101
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Ikebe J, Sakuraba S, Kono H. Adaptive lambda square dynamics simulation: an efficient conformational sampling method for biomolecules. J Comput Chem 2013; 35:39-50. [PMID: 24166005 DOI: 10.1002/jcc.23462] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2013] [Revised: 08/23/2013] [Accepted: 09/20/2013] [Indexed: 11/10/2022]
Abstract
A novel, efficient sampling method for biomolecules is proposed. The partial multicanonical molecular dynamics (McMD) was recently developed as a method that improved generalized ensemble (GE) methods to focus sampling only on a part of a system (GEPS); however, it was not tested well. We found that partial McMD did not work well for polylysine decapeptide and gave significantly worse sampling efficiency than a conventional GE. Herein, we elucidate the fundamental reason for this and propose a novel GEPS, adaptive lambda square dynamics (ALSD), which can resolve the problem faced when using partial McMD. We demonstrate that ALSD greatly increases the sampling efficiency over a conventional GE. We believe that ALSD is an effective method and is applicable to the conformational sampling of larger and more complicated biomolecule systems.
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Affiliation(s)
- Jinzen Ikebe
- Molecular Modeling and Simulation Group, Japan Atomic Energy Agency, 8-1-7 Umemidai, Kizugawa, Kyoto, 619-0215, Japan
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102
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Morishita T, Itoh SG, Okumura H, Mikami M. On-the-fly reconstruction of free-energy profiles using logarithmic mean-force dynamics. J Comput Chem 2013; 34:1375-84. [PMID: 23460528 DOI: 10.1002/jcc.23267] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2012] [Revised: 01/10/2013] [Accepted: 02/07/2013] [Indexed: 11/11/2022]
Abstract
Mean-force dynamics (MFD), which is a fictitious dynamics for a set of collective variables on a potential of mean-force, is a powerful algorithm to efficiently explore free-energy landscapes. Recently, we have introduced logarithmic MFD (LogMFD) (Morishita et al., Phys. Rev. E 2012, 85, 066702) which overcomes difficulties encounterd in free-energy calculations using standard approaches such as thermodynamic integration. Here, we present a guide to implementing LogMFD calculations paying attention to the practical issues in choosing the parameters in LogMFD. A primary focus is given to the effect of the parameters on the accuracy of the reconstructed free-energy profiles. A recipe for reducing the errors due to energy dissipation is presented. We also demonstrate that multidimensional free-energy landscapes can be reconstructed on-the-fly using LogMFD, which cannot be accomplished using any other free-energy calculation techniques.
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Affiliation(s)
- Tetsuya Morishita
- Nanosystem Research Institute, National Institute of Advanced Industrial Science and Technology, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
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103
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Wickstrom L, He P, Gallicchio E, Levy RM. Large scale affinity calculations of cyclodextrin host-guest complexes: Understanding the role of reorganization in the molecular recognition process. J Chem Theory Comput 2013; 9:3136-3150. [PMID: 25147485 DOI: 10.1021/ct400003r] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Host-guest inclusion complexes are useful models for understanding the structural and energetic aspects of molecular recognition. Due to their small size relative to much larger protein-ligand complexes, converged results can be obtained rapidly for these systems thus offering the opportunity to more reliably study fundamental aspects of the thermodynamics of binding. In this work, we have performed a large scale binding affinity survey of 57 β-cyclodextrin (CD) host guest systems using the binding energy distribution analysis method (BEDAM) with implicit solvation (OPLS-AA/AGBNP2). Converged estimates of the standard binding free energies are obtained for these systems by employing techniques such as parallel Hamitionian replica exchange molecular dynamics, conformational reservoirs and multistate free energy estimators. Good agreement with experimental measurements is obtained in terms of both numerical accuracy and affinity rankings. Overall, average effective binding energies reproduce affinity rank ordering better than the calculated binding affinities, even though calculated binding free energies, which account for effects such as conformational strain and entropy loss upon binding, provide lower root mean square errors when compared to measurements. Interestingly, we find that binding free energies are superior rank order predictors for a large subset containing the most flexible guests. The results indicate that, while challenging, accurate modeling of reorganization effects can lead to ligand design models of superior predictive power for rank ordering relative to models based only on ligand-receptor interaction energies.
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Affiliation(s)
- Lauren Wickstrom
- BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854 ; Department of Chemistry, Lehman College, The City University of New York, Bronx, NY 10468
| | - Peng He
- BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854
| | - Emilio Gallicchio
- BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854
| | - Ronald M Levy
- BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854
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104
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Law SM, Zhang BW, Brooks CL. pH-sensitive residues in the p19 RNA silencing suppressor protein from carnation Italian ringspot virus affect siRNA binding stability. Protein Sci 2013; 22:595-604. [PMID: 23450521 DOI: 10.1002/pro.2243] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2012] [Revised: 02/06/2013] [Accepted: 02/10/2013] [Indexed: 01/08/2023]
Abstract
Tombusviruses, such as Carnation Italian ringspot virus (CIRV), encode a protein homodimer called p19 that is capable of suppressing RNA silencing in their infected hosts by binding to and sequestering short-interfering RNA (siRNA) away from the RNA silencing pathway. P19 binding stability has been shown to be sensitive to changes in pH but the specific amino acid residues involved have remained unclear. Using constant pH molecular dynamics simulations, we have identified key pH-dependent residues that affect CIRV p19-siRNA binding stability at various pH ranges based on calculated changes in the free energy contribution from each titratable residue. At high pH, the deprotonation of Lys60, Lys67, Lys71, and Cys134 has the largest effect on the binding stability. Similarly, deprotonation of several acidic residues (Asp9, Glu12, Asp20, Glu35, and/or Glu41) at low pH results in a decrease in binding stability. At neutral pH, residues Glu17 and His132 provide a small increase in the binding stability and we find that the optimal pH range for siRNA binding is between 7.0 and 10.0. Overall, our findings further inform recent experiments and are in excellent agreement with data on the pH-dependent binding profile.
