1
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Parkin D, Takano M. The Intrinsic Radius as a Key Parameter in the Generalized Born Model to Adjust Protein-Protein Electrostatic Interaction. Int J Mol Sci 2023; 24:ijms24054700. [PMID: 36902130 PMCID: PMC10003186 DOI: 10.3390/ijms24054700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 01/30/2023] [Accepted: 02/19/2023] [Indexed: 03/05/2023] Open
Abstract
The generalized Born (GB) model is an extension of the continuum dielectric theory of Born solvation energy and is a powerful method for accelerating the molecular dynamic (MD) simulations of charged biological molecules in water. While the effective dielectric constant of water that varies as a function of the separation distance between solute molecules is incorporated into the GB model, adjustment of the parameters is indispensable for accurate calculation of the Coulomb (electrostatic) energy. One of the key parameters is the lower limit of the spatial integral of the energy density of the electric field around a charged atom, known as the intrinsic radius ρ. Although ad hoc adjustment of ρ has been conducted to improve the Coulombic (ionic) bond stability, the physical mechanism by which ρ affects the Coulomb energy remains unclear. Via energetic analysis of three differently sized systems, here, we clarify that the Coulomb bond stability increases with increasing ρ and that the increased stability is caused by the interaction energy term, not by the self-energy (desolvation energy) term, as was supposed previously. Our results suggest that the use of larger values for the intrinsic radii of hydrogen and oxygen atoms, together with the use of a relatively small value for the spatial integration cutoff in the GB model, can better reproduce the Coulombic attraction between protein molecules.
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Affiliation(s)
- Dan Parkin
- Research Institute for Science and Engineering, Waseda University, Okubo 3-4-1, Sinjuku-ku, Tokyo 169-8555, Japan
| | - Mitsunori Takano
- Research Institute for Science and Engineering, Waseda University, Okubo 3-4-1, Sinjuku-ku, Tokyo 169-8555, Japan
- Department of Pure and Applied Physics, Waseda University, Okubo 3-4-1, Sinjuku-ku, Tokyo 169-8555, Japan
- Correspondence: ; Tel.: +81-3-5286-3512
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2
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Abstract
It would often be useful in computer simulations to use an implicit description of solvation effects, instead of explicitly representing the individual solvent molecules. Continuum dielectric models often work well in describing the thermodynamic aspects of aqueous solvation and can be very efficient compared to the explicit treatment of the solvent. Here, we review a particular class of so-called fast implicit solvent models, generalized Born (GB) models, which are widely used for molecular dynamics (MD) simulations of proteins and nucleic acids. These approaches model hydration effects and provide solvent-dependent forces with efficiencies comparable to molecular-mechanics calculations on the solute alone; as such, they can be incorporated into MD or other conformational searching strategies in a straightforward manner. The foundations of the GB model are reviewed, followed by examples of newer, emerging models and examples of important applications. We discuss their strengths and weaknesses, both for fidelity to the underlying continuum model and for the ability to replace explicit consideration of solvent molecules in macromolecular simulations.
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Affiliation(s)
- Alexey V Onufriev
- Departments of Computer Science and Physics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24060, USA;
| | - David A Case
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA;
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3
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Bansal N, Zheng Z, Song LF, Pei J, Merz KM. The Role of the Active Site Flap in Streptavidin/Biotin Complex Formation. J Am Chem Soc 2018; 140:5434-5446. [PMID: 29607642 DOI: 10.1021/jacs.8b00743] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Obtaining a detailed description of how active site flap motion affects substrate or ligand binding will advance structure-based drug design (SBDD) efforts on systems including the kinases, HSP90, HIV protease, ureases, etc. Through this understanding, we will be able to design better inhibitors and better proteins that have desired functions. Herein we address this issue by generating the relevant configurational states of a protein flap on the molecular energy landscape using an approach we call MTFlex-b and then following this with a procedure to estimate the free energy associated with the motion of the flap region. To illustrate our overall workflow, we explored the free energy changes in the streptavidin/biotin system upon introducing conformational flexibility in loop3-4 in the biotin unbound ( apo) and bound ( holo) state. The free energy surfaces were created using the Movable Type free energy method, and for further validation, we compared them to potential of mean force (PMF) generated free energy surfaces using MD simulations employing the FF99SBILDN and FF14SB force fields. We also estimated the free energy thermodynamic cycle using an ensemble of closed-like and open-like end states for the ligand unbound and bound states and estimated the binding free energy to be approximately -16.2 kcal/mol (experimental -18.3 kcal/mol). The good agreement between MTFlex-b in combination with the MT method with experiment and MD simulations supports the effectiveness of our strategy in obtaining unique insights into the motions in proteins that can then be used in a range of biological and biomedical applications.
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Affiliation(s)
- Nupur Bansal
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Zheng Zheng
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Lin Frank Song
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Jun Pei
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States.,Institute for Cyber Enabled Research , Michigan State University , 567 Wilson Road , East Lansing , Michigan 48824 , United States
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4
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Chakravorty A, Jia Z, Li L, Zhao S, Alexov E. Reproducing the Ensemble Average Polar Solvation Energy of a Protein from a Single Structure: Gaussian-Based Smooth Dielectric Function for Macromolecular Modeling. J Chem Theory Comput 2018; 14:1020-1032. [PMID: 29350933 PMCID: PMC9885857 DOI: 10.1021/acs.jctc.7b00756] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Typically, the ensemble average polar component of solvation energy (ΔGpolarsolv) of a macromolecule is computed using molecular dynamics (MD) or Monte Carlo (MC) simulations to generate conformational ensemble and then single/rigid conformation solvation energy calculation is performed on each snapshot. The primary objective of this work is to demonstrate that Poisson-Boltzmann (PB)-based approach using a Gaussian-based smooth dielectric function for macromolecular modeling previously developed by us (Li et al. J. Chem. Theory Comput. 2013, 9 (4), 2126-2136) can reproduce that ensemble average (ΔGpolarsolv) of a protein from a single structure. We show that the Gaussian-based dielectric model reproduces the ensemble average ΔGpolarsolv(⟨ΔGpolarsolv⟩) from an energy-minimized structure of a protein regardless of the minimization environment (structure minimized in vacuo, implicit or explicit waters, or crystal structure); the best case, however, is when it is paired with an in vacuo-minimized structure. In other minimization environments (implicit or explicit waters or crystal structure), the traditional two-dielectric model can still be selected with which the model produces correct solvation energies. Our observations from this work reflect how the ability to appropriately mimic the motion of residues, especially the salt bridge residues, influences a dielectric model's ability to reproduce the ensemble average value of polar solvation free energy from a single in vacuo-minimized structure.
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Affiliation(s)
- Arghya Chakravorty
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA
| | - Zhe Jia
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA
| | - Lin Li
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA
| | - Shan Zhao
- Departement of Mathematics, College of Arts and Sciences, University of Alabama, Tuscaloosa, Alabama 35487, USA
| | - Emil Alexov
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA.,Corresponding Author Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA.
