The Experts below are selected from a list of 4284 Experts worldwide ranked by ideXlab platform
Roland L Dunbrack - One of the best experts on this subject based on the ideXlab platform.
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a smoothed backbone dependent Rotamer library for proteins derived from adaptive kernel density estimates and regressions
2011Co-Authors: Maxim V Shapovalov, Roland L DunbrackAbstract:Rotamer libraries are used in protein structure determination, prediction, and design. The backbone-dependent Rotamer library consists of Rotamer frequencies, mean dihedral angles, and variances as a function of the backbone dihedral angles. Structure prediction and design methods that employ backbone flexibility would strongly benefit from smoothly varying probabilities and angles. A new version of the backbone-dependent Rotamer library has been developed using adaptive kernel density estimates for the Rotamer frequencies and adaptive kernel regression for the mean dihedral angles and variances. This formulation allows for evaluation of the Rotamer probabilities, mean angles, and variances as a smooth and continuous function of phi and psi. Continuous probability density estimates for the nonRotameric degrees of freedom of amides, carboxylates, and aromatic side chains have been modeled as a function of the backbone dihedrals and Rotamers of the remaining degrees of freedom. New backbone-dependent Rotamer libraries at varying levels of smoothing are available from http://dunbrack.fccc.edu.
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development of a Rotamer library for use in beta peptide foldamer computational design
2010Co-Authors: Scott J Shandler, Roland L Dunbrack, Maxim V Shapovalov, William F DegradoAbstract:Foldamers present a particularly difficult challenge for accurate computational design compared to the case for conventional peptide and protein design due to the lack of a large body of structural data to allow parametrization of Rotamer libraries and energies. We therefore explored the use of molecular mechanics for constructing Rotamer libraries for non-natural foldamer backbones. We first evaluated the accuracy of molecular mechanics (MM) for the prediction of Rotamer probability distributions in the crystal structures of proteins is explored. The van der Waals radius, dielectric constant and effective Boltzmann temperature were systematically varied to maximize agreement with experimental data. Boltzmann-weighted probabilities from these molecular mechanics energies compare well with database-derived probabilities for both an idealized alpha-helix (R = 0.95) as well as beta-strand conformations (R = 0.92). Based on these parameters, de novo Rotamer probabilities for secondary structures of peptides built from beta-amino acids were determined. To limit computational complexity, it is useful to establish a residue-specific criterion for excluding rare, high-energy Rotamers from the library. This is accomplished by including only those Rotamers with probability greater than a given threshold (e.g., 10%) of the random value, defined as 1/n where n is the number of potential Rotamers for each residue type.
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Rotamer libraries in the 21st century
2002Co-Authors: Roland L DunbrackAbstract:Rotamer libraries are widely used in protein structure prediction, protein design, and structure refinement. As the size of the structure data base has increased rapidly in recent years, it has become possible to derive well-refined Rotamer libraries using strict criteria for data inclusion and for studying dependence of Rotamer populations and dihedral angles on local structural features.
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bayesian statistical analysis of protein side chain Rotamer preferences
1997Co-Authors: Roland L Dunbrack, Fred E CohenAbstract:We present a Bayesian statistical analysis of the conformations of side chains in proteins from the Protein Data Bank. This is an extension of the backbone-dependent Rotamer library, and includes Rotamer populations and average chi angles for a full range of phi, psi values. The Bayesian analysis used here provides a rigorous statistical method for taking account of varying amounts of data. Bayesian statistics requires the assumption of a prior distribution for parameters over their range of possible values. This prior distribution can be derived from previous data or from pooling some of the present data. The prior distribution is combined with the data to form the posterior distribution, which is a compromise between the prior distribution and the data. For the chi 2, chi 3, and chi 4 Rotamer prior distributions, we assume that the probability of each Rotamer type is dependent only on the previous chi Rotamer in the chain. For the backbone-dependence of the chi 1 Rotamers, we derive prior distributions from the product of the phi-dependent and psi-dependent probabilities. Molecular mechanics calculations with the CHARMM22 potential show a strong similarity with the experimental distributions, indicating that proteins attain their lowest energy Rotamers with respect to local backbone-side-chain interactions. The new library is suitable for use in homology modeling, protein folding simulations, and the refinement of X-ray and NMR structures.
