The Experts below are selected from a list of 8280 Experts worldwide ranked by ideXlab platform
William L Jorgensen - One of the best experts on this subject based on the ideXlab platform.
-
development and testing of the OPLS aa m force field for rna
Journal of Chemical Theory and Computation, 2019Co-Authors: Michael J Robertson, Julian Tiradorives, Yue Qian, Matthew C Robinson, William L JorgensenAbstract:Significant improvements have been made to the OPLS-AA force field for modeling RNA. New torsional potentials were optimized based on density functional theory (DFT) scans at the ωB97X-D/6-311++G(d,p) level for potential energy surfaces of the backbone α and γ dihedral angles. In combination with previously reported improvements for the sugar puckering and glycosidic torsion terms, the new force field was validated through diverse molecular dynamics simulations for RNAs in aqueous solution. Results for dinucleotides and tetranucleotides revealed both accurate reproduction of 3 J couplings from NMR and the avoidance of several unphysical states observed with other force fields. Simulations of larger systems with noncanonical motifs showed significant structural improvements over the previous OPLS-AA parameters. The new force field, OPLS-AA/M, is expected to perform competitively with other recent RNA force fields and to be compatible with OPLS-AA models for proteins and small molecules.
-
improved peptide and protein torsional energetics with the OPLS aa force field
Journal of Chemical Theory and Computation, 2015Co-Authors: Michael J Robertson, Julian Tiradorives, William L JorgensenAbstract:The development and validation of new peptide dihedral parameters are reported for the OPLS-AA force field. High accuracy quantum chemical methods were used to scan φ, ψ, χ1, and χ2 potential energy surfaces for blocked dipeptides. New Fourier coefficients for the dihedral angle terms of the OPLS-AA force field were fit to these surfaces, utilizing a Boltzmann-weighted error function and systematically examining the effects of weighting temperature. To prevent overfitting to the available data, a minimal number of new residue-specific and peptide-specific torsion terms were developed. Extensive experimental solution-phase and quantum chemical gas-phase benchmarks were used to assess the quality of the new parameters, named OPLS-AA/M, demonstrating significant improvement over previous OPLS-AA force fields. A Boltzmann weighting temperature of 2000 K was determined to be optimal for fitting the new Fourier coefficients for dihedral angle parameters. Conclusions are drawn from the results for best practices...
-
treatment of halogen bonding in the OPLS aa force field application to potent anti hiv agents
Journal of Chemical Theory and Computation, 2012Co-Authors: William L Jorgensen, Patric SchymanAbstract:The representation of chlorine, bromine, and iodine in aryl halides has been modified in the OPLS-AA and OPLS/CM1A force fields in order to incorporate halogen bonding. The enhanced force fields, OPLS-AAx and OPLS/CM1Ax, have been tested in calculations on gas-phase complexes of halobenzenes with Lewis bases, and for free energies of hydration, densities, and heats of vaporization of halobenzenes. Comparisons with results of MP2/aug-cc-pVDZ(-PP) calculations for the complexes are included. Implementation in the MCPRO software also allowed computation of relative free energies of binding for a series of HIV reverse transcriptase inhibitors via Monte Carlo/free-energy perturbation calculations. The results support the notion that the activity of an unusually potent chloro analog likely benefits from halogen bonding with the carbonyl group of a proline residue.
-
exploring solvent effects upon the menshutkin reaction using a polarizable force field
Journal of Physical Chemistry B, 2010Co-Authors: Orlando Acevedo, William L JorgensenAbstract:The energetics of the Menshutkin reaction between triethylamine and ethyl iodide have been computed using B3LYP and MP2 with the LANL2DZ, LANL2DZd, SVP, MIDI!, 6-311G(d,p), and aug-cc-PVTZ basis sets. Small- and large-core energy-consistent relativistic pseudopotentials were employed. Solvent effect corrections were computed from QM/MM Monte Carlo simulations utilizing free-energy perturbation theory, PDDG/PM3, and both a nonpolarizable OPLS and polarizable OPLS-AAP force field. The B3LYP/MIDI! theory level provided the best ΔG‡ values with a mean absolute error (MAE) of 4.9 kcal/mol from experiment in cyclohexane, CCl4, THF, DMSO, acetonitrile, water, and methanol. However, the relative rates in cyclohexane, and to a certain extent CCl4, were determined to be greatly underestimated when using the nonpolarizable OPLS force field. An overall reduction in the MAE to 3.1 kcal/mol using B3LYP/MIDI!/OPLS-AAP demonstrated the need for a fully polarizable force field when computing solvent effects for highly dip...
