1
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Christoffer C, Kihara D. Modeling protein-nucleic acid complexes with extremely large conformational changes using Flex-LZerD. Proteomics 2023; 23:e2200322. [PMID: 36529945 PMCID: PMC10448949 DOI: 10.1002/pmic.202200322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/08/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Proteins and nucleic acids are key components in many processes in living cells, and interactions between proteins and nucleic acids are often crucial pathway components. In many cases, large flexibility of proteins as they interact with nucleic acids is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D atomic structures of such protein-nucleic acid complexes. When such structures are not yet experimentally determined, protein docking can be used to computationally generate useful structure models. However, such docking has long had the limitation that the consideration of flexibility is usually limited to small movements or to small structures. We previously developed a method of flexible protein docking which could model ordered proteins which undergo large-scale conformational changes, which we also showed was compatible with nucleic acids. Here, we elaborate on the ability of that pipeline, Flex-LZerD, to model specifically interactions between proteins and nucleic acids, and demonstrate that Flex-LZerD can model more interactions and types of conformational change than previously shown.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA
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2
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Christoffer C, Kihara D. Domain-Based Protein Docking with Extremely Large Conformational Changes. J Mol Biol 2022; 434:167820. [PMID: 36089054 PMCID: PMC9992458 DOI: 10.1016/j.jmb.2022.167820] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/31/2022] [Accepted: 09/03/2022] [Indexed: 11/17/2022]
Abstract
Proteins are key components in many processes in living cells, and physical interactions with other proteins and nucleic acids often form key parts of their functions. In many cases, large flexibility of proteins as they interact is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D structures of such protein complexes. When such structures are not yet experimentally determined, protein docking has long been present to computationally generate useful structure models. However, protein docking has long had the limitation that the consideration of flexibility is usually limited to very small movements or very small structures. Methods have been developed which handle minor flexibility via normal mode or other structure sampling, but new methods are required to model ordered proteins which undergo large-scale conformational changes to elucidate their function at the molecular level. Here, we present Flex-LZerD, a framework for docking such complexes. Via partial assembly multidomain docking and an iterative normal mode analysis admitting curvilinear motions, we demonstrate the ability to model the assembly of a variety of protein-protein and protein-nucleic acid complexes.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA.
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3
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Dhusia K, Wu Y. Classification of protein-protein association rates based on biophysical informatics. BMC Bioinformatics 2021; 22:408. [PMID: 34404340 PMCID: PMC8371850 DOI: 10.1186/s12859-021-04323-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 08/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Proteins form various complexes to carry out their versatile functions in cells. The dynamic properties of protein complex formation are mainly characterized by the association rates which measures how fast these complexes can be formed. It was experimentally observed that the association rates span an extremely wide range with over ten orders of magnitudes. Identification of association rates within this spectrum for specific protein complexes is therefore essential for us to understand their functional roles. RESULTS To tackle this problem, we integrate physics-based coarse-grained simulations into a neural-network-based classification model to estimate the range of association rates for protein complexes in a large-scale benchmark set. The cross-validation results show that, when an optimal threshold was selected, we can reach the best performance with specificity, precision, sensitivity and overall accuracy all higher than 70%. The quality of our cross-validation data has also been testified by further statistical analysis. Additionally, given an independent testing set, we can successfully predict the group of association rates for eight protein complexes out of ten. Finally, the analysis of failed cases suggests the future implementation of conformational dynamics into simulation can further improve model. CONCLUSIONS In summary, this study demonstrated that a new modeling framework that combines biophysical simulations with bioinformatics approaches is able to identify protein-protein interactions with low association rates from those with higher association rates. This method thereby can serve as a useful addition to a collection of existing experimental approaches that measure biomolecular recognition.