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Affiliation(s)
- Sean M Law
- Department of Chemistry and Biophysics Program, University of Michigan, Ann Arbor, Michigan 48109, USA
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105
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Abstract
The role of pH-dependent protonation equilibrium in modulating RNA dynamics and function is one of the key unanswered questions in RNA biology. Molecular dynamics (MD) simulations can provide insight into the mechanistic roles of protonated nucleotides, but it is only capable of modeling fixed protonation states and requires prior knowledge of the key residue's protonation state. Recently, we developed a framework for constant pH molecular dynamics simulations (CPHMDMSλD) of nucleic acids, where the nucleotides' protonation states are modeled as dynamic variables that are coupled to the structural dynamics of the RNA. In the present study, we demonstrate the application of CPHMDMSλD to the lead-dependent ribozyme; establishing the validity of this approach for modeling complex RNA structures. We show that CPHMDMSλD accurately predicts the direction of the pKa shifts and reproduces experimentally-measured microscopic pKa values with an average unsigned error of 1.3 pKa units. The effects of coupled titration states in RNA structures are modeled, and the importance of conformation sampling is highlighted. The general accuracy of CPHMDMSλD simulations in reproducing pH-dependent observables reported in this work demonstrates that constant pH simulations provides a powerful tool to investigate pH-dependent processes in nucleic acids.
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Affiliation(s)
- Garrett B Goh
- Department of Chemistry, University of Michigan, 930 N. University, Ann Arbor, Michigan 48109, United States
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106
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Feher M, Williams CI. Numerical Errors in Minimization Based Binding Energy Calculations. J Chem Inf Model 2012; 52:3200-12. [DOI: 10.1021/ci300298d] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Miklos Feher
- Campbell Family Institute for Breast Cancer Research, University Health
Network, Toronto Medical Discovery Tower, 101 College Street, Suite
5-361, Toronto, ON M5G 1L7, Canada
| | - Christopher I. Williams
- Chemical Computing Group, Suite 910, 1010 Sherbrooke Street West, Montreal,
QC H3A 2R7, Canada
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107
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Fomin ES, Alemasov NA. A study of the thermal stability of mutant barnase protein variants with MOLKERN software. ACTA ACUST UNITED AC 2012. [DOI: 10.1134/s2079059712060068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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108
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Carl N, Hodošček M, Vehar B, Konc J, Brooks BR, Janežič D. Correlating protein hot spot surface analysis using ProBiS with simulated free energies of protein-protein interfacial residues. J Chem Inf Model 2012; 52:2541-9. [PMID: 23009716 DOI: 10.1021/ci3003254] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A protocol was developed for the computational determination of the contribution of interfacial amino acid residues to the free energy of protein-protein binding. Thermodynamic integration, based on molecular dynamics simulation in CHARMM, was used to determine the free energy associated with single point mutations to glycine in a protein-protein interface. The hot spot amino acids found in this way were then correlated to structural similarity scores detected by the ProBiS algorithm for local structural alignment. We find that amino acids with high structural similarity scores contribute on average -3.19 kcal/mol to the free energy of protein-protein binding and are thus correlated with hot spot residues, while residues with low similarity scores contribute on average only -0.43 kcal/mol. This suggests that the local structural alignment method provides a good approximation of the contribution of a residue to the free energy of binding and is particularly useful for detection of hot spots in proteins with known structures but undetermined protein-protein complexes.
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Affiliation(s)
- Nejc Carl
- National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
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109
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Matthies MC, Bienert S, Torda AE. Dynamics in Sequence Space for RNA Secondary Structure Design. J Chem Theory Comput 2012; 8:3663-70. [PMID: 26593011 DOI: 10.1021/ct300267j] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
We have implemented a method for the design of RNA sequences that should fold to arbitrary secondary structures. A popular energy model allows one to take the derivative with respect to composition, which can then be interpreted as a force and used for Newtonian dynamics in sequence space. Combined with a negative design term, one can rapidly sample sequences which are compatible with a desired secondary structure via simulated annealing. Results for 360 structures were compared with those from another nucleic acid design program using measures such as the probability of the target structure and an ensemble-weighted distance to the target structure.