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prediction of protein side chain Rotamers from a backbone dependent Rotamer library a new homology modeling tool
1997Co-Authors: Michael J Bower, Fred E Cohen, Roland L DunbrackAbstract:Modeling by homology is the most accurate computational method for translating an amino acid sequence into a protein structure. Homology modeling can be divided into two sub-problems, placing the polypeptide backbone and adding side-chains. We present a method for rapidly predicting the conformations of protein side-chains, starting from main-chain coordinates alone. The method involves using fewer than ten Rotamers per residue from a backbone-dependent Rotamer library and a search to remove steric conflicts. The method is initially tested on 299 high resolution crystal structures by rebuilding side-chains onto the experimentally determined backbone structures. A total of 77% of χ1 and 66% of χ1+2 dihedral angles are predicted within 40° of their crystal structure values. We then tested the method on the entire database of known structures in the Protein Data Bank. The predictive accuracy of the algorithm was strongly correlated with the resolution of the structures. In an effort to simulate a realistic homology modeling problem, 9424 homology models were created using three different modeling strategies. For prediction purposes, pairs of structures were identified which shared between 30% and 90% sequence identity. One strategy results in 82% of χ1 and 72% χ1+2 dihedral angles predicted within 40 degrees of the target crystal structure values, suggesting that movements of the backbone associated with this degree of sequence identity are not large enough to disrupt the predictive ability of our method for non-native backbones. These results compared favorably with existing methods over a comprehensive data set.
Richard Bonneau - One of the best experts on this subject based on the ideXlab platform.
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Rotamer libraries for the high resolution design of β amino acid foldamers
2017Co-Authors: Andrew M Watkins, Douglas P Renfrew, Timothy W Craven, Paramjit S. Arora, Richard BonneauAbstract:Summary β-Amino acids offer attractive opportunities to develop biologically active peptidomimetics, either employed alone or in conjunction with natural α-amino acids. Owing to their potential for unique conformational preferences that deviate considerably from α-peptide geometries, β-amino acids greatly expand the possible chemistries and physical properties available to polyamide foldamers. Complete in silico support for designing new molecules incorporating non-natural amino acids typically requires representing their side-chain conformations as sets of discrete Rotamers for model refinement and sequence optimization. Such Rotamer libraries are key components of several state-of-the-art design frameworks. Here we report the development, incorporation in to the Rosetta macromolecular modeling suite, and validation of Rotamer libraries for β 3 -amino acids.
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Rotamer libraries for the high resolution design of beta amino acid foldamers
2016Co-Authors: Andrew W Watkins, Douglas P Renfrew, Timothy W Craven, Paramjit S. Arora, Richard BonneauAbstract:β-amino acids offer attractive opportunities to develop biologically active peptidomimetics, either employed alone or in conjunction with natural α-amino acids. Owing to their potential for unique conformational preferences that deviate considerably from α-peptide geometries, β-amino acids greatly expand the possible chemistries and physical properties available to polyamide foldamers. Complete in silico support for designing new molecules incorporating nonnatural amino acids typically requires representing their side chain conformations as sets of discrete Rotamers for model refinement and sequence optimization. Such Rotamer libraries are key components of several state of the art design frameworks. Here we report the development, incorporation in to the Rosetta macromolecular modeling suite, and validation of Rotamer libraries for β3-amino acids.
Valerie Daggett - One of the best experts on this subject based on the ideXlab platform.
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new dynamic Rotamer libraries data driven analysis of side chain conformational propensities
2016Co-Authors: Clarelouise Towse, Steven J Rysavy, Ivan Vulovic, Valerie DaggettAbstract:Most Rotamer libraries are generated from subsets of the PDB and do not fully represent the conformational scope of protein side chains. Previous attempts to rectify this sparse coverage of conformational space have involved application of weighting and smoothing functions. We resolve these limitations by using physics-based molecular dynamics simulations to determine more accurate frequencies of Rotameric states. This work forms part of our Dynameomics initiative and uses a set of 807 proteins selected to represent 97% of known autonomous protein folds, thereby eliminating the bias toward common topologies found within the PDB. Our Dynameomics derived Rotamer libraries encompass 4.8 × 10(9) Rotamers, sampled from at least 51,000 occurrences of each of 93,642 residues. Here, we provide a backbone-dependent Rotamer library, based on secondary structure ϕ/ψ regions, and an update to our 2011 backbone-independent library that addresses the doubling of our dataset since its original publication.