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Affiliation(s)
- Kalyani Dhusia
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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4
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Pan Y, Zhou S, Guan J. Computationally identifying hot spots in protein-DNA binding interfaces using an ensemble approach. BMC Bioinformatics 2020; 21:384. [PMID: 32938375 PMCID: PMC7495898 DOI: 10.1186/s12859-020-03675-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Protein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods. RESULTS Here, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones. CONCLUSIONS PreHots, which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/ .
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Affiliation(s)
- Yuliang Pan
- Department of Computer Science and Technology, Tongji University, No. 4800 Caoan Road, Shanghai, 201804, China
| | - Shuigeng Zhou
- Shanghai Key Laboratory of Intelligent Information Processing, and School of Computer Science, Fudan University, No. 220 Handan Road, Shanghai, 200433, China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, No. 4800 Caoan Road, Shanghai, 201804, China.
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5
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Gress A, Kalinina OV. SphereCon-a method for precise estimation of residue relative solvent accessible area from limited structural information. Bioinformatics 2020; 36:3372-3378. [PMID: 32154837 DOI: 10.1093/bioinformatics/btaa159] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 02/28/2020] [Accepted: 03/04/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION In proteins, solvent accessibility of individual residues is a factor contributing to their importance for protein function and stability. Hence one might wish to calculate solvent accessibility in order to predict the impact of mutations, their pathogenicity and for other biomedical applications. A direct computation of solvent accessibility is only possible if all atoms of a protein three-dimensional structure are reliably resolved. RESULTS We present SphereCon, a new precise measure that can estimate residue relative solvent accessibility (RSA) from limited data. The measure is based on calculating the volume of intersection of a sphere with a cone cut out in the direction opposite of the residue with surrounding atoms. We propose a method for estimating the position and volume of residue atoms in cases when they are not known from the structure, or when the structural data are unreliable or missing. We show that in cases of reliable input structures, SphereCon correlates almost perfectly with the directly computed RSA, and outperforms other previously suggested indirect methods. Moreover, SphereCon is the only measure that yields accurate results when the identities of amino acids are unknown. A significant novel feature of SphereCon is that it can estimate RSA from inter-residue distance and contact matrices, without any information about the actual atom coordinates. AVAILABILITY AND IMPLEMENTATION https://github.com/kalininalab/spherecon. CONTACT alexander.gress@helmholtz-hips.de. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alexander Gress
- Department of Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Campus E8.1, Saarbrücken 66123, Germany.,Graduate School of Computer Science, Saarland University, Saarbrücken 66123, Germany
| | - Olga V Kalinina
- Department of Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Campus E8.1, Saarbrücken 66123, Germany.,Medical Faculty, Saarland University, Homburg 66421, Germany
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6
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Gyulkhandanyan A, Rezaie AR, Roumenina L, Lagarde N, Fremeaux-Bacchi V, Miteva MA, Villoutreix BO. Analysis of protein missense alterations by combining sequence- and structure-based methods. Mol Genet Genomic Med 2020; 8:e1166. [PMID: 32096919 PMCID: PMC7196459 DOI: 10.1002/mgg3.1166] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/20/2020] [Accepted: 01/27/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Different types of in silico approaches can be used to predict the phenotypic consequence of missense variants. Such algorithms are often categorized as sequence based or structure based, when they necessitate 3D structural information. In addition, many other in silico tools, not dedicated to the analysis of variants, can be used to gain additional insights about the possible mechanisms at play. METHODS Here we applied different computational approaches to a set of 20 known missense variants present on different proteins (CYP, complement factor B, antithrombin and blood coagulation factor VIII). The tools that were used include fast computational approaches and web servers such as PolyPhen-2, PopMusic, DUET, MaestroWeb, SAAFEC, Missense3D, VarSite, FlexPred, PredyFlexy, Clustal Omega, meta-PPISP, FTMap, ClusPro, pyDock, PPM, RING, Cytoscape, and ChannelsDB. RESULTS We observe some conflicting results among the methods but, most of the time, the combination of several engines helped to clarify the potential impacts of the amino acid substitutions. CONCLUSION Combining different computational approaches including some that were not developed to investigate missense variants help to predict the possible impact of the amino acid substitutions. Yet, when the modified residues are involved in a salt-bridge, the tools tend to fail, even when the analysis is performed in 3D. Thus, interactive structural analysis with molecular graphics packages such as Chimera or PyMol or others are still needed to clarify automatic prediction.