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Affiliation(s)
- Marco C Matthies
- Centre for Bioinformatics, University of Hamburg, Bundesstr. 43, 20146 Hamburg, Germany
| | - Stefan Bienert
- Centre for Bioinformatics, University of Hamburg, Bundesstr. 43, 20146 Hamburg, Germany.,Biozentrum, University of Basel, Klingelbergstr. 50/70, 4056 Basel, Switzerland
| | - Andrew E Torda
- Centre for Bioinformatics, University of Hamburg, Bundesstr. 43, 20146 Hamburg, Germany
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110
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Cuendet MA, Tuckerman ME. Alchemical Free Energy Differences in Flexible Molecules from Thermodynamic Integration or Free Energy Perturbation Combined with Driven Adiabatic Dynamics. J Chem Theory Comput 2012; 8:3504-12. [DOI: 10.1021/ct300090z] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Michel A. Cuendet
- Department of Chemistry, New York
University, New York,
New York 10003, United States
| | - Mark E. Tuckerman
- Department of Chemistry, New York
University, New York,
New York 10003, United States
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111
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112
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Pietropaolo A. Exploring metal-driven stereoselectivity of glycopeptides by free-energy calculations. PURE APPL CHEM 2012. [DOI: 10.1351/pac-con-11-09-33] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A formalism to quantify the chemical stereoselectivity, based on free energy of binding calculations, is here discussed. It is used to explain the stereoselectivity of two diastereoisomeric frameworks, comprising the dimer of a copper(II)-peptide core of L- and D-carnosine, respectively, each bound to two chains of D-trehalose, in which copper(II) adopts a type-II coordination geometry. The stereocenter of carnosine is varied both L and D, giving rise to two diastereoisomers. A thermodynamic cycle crossing the formation of the two enantiomeric copper(II) peptide cores was devised. A harmonic restraining potential that depends only on the bond distance was added to ensure reversibility in bond formation and dissociation, for an accurate estimate of the free energy. The calculation of the free energy of binding between D-trehalose and the two enantiomeric copper(II) peptide cores reproduces the free-energy quantities observed from stability constants and isothermal titration calorimetry (ITC) measurements. This is an example of chirality selection based on free-energy difference.
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Affiliation(s)
- Adriana Pietropaolo
- 1Dipartimento di Scienze della Salute, Università Magna Graecia di Catanzaro, Viale Europa, 88100 Catanzaro, Italy
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113
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Abstract
One of the central problems in statistical mechanics is that of finding the density of states of a system. Knowledge of the density of states of a system is equivalent to knowledge of its fundamental equation, from which all thermodynamic quantities can be obtained. Over the past several years molecular simulations have made considerable strides in their ability to determine the density of states of complex fluids and materials. In this review we discuss some of the more promising approaches proposed in the recent literature along with their advantages and limitations.
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Affiliation(s)
- Sadanand Singh
- Department of Chemical and Biological Engineering, University of Wisconsin, Madison, WI 53706, USA
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114
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Locating binding poses in protein-ligand systems using reconnaissance metadynamics. Proc Natl Acad Sci U S A 2012; 109:5170-5. [PMID: 22440749 DOI: 10.1073/pnas.1201940109] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A molecular dynamics-based protocol is proposed for finding and scoring protein-ligand binding poses. This protocol uses the recently developed reconnaissance metadynamics method, which employs a self-learning algorithm to construct a bias that pushes the system away from the kinetic traps where it would otherwise remain. The exploration of phase space with this algorithm is shown to be roughly six to eight times faster than unbiased molecular dynamics and is only limited by the time taken to diffuse about the surface of the protein. We apply this method to the well-studied trypsin-benzamidine system and show that we are able to refind all the poses obtained from a reference EADock blind docking calculation. These poses can be scored based on the length of time the system remains trapped in the pose. Alternatively, one can perform dimensionality reduction on the output trajectory and obtain a map of phase space that can be used in more expensive free-energy calculations.
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115
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Zheng L, Yang W. Practically Efficient and Robust Free Energy Calculations: Double-Integration Orthogonal Space Tempering. J Chem Theory Comput 2012; 8:810-23. [DOI: 10.1021/ct200726v] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Lianqing Zheng
- Institute of Molecular
Biophysics,
Florida State University, Tallahassee, Florida 32306, United States
| | - Wei Yang
- Institute of Molecular
Biophysics,
Florida State University, Tallahassee, Florida 32306, United States
- Department of Chemistry and
Biochemistry, Florida State University, Tallahassee, Florida 32306,
United States
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116
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Wereszczynski J, McCammon JA. Statistical mechanics and molecular dynamics in evaluating thermodynamic properties of biomolecular recognition. Q Rev Biophys 2012; 45:1-25. [PMID: 22082669 PMCID: PMC3291752 DOI: 10.1017/s0033583511000096] [Citation(s) in RCA: 125] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Molecular recognition plays a central role in biochemical processes. Although well studied, understanding the mechanisms of recognition is inherently difficult due to the range of potential interactions, the molecular rearrangement associated with binding, and the time and length scales involved. Computational methods have the potential for not only complementing experiments that have been performed, but also in guiding future ones through their predictive abilities. In this review, we discuss how molecular dynamics (MD) simulations may be used in advancing our understanding of the thermodynamics that drive biomolecular recognition. We begin with a brief review of the statistical mechanics that form a basis for these methods. This is followed by a description of some of the most commonly used methods: thermodynamic pathways employing alchemical transformations and potential of mean force calculations, along with end-point calculations for free energy differences, and harmonic and quasi-harmonic analysis for entropic calculations. Finally, a few of the fundamental findings that have resulted from these methods are discussed, such as the role of configurational entropy and solvent in intermolecular interactions, along with selected results of the model system T4 lysozyme to illustrate potential and current limitations of these methods.
