1
|
Paria P, Chakraborty HJ, Behera BK. Identification of novel salt tolerance-associated proteins from the secretome of Enterococcus faecalis. World J Microbiol Biotechnol 2022; 38:177. [PMID: 35934729 DOI: 10.1007/s11274-022-03354-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/04/2022] [Indexed: 11/30/2022]
Abstract
The ability of bacteria to adapt to the external environment is fundamental for their survival. A halotolerant microorganism Enterococcus faecalis able to grow under high salt stress conditions was isolated in the present study. The SDS-PAGE analysis of the secretome showed a protein band with a molecular weight of 28 kDa, gradually increased with an increase in salt concentration, and the highest intensity was observed at 15% salt stress condition. LC-MS/MS analysis of this particular band identified fourteen different proteins, out of which nine proteins were uncharacterized. Further, the function of uncharacterized proteins was predicted based on structure-function relationship using a reverse template search approach deciphering uncharacterized protein into type III polyketide synthases, stress-induced protein-1, Eed-h3k79me3, ba42 protein, 3-methyladenine DNA glycosylase, Atxa protein, membrane-bound respiratory hydrogenase, type-i restriction-modification system methylation subunit and ManxA. STRING network analysis further a showed strong association among the proteins. The processes predicted involvement of these proteins in signal transduction, ions transport, synthesis of the protective layer, cellular homeostasis and regulation of gene expression and different metabolic pathways. Thus, the fourteen proteins identified in the secretome play an essential role in maintaining cellular homeostasis in E. faecalis under high-salinity stress. This may represent a novel and previously unreported strategy by E. faecalis to maintain their normal growth and physiology under high salinity conditions.
Collapse
Affiliation(s)
- Prasenjit Paria
- Aquatic Environmental Biotechnology and Nanotechnology Division, ICAR-Central Inland Fisheries Research Institute, Barrackpore, Kolkata, 700120, India
| | - Hirak Jyoti Chakraborty
- Aquatic Environmental Biotechnology and Nanotechnology Division, ICAR-Central Inland Fisheries Research Institute, Barrackpore, Kolkata, 700120, India
| | - Bijay Kumar Behera
- Aquatic Environmental Biotechnology and Nanotechnology Division, ICAR-Central Inland Fisheries Research Institute, Barrackpore, Kolkata, 700120, India.
| |
Collapse
|
2
|
Heteromerization of μ-opioid receptor and cholecystokinin B receptor through the third transmembrane domain of the μ-opioid receptor contributes to the anti-opioid effects of cholecystokinin octapeptide. Exp Mol Med 2018; 50:1-16. [PMID: 29780163 PMCID: PMC5960647 DOI: 10.1038/s12276-018-0090-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Revised: 01/21/2018] [Accepted: 03/06/2018] [Indexed: 11/08/2022] Open
Abstract
Activation of the cholecystokinin type B receptor (CCKBR) by cholecystokinin octapeptide (CCK-8) inhibits opioid analgesia. Chronic opiate treatment leads to an increase in the CCK-8 concentration and thus enhances the antagonism of CCK-8 against opioid analgesia; the underlying molecular mechanisms remain of great interest. In the present study, we validated the colocalization of the μ-opioid receptor (MOR) and CCKBR in pain signal transmission-related spinal cord dorsal horn and dorsal root ganglion neurons of rats. Co-immunoprecipitation (Co-IP) and fluorescence lifetime-imaging-microscopy-based fluorescence resonance energy transfer (FLIM-FRET) assays showed that MOR heteromerized with CCKBR directly in transfected HEK293 cells. Combined with MOR mutant construction, the third transmembrane domain of MOR (TM3MOR) was demonstrated to participate in heteromerization with CCKBR. Receptor ligand binding, ERK phosphorylation and cAMP assays showed that MOR heteromerization with CCKBR weakened the activity of MOR. A cell-penetrating interfering peptide consisting of TM3MOR and TAT (a transactivator of HIV-1) sequences from the N terminal to the C terminal disrupted the MOR-CCKBR interaction and restored the activity of MOR in transfected HEK293 cells. Furthermore, intrathecal application of the TM3MOR-TAT peptide alleviated CCK-8-injection-induced antagonism to morphine analgesia in rats. These results suggest a new molecular mechanism for CCK-8 antagonism to opioid analgesia in terms of G-protein-coupled receptor (GPCR) interaction through direct heteromerization. Our study may provide a potential strategy for pain management with opioid analgesics.