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Affiliation(s)
- Aram Gyulkhandanyan
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- Laboratory SABNP, University of Evry, INSERM U1204, Université Paris-Saclay, Evry, France
| | - Alireza R Rezaie
- Cardiovascular Biology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Lubka Roumenina
- INSERM, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France
- Sorbonne Universités, Paris, France
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Nathalie Lagarde
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- Laboratoire GBCM, EA7528, Conservatoire national des arts et métiers, Hesam Université, Paris, France
| | - Veronique Fremeaux-Bacchi
- INSERM, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France
- Sorbonne Universités, Paris, France
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
- Assistance Publique-Hôpitaux de Paris, Service d'Immunologie Biologique, Hôpital Européen Georges Pompidou, Paris, France
| | - Maria A Miteva
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- Inserm U1268 MCTR, CNRS UMR 8038 CiTCoM, Faculté de Pharmacie de Paris, Univ. De Paris, Paris, France
| | - Bruno O Villoutreix
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- INSERM, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, Université de Lille, Lille, France
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7
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Deryusheva EI, Machulin AV, Matyunin MA, Galzitskaya OV. Investigation of the Relationship between the S1 Domain and Its Molecular Functions Derived from Studies of the Tertiary Structure. Molecules 2019; 24:E3681. [PMID: 31614904 PMCID: PMC6832287 DOI: 10.3390/molecules24203681] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Accepted: 10/11/2019] [Indexed: 11/16/2022] Open
Abstract
S1 domain, a structural variant of one of the "oldest" OB-folds (oligonucleotide/oligosaccharide-binding fold), is widespread in various proteins in three domains of life: Bacteria, Eukaryotes, and Archaea. In this study, it was shown that S1 domains of bacterial, eukaryotic, and archaeal proteins have a low percentage of identity, which indicates the uniqueness of the scaffold and is associated with protein functions. Assessment of the predisposition of tertiary flexibility of S1 domains using computational and statistical tools showed similar structural features and revealed functional flexible regions that are potentially involved in the interaction of natural binding partners. In addition, we analyzed the relative number and distribution of S1 domains in all domains of life and established specific features based on sequences and structures associated with molecular functions. The results correlate with the presence of repeats of the S1 domain in proteins containing the S1 domain in the range from one (bacterial and archaeal) to 15 (eukaryotic) and, apparently, are associated with the need for individual proteins to increase the affinity and specificity of protein binding to ligands.
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Affiliation(s)
- Evgenia I Deryusheva
- Institute for Biological Instrumentation, Federal Research Center "Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences", 142290 Pushchino, Moscow Region, Russia.
| | - Andrey V Machulin
- Skryabin Institute of Biochemistry and Physiology of Microorganisms, Russian Academy of Sciences, Federal Research Center "Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences", 142290 Pushchino, Moscow Region, Russia.
| | - Maxim A Matyunin
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Moscow Region, Russia.
| | - Oxana V Galzitskaya
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Moscow Region, Russia.
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Moscow Region, Russia.