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Affiliation(s)
- Jeff Wereszczynski
- Department of Chemistry and Biochemistry, University of California, San Diego, CA, USA.
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117
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Lapelosa M, Gallicchio E, Levy RM. Conformational Transitions and Convergence of Absolute Binding Free Energy Calculations. J Chem Theory Comput 2011; 8:47-60. [PMID: 22368530 DOI: 10.1021/ct200684b] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The Binding Energy Distribution Analysis Method (BEDAM) is employed to compute the standard binding free energies of a series of ligands to a FK506 binding protein (FKBP12) with implicit solvation. Binding free energy estimates are in reasonably good agreement with experimental affinities. The conformations of the complexes identified by the simulations are in good agreement with crystallographic data, which was not used to restrain ligand orientations. The BEDAM method is based on λ -hopping Hamiltonian parallel Replica Exchange (HREM) molecular dynamics conformational sampling, the OPLS-AA/AGBNP2 effective potential, and multi-state free energy estimators (MBAR). Achieving converged and accurate results depends on all of these elements of the calculation. Convergence of the binding free energy is tied to the level of convergence of binding energy distributions at critical intermediate states where bound and unbound states are at equilibrium, and where the rate of binding/unbinding conformational transitions is maximal. This finding mirrors similar observations in the context of order/disorder transitions as for example in protein folding. Insights concerning the physical mechanism of ligand binding and unbinding are obtained. Convergence for the largest FK506 ligand is achieved only after imposing strict conformational restraints, which however require accurate prior structural knowledge of the structure of the complex. The analytical AGBNP2 model is found to underestimate the magnitude of the hydrophobic driving force towards binding in these systems characterized by loosely packed protein-ligand binding interfaces. Rescoring of the binding energies using a numerical surface area model corrects this deficiency. This study illustrates the complex interplay between energy models, exploration of conformational space, and free energy estimators needed to obtain robust estimates from binding free energy calculations.
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Affiliation(s)
- Mauro Lapelosa
- 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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118
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Goh GB, Knight JL, Brooks CL. Constant pH Molecular Dynamics Simulations of Nucleic Acids in Explicit Solvent. J Chem Theory Comput 2011; 8:36-46. [PMID: 22337595 DOI: 10.1021/ct2006314] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The nucleosides of adenine and cytosine have pKa values of 3.50 and 4.08, respectively, and are assumed to be unprotonated under physiological conditions. However, evidence from recent NMR and X-Ray crystallography studies has revealed the prevalence of protonated adenine and cytosine in RNA macromolecules. Such nucleotides with elevated pKa values may play a role in stabilizing RNA structure and participate in the mechanism of ribozyme catalysis. With the work presented here, we establish the framework and demonstrate the first constant pH MD simulations (CPHMD) for nucleic acids in explicit solvent in which the protonation state is coupled to the dynamical evolution of the RNA system via λ-dynamics. We adopt the new functional form λ(Nexp) for λ that was recently developed for Multi-Site λ-Dynamics (MSλD) and demonstrate good sampling characteristics in which rapid and frequent transitions between the protonated and unprotonated states at pH = pKa are achieved. Our calculated pKa values of simple nucleotides are in a good agreement with experimentally measured values, with a mean absolute error of 0.24 pKa units. This work demonstrates that CPHMD can be used as a powerful tool to investigate pH-dependent biological properties of RNA macromolecules.
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Affiliation(s)
- Garrett B Goh
- Department of Chemistry, University of Michigan, 930 N. University, Ann Arbor, Michigan 48109, United States
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119
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Knight JL, Brooks CL. Applying efficient implicit nongeometric constraints in alchemical free energy simulations. J Comput Chem 2011; 32:3423-32. [PMID: 21919014 PMCID: PMC3196384 DOI: 10.1002/jcc.21921] [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: 06/23/2011] [Accepted: 07/29/2011] [Indexed: 11/08/2022]
Abstract
Several strategies have been developed for satisfying bond lengths, angle, and other geometric constraints in molecular dynamics simulations. Advanced variations of alchemical free energy perturbation simulations, however, also require nongeometric constraints. In our recently developed multisite λ-dynamics simulation method, the conventional λ parameters that are associated with the progress variables in alchemical transformations are treated as dynamic variables and are constrained such that: 0 ≤ λ(i) ≤ 1 and Σ(i = 1)(N) λ(i) = 1. Here, we present four functional forms of λ that implicitly satisfy these nongeometric constraints, whose values and forces are facile to compute and that yield stable simulations using a 2 fs integration timestep. Using model systems, we present the sampling characteristics of these functional forms and demonstrate the enhanced sampling profiles and improved convergence rates that are achieved by the functional form: λ(i) = e(c sinθ(i))/Σ(j = 1)(N) e(c sinθ(j)) that oscillates between λ(i) = 0 and λ(i) = 1 and has relatively steep transitions between these endpoints.