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the dynameomics Rotamer library amino acid side chain conformations and dynamics from comprehensive molecular dynamics simulations in water
2011Co-Authors: Alexander D Scouras, Valerie DaggettAbstract:We have recently completed systematic molecular dynamics simulations of 807 different proteins representing 95% of the known autonomous protein folds in an effort we refer to as Dynameomics. Here we focus on the analysis of side chain conformations and dynamics to create a dynamic Rotamer library. Overall this library is derived from 31,000 occurrences of each of 86,217 different residues, or 2.7 × 109 Rotamers. This dynamic library has 74% overlap of Rotamer distributions with Rotamer libraries derived from static high-resolution crystal structures. Seventy-five percent of the residues had an assignable primary conformation, and 68% of the residues had at least one significant alternate conformation. The average correlation time for switching between Rotamers ranged from 22 ps for Met to over 8 ns for Cys; this time decreased 20-fold on the surface of the protein and modestly for dihedral angles further from the main chain. Side chain S2 axis order parameters were calculated and they correlated well with those derived from NMR relaxation experiments (R = 0.9). Relationships relating the S2 axis order parameters to Rotamer occupancy were derived. Overall the Dynameomics Rotamer library offers a comprehensive depiction of side chain Rotamer preferences and dynamics in solution, and more realistic distributions for dynamic proteins in solution at ambient temperature than libraries derived from crystal structures, in particular charged surface residues are better represented. Details of the Rotamer library are presented here and the library itself can be downloaded at http://www.dynameomics.org.
Zbynek Heger - One of the best experts on this subject based on the ideXlab platform.
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Rotamer dynamics analysis of Rotamers in molecular dynamics simulations of proteins
2019Co-Authors: Yazan Haddad, Vojtech Adam, Zbynek HegerAbstract:Abstract Given by χ torsional angles, Rotamers describe the side-chain conformations of amino acid residues in a protein based on the rotational isomers (hence the word Rotamer). Constructed Rotamer libraries, based on either protein crystal structures or dynamics studies, are the tools for classifying Rotamers (torsional angles) in a way that reflect their frequency in nature. Rotamer libraries are routinely used in structure modeling and evaluation. In this perspective article, we would like to encourage researchers to apply Rotamer analyses beyond their traditional use. Molecular dynamics (MD) of proteins highlight the in silico behavior of molecules in solution and thus can identify favorable side-chain conformations. In this article, we used simple computational tools to study Rotamer dynamics (RD) in MD simulations. First, we isolated each frame in the MD trajectories in separate Protein Data Bank files via the cpptraj module in AMBER. Then, we extracted torsional angles via the Bio3D module in R language. The classification of torsional angles was also done in R according to the penultimate Rotamer library. RD analysis is useful for various applications such as protein folding, study of Rotamer-Rotamer relationship in protein-protein interaction, real-time correlation between secondary structures and Rotamers, study of flexibility of side chains in binding site for molecular docking preparations, use of RD as guide in functional analysis and study of structural changes caused by mutations, providing parameters for improving coarse-grained MD accuracy and speed, and many others. Major challenges facing RD to emerge as a new scientific field involve the validation of results via easy, inexpensive wet-lab methods. This realm is yet to be explored.
Bruce R Donald - One of the best experts on this subject based on the ideXlab platform.
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the minimized dead end elimination criterion and its application to protein redesign in a hybrid scoring and search algorithm for computing partition functions over molecular ensembles
2008Co-Authors: Ivelin S Georgiev, Ryan H Lilien, Bruce R DonaldAbstract:One of the main challenges for protein redesign is the efficient evaluation of a combinatorial number of candidate structures. The modeling of protein flexibility, typically by using a Rotamer library of commonly-observed low-energy side-chain conformations, further increases the complexity of the redesign problem. A dominant algorithm for protein redesign is Dead-End Elimination (DEE), which prunes the majority of candidate conformations by eliminating rigid Rotamers that provably are not part of the Global Minimum Energy Conformation (GMEC). The identified GMEC consists of rigid Rotamers (i.e., Rotamers that have not been energy-minimized) and is thus referred to as the rigid-GMEC. As a post-processing step, the conformations that survive DEE may be energy-minimized. When energy minimization is performed after pruning with DEE, the combined protein design process becomes heuristic, and is no longer provably accurate: a conformation that is pruned using rigid-Rotamer energies may subsequently minimize to a lower energy than the rigid-GMEC. That is, the rigid-GMEC and the conformation with the lowest energy among all energy-minimized conformations (the minimized-GMEC) are likely to be different. While the traditional DEE algorithm succeeds in not pruning Rotamers that are part of the rigid-GMEC, it makes no guarantees regarding the identification of the minimized-GMEC. In this paper we derive a novel, provable, and efficient DEE-like algorithm, called minimized-DEE (MinDEE), that guarantees that Rotamers belonging to the minimized-GMEC will not be pruned, while still pruning a combinatorial number of conformations. We show that MinDEE is useful not only in identifying the minimized-GMEC, but also as a filter in an ensemble-based scoring and search algorithm for protein redesign that exploits energy-minimized conformations. We compare our results both to our previous computational predictions of protein designs and to biological activity assays of predicted protein mutants. Our provable and efficient minimized-DEE algorithm is applicable in protein redesign, protein-ligand binding prediction, and computer-aided drug design.