Collapse
|
3
|
Abstract
The limitation of most HMMs is their inherent high dimensionality. Therefore we developed several variations of low complexity models that can be applied even to protein families with a few members. In this chapter we present these variations. All of them include the use of a hidden Markov model (HMM), with a small number of states (called reduced state-space HMM), which is trained with both amino acid sequence and secondary structure of proteins whose 3D structure is known and it is used for protein fold classification. We used data from Protein Data Bank and annotation from SCOP database for training and evaluation of the proposed HMM variations for a number of protein folds that belong to major structural classes. Results indicate that the variations have similar performance, or even better in some cases, on classifying proteins than SAM, which is a widely used HMM-based method for protein classification. The major advantage of the proposed variations is that we employed a small number of states and the algorithms used for training and scoring are of low complexity and thus relatively fast. The main variations examined include a version of the reduced state-space HMM with seven states (7-HMM), a version of the reduced state-space HMM with three states (3-HMM) and an optimized version of the reduced state-space HMM with three states, where an optimization process is applied to its scores (optimized 3-HMM).
Collapse
Affiliation(s)
- Christos Lampros
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, University Campus of Ioannina, GR45110, Ioannina, Greece
| | - Costas Papaloukas
- Department of Biological Applications and Technology, University of Ioannina, Ioannina, Greece
| | - Themis Exarchos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, University Campus of Ioannina, GR45110, Ioannina, Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, University Campus of Ioannina, GR45110, Ioannina, Greece.
| |
Collapse
|
4
|
Li C, Wu A, Peng Y, Wang J, Guo Y, Chen Z, Zhang H, Wang Y, Dong J, Wang L, Qin FXF, Cheng G, Deng T, Jiang T. Integrating computational modeling and functional assays to decipher the structure-function relationship of influenza virus PB1 protein. Sci Rep 2014; 4:7192. [PMID: 25424584 PMCID: PMC4244630 DOI: 10.1038/srep07192] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2014] [Accepted: 11/03/2014] [Indexed: 11/12/2022] Open
Abstract
The influenza virus PB1 protein is the core subunit of the heterotrimeric polymerase complex (PA, PB1 and PB2) in which PB1 is responsible for catalyzing RNA polymerization and binding to the viral RNA promoter. Among the three subunits, PB1 is the least known subunit so far in terms of its structural information. In this work, by integrating template-based structural modeling approach with all known sequence and functional information about the PB1 protein, we constructed a modeled structure of PB1. Based on this model, we performed mutagenesis analysis for the key residues that constitute the RNA template binding and catalytic (TBC) channel in an RNP reconstitution system. The results correlated well with the model and further identified new residues of PB1 that are critical for RNA synthesis. Moreover, we derived 5 peptides from the sequence of PB1 that form the TBC channel and 4 of them can inhibit the viral RNA polymerase activity. Interestingly, we found that one of them named PB1(491–515) can inhibit influenza virus replication by disrupting viral RNA promoter binding activity of polymerase. Therefore, this study has not only deepened our understanding of structure-function relationship of PB1, but also promoted the development of novel therapeutics against influenza virus.