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Affiliation(s)
- Jennifer L. Knight
- Department of Chemistry & Department of Biophysics. University of Michigan. 930 N. University Ave. Ann Arbor, MI 48109 USA
| | - Charles L. Brooks
- Department of Chemistry & Department of Biophysics. University of Michigan. 930 N. University Ave. Ann Arbor, MI 48109 USA
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120
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Arthur EJ, Yesselman JD, Brooks CL. Predicting extreme pKa shifts in staphylococcal nuclease mutants with constant pH molecular dynamics. Proteins 2011; 79:3276-86. [PMID: 22002886 PMCID: PMC3213318 DOI: 10.1002/prot.23195] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2011] [Revised: 09/09/2011] [Accepted: 09/09/2011] [Indexed: 11/11/2022]
Abstract
Accurate computational methods of determining protein and nucleic acid pK(a) values are vital to understanding pH-dependent processes in biological systems. In this article, we use the recently developed method constant pH molecular dynamics (CPHMD) to explore the calculation of highly perturbed pK(a) values in variants of staphylococcal nuclease (SNase). Simulations were performed using the replica exchange (REX) protocol for improved conformational sampling with eight temperature windows, and yielded converged proton populations in a total sampling time of 4 ns. Our REX-CPHMD simulations resulted in calculated pK(a) values with an average unsigned error (AUE) of 0.75 pK units for the acidic residues in Δ + PHS, a hyperstable variant of SNase. For highly pK(a)-perturbed SNase mutants with known crystal structures, our calculations yielded an AUE of 1.5 pK units and for those mutants based on modeled structures an AUE of 1.4 pK units was found. Although a systematic underestimate of pK shifts was observed in most of the cases for the highly perturbed pK mutants, correlations between conformational rearrangement and plasticity associated with the mutation and error in pK(a) prediction was not evident in the data. This study further extends the scope of electrostatic environments explored using the REX-CPHMD methodology and suggests that it is a reliable tool for rapidly characterizing ionizable amino acids within proteins even when modeled structures are employed.
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Affiliation(s)
- Evan J. Arthur
- Department of Chemistry, University of Michigan, 930 N. University Ave, Ann Arbor, MI 48109-1055
| | - Joseph D. Yesselman
- Biophysics Program, University of Michigan, 930 N. University Ave, Ann Arbor, MI 48109-1055
| | - Charles L. Brooks
- Department of Chemistry, University of Michigan, 930 N. University Ave, Ann Arbor, MI 48109-1055
- Biophysics Program, University of Michigan, 930 N. University Ave, Ann Arbor, MI 48109-1055
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121
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Lin Z, Liu H, Riniker S, van Gunsteren WF. On the Use of Enveloping Distribution Sampling (EDS) to Compute Free Enthalpy Differences between Different Conformational States of Molecules: Application to 310-, α-, and π-Helices. J Chem Theory Comput 2011; 7:3884-97. [DOI: 10.1021/ct200623b] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Zhixiong Lin
- Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH, 8093 Zürich, Switzerland
- School of Life Sciences and Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China (USTC), Hefei, Anhui 230027, People’s Republic of China
| | - Haiyan Liu
- School of Life Sciences and Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China (USTC), Hefei, Anhui 230027, People’s Republic of China
| | - Sereina Riniker
- Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH, 8093 Zürich, Switzerland
| | - Wilfred F. van Gunsteren
- Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH, 8093 Zürich, Switzerland
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122
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Wu P, Hu X, Yang W. λ-Meta Dynamics Approach To Compute Absolute Solvation Free Energy. J Phys Chem Lett 2011; 2:2099-2103. [PMID: 23678385 PMCID: PMC3652470 DOI: 10.1021/jz200808x] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We present a new approach to combine λ dynamics with meta-dynamics (named λ-meta dynamics) to compute free energy surface with respect to λ. Particularly, the λ-meta dynamics method extends meta-dynamics to a single virtual variable λ, i.e., the coupling parameter between solute and solvent, to compute absolute solvation free energy as an exemplary application. We demonstrate that λ-meta dynamics simulations can recover the accurate potential of mean force surface with respect to λ compared to the benchmark results from traditional λ-dynamics with umbrella sampling. The solvation free energy results for five small organic molecules from λ-meta dynamics simulations using the same filling scheme show that the statistical errors are within ±0.5 kcal/mol. The new λ-meta dynamics method is general and other variables such as order parameters to describe conformational changes can be easily combined with λ-meta dynamics. This should allow for efficient samplings on high-dimension free energy landscapes.