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a hausdorff based noe assignment algorithm using protein backbone determined from residual dipolar couplings and Rotamer patterns
2008Co-Authors: Jianyang Michael Zeng, Chittaranjan Tripathy, Pei Zhou, Bruce R DonaldAbstract:High-throughput structure determination based on solution Nuclear Magnetic Resonance (NMR) spectroscopy plays an important role in structural genomics. One of the main bottlenecks in NMR structure determination is the interpretation of NMR data to obtain a sufficient number of accurate distance restraints by assigning nuclear Overhauser effect (NOE) spectral peaks to pairs of protons. The difficulty in automated NOE assignment mainly lies in the ambiguities arising both from the resonance degeneracy of chemical shifts and from the uncertainty due to experimental errors in NOE peak positions. In this paper we present a novel NOE assignment algorithm, called HAusdorff-based NOE Assignment (HANA), that starts with a high-resolution protein backbone computed using only two residual dipolar couplings (RDCs) per residue37, 39, employs a Hausdorff-based pattern matching technique to deduce similarity between experimental and back-computed NOE spectra for each Rotamer from a statistically diverse library, and drives the selection of optimal position-specific Rotamers for filtering ambiguous NOE assignments. Our algorithm runs in time O(tn(3) +tn log t), where t is the maximum number of Rotamers per residue and n is the size of the protein. Application of our algorithm on biological NMR data for three proteins, namely, human ubiquitin, the zinc finger domain of the human DNA Y-polymerase Eta (pol η) and the human Set2-Rpb1 interacting domain (hSRI) demonstrates that our algorithm overcomes spectral noise to achieve more than 90% assignment accuracy. Additionally, the final structures calculated using our automated NOE assignments have backbone RMSD < 1.7 A and all-heavy-atom RMSD < 2.5 A from reference structures that were determined either by X-ray crystallography or traditional NMR approaches. These results show that our NOE assignment algorithm can be successfully applied to protein NMR spectra to obtain high-quality structures.
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a novel minimized dead end elimination criterion and its application to protein redesign in a hybrid scoring and search algorithm for computing partition functions over molecular ensembles
2006Co-Authors: Ivelin S Georgiev, Ryan H Lilien, Bruce R DonaldAbstract:Novel molecular function can be achieved by redesigning an enzyme's active site so that it will perform its chemical reaction on a novel substrate. One of the main challenges for protein redesign is the efficient evaluation of a combinatorial number of candidate structures. The modeling of protein flexibility, typically by using a Rotamer library of commonly-observed low-energy side-chain conformations, further increases the complexity of the redesign problem. A dominant algorithm for protein redesign is Dead-End Elimination (DEE), which prunes the majority of candidate conformations by eliminating rigid Rotamers that provably are not part of the Global Minimum Energy Conformation (GMEC). The identified GMEC consists of rigid Rotamers that have not been energy-minimized and is referred to as the rigid-GMEC. As a post-processing step, the conformations that survive DEE may be energy-minimized. When energy minimization is performed after pruning with DEE, the combined protein design process becomes heuristic, and is no longer provably accurate: That is, the rigid-GMEC and the conformation with the lowest energy among all energy-minimized conformations (the minimized-GMEC, or minGMEC) are likely to be different. While the traditional DEE algorithm succeeds in not pruning Rotamers that are part of the rigid-GMEC, it makes no guarantees regarding the identification of the minGMEC. In this paper we derive a novel, provable, and efficient DEE-like algorithm, called minimized-DEE (MinDEE), that guarantees that Rotamers belonging to the minGMEC will not be pruned, while still pruning a combinatorial number of conformations. We show that MinDEE is useful not only in identifying the minGMEC, but also as a filter in an ensemble-based scoring and search algorithm for protein redesign that exploits energy-minimized conformations. We compare our results both to our previous computational predictions of protein designs and to biological activity assays of predicted protein mutants. Our provable and efficient minimized-DEE algorithm is applicable in protein redesign, protein-ligand binding prediction, and computer-aided drug design.