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5
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Onufriev AV, Izadi S. Water models for biomolecular simulations. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2017. [DOI: 10.1002/wcms.1347] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Alexey V. Onufriev
- Department of Physics; Virginia Tech; Blacksburg VA USA
- Department of Computer Science; Virginia Tech; Blacksburg VA USA
- Center for Soft Matter and Biological Physics; Virginia Tech; Blacksburg VA USA
| | - Saeed Izadi
- Early Stage Pharmaceutical Development; Genentech Inc.; South San Francisco, CA USA
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6
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Feig M. Computational protein structure refinement: Almost there, yet still so far to go. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2017; 7:e1307. [PMID: 30613211 PMCID: PMC6319934 DOI: 10.1002/wcms.1307] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Rd., Room 218 BCH, East Lansing, MI, USA, ; 517-432-7439
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7
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Zhang B, Kilburg D, Eastman P, Pande VS, Gallicchio E. Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units. J Comput Chem 2017; 38:740-752. [PMID: 28160511 DOI: 10.1002/jcc.24745] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Revised: 01/05/2017] [Accepted: 01/08/2017] [Indexed: 11/07/2022]
Abstract
We present an algorithm to efficiently compute accurate volumes and surface areas of macromolecules on graphical processing unit (GPU) devices using an analytic model which represents atomic volumes by continuous Gaussian densities. The volume of the molecule is expressed by means of the inclusion-exclusion formula, which is based on the summation of overlap integrals among multiple atomic densities. The surface area of the molecule is obtained by differentiation of the molecular volume with respect to atomic radii. The many-body nature of the model makes a port to GPU devices challenging. To our knowledge, this is the first reported full implementation of this model on GPU hardware. To accomplish this, we have used recursive strategies to construct the tree of overlaps and to accumulate volumes and their gradients on the tree data structures so as to minimize memory contention. The algorithm is used in the formulation of a surface area-based non-polar implicit solvent model implemented as an open source plug-in (named GaussVol) for the popular OpenMM library for molecular mechanics modeling. GaussVol is 50 to 100 times faster than our best optimized implementation for the CPUs, achieving speeds in excess of 100 ns/day with 1 fs time-step for protein-sized systems on commodity GPUs. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Baofeng Zhang
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210
| | - Denise Kilburg
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York, 10016
| | - Peter Eastman
- Department of Bioengineering, Stanford University, Stanford, California, 94035
| | - Vijay S Pande
- Department of Chemistry, Stanford University, Stanford, California, 94035
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York, 10016
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8
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Izadi S, Anandakrishnan R, Onufriev AV. Implicit Solvent Model for Million-Atom Atomistic Simulations: Insights into the Organization of 30-nm Chromatin Fiber. J Chem Theory Comput 2016; 12:5946-5959. [PMID: 27748599 PMCID: PMC5649046 DOI: 10.1021/acs.jctc.6b00712] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Molecular dynamics (MD) simulations based on the implicit solvent generalized Born (GB) models can provide significant computational advantages over the traditional explicit solvent simulations. However, the standard GB becomes prohibitively expensive for all-atom simulations of large structures; the model scales poorly, ∼n2, with the number of solute atoms. Here we combine our recently developed optimal point charge approximation (OPCA) with the hierarchical charge partitioning (HCP) approximation to present an ∼n log n multiscale, yet fully atomistic, GB model (GB-HCPO). The HCP approximation exploits the natural organization of biomolecules (atoms, groups, chains, and complexes) to partition the structure into multiple hierarchical levels of components. OPCA approximates the charge distribution for each of these components by a small number of point charges so that the low order multipole moments of these components are optimally reproduced. The approximate charges are then used for computing electrostatic interactions with distant components, while the full set of atomic charges are used for nearby components. We show that GB-HCPO can deliver up to 2 orders of magnitude speedup compared to the standard GB, with minimal impact on its accuracy. For large structures, GB-HCPO can approach the same nominal speed, as in nanoseconds per day, as the highly optimized explicit-solvent simulation based on particle mesh Ewald (PME). The increase in the nominal simulation speed, relative to the standard GB, coupled with substantially faster sampling of conformational space, relative to the explicit solvent, makes GB-HCPO a suitable candidate for MD simulation of large atomistic systems in implicit solvent. As a practical demonstration, we use GB-HCPO simulation to refine a ∼1.16 million atom structure of 30 nm chromatin fiber (40 nucleosomes). The refined structure suggests important details about spatial organization of the linker DNA and the histone tails in the fiber: (1) the linker DNA fills the core region, allowing the H3 histone tails to interact with the linker DNA, which is consistent with experiment; (2) H3 and H4 tails are found mostly in the core of the structure, closer to the helical axis of the fiber, while H2A and H2B are mostly solvent exposed. Potential functional consequences of these findings are discussed. GB-HCPO is implemented in the open source MD software NAB in Amber 2016.
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Affiliation(s)
- Saeed Izadi
- Department of Biomedical Engineering and Mechanics, ‡Biomedical Division, Edward Via College of Osteopathic Medicine, ¶Department of Computer Science and Physics, and §Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute and State University , Blacksburg, Virginia 24061, United States
| | - Ramu Anandakrishnan
- Department of Biomedical Engineering and Mechanics, ‡Biomedical Division, Edward Via College of Osteopathic Medicine, ¶Department of Computer Science and Physics, and §Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute and State University , Blacksburg, Virginia 24061, United States
| | - Alexey V Onufriev
- Department of Biomedical Engineering and Mechanics, ‡Biomedical Division, Edward Via College of Osteopathic Medicine, ¶Department of Computer Science and Physics, and §Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute and State University , Blacksburg, Virginia 24061, United States
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9
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Deng N, Zhang BW, Levy RM. Connecting free energy surfaces in implicit and explicit solvent: an efficient method to compute conformational and solvation free energies. J Chem Theory Comput 2016; 11:2868-78. [PMID: 26236174 DOI: 10.1021/acs.jctc.5b00264] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
The ability to accurately model solvent effects on free energy surfaces is important for understanding many biophysical processes including protein folding and misfolding, allosteric transitions, and protein–ligand binding. Although all-atom simulations in explicit solvent can provide an accurate model for biomolecules in solution, explicit solvent simulations are hampered by the slow equilibration on rugged landscapes containing multiple basins separated by barriers. In many cases, implicit solvent models can be used to significantly speed up the conformational sampling; however, implicit solvent simulations do not fully capture the effects of a molecular solvent, and this can lead to loss of accuracy in the estimated free energies. Here we introduce a new approach to compute free energy changes in which the molecular details of explicit solvent simulations are retained while also taking advantage of the speed of the implicit solvent simulations. In this approach, the slow equilibration in explicit solvent, due to the long waiting times before barrier crossing, is avoided by using a thermodynamic cycle which connects the free energy basins in implicit solvent and explicit solvent using a localized decoupling scheme. We test this method by computing conformational free energy differences and solvation free energies of the model system alanine dipeptide in water. The free energy changes between basins in explicit solvent calculated using fully explicit solvent paths agree with the corresponding free energy differences obtained using the implicit/explicit thermodynamic cycle to within 0.3 kcal/mol out of ∼3 kcal/mol at only ∼8% of the computational cost. We note that WHAM methods can be used to further improve the efficiency and accuracy of the implicit/explicit thermodynamic cycle.
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Affiliation(s)
- Nanjie Deng
- Center for Biophysics & Computational Biology and Institute for Computational Molecular Sciences Temple University, Philadelphia, Pennsylvania 19122, United States
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10
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Messih MA, Lepore R, Tramontano A. LoopIng: a template-based tool for predicting the structure of protein loops. Bioinformatics 2015; 31:3767-72. [PMID: 26249814 PMCID: PMC4653384 DOI: 10.1093/bioinformatics/btv438] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 07/21/2015] [Indexed: 12/31/2022] Open
Abstract
Motivation: Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are not very effective in modeling their structure. However, loops are often involved in protein function, hence inferring their structure is important for predicting protein structure as well as function. Results: We describe a method, LoopIng, based on the Random Forest automated learning technique, which, given a target loop, selects a structural template for it from a database of loop candidates. Compared to the most recently available methods, LoopIng is able to achieve similar accuracy for short loops (4–10 residues) and significant enhancements for long loops (11–20 residues). The quality of the predictions is robust to errors that unavoidably affect the stem regions when these are modeled. The method returns a confidence score for the predicted template loops and has the advantage of being very fast (on average: 1 min/loop). Availability and implementation:www.biocomputing.it/looping Contact:anna.tramontano@uniroma1.it Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Rosalba Lepore
- Department of Physics, Sapienza University, 00185 Rome, Italy and
| | - Anna Tramontano
- Department of Physics, Sapienza University, 00185 Rome, Italy and Istituto Pasteur-Fondazione Cenci Bolognetti, Viale Regina Elena 291, 00161 Rome, Italy
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11
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Zachmann M, Mathias G, Antes I. Parameterization of the Hamiltonian Dielectric Solvent (HADES) Reaction-Field Method for the Solvation Free Energies of Amino Acid Side-Chain Analogs. Chemphyschem 2015; 16:1739-49. [PMID: 25820235 DOI: 10.1002/cphc.201402861] [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: 12/04/2014] [Revised: 02/02/2015] [Indexed: 11/10/2022]
Abstract
Optimization of the Hamiltonian dielectric solvent (HADES) method for biomolecular simulations in a dielectric continuum is presented with the goal of calculating accurate absolute solvation free energies while retaining the model's accuracy in predicting conformational free-energy differences. The solvation free energies of neutral and polar amino acid side-chain analogs calculated by using HADES, which may optionally include nonpolar contributions, were optimized against experimental data to reach a chemical accuracy of about 0.5 kcal mol(-1). The new parameters were evaluated for charged side-chain analogs. The HADES results were compared with explicit-solvent, generalized Born, Poisson-Boltzmann, and QM-based methods. The potentials of mean force (PMFs) between pairs of side-chain analogs obtained by using HADES and explicit-solvent simulations were used to evaluate the effects of the improved parameters optimized for solvation free energies on intermolecular potentials.