Collapse
Affiliation(s)
- Chunfeng Li
- 1] Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences &Peking Union Medical College, Beijing. 100005, China [2] Suzhou Institute of Systems Medicine, Suzhou. 215123, China
| | - Aiping Wu
- 1] Suzhou Institute of Systems Medicine, Suzhou. 215123, China [2] Key Laboratory of Protein &Peptide Pharmaceuticals, National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing. 100101, China
| | - Yousong Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha. 410082, China
| | - Jingfeng Wang
- Suzhou Institute of Systems Medicine, Suzhou. 215123, China
| | - Yang Guo
- MOH Key Laboratory of Systems Biology of Pahogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences &Peking Union Medical College, Beijing, 100730, China
| | - Zhigao Chen
- Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences &Peking Union Medical College, Beijing. 100005, China
| | - Hong Zhang
- Key Laboratory of Protein &Peptide Pharmaceuticals, National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing. 100101, China
| | - Yongqiang Wang
- Key Laboratory of Protein &Peptide Pharmaceuticals, National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing. 100101, China
| | - Jiuhong Dong
- Key Laboratory of Protein &Peptide Pharmaceuticals, National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing. 100101, China
| | - Lulan Wang
- Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences &Peking Union Medical College, Beijing. 100005, China
| | - F Xiao-Feng Qin
- 1] Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences &Peking Union Medical College, Beijing. 100005, China [2] Suzhou Institute of Systems Medicine, Suzhou. 215123, China
| | - Genhong Cheng
- 1] Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences &Peking Union Medical College, Beijing. 100005, China [2] Suzhou Institute of Systems Medicine, Suzhou. 215123, China [3] Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, CA 90095, USA
| | - Tao Deng
- MOH Key Laboratory of Systems Biology of Pahogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences &Peking Union Medical College, Beijing, 100730, China
| | - Taijiao Jiang
- 1] Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences &Peking Union Medical College, Beijing. 100005, China [2] Suzhou Institute of Systems Medicine, Suzhou. 215123, China [3] Key Laboratory of Protein &Peptide Pharmaceuticals, National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing. 100101, China
| |
Collapse
|
5
|
Lampros C, Simos T, Exarchos TP, Exarchos KP, Papaloukas C, Fotiadis DI. Assessment of optimized Markov models in protein fold classification. J Bioinform Comput Biol 2014; 12:1450016. [PMID: 25152041 DOI: 10.1142/s0219720014500164] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Protein fold classification is a challenging task strongly associated with the determination of proteins' structure. In this work, we tested an optimization strategy on a Markov chain and a recently introduced Hidden Markov Model (HMM) with reduced state-space topology. The proteins with unknown structure were scored against both these models. Then the derived scores were optimized following a local optimization method. The Protein Data Bank (PDB) and the annotation of the Structural Classification of Proteins (SCOP) database were used for the evaluation of the proposed methodology. The results demonstrated that the fold classification accuracy of the optimized HMM was substantially higher compared to that of the Markov chain or the reduced state-space HMM approaches. The proposed methodology achieved an accuracy of 41.4% on fold classification, while Sequence Alignment and Modeling (SAM), which was used for comparison, reached an accuracy of 38%.
Collapse
Affiliation(s)
- Christos Lampros
- Department of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece
| | | | | | | | | | | |
Collapse
|
6
|
Joseph AP, de Brevern AG. From local structure to a global framework: recognition of protein folds. J R Soc Interface 2014; 11:20131147. [PMID: 24740960 DOI: 10.1098/rsif.2013.1147] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.
Collapse
Affiliation(s)
- Agnel Praveen Joseph
- Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Oxford, , Didcot OX11 0QX, UK
| | | |
Collapse
|
7
|
Improvement in low-homology template-based modeling by employing a model evaluation method with focus on topology. PLoS One 2014; 9:e89935. [PMID: 24587135 PMCID: PMC3935967 DOI: 10.1371/journal.pone.0089935] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2013] [Accepted: 01/24/2014] [Indexed: 01/22/2023] Open
Abstract
Many template-based modeling (TBM) methods have been developed over the recent years that allow for protein structure prediction and for the study of structure-function relationships for proteins. One major problem all TBM algorithms face, however, is their unsatisfactory performance when proteins under consideration are low-homology. To improve the performance of TBM methods for such targets, a novel model evaluation method was developed here, and named MEFTop. Our novel method focuses on evaluating the topology by using two novel groups of features. These novel features included secondary structure element (SSE) contact information and 3-dimensional topology information. By combining MEFTop algorithm with FR-t5, a threading program developed by our group, we found that this modified TBM program, which was named FR-t5-M, exhibited significant improvements in predictive abilities for low-homology protein targets. We further showed that the MEFTop could be a generalized method to improve threading programs for low-homology protein targets. The softwares (FR-t5-M and MEFTop) are available to non-commercial users at our website: http://jianglab.ibp.ac.cn/lims/FRt5M/FRt5M.html.