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8
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Deryusheva EI, Denesyuk AI, Denessiouk K, Uversky VN, Permyakov SE, Permyakov EA. On the relationship between the conserved ‘black’ and ‘gray’ structural clusters and intrinsic disorder in parvalbumins. Int J Biol Macromol 2018; 120:1055-1062. [DOI: 10.1016/j.ijbiomac.2018.08.183] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Revised: 08/28/2018] [Accepted: 08/30/2018] [Indexed: 12/20/2022]
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9
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Putz I, Brock O. Elastic network model of learned maintained contacts to predict protein motion. PLoS One 2017; 12:e0183889. [PMID: 28854238 PMCID: PMC5576689 DOI: 10.1371/journal.pone.0183889] [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: 10/16/2016] [Accepted: 08/14/2017] [Indexed: 12/21/2022] Open
Abstract
We present a novel elastic network model, lmcENM, to determine protein motion even for localized functional motions that involve substantial changes in the protein's contact topology. Existing elastic network models assume that the contact topology remains unchanged throughout the motion and are thus most appropriate to simulate highly collective function-related movements. lmcENM uses machine learning to differentiate breaking from maintained contacts. We show that lmcENM accurately captures functional transitions unexplained by the classical ENM and three reference ENM variants, while preserving the simplicity of classical ENM. We demonstrate the effectiveness of our approach on a large set of proteins covering different motion types. Our results suggest that accurately predicting a "deformation-invariant" contact topology offers a promising route to increase the general applicability of ENMs. We also find that to correctly predict this contact topology a combination of several features seems to be relevant which may vary slightly depending on the protein. Additionally, we present case studies of two biologically interesting systems, Ferric Citrate membrane transporter FecA and Arachidonate 15-Lipoxygenase.
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Affiliation(s)
- Ines Putz
- Robotics and Biology Laboratory, Department of Computer Science and Electrical Engineering, Technische Universität Berlin, Berlin, Berlin, Germany
| | - Oliver Brock
- Robotics and Biology Laboratory, Department of Computer Science and Electrical Engineering, Technische Universität Berlin, Berlin, Berlin, Germany
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10
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Peterson L, Jamroz M, Kolinski A, Kihara D. Predicting Real-Valued Protein Residue Fluctuation Using FlexPred. Methods Mol Biol 2017; 1484:175-186. [PMID: 27787827 DOI: 10.1007/978-1-4939-6406-2_13] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The conventional view of a protein structure as static provides only a limited picture. There is increasing evidence that protein dynamics are often vital to protein function including interaction with partners such as other proteins, nucleic acids, and small molecules. Considering flexibility is also important in applications such as computational protein docking and protein design. While residue flexibility is partially indicated by experimental measures such as the B-factor from X-ray crystallography and ensemble fluctuation from nuclear magnetic resonance (NMR) spectroscopy as well as computational molecular dynamics (MD) simulation, these techniques are resource-intensive. In this chapter, we describe the web server and stand-alone version of FlexPred, which rapidly predicts absolute per-residue fluctuation from a three-dimensional protein structure. On a set of 592 nonredundant structures, comparing the fluctuations predicted by FlexPred to the observed fluctuations in MD simulations showed an average correlation coefficient of 0.669 and an average root mean square error of 1.07 Å. FlexPred is available at http://kiharalab.org/flexPred/ .
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Affiliation(s)
- Lenna Peterson
- Department of Biological Sciences, College of Science, Purdue University, 915 W. State Street, West Lafayette, IN, 47907-2054, USA
| | - Michal Jamroz
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, Warszawa, 02-093, Poland
| | - Andrzej Kolinski
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, Warszawa, 02-093, Poland
| | - Daisuke Kihara
- Department of Biological Sciences, College of Science, Purdue University, 915 W. State Street, West Lafayette, IN, 47907-2054, USA. .,Department of Computer Science, College of Science, Purdue University, 305 N. University Street, West Lafayette, IN, 47907-2107, USA.
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11
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Luna-Martínez OD, Vidal-Limón A, Villalba-Velázquez MI, Sánchez-Alcalá R, Garduño-Juárez R, Uversky VN, Becerril B. Simple approach for ranking structure determining residues. PeerJ 2016; 4:e2136. [PMID: 27366642 PMCID: PMC4924125 DOI: 10.7717/peerj.2136] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 05/25/2016] [Indexed: 12/30/2022] Open
Abstract
Mutating residues has been a common task in order to study structural properties of the protein of interest. Here, we propose and validate a simple method that allows the identification of structural determinants; i.e., residues essential for preservation of the stability of global structure, regardless of the protein topology. This method evaluates all of the residues in a 3D structure of a given globular protein by ranking them according to their connectivity and movement restrictions without topology constraints. Our results matched up with sequence-based predictors that look up for intrinsically disordered segments, suggesting that protein disorder can also be described with the proposed methodology.