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Affiliation(s)
- Martin Zachmann
- Theoretical Chemical Biology and Protein Modelling Group, Technische Universiät München (Germany)
| | - Gerald Mathias
- Lehrstuhl für Biomolekulare Optik, Ludwig-Maximilians Universität München (Germany).
| | - Iris Antes
- Theoretical Chemical Biology and Protein Modelling Group, Technische Universiät München (Germany).
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12
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Kleinjung J, Fraternali F. Design and application of implicit solvent models in biomolecular simulations. Curr Opin Struct Biol 2014; 25:126-34. [PMID: 24841242 PMCID: PMC4045398 DOI: 10.1016/j.sbi.2014.04.003] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 04/07/2014] [Accepted: 04/09/2014] [Indexed: 11/23/2022]
Abstract
Implicit solvent replaces explicit water by a potential of mean force. Popular models are SASA, VOL and Generalized Born. Implicit solvent is used in MD, protein modelling, folding, design, prediction and drug screening. Large-scale simulations allow for parametrisation via force matching. Application to nucleic acids and membranes is challenging.
We review implicit solvent models and their parametrisation by introducing the concepts and recent devlopments of the most popular models with a focus on parametrisation via force matching. An overview of recent applications of the solvation energy term in protein dynamics, modelling, design and prediction is given to illustrate the usability and versatility of implicit solvation in reproducing the physical behaviour of biomolecular systems. Limitations of implicit modes are discussed through the example of more challenging systems like nucleic acids and membranes.
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Affiliation(s)
- Jens Kleinjung
- Division of Mathematical Biology, MRC National Institute for Medical Research, The Ridgeway, London NW7 1AA, United Kingdom
| | - Franca Fraternali
- Randall Division of Cell and Molecular Biophysics, King's College London, New Hunt's House, London SE1 1UL, United Kingdom.
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13
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Mukhopadhyay A, Aguilar BH, Tolokh IS, Onufriev AV. Introducing Charge Hydration Asymmetry into the Generalized Born Model. J Chem Theory Comput 2014; 10:1788-1794. [PMID: 24803871 PMCID: PMC3985468 DOI: 10.1021/ct4010917] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2013] [Indexed: 12/15/2022]
Abstract
The effect of charge hydration asymmetry (CHA)-non-invariance of solvation free energy upon solute charge inversion-is missing from the standard linear response continuum electrostatics. The proposed charge hydration asymmetric-generalized Born (CHA-GB) approximation introduces this effect into the popular generalized Born (GB) model. The CHA is added to the GB equation via an analytical correction that quantifies the specific propensity of CHA of a given water model; the latter is determined by the charge distribution within the water model. Significant variations in CHA seen in explicit water (TIP3P, TIP4P-Ew, and TIP5P-E) free energy calculations on charge-inverted "molecular bracelets" are closely reproduced by CHA-GB, with the accuracy similar to models such as SEA and 3D-RISM that go beyond the linear response. Compared against reference explicit (TIP3P) electrostatic solvation free energies, CHA-GB shows about a 40% improvement in accuracy over the canonical GB, tested on a diverse set of 248 rigid small neutral molecules (root mean square error, rmse = 0.88 kcal/mol for CHA-GB vs 1.24 kcal/mol for GB) and 48 conformations of amino acid analogs (rmse = 0.81 kcal/mol vs 1.26 kcal/mol). CHA-GB employs a novel definition of the dielectric boundary that does not subsume the CHA effects into the intrinsic atomic radii. The strategy leads to finding a new set of intrinsic atomic radii optimized for CHA-GB; these radii show physically meaningful variation with the atom type, in contrast to the radii set optimized for GB. Compared to several popular radii sets used with the original GB model, the new radii set shows better transferability between different classes of molecules.
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Affiliation(s)
| | - Boris H. Aguilar
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Igor S. Tolokh
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Alexey V. Onufriev
- Department
of Physics, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
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14
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Stein A, Kortemme T. Improvements to robotics-inspired conformational sampling in rosetta. PLoS One 2013; 8:e63090. [PMID: 23704889 PMCID: PMC3660577 DOI: 10.1371/journal.pone.0063090] [Citation(s) in RCA: 140] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Accepted: 03/28/2013] [Indexed: 02/04/2023] Open
Abstract
To accurately predict protein conformations in atomic detail, a computational method must be capable of sampling models sufficiently close to the native structure. All-atom sampling is difficult because of the vast number of possible conformations and extremely rugged energy landscapes. Here, we test three sampling strategies to address these difficulties: conformational diversification, intensification of torsion and omega-angle sampling and parameter annealing. We evaluate these strategies in the context of the robotics-based kinematic closure (KIC) method for local conformational sampling in Rosetta on an established benchmark set of 45 12-residue protein segments without regular secondary structure. We quantify performance as the fraction of sub-Angstrom models generated. While improvements with individual strategies are only modest, the combination of intensification and annealing strategies into a new “next-generation KIC” method yields a four-fold increase over standard KIC in the median percentage of sub-Angstrom models across the dataset. Such improvements enable progress on more difficult problems, as demonstrated on longer segments, several of which could not be accurately remodeled with previous methods. Given its improved sampling capability, next-generation KIC should allow advances in other applications such as local conformational remodeling of multiple segments simultaneously, flexible backbone sequence design, and development of more accurate energy functions.
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Affiliation(s)
- Amelie Stein
- California Institute for Quantitative Biomedical Research and Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- * E-mail: (AS); (TK)
| | - Tanja Kortemme
- California Institute for Quantitative Biomedical Research and Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- * E-mail: (AS); (TK)
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15
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Chys P, Chacón P. Random Coordinate Descent with Spinor-matrices and Geometric Filters for Efficient Loop Closure. J Chem Theory Comput 2013; 9:1821-9. [PMID: 26587638 DOI: 10.1021/ct300977f] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Protein loop closure constitutes a critical step in loop and protein modeling whereby geometrically feasible loops must be found between two given anchor residues. Here, a new analytic/iterative algorithm denoted random coordinate descent (RCD) to perform protein loop closure is described. The algorithm solves loop closure through minimization as in cyclic coordinate descent but selects bonds for optimization randomly, updates loop conformations by spinor-matrices, performs loop closure in both chain directions, and uses a set of geometric filters to yield efficient conformational sampling. Geometric filters allow one to detect clashes and constrain dihedral angles on the fly. The RCD algorithm is at least comparable to state of the art loop closure algorithms due to an excellent balance between efficiency and intrinsic sampling capability. Furthermore, its efficiency allows one to improve conformational sampling by increasing the sampling number without much penalty. Overall, RCD turns out to be accurate, fast, robust, and applicable over a wide range of loop lengths. Because of the versatility of RCD, it is a solid alternative for integration with current loop modeling strategies.
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Affiliation(s)
- Pieter Chys
- Structural Bioinformatics Group, Biological Chemical Physics Department, Institute of Physical Chemistry Rocasolano (IQFR), Consejo Superior de Investigaciones Cientı́ficas (CSIC), Calle de Serrano 119, Madrid 28006, Spain
| | - Pablo Chacón
- Structural Bioinformatics Group, Biological Chemical Physics Department, Institute of Physical Chemistry Rocasolano (IQFR), Consejo Superior de Investigaciones Cientı́ficas (CSIC), Calle de Serrano 119, Madrid 28006, Spain
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16
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Li Y. Conformational sampling in template-free protein loop structure modeling: an overview. Comput Struct Biotechnol J 2013; 5:e201302003. [PMID: 24688696 PMCID: PMC3962101 DOI: 10.5936/csbj.201302003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2012] [Revised: 01/23/2013] [Accepted: 01/28/2013] [Indexed: 01/04/2023] Open
Abstract
Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a "mini protein folding problem" under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increasing number of known structures available in PDB. This mini review provides an overview on the recent computational approaches for loop structure modeling. In particular, we focus on the approaches of sampling loop conformation space, which is a critical step to obtain high resolution models in template-free methods. We review the potential energy functions for loop modeling, loop buildup mechanisms to satisfy geometric constraints, and loop conformation sampling algorithms. The recent loop modeling results are also summarized.