Collapse
|
8
|
Wang H, He Z, Zhang C, Zhang L, Xu D. Transmembrane protein alignment and fold recognition based on predicted topology. PLoS One 2013; 8:e69744. [PMID: 23894534 PMCID: PMC3716705 DOI: 10.1371/journal.pone.0069744] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2012] [Accepted: 06/15/2013] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Although Transmembrane Proteins (TMPs) are highly important in various biological processes and pharmaceutical developments, general prediction of TMP structures is still far from satisfactory. Because TMPs have significantly different physicochemical properties from soluble proteins, current protein structure prediction tools for soluble proteins may not work well for TMPs. With the increasing number of experimental TMP structures available, template-based methods have the potential to become broadly applicable for TMP structure prediction. However, the current fold recognition methods for TMPs are not as well developed as they are for soluble proteins. METHODOLOGY We developed a novel TMP Fold Recognition method, TMFR, to recognize TMP folds based on sequence-to-structure pairwise alignment. The method utilizes topology-based features in alignment together with sequence profile and solvent accessibility. It also incorporates a gap penalty that depends on predicted topology structure segments. Given the difference between α-helical transmembrane protein (αTMP) and β-strands transmembrane protein (βTMP), parameters of scoring functions are trained respectively for these two protein categories using 58 αTMPs and 17 βTMPs in a non-redundant training dataset. RESULTS We compared our method with HHalign, a leading alignment tool using a non-redundant testing dataset including 72 αTMPs and 30 βTMPs. Our method achieved 10% and 9% better accuracies than HHalign in αTMPs and βTMPs, respectively. The raw score generated by TMFR is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition. The result demonstrates TMFR provides an effective TMP-specific fold recognition and alignment method.
Collapse
Affiliation(s)
- Han Wang
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, People’s Republic of China
- Department of Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, United States of America
| | - Zhiquan He
- Department of Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, United States of America
| | - Chao Zhang
- Department of Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, United States of America
| | - Li Zhang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, People’s Republic of China
| | - Dong Xu
- Department of Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri, United States of America
| |
Collapse
|
9
|
Wang L, Li J, Wang X, Liu W, Zhang XC, Li X, Rao Z. Structure analysis of the extracellular domain reveals disulfide bond forming-protein properties of Mycobacterium tuberculosis Rv2969c. Protein Cell 2013; 4:628-40. [PMID: 23828196 DOI: 10.1007/s13238-013-3033-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2013] [Accepted: 05/28/2013] [Indexed: 11/25/2022] Open
Abstract
Disulfide bond-forming (Dsb) protein is a bacterial periplasmic protein that is essential for the correct folding and disulfide bond formation of secreted or cell wallassociated proteins. DsbA introduces disulfide bonds into folding proteins, and is re-oxidized through interaction with its redox partner DsbB. Mycobacterium tuberculosis, a Gram-positive bacterium, expresses a DsbA-like protein ( Rv2969c), an extracellular protein that has its Nterminus anchored in the cell membrane. Since Rv2969c is an essential gene, crucial for disulfide bond formation, research of DsbA may provide a target of a new class of anti-bacterial drugs for treatment of M.tuberculosis infection. In the present work, the crystal structures of the extracellular region of Rv2969c (Mtb DsbA) were determined in both its reduced and oxidized states. The overall structure of Mtb DsbA can be divided into two domains: a classical thioredoxin-like domain with a typical CXXC active site, and an α-helical domain. It largely resembles its Escherichia coli homologue EcDsbA, however, it possesses a truncated binding groove; in addition, its active site is surrounded by an acidic, rather than hydrophobic surface. In our oxidoreductase activity assay, Mtb DsbA exhibited a different substrate specificity when compared to EcDsbA. Moreover, structural analysis revealed a second disulfide bond in Mtb DsbA, which is rare in the previously reported DsbA structures, and is assumed to contribute to the overall stability of Mtb DsbA. To investigate the disulphide formation pathway in M.tuberculosis, we modeled Mtb Vitamin K epoxide reductase (Mtb VKOR), a binding partner of Mtb DsbA, to Mtb DsbA.