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123
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Knight JL, Brooks CL. Multi-Site λ-dynamics for simulated Structure-Activity Relationship studies. J Chem Theory Comput 2011; 7:2728-2739. [PMID: 22125476 DOI: 10.1021/ct200444f] [Citation(s) in RCA: 76] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Multi-Site λ-dynamics (MSλD) is a new free energy simulation method that is based on λ-dynamics. It has been developed to enable multiple substituents at multiple sites on a common ligand core to be modeled simultaneously and their free energies assessed. The efficacy of MSλD for estimating relative hydration free energies and relative binding affinties is demonstrated using three test systems. Model compounds representing multiple identical benzene, dihydroxybenzene and dimethoxybenzene molecules show total combined MSλD trajectory lengths of ~1.5 ns are sufficient to reliably achieve relative hydration free energy estimates within 0.2 kcal/mol and are less sensitive to the number of trajectories that are used to generate these estimates for hybrid ligands that contain up to ten substituents modeled at a single site or five substituents modeled at each of two sites. Relative hydration free energies among six benzene derivatives calculated from MSλD simulations are in very good agreement with those from alchemical free energy simulations (with average unsigned differences of 0.23 kcal/mol and R(2)=0.991) and experiment (with average unsigned errors of 1.8 kcal/mol and R(2)=0.959). Estimates of the relative binding affinities among 14 inhibitors of HIV-1 reverse transcriptase obtained from MSλD simulations are in reasonable agreement with those from traditional free energy simulations and experiment (average unsigned errors of 0.9 kcal/mol and R(2)=0.402). For the same level of accuracy and precision MSλD simulations are achieved ~20-50 times faster than traditional free energy simulations and thus with reliable force field parameters can be used effectively to screen tens to hundreds of compounds in structure-based drug design applications.
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Affiliation(s)
- Jennifer L Knight
- Department of Chemistry & Department of Biophysics. University of Michigan. 930 N. University Ave. Ann Arbor, MI 48109 USA
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124
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Umbrella sampling. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2011. [DOI: 10.1002/wcms.66] [Citation(s) in RCA: 606] [Impact Index Per Article: 46.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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125
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Boresch S, Bruckner S. Avoiding the van der Waals endpoint problem using serial atomic insertion. J Comput Chem 2011; 32:2449-58. [PMID: 21607991 DOI: 10.1002/jcc.21829] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2010] [Revised: 03/08/2011] [Accepted: 04/02/2011] [Indexed: 11/07/2022]
Abstract
In the past analyses of the so-called van der Waals end point problem focused on thermodynamic integration. Here we investigate which of the recommendations, such as the need for soft-core potentials, are still valid when Bennett's acceptance ratio method is used. We show that in combination with Bennett's acceptance ratio method intermediate states characterized by the coupling parameter λ can be replaced by intermediate states in which Lennard-Jones interactions are turned on or off on an "atom by atom" basis. By doing so, there is no necessity to use soft-core potentials. In fact, one can compute free energy differences without dedicated code, making it possible to use any molecular dynamics program to compute alchemical free energy differences. Such an approach, which we illustrate by several examples, makes it possible to exploit the tremendous computational power of the graphics processing unit.
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Affiliation(s)
- Stefan Boresch
- Department of Computational Biological Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.
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126
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Shim J, MacKerell AD. Computational ligand-based rational design: Role of conformational sampling and force fields in model development. MEDCHEMCOMM 2011; 2:356-370. [PMID: 21716805 PMCID: PMC3123535 DOI: 10.1039/c1md00044f] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.
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127
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De Jong DH, Schäfer LV, De Vries AH, Marrink SJ, Berendsen HJC, Grubmüller H. Determining equilibrium constants for dimerization reactions from molecular dynamics simulations. J Comput Chem 2011; 32:1919-28. [DOI: 10.1002/jcc.21776] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2010] [Accepted: 01/26/2011] [Indexed: 11/07/2022]
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128
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Kramer C, Gedeck P. Global Free Energy Scoring Functions Based on Distance-Dependent Atom-Type Pair Descriptors. J Chem Inf Model 2011; 51:707-20. [DOI: 10.1021/ci100473d] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Christian Kramer
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1, Novartis Campus, CH-4056 Basel, Switzerland
| | - Peter Gedeck
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1, Novartis Campus, CH-4056 Basel, Switzerland
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129
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Gallicchio E, Levy RM. Advances in all atom sampling methods for modeling protein-ligand binding affinities. Curr Opin Struct Biol 2011; 21:161-6. [PMID: 21339062 DOI: 10.1016/j.sbi.2011.01.010] [Citation(s) in RCA: 88] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2010] [Revised: 01/25/2011] [Accepted: 01/27/2011] [Indexed: 01/23/2023]
Abstract
Conformational dynamics plays a fundamental role in the regulation of molecular recognition processes. Conformational heterogeneity and entropy variations upon binding, although not always evident from the analysis of structural data, can substantially affect affinity and specificity. Computer modeling is able to provide some of the most direct insights into these aspects of molecular recognition. We review recent physics-based computational studies that employ advanced conformational sampling algorithms and effective potentials to model the three main classes of degrees of freedom relevant to the binding process: ligand positioning relative to the receptor, ligand and receptor internal reorganization, and hydration. Collectively these studies show that all of these elements are important for proper modeling of protein-ligand interactions.
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Affiliation(s)
- Emilio Gallicchio
- BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
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130
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Paciello G, Acquaviva A, Ficarra E, Deriu MA, Macii E. A molecular dynamics study of a miRNA:mRNA interaction. J Mol Model 2011; 17:2895-906. [DOI: 10.1007/s00894-011-0991-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2010] [Accepted: 01/24/2011] [Indexed: 12/25/2022]
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131
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Gallicchio E, Levy RM. Recent theoretical and computational advances for modeling protein-ligand binding affinities. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2011; 85:27-80. [PMID: 21920321 DOI: 10.1016/b978-0-12-386485-7.00002-8] [Citation(s) in RCA: 93] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We review recent theoretical and algorithmic advances for the modeling of protein ligand binding free energies. We first describe a statistical mechanics theory of noncovalent association, with particular focus on deriving the fundamental formulas on which computational methods are based. The second part reviews the main computational models and algorithms in current use or development, pointing out the relations with each other and with the theory developed in the first part. Particular emphasis is given to the modeling of conformational reorganization and entropic effect. The methods reviewed are free energy perturbation, double decoupling, the Binding Energy Distribution Analysis Method, the potential of mean force method, mining minima and MM/PBSA. These models have different features and limitations, and their ranges of applicability vary correspondingly. Yet their origins can all be traced back to a single fundamental theory.