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Affiliation(s)
- Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
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17
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Narayanan C, Weinstock DS, Wu KP, Baum J, Levy RM. Investigation of the Polymeric Properties of α-Synuclein and Comparison with NMR Experiments: A Replica Exchange Molecular Dynamics Study. J Chem Theory Comput 2012; 8:3929-3942. [PMID: 23162382 PMCID: PMC3496295 DOI: 10.1021/ct300241t] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Intrinsically disordered proteins (IDPs) have been shown to be involved in a number of cellular functions, in addition to their predominance in diseased states. α-synuclein may be described as one such IDP implicated in the pathology of Parkinson's disease. Understanding the conformational characteristics of the monomeric state of α-synuclein is necessary for understanding the role of the monomer conformation in aggregation. Polymer theories have been applied to investigate the statistical properties of homopolymeric IDPs. Here we use Replica Exchange Molecular Dynamics (REMD) simulations using temperature as a proxy for solvent quality to examine how well these theories developed for homopolymeric chains describe heteropolymeric α-synuclein. Our results indicate that α-synuclein behaves like a homopolymer at the extremes of solvent quality, while in the intermediate solvent regime, the uneven distribution of charged residues along the sequence strongly influences the conformations adopted by the chain. We refine the ensemble extracted from the REMD simulations of α-synuclein, which shows the best qualitative agreement with experiment, by fitting to the experimental NMR Residual Dipolar Couplings (RDCs) and Paramagnetic Relaxation Enhancements (PREs). Our results demonstrate that the detailed shape of the RDC patterns are sensitive to the angular correlations that are local in sequence while longer range anti-correlations which arise from packing constraints affect the RDC magnitudes.
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Affiliation(s)
- Chitra Narayanan
- Graduate Program in Biochemistry, Rutgers University, Piscataway NJ 08854
| | - Daniel S. Weinstock
- BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway NJ 08854
| | - Kuen-Phon Wu
- Department of Chemistry and Chemical Biology, Rutgers University, 610 Taylor Road, Piscataway NJ 08854
| | - Jean Baum
- BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway NJ 08854
- Department of Chemistry and Chemical Biology, Rutgers University, 610 Taylor Road, Piscataway NJ 08854
| | - Ronald M. Levy
- BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway NJ 08854
- Department of Chemistry and Chemical Biology, Rutgers University, 610 Taylor Road, Piscataway NJ 08854
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18
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Flick J, Tristram F, Wenzel W. Modeling loop backbone flexibility in receptor-ligand docking simulations. J Comput Chem 2012; 33:2504-15. [DOI: 10.1002/jcc.23087] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2012] [Revised: 06/15/2012] [Accepted: 07/09/2012] [Indexed: 12/20/2022]
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19
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Abstract
The prediction of loop structures is considered one of the main challenges in the protein folding problem. Regardless of the dependence of the overall algorithm on the protein data bank, the flexibility of loop regions dictates the need for special attention to their structures. In this article, we present algorithms for loop structure prediction with fixed stem and flexible stem geometry. In the flexible stem geometry problem, only the secondary structure of three stem residues on either side of the loop is known. In the fixed stem geometry problem, the structure of the three stem residues on either side of the loop is also known. Initial loop structures are generated using a probability database for the flexible stem geometry problem, and using torsion angle dynamics for the fixed stem geometry problem. Three rotamer optimization algorithms are introduced to alleviate steric clashes between the generated backbone structures and the side chain rotamers. The structures are optimized by energy minimization using an all-atom force field. The optimized structures are clustered using a traveling salesman problem-based clustering algorithm. The structures in the densest clusters are then utilized to refine dihedral angle bounds on all amino acids in the loop. The entire procedure is carried out for a number of iterations, leading to improved structure prediction and refined dihedral angle bounds. The algorithms presented in this article have been tested on 3190 loops from the PDBSelect25 data set and on targets from the recently concluded CASP9 community-wide experiment.
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Affiliation(s)
- A. Subramani
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544-5263, U.S.A
| | - C. A. Floudas
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544-5263, U.S.A
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20
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Skliros A, Zimmermann MT, Chakraborty D, Saraswathi S, Katebi AR, Leelananda SP, Kloczkowski A, Jernigan RL. The importance of slow motions for protein functional loops. Phys Biol 2012; 9:014001. [PMID: 22314977 DOI: 10.1088/1478-3975/9/1/014001] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Loops in proteins that connect secondary structures such as alpha-helix and beta-sheet, are often on the surface and may play a critical role in some functions of a protein. The mobility of loops is central for the motional freedom and flexibility requirements of active-site loops and may play a critical role for some functions. The structures and behaviors of loops have not been studied much in the context of the whole structure and its overall motions, especially how these might be coupled. Here we investigate loop motions by using coarse-grained structures (C(α) atoms only) to solve the motions of the system by applying Lagrange equations with elastic network models to learn about which loops move in an independent fashion and which move in coordination with domain motions, faster and slower, respectively. The normal modes of the system are calculated using eigen-decomposition of the stiffness matrix. The contribution of individual modes and groups of modes is investigated for their effects on all residues in each loop by using Fourier analyses. Our results indicate overall that the motions of functional sets of loops behave in similar ways as the whole structure. But overall only a relatively few loops move in coordination with the dominant slow modes of motion, and these are often closely related to function.
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Affiliation(s)
- Aris Skliros
- L. H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA
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21
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Adhikari AN, Peng J, Wilde M, Xu J, Freed KF, Sosnick TR. Modeling large regions in proteins: applications to loops, termini, and folding. Protein Sci 2012; 21:107-21. [PMID: 22095743 PMCID: PMC3323786 DOI: 10.1002/pro.767] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2011] [Revised: 11/02/2011] [Accepted: 11/06/2011] [Indexed: 11/10/2022]
Abstract
Template-based methods for predicting protein structure provide models for a significant portion of the protein but often contain insertions or chain ends (InsEnds) of indeterminate conformation. The local structure prediction "problem" entails modeling the InsEnds onto the rest of the protein. A well-known limit involves predicting loops of ≤12 residues in crystal structures. However, InsEnds may contain as many as ~50 amino acids, and the template-based model of the protein itself may be imperfect. To address these challenges, we present a free modeling method for predicting the local structure of loops and large InsEnds in both crystal structures and template-based models. The approach uses single amino acid torsional angle "pivot" moves of the protein backbone with a C(β) level representation. Nevertheless, our accuracy for loops is comparable to existing methods. We also apply a more stringent test, the blind structure prediction and refinement categories of the CASP9 tournament, where we improve the quality of several homology based models by modeling InsEnds as long as 45 amino acids, sizes generally inaccessible to existing loop prediction methods. Our approach ranks as one of the best in the CASP9 refinement category that involves improving template-based models so that they can function as molecular replacement models to solve the phase problem for crystallographic structure determination.
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Affiliation(s)
- Aashish N Adhikari
- Department of Chemistry, The University of ChicagoChicago, Illinois 60637
- The James Franck Institute, The University of ChicagoChicago, Illinois 60637
| | - Jian Peng
- Toyota Technological Institute at ChicagoChicago, Illinois 60637
| | - Michael Wilde
- Department of Biochemistry and Molecular Biology, The University of ChicagoChicago, Illinois 60637
| | - Jinbo Xu
- Toyota Technological Institute at ChicagoChicago, Illinois 60637
| | - Karl F Freed
- Department of Chemistry, The University of ChicagoChicago, Illinois 60637
- The James Franck Institute, The University of ChicagoChicago, Illinois 60637
- Computation Institute, The University of Chicago and Argonne National LaboratoryChicago, Illinois 60637
| | - Tobin R Sosnick
- Computation Institute, The University of Chicago and Argonne National LaboratoryChicago, Illinois 60637
- Department of Biochemistry and Molecular Biology, The University of ChicagoChicago, Illinois 60637
- Institute for Biophysical Dynamics, The University of ChicagoChicago, Illinois 60637
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22
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Wickstrom L, Gallicchio E, Levy RM. The linear interaction energy method for the prediction of protein stability changes upon mutation. Proteins 2011; 80:111-25. [PMID: 22038697 DOI: 10.1002/prot.23168] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2011] [Revised: 07/28/2011] [Accepted: 08/06/2011] [Indexed: 12/25/2022]
Abstract
The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance.
