1
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Györffy D, Závodszky P, Szilágyi A. A Kinetic Transition Network Model Reveals the Diversity of Protein Dimer Formation Mechanisms. Biomolecules 2023; 13:1708. [PMID: 38136580 PMCID: PMC10741920 DOI: 10.3390/biom13121708] [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: 11/06/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Protein homodimers have been classified as three-state or two-state dimers depending on whether a folded monomer forms before association, but the details of the folding-binding mechanisms are poorly understood. Kinetic transition networks of conformational states have provided insight into the folding mechanisms of monomeric proteins, but extending such a network to two protein chains is challenging as all the relative positions and orientations of the chains need to be included, greatly increasing the number of degrees of freedom. Here, we present a simplification of the problem by grouping all states of the two chains into two layers: a dissociated and an associated layer. We combined our two-layer approach with the Wako-Saito-Muñoz-Eaton method and used Transition Path Theory to investigate the dimer formation kinetics of eight homodimers. The analysis reveals a remarkable diversity of dimer formation mechanisms. Induced folding, conformational selection, and rigid docking are often simultaneously at work, and their contribution depends on the protein concentration. Pre-folded structural elements are always present at the moment of association, and asymmetric binding mechanisms are common. Our two-layer network approach can be combined with various methods that generate discrete states, yielding new insights into the kinetics and pathways of flexible binding processes.
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Affiliation(s)
- Dániel Györffy
- Systems Biology of Reproduction Research Group, Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, 1117 Budapest, Hungary;
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary
| | - Péter Závodszky
- Structural Biophysics Research Group, Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, 1117 Budapest, Hungary;
| | - András Szilágyi
- Systems Biology of Reproduction Research Group, Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, 1117 Budapest, Hungary;
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2
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Singer CM, Joy D, Jacobs DJ, Nesmelova IV. Rigidity and flexibility characteristics of DD[E/D]-transposases Mos1 and Sleeping Beauty. Proteins 2018; 87:313-325. [PMID: 30582767 DOI: 10.1002/prot.25653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 12/06/2018] [Accepted: 12/19/2018] [Indexed: 11/05/2022]
Abstract
DD[E/D]-transposases catalyze the multistep reaction of cut-and-paste DNA transposition. Structurally, several DD[E/D]-transposases have been characterized, revealing a multi-domain structure with the catalytic domain possessing the RNase H-like structural motif that brings three catalytic residues (D, D, and E or D) into close proximity for the catalysis. However, the dynamic behavior of DD[E/D]-transposases during transposition remains poorly understood. Here, we analyze the rigidity and flexibility characteristics of two representative DD[E/D]-transposases Mos1 and Sleeping Beauty (SB) using the minimal distance constraint model (mDCM). We find that the catalytic domain of both transposases is globally rigid, with the notable exception of the clamp loop being flexible in the DNA-unbound form. Within this globally rigid structure, the central β-sheet of the RNase H-like motif is much less rigid in comparison to its surrounding α-helices, forming a cage-like structure. The comparison of the original SB transposase to its hyperactive version SB100X reveals the region where the change in flexibility/rigidity correlates with increased activity. This region is found to be within the RNase H-like structural motif and comprise the loop leading from beta-strand B3 to helix H1, helices H1 and H2, which are located on the same side of the central beta-sheet, and the loop between helix H3 and beta-strand B5. We further identify the RKEN214-217DAVQ mutations of the set of hyperactive mutations within the catalytic domain of SB transposase to be the driving factor that induces change in residue-pair rigidity correlations within SB transposase. Given that a signature RNase H-like structural motif is found in DD[E/D]-transposases and, more broadly, in a large superfamily of polynucleotidyl transferases, our results are relevant to these proteins as well.