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Affiliation(s)
- Oscar D Luna-Martínez
- Instituto de Biotecnología, Universidad Nacional Autónoma de México , Cuernavaca , Morelos , Mexico
| | - Abraham Vidal-Limón
- División de Biología Molecular, Instituto Potosino de Investigación Científica y Tecnológica , San Luis Potosí , Mexico
| | | | - Rosalba Sánchez-Alcalá
- Instituto de Biotecnología, Universidad Nacional Autónoma de México , Cuernavaca , Morelos , Mexico
| | - Ramón Garduño-Juárez
- Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México , Cuernavaca , Morelos , Mexico
| | - Vladimir N Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, University of South Florida, Tampa, FL, United States; Institute for Biological Instrumentation, Russian Academy of Sciences, Puschino, Moscow Region, Russia; Laboratory of Structural Dynamics, Stability and Folding of Proteins, Russian Academy of Sciences, St. Petersburg, Russia
| | - Baltazar Becerril
- Instituto de Biotecnología, Universidad Nacional Autónoma de México , Cuernavaca , Morelos , Mexico
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12
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Abstract
Motivation: Comparing protein tertiary structures is a fundamental procedure in structural biology and protein bioinformatics. Structure comparison is important particularly for evaluating computational protein structure models. Most of the model structure evaluation methods perform rigid body superimposition of a structure model to its crystal structure and measure the difference of the corresponding residue or atom positions between them. However, these methods neglect intrinsic flexibility of proteins by treating the native structure as a rigid molecule. Because different parts of proteins have different levels of flexibility, for example, exposed loop regions are usually more flexible than the core region of a protein structure, disagreement of a model to the native needs to be evaluated differently depending on the flexibility of residues in a protein. Results: We propose a score named FlexScore for comparing protein structures that consider flexibility of each residue in the native state of proteins. Flexibility information may be extracted from experiments such as NMR or molecular dynamics simulation. FlexScore considers an ensemble of conformations of a protein described as a multivariate Gaussian distribution of atomic displacements and compares a query computational model with the ensemble. We compare FlexScore with other commonly used structure similarity scores over various examples. FlexScore agrees with experts’ intuitive assessment of computational models and provides information of practical usefulness of models. Availability and implementation:https://bitbucket.org/mjamroz/flexscore Contact:dkihara@purdue.edu Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Michal Jamroz
- Department of Chemistry, University of Warsaw, Warsaw, 02-093, Poland
| | - Andrzej Kolinski
- Department of Chemistry, University of Warsaw, Warsaw, 02-093, Poland
| | - Daisuke Kihara
- Department of Biological Sciences Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
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13
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Stepanenko OV, Roginskii DO, Stepanenko OV, Kuznetsova IM, Uversky VN, Turoverov KK. Structure and stability of recombinant bovine odorant-binding protein: II. Unfolding of the monomeric forms. PeerJ 2016; 4:e1574. [PMID: 27114857 PMCID: PMC4841237 DOI: 10.7717/peerj.1574] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Accepted: 12/16/2015] [Indexed: 01/27/2023] Open
Abstract
In a family of monomeric odorant-binding proteins (OBPs), bovine OBP (bOBP), that lacks conserved disulfide bond found in other OBPs, occupies unique niche because of its ability to form domain-swapped dimers. In this study, we analyzed conformational stabilities of the recombinant bOBP and its monomeric variants, the bOBP-Gly121+ mutant containing an additional glycine residue after the residue 121 of the bOBP, and the GCC-bOBP mutant obtained from the bOBP-Gly121+ form by introduction of the Trp64Cys/His155Cys double mutation to restore the canonical disulfide bond. We also analyzed the effect of the natural ligand binding on the conformational stabilities of these bOBP variants. Our data are consistent with the conclusion that the unfolding-refolding pathways of the recombinant bOBP and its mutant monomeric forms bOBP-Gly121+ and GCC-bOBP are similar and do not depend on the oligomeric status of the protein. This clearly shows that the information on the unfolding-refolding mechanism is encoded in the structure of the bOBP monomers. However, the process of the bOBP unfolding is significantly complicated by the formation of the domain-swapped dimer, and the rates of the unfolding-refolding reactions essentially depend on the conditions in which the protein is located.