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Affiliation(s)
- Lauren Wickstrom
- Department of Chemistry and Chemical Biology, BioMaPS Institute for Quantitative Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
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23
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Nekouzadeh A, Rudy Y. Three-residue loop closure in proteins: a new kinematic method reveals a locus of connected loop conformations. J Comput Chem 2011; 32:2515-25. [PMID: 21618253 PMCID: PMC4154380 DOI: 10.1002/jcc.21812] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2010] [Revised: 03/09/2011] [Accepted: 03/20/2011] [Indexed: 11/05/2022]
Abstract
The closure of a three-residue loop was studied using a developed kinematic method. It was shown that there are infinite number of three-residue loops (a locus of conformations), which can connect two segments of a polypeptide. This adds to the current understanding of a finite number of conformations for three-residue loop-closure. In the developed method, some of the equations can be solved analytically to reduce the computation cost. Benefiting from the reduced computation time, we determined all the relative positions of two polypeptide segments that can be connected by a three-residue loop.
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Affiliation(s)
- Ali Nekouzadeh
- Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, USA.
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24
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Zhao S, Zhu K, Li J, Friesner RA. Progress in super long loop prediction. Proteins 2011; 79:2920-35. [PMID: 21905115 DOI: 10.1002/prot.23129] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2010] [Revised: 05/06/2011] [Accepted: 06/15/2011] [Indexed: 11/07/2022]
Abstract
Sampling errors are very common in super long loop (referring here to loops that have more than thirteen residues) prediction, simply because the sampling space is vast. We have developed a dipeptide segment sampling algorithm to solve this problem. As a first step in evaluating the performance of this algorithm, it was applied to the problem of reconstructing loops in native protein structures. With a newly constructed test set of 89 loops ranging from 14 to 17 residues, this method obtains average/median global backbone root-mean-square deviations (RMSDs) to the native structure (superimposing the body of the protein, not the loop itself) of 1.46/0.68 Å. Specifically, results for loops of various lengths are 1.19/0.67 Å for 36 fourteen-residue loops, 1.55/0.75 Å for 30 fifteen-residue loops, 1.43/0.80 Å for 14 sixteen-residue loops, and 2.30/1.92 Å for nine seventeen-residue loops. In the vast majority of cases, the method locates energy minima that are lower than or equal to that of the minimized native loop, thus indicating that the new sampling method is successful and rarely limits prediction accuracy. Median RMSDs are substantially lower than the averages because of a small number of outliers. The causes of these failures are examined in some detail, and some can be attributed to flaws in the energy function, such as π-π interactions are not accurately accounted for by the OPLS-AA force field we employed in this study. By introducing a new energy model which has a superior description of π-π interactions, significantly better results were achieved for quite a few former outliers. Crystal packing is explicitly included in order to provide a fair comparison with crystal structures.
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Affiliation(s)
- Suwen Zhao
- Department of Chemistry, Columbia University, New York, New York 1027, USA
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25
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Li J, Abel R, Zhu K, Cao Y, Zhao S, Friesner RA. The VSGB 2.0 model: a next generation energy model for high resolution protein structure modeling. Proteins 2011; 79:2794-812. [PMID: 21905107 DOI: 10.1002/prot.23106] [Citation(s) in RCA: 698] [Impact Index Per Article: 53.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2010] [Revised: 05/03/2011] [Accepted: 05/13/2011] [Indexed: 02/06/2023]
Abstract
A novel energy model (VSGB 2.0) for high resolution protein structure modeling is described, which features an optimized implicit solvent model as well as physics-based corrections for hydrogen bonding, π-π interactions, self-contact interactions, and hydrophobic interactions. Parameters of the VSGB 2.0 model were fit to a crystallographic database of 2239 single side chain and 100 11-13 residue loop predictions. Combined with an advanced method of sampling and a robust algorithm for protonation state assignment, the VSGB 2.0 model was validated by predicting 115 super long loops up to 20 residues. Despite the dramatically increasing difficulty in reconstructing longer loops, a high accuracy was achieved: all of the lowest energy conformations have global backbone RMSDs better than 2.0 Å from the native conformations. Average global backbone RMSDs of the predictions are 0.51, 0.63, 0.70, 0.62, 0.80, 1.41, and 1.59 Å for 14, 15, 16, 17, 18, 19, and 20 residue loop predictions, respectively. When these results are corrected for possible statistical bias as explained in the text, the average global backbone RMSDs are 0.61, 0.71, 0.86, 0.62, 1.06, 1.67, and 1.59 Å. Given the precision and robustness of the calculations, we believe that the VSGB 2.0 model is suitable to tackle "real" problems, such as biological function modeling and structure-based drug discovery.
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Affiliation(s)
- Jianing Li
- Department of Chemistry, Columbia University, New York, New York 10027, USA
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26
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Rapp C, Kalyanaraman C, Schiffmiller A, Schoenbrun EL, Jacobson MP. A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series. J Chem Inf Model 2011; 51:2082-9. [PMID: 21780805 DOI: 10.1021/ci200033n] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
We introduce the "Prime-ligand" method for ranking ligands in congeneric series. The method employs a single scoring function, the OPLS-AA/GBSA molecular mechanics/implicit solvent model, for all stages of sampling and scoring. We evaluate the method using 12 test sets of congeneric series for which experimental binding data is available in the literature, as well as the structure of one member of the series bound to the protein. Ligands are "docked" by superimposing a common stem fragment among the compounds in the series using a crystal complex from the Protein Data Bank and sampling the conformational space of the variable region. Our results show good correlation between our predicted rankings and the experimental data for cases in which binding affinities differ by at least 1 order of magnitude. For 11 out of 12 cases, >90% of such ligand pairs could be correctly ranked, while for the remaining case, Factor Xa, 76% of such pairs were correctly ranked. A small number of compounds could not be docked using the current protocol because of the large size of functional groups that could not be accommodated by a rigid receptor. CPU requirements for the method, involving CPU minutes per ligand, are modest compared with more rigorous methods that use similar force fields, such as free energy perturbation. We also benchmark the scoring function using series of ligands bound to the same protein within the CSAR data set. We demonstrate that energy minimization of ligands in the crystal structures is critical to obtain any correlation with experimentally determined binding affinities.
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Affiliation(s)
- Chaya Rapp
- Department of Chemistry, Stern College for Women, Yeshiva University, New York, New York, United States
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27
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Olson MA, Chaudhury S, Lee MS. Comparison between self-guided Langevin dynamics and molecular dynamics simulations for structure refinement of protein loop conformations. J Comput Chem 2011; 32:3014-22. [PMID: 21793008 DOI: 10.1002/jcc.21883] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2011] [Accepted: 06/19/2011] [Indexed: 11/09/2022]
Abstract
This article presents a comparative analysis of two replica-exchange simulation methods for the structure refinement of protein loop conformations, starting from low-resolution predictions. The methods are self-guided Langevin dynamics (SGLD) and molecular dynamics (MD) with a Nosé-Hoover thermostat. We investigated a small dataset of 8- and 12-residue loops, with the shorter loops placed initially from a coarse-grained lattice model and the longer loops from an enumeration assembly method (the Loopy program). The CHARMM22 + CMAP force field with a generalized Born implicit solvent model (molecular-surface parameterized GBSW2) was used to explore conformational space. We also assessed two empirical scoring methods to detect nativelike conformations from decoys: the all-atom distance-scaled ideal-gas reference state (DFIRE-AA) statistical potential and the Rosetta energy function. Among the eight-residue loop targets, SGLD out performed MD in all cases, with a median of 0.48 Å reduction in global root-mean-square deviation (RMSD) of the loop backbone coordinates from the native structure. Among the more challenging 12-residue loop targets, SGLD improved the prediction accuracy over MD by a median of 1.31 Å, representing a substantial improvement. The overall median RMSD for SGLD simulations of 12-residue loops was 0.91 Å, yielding refinement of a median 2.70 Å from initial loop placement. Results from DFIRE-AA and the Rosetta model applied to rescoring conformations failed to improve the overall detection calculated from the CHARMM force field. We illustrate the advantage of SGLD over the MD simulation model by presenting potential-energy landscapes for several loop predictions. Our results demonstrate that SGLD significantly outperforms traditional MD in the generation and populating of nativelike loop conformations and that the CHARMM force field performs comparably to other empirical force fields in identifying these conformations from the resulting ensembles.