Collapse
Affiliation(s)
- Lu Wang
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | | | | | | | | | | | | |
Collapse
|
10
|
Maadooliat M, Gao X, Huang JZ. Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles. Brief Bioinform 2012; 14:724-36. [PMID: 22926831 DOI: 10.1093/bib/bbs052] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence-structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu.edu/∼madoliat/LagSVD) that can be used to produce informative animations.
Collapse
Affiliation(s)
- Mehdi Maadooliat
- Mathematical and Computer Sciences and Engineering Division, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia, . Jianhua Z. Huang, Department of Statistics, 447 Blocker Building, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143 (USA), E-mail:
| | | | | |
Collapse
|
11
|
Zhao F, Xu J. A position-specific distance-dependent statistical potential for protein structure and functional study. Structure 2012; 20:1118-26. [PMID: 22608968 PMCID: PMC3372698 DOI: 10.1016/j.str.2012.04.003] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2012] [Revised: 04/09/2012] [Accepted: 04/10/2012] [Indexed: 10/28/2022]
Abstract
Although studied extensively, designing highly accurate protein energy potential is still challenging. A lot of knowledge-based statistical potentials are derived from the inverse of the Boltzmann law and consist of two major components: observed atomic interacting probability and reference state. These potentials mainly distinguish themselves in the reference state and use a similar simple counting method to estimate the observed probability, which is usually assumed to correlate with only atom types. This article takes a rather different view on the observed probability and parameterizes it by the protein sequence profile context of the atoms and the radius of the gyration, in addition to atom types. Experiments confirm that our position-specific statistical potential outperforms currently the popular ones in several decoy discrimination tests. Our results imply that, in addition to reference state, the observed probability also makes energy potentials different and evolutionary information greatly boost performance of energy potentials.
Collapse
Affiliation(s)
- Feng Zhao
- Toyota Technological Institute at Chicago, Chicago IL, USA 60637
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago IL, USA 60637
| |
Collapse
|
12
|
Ceres N, Lavery R. Coarse-grain Protein Models. INNOVATIONS IN BIOMOLECULAR MODELING AND SIMULATIONS 2012. [DOI: 10.1039/9781849735049-00219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Coarse-graining is a powerful approach for modeling biomolecules that, over the last few decades, has been extensively applied to proteins. Coarse-grain models offer access to large systems and to slow processes without becoming computationally unmanageable. In addition, they are very versatile, enabling both the protein representation and the energy function to be adapted to the biological problem in hand. This review concentrates on modeling soluble proteins and their assemblies. It presents an overview of the coarse-grain representations, of the associated interaction potentials, and of the optimization procedures used to define them. It then shows how coarse-grain models have been used to understand processes involving proteins, from their initial folding to their functional properties, their binary interactions, and the assembly of large complexes.
Collapse
Affiliation(s)
- N. Ceres
- Bases Moléculaires et Structurales des Systèmes Infectieux Université Lyon1/CNRS UMR 5086, IBCP, 7 Passage du Vercors, 69367, Lyon France
| | - R. Lavery
- Bases Moléculaires et Structurales des Systèmes Infectieux Université Lyon1/CNRS UMR 5086, IBCP, 7 Passage du Vercors, 69367, Lyon France
| |
Collapse
|