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Affiliation(s)
- Christopher M Singer
- Department of Physics and Optical Science, University of North Carolina, Charlotte, North Carolina
| | - Diana Joy
- Department of Physics and Optical Science, University of North Carolina, Charlotte, North Carolina
| | - Donald J Jacobs
- Department of Physics and Optical Science, University of North Carolina, Charlotte, North Carolina.,Center for Biomedical Engineering, University of North Carolina, Charlotte, North Carolina
| | - Irina V Nesmelova
- Department of Physics and Optical Science, University of North Carolina, Charlotte, North Carolina.,Center for Biomedical Engineering, University of North Carolina, Charlotte, North Carolina
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3
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Sawle L, Huihui J, Ghosh K. All-Atom Simulations Reveal Protein Charge Decoration in the Folded and Unfolded Ensemble Is Key in Thermophilic Adaptation. J Chem Theory Comput 2017; 13:5065-5075. [DOI: 10.1021/acs.jctc.7b00545] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lucas Sawle
- Department of Physics and
Astronomy, University of Denver, Denver, Colorado 80208, United States
| | - Jonathan Huihui
- Department of Physics and
Astronomy, University of Denver, Denver, Colorado 80208, United States
| | - Kingshuk Ghosh
- Department of Physics and
Astronomy, University of Denver, Denver, Colorado 80208, United States
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4
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Wahome N, Sully E, Singer C, Thomas JC, Hu L, Joshi SB, Volkin DB, Fang J, Karanicolas J, Jacobs DJ, Mantis NJ, Middaugh CR. Novel Ricin Subunit Antigens With Enhanced Capacity to Elicit Toxin-Neutralizing Antibody Responses in Mice. J Pharm Sci 2016; 105:1603-1613. [PMID: 26987947 PMCID: PMC4846473 DOI: 10.1016/j.xphs.2016.02.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Revised: 01/26/2016] [Accepted: 02/09/2016] [Indexed: 02/07/2023]
Abstract
RiVax is a candidate ricin toxin subunit vaccine antigen that has proven to be safe in human phase I clinical trials. In this study, we introduced double and triple cavity-filling point mutations into the RiVax antigen with the expectation that stability-enhancing modifications would have a beneficial effect on overall immunogenicity of the recombinant proteins. We demonstrate that 2 RiVax triple mutant derivatives, RB (V81L/C171L/V204I) and RC (V81I/C171L/V204I), when adsorbed to aluminum salts adjuvant and tested in a mouse prime-boost-boost regimen were 5- to 10-fold more effective than RiVax at eliciting toxin-neutralizing serum IgG antibody titers. Increased toxin neutralizing antibody values and seroconversion rates were evident at different antigen dosages and within 7 days after the first booster. Quantitative stability/flexibility relationships analysis revealed that the RB and RC mutations affect rigidification of regions spanning residues 98-103, which constitutes a known immunodominant neutralizing B-cell epitope. A more detailed understanding of the immunogenic nature of RB and RC may provide insight into the fundamental relationship between local protein stability and antibody reactivity.
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Affiliation(s)
- Newton Wahome
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047
| | - Erin Sully
- Division of Infectious Disease, Wadsworth Center, New York State Department of Health, Albany, New York 12208
| | - Christopher Singer
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, North Carolina 28223
| | - Justin C Thomas
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047
| | - Lei Hu
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047
| | - Sangeeta B Joshi
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047
| | - David B Volkin
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047
| | - Jianwen Fang
- Applied Bioinformatics Laboratory, Department of Medicinal Chemistry, University of Kansas, Lawrence, Kansas 66047
| | - John Karanicolas
- Department of Molecular Biosciences, Center for Computational Biology, University of Kansas, Lawrence, Kansas 66045
| | - Donald J Jacobs
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, North Carolina 28223.
| | - Nicholas J Mantis
- Division of Infectious Disease, Wadsworth Center, New York State Department of Health, Albany, New York 12208; Department of Biomedical Sciences, School of Public Health, University at Albany, Albany, New York 12201.
| | - C Russell Middaugh
- Department of Pharmaceutical Chemistry, Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047.