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Affiliation(s)
- Mark A Olson
- Department of Cell Biology and Biochemistry, US Army Medical Research Institute of Infectious Diseases, Fredrick, Maryland 21702, USA.
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28
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Li Y, Rata I, Jakobsson E. Sampling multiple scoring functions can improve protein loop structure prediction accuracy. J Chem Inf Model 2011; 51:1656-66. [PMID: 21702492 PMCID: PMC3211142 DOI: 10.1021/ci200143u] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Accurately predicting loop structures is important for understanding functions of many proteins. In order to obtain loop models with high accuracy, efficiently sampling the loop conformation space to discover reasonable structures is a critical step. In loop conformation sampling, coarse-grain energy (scoring) functions coupling with reduced protein representations are often used to reduce the number of degrees of freedom as well as sampling computational time. However, due to implicitly considering many factors by reduced representations, the coarse-grain scoring functions may have potential insensitivity and inaccuracy, which can mislead the sampling process and consequently ignore important loop conformations. In this paper, we present a new computational sampling approach to obtain reasonable loop backbone models, so-called the Pareto optimal sampling (POS) method. The rationale of the POS method is to sample the function space of multiple, carefully selected scoring functions to discover an ensemble of diversified structures yielding Pareto optimality to all sampled conformations. The POS method can efficiently tolerate insensitivity and inaccuracy in individual scoring functions and thereby lead to significant accuracy improvement in loop structure prediction. We apply the POS method to a set of 4-12-residue loop targets using a function space composed of backbone-only Rosetta and distance-scale finite ideal-gas reference (DFIRE) and a triplet backbone dihedral potential developed in our lab. Our computational results show that in 501 out of 502 targets, the model sets generated by POS contain structure models are within subangstrom resolution. Moreover, the top-ranked models have a root mean square deviation (rmsd) less than 1 A in 96.8, 84.1, and 72.2% of the short (4-6 residues), medium (7-9 residues), and long (10-12 residues) targets, respectively, when the all-atom models are generated by local optimization from the backbone models and are ranked by our recently developed Pareto optimal consensus (POC) method. Similar sampling effectiveness can also be found in a set of 13-residue loop targets.
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Affiliation(s)
- Yaohang Li
- Department of Computer Science, Old Dominion University
| | - Ionel Rata
- Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign
| | - Eric Jakobsson
- Department of Molecular and Integrative Physiology, Beckman Institute, and National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
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29
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Grebner C, Becker J, Stepanenko S, Engels B. Efficiency of tabu-search-based conformational search algorithms. J Comput Chem 2011; 32:2245-53. [DOI: 10.1002/jcc.21807] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2011] [Revised: 03/10/2011] [Accepted: 03/10/2011] [Indexed: 01/02/2023]
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30
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Arnautova YA, Abagyan RA, Totrov M. Development of a new physics-based internal coordinate mechanics force field and its application to protein loop modeling. Proteins 2011; 79:477-98. [PMID: 21069716 PMCID: PMC3057902 DOI: 10.1002/prot.22896] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
We report the development of internal coordinate mechanics force field (ICMFF), new force field parameterized using a combination of experimental data for crystals of small molecules and quantum mechanics calculations. The main features of ICMFF include: (a) parameterization for the dielectric constant relevant to the condensed state (ε = 2) instead of vacuum, (b) an improved description of hydrogen-bond interactions using duplicate sets of van der Waals parameters for heavy atom-hydrogen interactions, and (c) improved backbone covalent geometry and energetics achieved using novel backbone torsional potentials and inclusion of the bond angles at the C(α) atoms into the internal variable set. The performance of ICMFF was evaluated through loop modeling simulations for 4-13 residue loops. ICMFF was combined with a solvent-accessible surface area solvation model optimized using a large set of loop decoys. Conformational sampling was carried out using the biased probability Monte Carlo method. Average/median backbone root-mean-square deviations of the lowest energy conformations from the native structures were 0.25/0.21 Å for four residues loops, 0.84/0.46 Å for eight residue loops, and 1.16/0.73 Å for 12 residue loops. To our knowledge, these results are significantly better than or comparable with those reported to date for any loop modeling method that does not take crystal packing into account. Moreover, the accuracy of our method is on par with the best previously reported results obtained considering the crystal environment. We attribute this success to the high accuracy of the new ICM force field achieved by meticulous parameterization, to the optimized solvent model, and the efficiency of the search method.
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Affiliation(s)
- Yelena A Arnautova
- Molsoft LLC, 3366 North Torrey Pines Court, Suite 300, La Jolla, California 92037, USA
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31
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Abstract
Loop modeling is crucial for high-quality homology model construction outside conserved secondary structure elements. Dozens of loop modeling protocols involving a range of database and ab initio search algorithms and a variety of scoring functions have been proposed. Knowledge-based loop modeling methods are very fast and some can successfully and reliably predict loops up to about eight residues long. Several recent ab initio loop simulation methods can be used to construct accurate models of loops up to 12-13 residues long, albeit at a substantial computational cost. Major current challenges are the simulations of loops longer than 12-13 residues, the modeling of multiple interacting flexible loops, and the sensitivity of the loop predictions to the accuracy of the loop environment.
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32
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Shang Y, Nguyen H, Wickstrom L, Okur A, Simmerling C. Improving the description of salt bridge strength and geometry in a Generalized Born model. J Mol Graph Model 2010; 29:676-84. [PMID: 21168352 DOI: 10.1016/j.jmgm.2010.11.013] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2010] [Accepted: 11/22/2010] [Indexed: 10/18/2022]
Abstract
The Generalized Born (GB) solvent model is widely used in molecular dynamics simulations because it can be less computationally expensive and it samples conformational changes more efficiently than explicit solvent simulations. Meanwhile, great efforts have been made in the past to improve its precision and accuracy. Previous studies have shown that reducing intrinsic GB radii of some hydrogen atoms would improve AMBER GB-HCT solvent model's accuracy on salt bridges. Here we present our finding that similar correction also shows dramatic improvement for the AMBER GB-OBC solvent model. Potential of mean force and cluster analysis for small peptide replica exchange molecular dynamics simulations suggested that new radii GB simulation with ff99SB/GB-OBC corrected salt bridge strength and achieved significantly higher geometry similarity with TIP3P simulation. Improved performance in 60 ns HIV-1 protease GB simulation further validated this approach for large systems.
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Affiliation(s)
- Yi Shang
- Graduate Program in Molecular and Cellular Biology, Stony Brook University, Stony Brook, NY 11794, USA.
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Larsson P, Lindahl E. A high-performance parallel-generalized Born implementation enabled by tabulated interaction rescaling. J Comput Chem 2010; 31:2593-600. [PMID: 20740558 DOI: 10.1002/jcc.21552] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Implicit solvent representations, in general, and generalized Born models, in particular, provide an attractive way to reduce the number of interactions and degrees of freedom in a system. The instantaneous relaxation of the dielectric shielding provided by an implicit solvent model can be extremely efficient for high-throughput and Monte Carlo studies, and a reduced system size can also remove a lot of statistical noise. Despite these advantages, it has been difficult for generalized Born implementations to significantly outperform optimized explicit-water simulations due to more complex functional forms and the two extra interaction stages necessary to calculate Born radii and the derivative chain rule terms contributing to the force. Here, we present a method that uses a rescaling transformation to make the standard generalized Born expression a function of a single variable, which enables an efficient tabulated implementation on any modern CPU hardware. The total performance is within a factor 2 of simulations in vacuo. The algorithm has been implemented in Gromacs, including single-instruction multiple-data acceleration, for three different Born radius models and corresponding chain rule terms. We have also adapted the model to work with the virtual interaction sites commonly used for hydrogens to enable long-time steps, which makes it possible to achieve a simulation performance of 0.86 micros/day for BBA5 with 1-nm cutoff on a single quad-core desktop processor. Finally, we have also implemented a set of streaming kernels without neighborlists to accelerate the non-cutoff setup occasionally used for implicit solvent simulations of small systems.