-
free energies of hydration from a generalized born model and an all atom force field
Journal of Physical Chemistry B, 2004Co-Authors: William L Jorgensen, And Jakob P Ulmschneider, Julian TiradorivesAbstract:The generalized Born/surface area (GB/SA) model of Still and co-workers was originally developed using partial atomic charges for organic molecules and ions from the OPLS united-atom force field. An efficient implementation of the GB/SA approach with the OPLS-AA (all-atom) force field is described here. Migration to the OPLS-AA model allows much broader application, and it also yields improved accuracy in predicting free energies of hydration. For 75 diverse, neutral organic molecules, the mean unsigned error is 0.6 kcal/mol with the OPLS-AA GB/SA model. Furthermore, effects of hydration on conformational equilibria are shown to be well represented, and results for free energies of hydration of a wide variety of ions are also in close accord with experimental data. As an even more general alternative, the use of partial charges from the CM1A procedure of Cramer, Truhlar, and co-workers has been tested on more than 400 organic molecules and ions. OPLS-AA force field parameters are also reported for primary alkyl halides, halobenzenes, and numerous ions.
Johan Trygg - One of the best experts on this subject based on the ideXlab platform.
-
variable influence on projection vip for OPLS models and its applicability in multivariate time series analysis
Chemometrics and Intelligent Laboratory Systems, 2015Co-Authors: Beatriz Galindoprieto, Lennart Eriksson, Johan TryggAbstract:Abstract Recently a new parameter to infer variable importance in orthogonal projections to latent structures (OPLS) was presented. Called OPLS-VIP (variable influence on projection), this paramete ...
-
orthogonal pls OPLS modeling for improved analysis and interpretation in drug design
Molecular Informatics, 2012Co-Authors: Lennart Eriksson, Josefin Rosen, Erik Johansson, Johan TryggAbstract:Partial least squares (PLS) regression is a flexible data analytical approach, which can be made even more versatile and useful by various modifications. In this article we describe the extension into orthogonal PLS modeling, in terms of two new methods, called OPLS and O2PLS, with similar prediction capacity but improved model interpretation.
-
cv anova for significance testing of pls and OPLS models
Journal of Chemometrics, 2008Co-Authors: Lennart Eriksson, Johan Trygg, Svante WoldAbstract:This report describes significance testing for PLS and OPLS® (orthogonal PLS) models. The testing is applicable to single-Y cases and is based on ANOVA of the cross-validated residuals (CV-ANOVA). Two variants of the CV-ANOVA are introduced. The first is based on the cross-validated predictive residuals of the PLS or OPLS model while the second works with the cross-validated predictive score values of the OPLS model. The two CV-ANOVA diagnostics are shown to work well in those cases where PLS and OPLS work well, that is, for data with many and correlated variables, missing data, etc. The utility of the CV-ANOVA diagnostic is demonstrated using three datasets related to (i) the monitoring of an industrial de-inking process; (ii) a pharmaceutical QSAR problem and (iii) a multivariate calibration application from a sugar refinery. Copyright © 2008 John Wiley & Sons, Ltd.
-
k OPLS package kernel based orthogonal projections to latent structures for prediction and interpretation in feature space
BMC Bioinformatics, 2008Co-Authors: Max Bylesjo, Jeremy K Nicholson, Elaine Holmes, Mattias Rantalainen, Johan TryggAbstract:Background Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.
-
OPLS methodology for analysis of pre processing effects on spectroscopic data
Chemometrics and Intelligent Laboratory Systems, 2006Co-Authors: Jon Gabrielsson, Hans Jonsson, Christian Airiau, Bernd Schmidt, Richard E A Escott, Johan TryggAbstract:Abstract Pre-processing of spectroscopic data is commonly applied to remove unwanted systematic variation. Possible loss of information and ambiguity regarding discarded variation are issues that complicate pre-treatment of data. In this paper, OPLS methodology is applied to evaluate different techniques for pre-processing of spectroscopic data gathered from a batch process. The objective is to present a rational scheme for analysis of pre-processing in order to understand the influence and effect of pre-treatment. O2PLS uses linear regression to divide the systematic variation in X and Y into three parts; one part with joint X – Y covariation, i.e. related to both X and Y , one part of X with Y -orthogonal variation and one part of Y with X -orthogonal variation. All of the investigated pre-treatment methods removed an additive baseline as expected. In the analysis of raw and differentiated data variation associated with the baseline was found in the Y -orthogonal part of X . Orthogonal information was also found in Y , which suggests that this pre-processing procedure not only removed variation. This would have been more difficult to detect without the O2PLS model since both raw and differentiated data must be analysed simultaneously. Development of a knowledge based strategy with OPLS methodology is an important step towards eliminating trial and error approaches to pre-processing.
Mattias Rantalainen - One of the best experts on this subject based on the ideXlab platform.
-
non linear modeling of 1h nmr metabonomic data using kernel based orthogonal projections to latent structures optimized by simulated annealing
Analytica Chimica Acta, 2011Co-Authors: Judith M Fonville, Max Bylesjo, Muireann Coen, Jeremy K Nicholson, Elaine Holmes, John C Lindon, Mattias RantalainenAbstract:Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and a study of Trypanosoma brucei brucei infection. Automated and user-friendly procedures for the kernel-optimization have been incorporated into version 1.1.1 of the freely available K-OPLS software package for both R and Matlab to enable easy application of K-OPLS for non-linear prediction modeling.