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Affiliation(s)
- Olga V. Stepanenko
- Laboratory of Structural Dynamics, Stability and Folding of Proteins, Institute of Cytology, Russian Academy of Sciences, St. Petersburg, Russia
| | - Denis O. Roginskii
- Laboratory of Structural Dynamics, Stability and Folding of Proteins, Institute of Cytology, Russian Academy of Sciences, St. Petersburg, Russia
| | - Olesya V. Stepanenko
- Laboratory of Structural Dynamics, Stability and Folding of Proteins, Institute of Cytology, Russian Academy of Sciences, St. Petersburg, Russia
| | - Irina M. Kuznetsova
- Laboratory of Structural Dynamics, Stability and Folding of Proteins, Institute of Cytology, Russian Academy of Sciences, St. Petersburg, Russia
| | - Vladimir N. Uversky
- Laboratory of Structural Dynamics, Stability and Folding of Proteins, Institute of Cytology, Russian Academy of Sciences, St. Petersburg, Russia
- Department of Molecular Medicine and USF Health Byrd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Konstantin K. Turoverov
- Laboratory of Structural Dynamics, Stability and Folding of Proteins, Institute of Cytology, Russian Academy of Sciences, St. Petersburg, Russia
- Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
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14
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Solomon O, Bazak L, Levanon EY, Amariglio N, Unger R, Rechavi G, Eyal E. Characterizing of functional human coding RNA editing from evolutionary, structural, and dynamic perspectives. Proteins 2014; 82:3117-31. [DOI: 10.1002/prot.24672] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Revised: 07/28/2014] [Accepted: 08/11/2014] [Indexed: 12/29/2022]
Affiliation(s)
- Oz Solomon
- Cancer Research Center; Chaim Sheba Medical Center; Tel Hashomer 52621 Ramat Gan Israel
- The Everard & Mina Goodman Faculty of Life Sciences; Bar-Ilan University; Ramat Gan 52900 Israel
| | - Lily Bazak
- The Everard & Mina Goodman Faculty of Life Sciences; Bar-Ilan University; Ramat Gan 52900 Israel
| | - Erez Y. Levanon
- The Everard & Mina Goodman Faculty of Life Sciences; Bar-Ilan University; Ramat Gan 52900 Israel
| | - Ninette Amariglio
- Cancer Research Center; Chaim Sheba Medical Center; Tel Hashomer 52621 Ramat Gan Israel
| | - Ron Unger
- The Everard & Mina Goodman Faculty of Life Sciences; Bar-Ilan University; Ramat Gan 52900 Israel
| | - Gideon Rechavi
- Cancer Research Center; Chaim Sheba Medical Center; Tel Hashomer 52621 Ramat Gan Israel
- Sackler School of Medicine; Tel Aviv University; Tel Aviv 69978 Israel
| | - Eran Eyal
- Cancer Research Center; Chaim Sheba Medical Center; Tel Hashomer 52621 Ramat Gan Israel
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Jamroz M, Kolinski A, Kmiecik S. CABS-flex predictions of protein flexibility compared with NMR ensembles. ACTA ACUST UNITED AC 2014; 30:2150-4. [PMID: 24735558 PMCID: PMC4103595 DOI: 10.1093/bioinformatics/btu184] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Motivation: Identification of flexible regions of protein structures is important for understanding of their biological functions. Recently, we have developed a fast approach for predicting protein structure fluctuations from a single protein model: the CABS-flex. CABS-flex was shown to be an efficient alternative to conventional all-atom molecular dynamics (MD). In this work, we evaluate CABS-flex and MD predictions by comparison with protein structural variations within NMR ensembles. Results: Based on a benchmark set of 140 proteins, we show that the relative fluctuations of protein residues obtained from CABS-flex are well correlated to those of NMR ensembles. On average, this correlation is stronger than that between MD and NMR ensembles. In conclusion, CABS-flex is useful and complementary to MD in predicting protein regions that undergo conformational changes as well as the extent of such changes. Availability and implementation: The CABS-flex is freely available to all users at http://biocomp.chem.uw.edu.pl/CABSflex. Contact: sekmi@chem.uw.edu.pl Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Michal Jamroz