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5
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Park IH, Venable JD, Steckler C, Cellitti SE, Lesley SA, Spraggon G, Brock A. Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis. J Chem Inf Model 2015; 55:1914-25. [PMID: 26241692 DOI: 10.1021/acs.jcim.5b00185] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure, and dynamics. More recently, hydrogen exchange mass spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from molecular dynamics (MD) simulation snapshots is used to determine partitioning over bonded and nonbonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models.
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Affiliation(s)
- In-Hee Park
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - John D Venable
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Caitlin Steckler
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States.,Joint Center for Structural Genomics , La Jolla, California 92037, United States
| | - Susan E Cellitti
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Scott A Lesley
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States.,Department of Integrative Structural and Computational Biology, The Scripps Research Institute , La Jolla, California 92037, United States.,Joint Center for Structural Genomics , La Jolla, California 92037, United States
| | - Glen Spraggon
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
| | - Ansgar Brock
- Genomics Institute of the Novartis Research Foundation , 10675 John Jay Hopkins Drive, San Diego, California 92121, United States
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6
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Brown JR, Livesay DR. Flexibility Correlation between Active Site Regions Is Conserved across Four AmpC β-Lactamase Enzymes. PLoS One 2015; 10:e0125832. [PMID: 26018804 PMCID: PMC4446314 DOI: 10.1371/journal.pone.0125832] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Accepted: 03/26/2015] [Indexed: 11/24/2022] Open
Abstract
β-lactamases are bacterial enzymes that confer resistance to β-lactam antibiotics, such as penicillins and cephalosporins. There are four classes of β-lactamase enzymes, each with characteristic sequence and structure properties. Enzymes from class A are the most common and have been well characterized across the family; however, less is known about how physicochemical properties vary across the C and D families. In this report, we compare the dynamical properties of four AmpC (class C) β-lactamases using our distance constraint model (DCM). The DCM reliably predicts thermodynamic and mechanical properties in an integrated way. As a consequence, quantitative stability/flexibility relationships (QSFR) can be determined and compared across the whole family. The DCM calculates a large number of QSFR metrics. Perhaps the most useful is the flexibility index (FI), which quantifies flexibility along the enzyme backbone. As typically observed in other systems, FI is well conserved across the four AmpC enzymes. Cooperativity correlation (CC), which quantifies intramolecular couplings within structure, is rarely conserved across protein families; however, it is in AmpC. In particular, the bulk of each structure is composed of a large rigid cluster, punctuated by three flexibly correlated regions located at the active site. These regions include several catalytic residues and the Ω-loop. This evolutionary conservation combined with active their site location strongly suggests that these coupled dynamical modes are important for proper functioning of the enzyme.
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Affiliation(s)
- Jenna R. Brown
- Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC, 28262, United States of America
| | - Dennis R. Livesay
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, 28262, United States of America
- * E-mail:
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7
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Brown MC, Verma D, Russell C, Jacobs DJ, Livesay DR. A case study comparing quantitative stability-flexibility relationships across five metallo-β-lactamases highlighting differences within NDM-1. Methods Mol Biol 2014; 1084:227-38. [PMID: 24061924 PMCID: PMC4676803 DOI: 10.1007/978-1-62703-658-0_12] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The Distance Constraint Model (DCM) is an ensemble-based biophysical model that integrates thermodynamic and mechanical viewpoints of protein structure. The DCM outputs a large number of structural characterizations that collectively allow for Quantified Stability-Flexibility Relationships (QSFR) to be identified and compared across protein families. Using five metallo-β-lactamases (MBLs) as a representative set, we demonstrate how QSFR properties are both conserved and varied across protein families. Similar to our characterizations on other protein families, the backbone flexibility of the five MBLs are overall visually conserved, yet there are interesting specific quantitative differences. For example, the plasmid-encoded NDM-1 enzyme, which leads to a fast spreading drug-resistant version of Klebsiella pneumoniae, has several regions of significantly increased rigidity relative to the other four. In addition, the set of intramolecular couplings within NDM-1 are also atypical. While long-range couplings frequently vary significantly across protein families, NDM-1 is distinct because it has limited correlated flexibility, which is isolated within the active site S3/S4 and S11/H6 loops. These loops are flexibly correlated in the other members, suggesting it is important to function, but the others also have significant amounts of correlated flexibility throughout the rest of their structures.