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Affiliation(s)
- Emilio Gallicchio
- Department of Chemistry and Chemical Biology, BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, New Jersey, USA
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132
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133
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Zhou Y, Duan Y, Yang Y, Faraggi E, Lei H. Trends in template/fragment-free protein structure prediction. Theor Chem Acc 2011; 128:3-16. [PMID: 21423322 PMCID: PMC3030773 DOI: 10.1007/s00214-010-0799-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2010] [Accepted: 08/15/2010] [Indexed: 12/13/2022]
Abstract
Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward.
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Affiliation(s)
- Yaoqi Zhou
- School of Informatics, Indiana Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indiana University Purdue University, 719 Indiana Ave #319, Walker Plaza Building, Indianapolis, IN 46202 USA
| | - Yong Duan
- UC Davis Genome Center and Department of Applied Science, University of California, One Shields Avenue, Davis, CA USA
- College of Physics, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074 Wuhan, China
| | - Yuedong Yang
- School of Informatics, Indiana Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indiana University Purdue University, 719 Indiana Ave #319, Walker Plaza Building, Indianapolis, IN 46202 USA
| | - Eshel Faraggi
- School of Informatics, Indiana Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indiana University Purdue University, 719 Indiana Ave #319, Walker Plaza Building, Indianapolis, IN 46202 USA
| | - Hongxing Lei
- UC Davis Genome Center and Department of Applied Science, University of California, One Shields Avenue, Davis, CA USA
- Beijing Institute of Genomics, Chinese Academy of Sciences, 100029 Beijing, China
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134
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Applications of computational science for understanding enzymatic deconstruction of cellulose. Curr Opin Biotechnol 2010; 22:231-8. [PMID: 21168322 DOI: 10.1016/j.copbio.2010.11.005] [Citation(s) in RCA: 80] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2010] [Accepted: 11/07/2010] [Indexed: 10/18/2022]
Abstract
Understanding the molecular-level mechanisms that enzymes employ to deconstruct plant cell walls is a fundamental scientific challenge with significant ramifications for renewable fuel production from biomass. In nature, bacteria and fungi use enzyme cocktails that include processive and non-processive cellulases and hemicellulases to convert cellulose and hemicellulose to soluble sugars. Catalyzed by an accelerated biofuels R&D portfolio, there is now a wealth of new structural and experimental insights related to cellulases and the structure of plant cell walls. From this background, computational approaches commonly used in other fields are now poised to offer insights complementary to experiments designed to probe mechanisms of plant cell wall deconstruction. Here we outline the current status of computational approaches for a collection of critical problems in cellulose deconstruction. We discuss path sampling methods to measure rates of elementary steps of enzyme action, coarse-grained modeling for understanding macromolecular, cellulosomal complexes, methods to screen for enzyme improvements, and studies of cellulose at the molecular level. Overall, simulation is a complementary tool to understand carbohydrate-active enzymes and plant cell walls, which will enable industrial processes for the production of advanced, renewable fuels.
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135
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Lettieri S, Mamonov AB, Zuckerman DM. Extending fragment-based free energy calculations with library Monte Carlo simulation: annealing in interaction space. J Comput Chem 2010; 32:1135-43. [PMID: 21387340 DOI: 10.1002/jcc.21695] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2010] [Accepted: 09/10/2010] [Indexed: 11/09/2022]
Abstract
Pre-calculated libraries of molecular fragment configurations have previously been used as a basis for both equilibrium sampling (via library-based Monte Carlo) and for obtaining absolute free energies using a polymer-growth formalism. Here, we combine the two approaches to extend the size of systems for which free energies can be calculated. We study a series of all-atom poly-alanine systems in a simple dielectric solvent and find that precise free energies can be obtained rapidly. For instance, for 12 residues, less than an hour of single-processor time is required. The combined approach is formally equivalent to the annealed importance sampling algorithm; instead of annealing by decreasing temperature, however, interactions among fragments are gradually added as the molecule is grown. We discuss implications for future binding affinity calculations in which a ligand is grown into a binding site.