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, Warsaw 02-093, Poland
| | - Andrzej Kolinski
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, Warsaw 02-093, Poland
| | - Sebastian Kmiecik
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, Warsaw 02-093, Poland
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Jamroz M, Kolinski A, Kmiecik S. CABS-flex: Server for fast simulation of protein structure fluctuations. Nucleic Acids Res 2013; 41:W427-31. [PMID: 23658222 PMCID: PMC3692091 DOI: 10.1093/nar/gkt332] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
The CABS-flex server (http://biocomp.chem.uw.edu.pl/CABSflex) implements CABS-model–based protocol for the fast simulations of near-native dynamics of globular proteins. In this application, the CABS model was shown to be a computationally efficient alternative to all-atom molecular dynamics—a classical simulation approach. The simulation method has been validated on a large set of molecular dynamics simulation data. Using a single input (user-provided file in PDB format), the CABS-flex server outputs an ensemble of protein models (in all-atom PDB format) reflecting the flexibility of the input structure, together with the accompanying analysis (residue mean-square-fluctuation profile and others). The ensemble of predicted models can be used in structure-based studies of protein functions and interactions.
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Affiliation(s)
- Michal Jamroz
- Faculty of Chemistry, University of Warsaw, Warsaw 02-093, Poland
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17
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Jamroz M, Orozco M, Kolinski A, Kmiecik S. Consistent View of Protein Fluctuations from All-Atom Molecular Dynamics and Coarse-Grained Dynamics with Knowledge-Based Force-Field. J Chem Theory Comput 2012; 9:119-25. [PMID: 26589015 DOI: 10.1021/ct300854w] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
It is widely recognized that atomistic Molecular Dynamics (MD), a classical simulation method, captures the essential physics of protein dynamics. That idea is supported by a theoretical study showing that various MD force-fields provide a consensus picture of protein fluctuations in aqueous solution [Rueda, M. et al. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 796-801]. However, atomistic MD cannot be applied to most biologically relevant processes due to its limitation to relatively short time scales. Much longer time scales can be accessed by properly designed coarse-grained models. We demonstrate that the aforementioned consensus view of protein dynamics from short (nanosecond) time scale MD simulations is fairly consistent with the dynamics of the coarse-grained protein model - the CABS model. The CABS model employs stochastic dynamics (a Monte Carlo method) and a knowledge-based force-field, which is not biased toward the native structure of a simulated protein. Since CABS-based dynamics allows for the simulation of entire folding (or multiple folding events) in a single run, integration of the CABS approach with all-atom MD promises a convenient (and computationally feasible) means for the long-time multiscale molecular modeling of protein systems with atomistic resolution.
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Affiliation(s)
- Michal Jamroz
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
| | - Modesto Orozco
- IRB - BSC Joint Research Program in Computational Biology, Institute for Research in Biomedicine , Josep Samitier 1-5, Barcelona 08028, Spain.,Department of Biochemistry, Universitat of Barcelona , Gran Via de les Corts Catalanes, 585 08007 Barcelona, Spain
| | - Andrzej Kolinski
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
| | - Sebastian Kmiecik
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
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