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Affiliation(s)
- Per Larsson
- Center for Biomembrane Research, Department of Biochemistry & Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden
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Gallicchio E, Lapelosa M, Levy RM. The Binding Energy Distribution Analysis Method (BEDAM) for the Estimation of Protein-Ligand Binding Affinities. J Chem Theory Comput 2010; 6:2961-2977. [PMID: 21116484 DOI: 10.1021/ct1002913] [Citation(s) in RCA: 125] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The Binding Energy Distribution Analysis Method (BEDAM) for the computation of receptor-ligand standard binding free energies with implicit solvation is presented. The method is based on a well established statistical mechanics theory of molecular association. It is shown that, in the context of implicit solvation, the theory is homologous to the test particle method of solvation thermodynamics with the solute-solvent potential represented by the effective binding energy of the protein-ligand complex. Accordingly, in BEDAM the binding constant is computed by means of a weighted integral of the probability distribution of the binding energy obtained in the canonical ensemble in which the ligand is positioned in the binding site but the receptor and the ligand interact only with the solvent continuum. It is shown that the binding energy distribution encodes all of the physical effects of binding. The balance between binding enthalpy and entropy is seen in our formalism as a balance between favorable and unfavorable binding modes which are coupled through the normalization of the binding energy distribution function. An efficient computational protocol for the binding energy distribution based on the AGBNP2 implicit solvent model, parallel Hamiltonian replica exchange sampling and histogram reweighting is developed. Applications of the method to a set of known binders and non-binders of the L99A and L99A/M102Q mutants of T4 lysozyme receptor are illustrated. The method is able to discriminate without error binders from non-binders, and the computed standard binding free energies of the binders are found to be in good agreement with experimental measurements. Analysis of the results reveals that the binding affinities of these systems reflect the contributions from multiple conformations spanning a wide range of binding energies.
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Affiliation(s)
- Emilio Gallicchio
- BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers the State University of New Jersey, Piscataway, NJ 08854
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Li Y, Rata I, Chiu SW, Jakobsson E. Improving predicted protein loop structure ranking using a Pareto-optimality consensus method. BMC STRUCTURAL BIOLOGY 2010; 10:22. [PMID: 20642859 PMCID: PMC2914074 DOI: 10.1186/1472-6807-10-22] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2009] [Accepted: 07/20/2010] [Indexed: 11/10/2022]
Abstract
Background Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction. Results We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of ~20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD < 0.5A from the native) as top-ranked, and selecting at least one near-native model in the top-5-ranked models, respectively. Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops. Furthermore, the POC method outperforms the other popularly-used consensus strategies in model ranking, such as rank-by-number, rank-by-rank, rank-by-vote, and regression-based methods. Conclusions By integrating multiple knowledge- and physics-based scoring functions based on Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set.
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Affiliation(s)
- Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.
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36
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Danielson ML, Lill MA. New computational method for prediction of interacting protein loop regions. Proteins 2010; 78:1748-59. [PMID: 20186974 DOI: 10.1002/prot.22690] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Flexible loop regions of proteins play a crucial role in many biological functions such as protein-ligand recognition, enzymatic catalysis, and protein-protein association. To date, most computational methods that predict the conformational states of loops only focus on individual loop regions. However, loop regions are often spatially in close proximity to one another and their mutual interactions stabilize their conformations. We have developed a new method, titled CorLps, capable of simultaneously predicting such interacting loop regions. First, an ensemble of individual loop conformations is generated for each loop region. The members of the individual ensembles are combined and are accepted or rejected based on a steric clash filter. After a subsequent side-chain optimization step, the resulting conformations of the interacting loops are ranked by the statistical scoring function DFIRE that originated from protein structure prediction. Our results show that predicting interacting loops with CorLps is superior to sequential prediction of the two interacting loop regions, and our method is comparable in accuracy to single loop predictions. Furthermore, improved predictive accuracy of the top-ranked solution is achieved for 12-residue length loop regions by diversifying the initial pool of individual loop conformations using a quality threshold clustering algorithm.
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Affiliation(s)
- Matthew L Danielson
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, USA
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Application of biasing-potential replica-exchange simulations for loop modeling and refinement of proteins in explicit solvent. Proteins 2010; 78:2809-19. [DOI: 10.1002/prot.22796] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Choi Y, Deane CM. FREAD revisited: Accurate loop structure prediction using a database search algorithm. Proteins 2010; 78:1431-40. [PMID: 20034110 DOI: 10.1002/prot.22658] [Citation(s) in RCA: 121] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Loops are the most variable regions of protein structure and are, in general, the least accurately predicted. Their prediction has been approached in two ways, ab initio and database search. In recent years, it has been thought that ab initio methods are more powerful. In light of the continued rapid expansion in the number of known protein structures, we have re-evaluated FREAD, a database search method and demonstrate that the power of database search methods may have been underestimated. We found that sequence similarity as quantified by environment specific substitution scores can be used to significantly improve prediction. In fact, FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than the ab initio methods of MODELLER, PLOP, and RAPPER. Within this subset, FREAD's predictive ability is length independent, in general, producing results within 2A RMSD, compared to an average of over 10A for loop length 20 for any of the other tested methods. We also benchmarked the prediction protocols on a set of 212 loops from the model structures in CASP 7 and 8. An extended version of FREAD is able to make predictions for 127 of these, it gives the best prediction of the methods tested in 61 of these cases. In examining FREAD's ability to predict in the model environment, we found that whole structure quality did not affect the quality of loop predictions.
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Affiliation(s)
- Yoonjoo Choi
- Department of Statistics, Oxford University, United Kingdom.
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Berrondo M, Gray JJ, Schleif R. Computational predictions of the mutant behavior of AraC. J Mol Biol 2010; 398:462-70. [PMID: 20338183 DOI: 10.1016/j.jmb.2010.03.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2009] [Revised: 02/16/2010] [Accepted: 03/11/2010] [Indexed: 11/29/2022]
Abstract
An algorithm implemented in Rosetta correctly predicts the folding capabilities of the 17-residue N-terminal arm of the AraC gene regulatory protein when arabinose is bound to the protein and the dramatically different structure of this arm when arabinose is absent. The transcriptional activity of 43 mutant AraC proteins with alterations in the arm sequences was measured in vivo and compared with their predicted folding properties. Seventeen of the mutants possessed regulatory properties that could be directly compared with folding predictions. Sixteen of the 17 mutants were correctly predicted. The algorithm predicts that the N-terminal arm sequences of AraC homologs fold to the Escherichia coli AraC arm structure. In contrast, it predicts that random sequences of the same length and many partially randomized E. coli arm sequences do not fold to the E. coli arm structure. The high level of success shows that relatively "simple" computational methods can in some cases predict the behavior of mutant proteins with good reliability.
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Affiliation(s)
- Monica Berrondo
- Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA
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40
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Nikiforovich GV, Taylor CM, Marshall GR, Baranski TJ. Modeling the possible conformations of the extracellular loops in G-protein-coupled receptors. Proteins 2010; 78:271-85. [PMID: 19731375 DOI: 10.1002/prot.22537] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
This study presents the results of a de novo approach modeling possible conformational dynamics of the extracellular (EC) loops in G-protein-coupled receptors (GPCRs), specifically in bovine rhodopsin (bRh), squid rhodopsin (sRh), human beta-2 adrenergic receptor (beta2AR), turkey beta-1 adrenergic receptor (beta1AR), and human A2 adenosine receptor (A2AR). The approach deliberately sacrificed a detailed description of any particular 3D structure of the loops in GPCRs in favor of a less precise description of many possible structures. Despite this, the approach found ensembles of the low-energy conformers of the EC loops that contained structures close to the available X-ray snapshots. For the smaller EC1 and EC3 loops (6-11 residues), our results were comparable with the best recent results obtained by other authors using much more sophisticated techniques. For the larger EC2 loops (25-34 residues), our results consistently yielded structures significantly closer to the X-ray snapshots than the results of the other authors for loops of similar size. The results suggested possible large-scale movements of the EC loops in GPCRs that might determine their conformational dynamics. The approach was also validated by accurately reproducing the docking poses for low-molecular-weight ligands in beta2AR, beta1AR, and A2AR, demonstrating the possible influence of the conformations of the EC loops on the binding sites of ligands. The approach correctly predicted the system of disulfide bridges between the EC loops in A2AR and elucidated the probable pathways for forming this system.