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Affiliation(s)
- Steven Lettieri
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
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136
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Seeliger D, de Groot BL. Protein thermostability calculations using alchemical free energy simulations. Biophys J 2010; 98:2309-16. [PMID: 20483340 DOI: 10.1016/j.bpj.2010.01.051] [Citation(s) in RCA: 147] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2009] [Revised: 01/15/2010] [Accepted: 01/21/2010] [Indexed: 12/29/2022] Open
Abstract
Thermal stability of proteins is crucial for both biotechnological and therapeutic applications. Rational protein engineering therefore frequently aims at increasing thermal stability by introducing stabilizing mutations. The accurate prediction of the thermodynamic consequences caused by mutations, however, is highly challenging as thermal stability changes are caused by alterations in the free energy of folding. Growing computational power, however, increasingly allows us to use alchemical free energy simulations, such as free energy perturbation or thermodynamic integration, to calculate free energy differences with relatively high accuracy. In this article, we present an automated protocol for setting up alchemical free energy calculations for mutations of naturally occurring amino acids (except for proline) that allows an unprecedented, automated screening of large mutant libraries. To validate the developed protocol, we calculated thermodynamic stability differences for 109 mutations in the microbial Ribonuclease Barnase. The obtained quantitative agreement with experimental data illustrates the potential of the approach in protein engineering and design.
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Affiliation(s)
- Daniel Seeliger
- Computational Biomolecular Dynamics Group, Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany
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137
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Ratkova EL, Chuev GN, Sergiievskyi VP, Fedorov MV. An Accurate Prediction of Hydration Free Energies by Combination of Molecular Integral Equations Theory with Structural Descriptors. J Phys Chem B 2010; 114:12068-79. [DOI: 10.1021/jp103955r] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Ekaterina L. Ratkova
- The Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, Leipzig, 04103, Germany, and Institute of Theoretical and Experimental Biophysics, Russian Academy of Science, Pushchino, Moscow Region, 142290, Russia
| | - Gennady N. Chuev
- The Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, Leipzig, 04103, Germany, and Institute of Theoretical and Experimental Biophysics, Russian Academy of Science, Pushchino, Moscow Region, 142290, Russia
| | - Volodymyr P. Sergiievskyi
- The Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, Leipzig, 04103, Germany, and Institute of Theoretical and Experimental Biophysics, Russian Academy of Science, Pushchino, Moscow Region, 142290, Russia
| | - Maxim V. Fedorov
- The Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, Leipzig, 04103, Germany, and Institute of Theoretical and Experimental Biophysics, Russian Academy of Science, Pushchino, Moscow Region, 142290, Russia
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138
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Palmer DS, Sergiievskyi VP, Jensen F, Fedorov MV. Accurate calculations of the hydration free energies of druglike molecules using the reference interaction site model. J Chem Phys 2010; 133:044104. [DOI: 10.1063/1.3458798] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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139
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Michel J, Essex JW. Prediction of protein-ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations. J Comput Aided Mol Des 2010; 24:639-58. [PMID: 20509041 DOI: 10.1007/s10822-010-9363-3] [Citation(s) in RCA: 187] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2010] [Accepted: 05/03/2010] [Indexed: 11/26/2022]
Abstract
Many limitations of current computer-aided drug design arise from the difficulty of reliably predicting the binding affinity of a small molecule to a biological target. There is thus a strong interest in novel computational methodologies that claim predictions of greater accuracy than current scoring functions, and at a throughput compatible with the rapid pace of drug discovery in the pharmaceutical industry. Notably, computational methodologies firmly rooted in statistical thermodynamics have received particular attention in recent years. Yet free energy calculations can be daunting to learn for a novice user because of numerous technical issues and various approaches advocated by experts in the field. The purpose of this article is to provide an overview of the current capabilities of free energy calculations and to discuss the applicability of this technology to drug discovery.
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Affiliation(s)
- Julien Michel
- Institute of Structural and Molecular Biology, The University of Edinburgh, Edinburgh, UK.
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140
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Computing Free-Energy Profiles Using Multidimensional Potentials of Mean Force and Polynomial Quadrature Methods. ACTA ACUST UNITED AC 2010. [DOI: 10.1016/s1574-1400(10)06003-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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141
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QM/MM Alchemical Free Energy Simulations: Challenges and Recent Developments. ANNUAL REPORTS IN COMPUTATIONAL CHEMISTRY 2010. [DOI: 10.1016/s1574-1400(10)06004-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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142
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Merz KM. Limits of Free Energy Computation for Protein-Ligand Interactions. J Chem Theory Comput 2010; 6:1018-1027. [PMID: 20467461 PMCID: PMC2866028 DOI: 10.1021/ct100102q] [Citation(s) in RCA: 72] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A detailed error analysis is presented for the computation of protein-ligand interaction energies. In particular, we show that it is probable that even highly accurate computed binding free energies have errors that represent a large percentage of the target free energies of binding. This is due to the observation that the error for computed energies quasi-linearly increases with the increasing number of interactions present in a protein-ligand complex. This principle is expected to hold true for any system that involves an ever increasing number of inter or intra-molecular interactions (e.g. ab initio protein folding). We introduce the concept of best-case scenario errors (BCS(errors)) that can be routinely applied to docking and scoring exercises and used to provide errors bars for the computed binding free energies. These BCS(errors) form a basis by which one can evaluate the outcome of a docking and scoring exercise. Moreover, the resultant error analysis enables the formation of an hypothesis that defines the best direction to proceed in order to improve scoring functions used in molecular docking studies.
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Affiliation(s)
- Kenneth M Merz
- Colonel Allan R. and Margaret G. Crow Term Professor, Department of Chemistry, Quantum Theory Project, 2328 New Physics Building, PO Box 118435, University of Florida, Gainesville, Florida 32611-8435,
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