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Affiliation(s)
- Matthew C. Brown
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28262
| | - Deeptak Verma
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28262
| | - Christian Russell
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28262
| | - Donald J. Jacobs
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, NC 28262
| | - Dennis R. Livesay
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28262, To whom correspondence should be addressed:
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8
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Meilikhov EZ, Farzetdinova RM. Rigidity loss of protein macromolecule induced by force--effective field theory. Proteins 2013; 82:966-74. [PMID: 24323674 DOI: 10.1002/prot.24471] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2013] [Revised: 10/24/2013] [Accepted: 11/04/2013] [Indexed: 11/09/2022]
Abstract
In the framework of the effective field theory for the order parameter, which characterizes the degree of deviating the protein globule structure from its native state, the phase transition of the protein macromolecule from the elastic state into the plastic one under its mechanical stretching is considered. Elastic properties of a protein are studied as a function of the applied force, temperature, and the mean coordination number of the protein "network."
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Affiliation(s)
- E Z Meilikhov
- Kurchatov Institute, 123182, Moscow, Russia; Department of General Physics, Moscow Institute of Physics and Technology (State University), 9, Institutsky lane, Dolgoprudny, 141707, Russia
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9
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Ruvinsky AM, Kirys T, Tuzikov AV, Vakser IA. Ensemble-based characterization of unbound and bound states on protein energy landscape. Protein Sci 2013; 22:734-44. [PMID: 23526684 DOI: 10.1002/pro.2256] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2012] [Revised: 02/02/2013] [Accepted: 03/15/2013] [Indexed: 11/07/2022]
Abstract
Physicochemical description of numerous cell processes is fundamentally based on the energy landscapes of protein molecules involved. Although the whole energy landscape is difficult to reconstruct, increased attention to particular targets has provided enough structures for mapping functionally important subspaces associated with the unbound and bound protein structures. The subspace mapping produces a discrete representation of the landscape, further called energy spectrum. We compiled and characterized ensembles of bound and unbound conformations of six small proteins and explored their spectra in implicit solvent. First, the analysis of the unbound-to-bound changes points to conformational selection as the binding mechanism for four proteins. Second, results show that bound and unbound spectra often significantly overlap. Moreover, the larger the overlap the smaller the root mean square deviation (RMSD) between the bound and unbound conformational ensembles. Third, the center of the unbound spectrum has a higher energy than the center of the corresponding bound spectrum of the dimeric and multimeric states for most of the proteins. This suggests that the unbound states often have larger entropy than the bound states. Fourth, the exhaustively long minimization, making small intrarotamer adjustments (all-atom RMSD ≤ 0.7 Å), dramatically reduces the distance between the centers of the bound and unbound spectra as well as the spectra extent. It condenses unbound and bound energy levels into a thin layer at the bottom of the energy landscape with the energy spacing that varies between 0.8-4.6 and 3.5-10.5 kcal/mol for the unbound and bound states correspondingly. Finally, the analysis of protein energy fluctuations showed that protein vibrations itself can excite the interstate transitions, including the unbound-to-bound ones.
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Affiliation(s)
- Anatoly M Ruvinsky
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas 66047, USA.