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41
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Lin YL, Gao J. Internal proton transfer in the external pyridoxal 5'-phosphate Schiff base in dopa decarboxylase. Biochemistry 2010; 49:84-94. [PMID: 19938875 DOI: 10.1021/bi901790e] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Combined quantum mechanical and molecular mechanical (QM/MM) simulations of dopa decarboxylase have been carried out to elucidate the factors that contribute to the tautomeric equilibrium of the intramolecular proton transfer in the external PLP-L-dopa Schiff base. The presence of a carboxylate anion on the alpha-carbon of the Schiff base stabilizes the zwitterions and shifts the equilibrium in favor of the oxoenamine tautomer (protonated Schiff base). Moreover, protonation of the PLP pyridine nitrogen further drives the equilibrium toward the oxoenamine direction. On the other hand, solvent effects favor the hydroxyimine configuration, although the equilibrium favors the oxoenamine isomer with a methyl group as the substituent on the imino nitrogen. In dopa decarboxylase, the hydroxyimine form of the PLP(H+)-L-dopa Schiff base is predicted to be the major isomer with a relative free energy of -1.3 kcal/mol over that of the oxoenamine isomer. Both Asp271 and Lys303 stabilize the hydroxyimine configuration through hydrogen-bonding interactions with the pyridine nitrogen of the PLP and the imino nitrogen of the Schiff base, respectively. Interestingly, Thr246 plays a double role in the intramolecular proton transfer process, in which it initially donates a hydrogen bond to the phenolate oxygen in the oxoenamine configuration and then switches to a hydrogen bond acceptor from the phenolic hydroxyl group in the hydroxyimine tautomer.
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Affiliation(s)
- Yen-lin Lin
- Department of Chemistry and Digital Technology Center, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, USA
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42
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Mandell DJ, Coutsias EA, Kortemme T. Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling. Nat Methods 2009; 6:551-2. [PMID: 19644455 DOI: 10.1038/nmeth0809-551] [Citation(s) in RCA: 339] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Abstract
The AGBNP2 implicit solvent model, an evolution of the Analytical Generalized Born plus Non-Polar (AGBNP) model we have previously reported, is presented with the aim of modeling hydration effects beyond those described by conventional continuum dielectric representations. A new empirical hydration free energy component based on a procedure to locate and score hydration sites on the solute surface is introduced to model first solvation shell effects, such as hydrogen bonding, which are poorly described by continuum dielectric models. This new component is added to the Generalized Born and non-polar AGBNP terms. Also newly introduced is an analytical Solvent Excluded Volume (SEV) model which improves the solute volume description by reducing the effect of spurious high-dielectric interstitial spaces present in conventional van der Waals representations. The AGBNP2 model is parametrized and tested with respect to experimental hydration free energies of small molecules and the results of explicit solvent simulations. Modeling the granularity of water is one of the main design principles employed for the the first shell solvation function and the SEV model, by requiring that water locations have a minimum available volume based on the size of a water molecule. It is shown that the new volumetric model produces Born radii and surface areas in good agreement with accurate numerical evaluations of these quantities. The results of molecular dynamics simulations of a series of mini-proteins show that the new model produces conformational ensembles in substantially better agreement with reference explicit solvent ensembles than the original AGBNP model with respect to both structural and energetics measures.
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Affiliation(s)
- Emilio Gallicchio
- Department of Chemistry and Chemical Biology and BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway NJ 08854
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Velez-Vega C, Fenwick MK, Escobedo FA. Simulated mutagenesis of the hypervariable loops of a llama VHH domain for the recovery of canonical conformations. J Phys Chem B 2009; 113:1785-95. [PMID: 19132876 DOI: 10.1021/jp805866j] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In this work, wildtype and mutated hypervariable regions of an anti-hCG llama VHH antibody were simulated via a molecular dynamics replica exchange method (REM). Seven mutants were simulated with the goal of identifying structural determinants that return the noncanonical H1 loop of the wildtype antibody to the type 1 canonical structure predicted by database methods formulated for conventional antibodies. Two cases with three point mutations yielded a stable type 1 H1 structure. In addition, other mutants with fewer mutations showed evidence of such conformations. Overall, the mutagenesis results suggest a marked influence of interloop interactions on the attainment of canonical conformations for this antibody. On the methodological front, a novel REM scheme was developed to quickly screen diverse mutants based on their relative propensities for attaining favorable structures. This multimutant REM (MMREM) was used to successfully identify mutations that stabilize a canonical H1 loop grafted on the llama antibody scaffold. The use of MMREM and REM for screening mutants and assessing structural stability may be useful in the rational design of antibody hypervariable loops.
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Affiliation(s)
- Camilo Velez-Vega
- School of Chemical and Biomolecular Engineering, Department of Molecular Medicine, Cornell University, Ithaca, New York 14853, USA
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45
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Sivasubramanian A, Sircar A, Chaudhury S, Gray JJ. Toward high-resolution homology modeling of antibody Fv regions and application to antibody-antigen docking. Proteins 2009; 74:497-514. [PMID: 19062174 DOI: 10.1002/prot.22309] [Citation(s) in RCA: 145] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
High-resolution homology models are useful in structure-based protein engineering applications, especially when a crystallographic structure is unavailable. Here, we report the development and implementation of RosettaAntibody, a protocol for homology modeling of antibody variable regions. The protocol combines comparative modeling of canonical complementarity determining region (CDR) loop conformations and de novo loop modeling of CDR H3 conformation with simultaneous optimization of V(L)-V(H) rigid-body orientation and CDR backbone and side-chain conformations. The protocol was tested on a benchmark of 54 antibody crystal structures. The median root mean square deviation (rmsd) of the antigen binding pocket comprised of all the CDR residues was 1.5 A with 80% of the targets having an rmsd lower than 2.0 A. The median backbone heavy atom global rmsd of the CDR H3 loop prediction was 1.6, 1.9, 2.4, 3.1, and 6.0 A for very short (4-6 residues), short (7-9), medium (10-11), long (12-14) and very long (17-22) loops, respectively. When the set of ten top-scoring antibody homology models are used in local ensemble docking to antigen, a moderate-to-high accuracy docking prediction was achieved in seven of fifteen targets. This success in computational docking with high-resolution homology models is encouraging, but challenges still remain in modeling antibody structures for sequences with long H3 loops. This first large-scale antibody-antigen docking study using homology models reveals the level of "functional accuracy" of these structural models toward protein engineering applications.
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Affiliation(s)
- Arvind Sivasubramanian
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, Maryland 21218, USA
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Lapelosa M, Gallicchio E, Arnold GF, Arnold E, Levy RM. In silico vaccine design based on molecular simulations of rhinovirus chimeras presenting HIV-1 gp41 epitopes. J Mol Biol 2009; 385:675-91. [PMID: 19026659 PMCID: PMC2649764 DOI: 10.1016/j.jmb.2008.10.089] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2008] [Revised: 09/15/2008] [Accepted: 10/31/2008] [Indexed: 11/28/2022]
Abstract
A cluster of promising epitopes for the development of human immunodeficiency virus (HIV) vaccines is located in the membrane-proximal external region (MPER) of the gp41 subunit of the HIV envelope spike structure. The crystal structure of the peptide corresponding to the so-called ELDKWA epitope (HIV-1 HxB2 gp41 residues 662-668), in complex with the corresponding broadly neutralizing human monoclonal antibody 2F5, provides a target for structure-based vaccine design strategies aimed at finding macromolecular carriers that are able to present this MPER-derived epitope with optimal antigenic activity. To this end, a series of replica exchange molecular dynamics computer simulations was conducted to characterize the distributions of conformations of ELDKWA-based epitopes inserted into a rhinovirus carrier and to identify those with the highest fraction of conformations that are able to bind 2F5. The length, hydrophobic character, and precise site of insertion were found to be critical for achieving structural similarity to the target crystal structure. A construct with a high degree of complementarity to the corresponding determinant region of 2F5 was obtained. This construct was employed to build a high-resolution structural model of the complex between the 2F5 antibody and the chimeric human rhinovirus type 14:HIV-1 ELDKWA virus particle. Additional simulations, which were conducted to study the conformational propensities of the ELDKWA region in solution, confirm the hypothesis that the ELDKWA region of gp41 is highly flexible and capable of assuming helical conformations (as in the postfusion helical bundle structure) and beta-turn conformations (as in the complex with the 2F5 antibody). These results also suggest that the ELDKWA epitope can be involved in intramolecular--and likely intermolecular--hydrophobic interactions. This tendency offers an explanation for the observation that mutations decreasing the hydrophobic character of the MPER in many cases result in conformational changes that increase the affinity of this region for the 2F5 antibody.
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Affiliation(s)
- Mauro Lapelosa
- BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ 08854, USA
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