction accuracy reaches 43.6%.
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Affiliation(s)
- A G De Brevern
- 1 INSERM, U726, Equipe de Bioinformatique Genomique et Moleculaire (EBGM), Universite Paris 7,case 7113, 2, place Jussieu, 75251 Paris Cedex 05, France.
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Etchebest C, Benros C, Bornot A, Camproux AC, de Brevern AG. A reduced amino acid alphabet for understanding and designing protein adaptation to mutation. EUROPEAN BIOPHYSICS JOURNAL: EBJ 2007; 36:1059-69. [PMID: 17565494 DOI: 10.1007/s00249-007-0188-5] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2007] [Revised: 05/05/2007] [Accepted: 05/07/2007] [Indexed: 10/23/2022]
Abstract
Protein sequence world is considerably larger than structure world. In consequence, numerous non-related sequences may adopt similar 3D folds and different kinds of amino acids may thus be found in similar 3D structures. By grouping together the 20 amino acids into a smaller number of representative residues with similar features, sequence world simplification may be achieved. This clustering hence defines a reduced amino acid alphabet (reduced AAA). Numerous works have shown that protein 3D structures are composed of a limited number of building blocks, defining a structural alphabet. We previously identified such an alphabet composed of 16 representative structural motifs (5-residues length) called Protein Blocks (PBs). This alphabet permits to translate the structure (3D) in sequence of PBs (1D). Based on these two concepts, reduced AAA and PBs, we analyzed the distributions of the different kinds of amino acids and their equivalences in the structural context. Different reduced sets were considered. Recurrent amino acid associations were found in all the local structures while other were specific of some local structures (PBs) (e.g Cysteine, Histidine, Threonine and Serine for the alpha-helix Ncap). Some similar associations are found in other reduced AAAs, e.g Ile with Val, or hydrophobic aromatic residues Trp with Phe and Tyr. We put into evidence interesting alternative associations. This highlights the dependence on the information considered (sequence or structure). This approach, equivalent to a substitution matrix, could be useful for designing protein sequence with different features (for instance adaptation to environment) while preserving mainly the 3D fold.
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Affiliation(s)
- C Etchebest
- Equipe de Bioinformatique Génomique et Moléculaire (EBGM), INSERM UMR-S 726, Université Denis DIDEROT, Paris 7, case 7113, 2, place Jussieu, 75251, Paris, France
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Dong QW, Wang XL, Lin L. Methods for optimizing the structure alphabet sequences of proteins. Comput Biol Med 2007; 37:1610-6. [PMID: 17493604 DOI: 10.1016/j.compbiomed.2007.03.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2006] [Accepted: 03/16/2007] [Indexed: 11/24/2022]
Abstract
Protein structure prediction based on fragment assemble has made great progress in recent years. Local protein structure prediction is receiving increased attention. One essential step of local protein structure prediction method is that the three-dimensional conformations must be compressed into one-dimensional series of letters of a structural alphabet. The traditional method assigns each structure fragment the structure alphabet that has the best local structure similarity. However, such locally optimal structure alphabet sequence does not guarantee to produce the globally optimal structure. This study presents two efficient methods trying to find the optimal structure alphabet sequence, which can model the native structures as accuracy as possible. First, a 28-letter structure alphabet is derived by clustering fragment in Cartesian space with fragment length of seven residues. The average quantization error of the 28 letters is 0.82 A in term of root mean square deviation. Then, two efficient methods are presented to encode the protein structures into series of structure alphabet letters, that is, the greedy and dynamic programming algorithm. They are tested on PDB database using the structure alphabet developed in Cartesian coordinates space (our structure alphabet) and in torsion angles space (the PB structure alphabet), respectively. The experimental results show that these two methods can find the approximately optimal structure alphabet sequences by searching a small fraction of the modeling space. The traditional local-optimization method achieves 26.27 A root mean square deviations between the reconstructed structures and the native one, while the modeling accuracy is improved to 3.28 A by the greedy algorithm. The results are helpful for local protein structure prediction.
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Affiliation(s)
- Qi-wen Dong
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
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