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10
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Brock A. Fragmentation hydrogen exchange mass spectrometry: A review of methodology and applications. Protein Expr Purif 2012; 84:19-37. [DOI: 10.1016/j.pep.2012.04.009] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2012] [Accepted: 04/13/2012] [Indexed: 01/19/2023]
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11
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Gipson B, Hsu D, Kavraki LE, Latombe JC. Computational models of protein kinematics and dynamics: beyond simulation. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2012; 5:273-91. [PMID: 22524225 PMCID: PMC4866812 DOI: 10.1146/annurev-anchem-062011-143024] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Physics-based simulation represents a powerful method for investigating the time-varying behavior of dynamic protein systems at high spatial and temporal resolution. Such simulations, however, can be prohibitively difficult or lengthy for large proteins or when probing the lower-resolution, long-timescale behaviors of proteins generally. Importantly, not all questions about a protein system require full space and time resolution to produce an informative answer. For instance, by avoiding the simulation of uncorrelated, high-frequency atomic movements, a larger, domain-level picture of protein dynamics can be revealed. The purpose of this review is to highlight the growing body of complementary work that goes beyond simulation. In particular, this review focuses on methods that address kinematics and dynamics, as well as those that address larger organizational questions and can quickly yield useful information about the long-timescale behavior of a protein.
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Affiliation(s)
- Bryant Gipson
- Computer Science Department, Rice University, Houston, Texas 77005, USA.
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12
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González LC, Wang H, Livesay DR, Jacobs DJ. Calculating ensemble averaged descriptions of protein rigidity without sampling. PLoS One 2012; 7:e29176. [PMID: 22383947 PMCID: PMC3285152 DOI: 10.1371/journal.pone.0029176] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2011] [Accepted: 11/22/2011] [Indexed: 11/30/2022] Open
Abstract
Previous works have demonstrated that protein rigidity is related to thermodynamic stability, especially under conditions that favor formation of native structure. Mechanical network rigidity properties of a single conformation are efficiently calculated using the integer body-bar Pebble Game (PG) algorithm. However, thermodynamic properties require averaging over many samples from the ensemble of accessible conformations to accurately account for fluctuations in network topology. We have developed a mean field Virtual Pebble Game (VPG) that represents the ensemble of networks by a single effective network. That is, all possible number of distance constraints (or bars) that can form between a pair of rigid bodies is replaced by the average number. The resulting effective network is viewed as having weighted edges, where the weight of an edge quantifies its capacity to absorb degrees of freedom. The VPG is interpreted as a flow problem on this effective network, which eliminates the need to sample. Across a nonredundant dataset of 272 protein structures, we apply the VPG to proteins for the first time. Our results show numerically and visually that the rigidity characterizations of the VPG accurately reflect the ensemble averaged properties. This result positions the VPG as an efficient alternative to understand the mechanical role that chemical interactions play in maintaining protein stability.
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Affiliation(s)
- Luis C. González
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Hui Wang
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Dennis R. Livesay
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
- * E-mail: (DRL); (DJJ)
| | - Donald J. Jacobs
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
- * E-mail: (DRL); (DJJ)
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13
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Abstract
The distance constraint model (DCM) is a unique computational modeling paradigm that integrates mechanical and thermodynamic descriptions of macromolecular structure. That is, network rigidity calculations are used to account for nonadditivity within entropy components, thus restoring the utility of free-energy decomposition. The DCM outputs a large number of structural characterizations that collectively allow for quantified stability-flexibility relationships (QSFR) to be identified. In this review, we describe the theoretical underpinnings of the DCM and introduce several common QSFR metrics. Application of the DCM across protein families highlights the sensitivity within the set of protein structure residue-to-residue couplings. Further, we have developed a perturbation method to identify putative allosteric sites, where large changes in QSFR upon rigidification (mimicking ligand-binding) detect sites likely to invoke allosteric changes.
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14
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Wallnoefer HG, Liedl KR, Fox T. A GRID-Derived Water Network Stabilizes Molecular Dynamics Computer Simulations of a Protease. J Chem Inf Model 2011; 51:2860-7. [DOI: 10.1021/ci200138u] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Hannes G. Wallnoefer
- Computational Chemistry, Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co., KG, 88397 Biberach, Germany
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 52a, 6020 Innsbruck, Austria
| | - Klaus R. Liedl
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 52a, 6020 Innsbruck, Austria
| | - Thomas Fox
- Computational Chemistry, Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co., KG, 88397 Biberach, Germany
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