-
k OPLS package kernel based orthogonal projections to latent structures for prediction and interpretation in feature space
BMC Bioinformatics, 2008Co-Authors: Max Bylesjo, Jeremy K Nicholson, Elaine Holmes, Mattias Rantalainen, Johan TryggAbstract:Background Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.
-
OPLS discriminant analysis combining the strengths of pls da and simca classification
Journal of Chemometrics, 2006Co-Authors: Max Bylesjo, Jeremy K Nicholson, Elaine Holmes, Mattias Rantalainen, Olivier Cloarec, Johan TryggAbstract:The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classific ...
Julian Tiradorives - One of the best experts on this subject based on the ideXlab platform.
-
development and testing of the OPLS aa m force field for rna
Journal of Chemical Theory and Computation, 2019Co-Authors: Michael J Robertson, Julian Tiradorives, Yue Qian, Matthew C Robinson, William L JorgensenAbstract:Significant improvements have been made to the OPLS-AA force field for modeling RNA. New torsional potentials were optimized based on density functional theory (DFT) scans at the ωB97X-D/6-311++G(d,p) level for potential energy surfaces of the backbone α and γ dihedral angles. In combination with previously reported improvements for the sugar puckering and glycosidic torsion terms, the new force field was validated through diverse molecular dynamics simulations for RNAs in aqueous solution. Results for dinucleotides and tetranucleotides revealed both accurate reproduction of 3 J couplings from NMR and the avoidance of several unphysical states observed with other force fields. Simulations of larger systems with noncanonical motifs showed significant structural improvements over the previous OPLS-AA parameters. The new force field, OPLS-AA/M, is expected to perform competitively with other recent RNA force fields and to be compatible with OPLS-AA models for proteins and small molecules.
-
improved peptide and protein torsional energetics with the OPLS aa force field
Journal of Chemical Theory and Computation, 2015Co-Authors: Michael J Robertson, Julian Tiradorives, William L JorgensenAbstract:The development and validation of new peptide dihedral parameters are reported for the OPLS-AA force field. High accuracy quantum chemical methods were used to scan φ, ψ, χ1, and χ2 potential energy surfaces for blocked dipeptides. New Fourier coefficients for the dihedral angle terms of the OPLS-AA force field were fit to these surfaces, utilizing a Boltzmann-weighted error function and systematically examining the effects of weighting temperature. To prevent overfitting to the available data, a minimal number of new residue-specific and peptide-specific torsion terms were developed. Extensive experimental solution-phase and quantum chemical gas-phase benchmarks were used to assess the quality of the new parameters, named OPLS-AA/M, demonstrating significant improvement over previous OPLS-AA force fields. A Boltzmann weighting temperature of 2000 K was determined to be optimal for fitting the new Fourier coefficients for dihedral angle parameters. Conclusions are drawn from the results for best practices...
-
free energies of hydration from a generalized born model and an all atom force field
Journal of Physical Chemistry B, 2004Co-Authors: William L Jorgensen, And Jakob P Ulmschneider, Julian TiradorivesAbstract:The generalized Born/surface area (GB/SA) model of Still and co-workers was originally developed using partial atomic charges for organic molecules and ions from the OPLS united-atom force field. An efficient implementation of the GB/SA approach with the OPLS-AA (all-atom) force field is described here. Migration to the OPLS-AA model allows much broader application, and it also yields improved accuracy in predicting free energies of hydration. For 75 diverse, neutral organic molecules, the mean unsigned error is 0.6 kcal/mol with the OPLS-AA GB/SA model. Furthermore, effects of hydration on conformational equilibria are shown to be well represented, and results for free energies of hydration of a wide variety of ions are also in close accord with experimental data. As an even more general alternative, the use of partial charges from the CM1A procedure of Cramer, Truhlar, and co-workers has been tested on more than 400 organic molecules and ions. OPLS-AA force field parameters are also reported for primary alkyl halides, halobenzenes, and numerous ions.
Max Bylesjo - One of the best experts on this subject based on the ideXlab platform.
-
non linear modeling of 1h nmr metabonomic data using kernel based orthogonal projections to latent structures optimized by simulated annealing
Analytica Chimica Acta, 2011Co-Authors: Judith M Fonville, Max Bylesjo, Muireann Coen, Jeremy K Nicholson, Elaine Holmes, John C Lindon, Mattias RantalainenAbstract:Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and a study of Trypanosoma brucei brucei infection. Automated and user-friendly procedures for the kernel-optimization have been incorporated into version 1.1.1 of the freely available K-OPLS software package for both R and Matlab to enable easy application of K-OPLS for non-linear prediction modeling.
-
k OPLS package kernel based orthogonal projections to latent structures for prediction and interpretation in feature space
BMC Bioinformatics, 2008Co-Authors: Max Bylesjo, Jeremy K Nicholson, Elaine Holmes, Mattias Rantalainen, Johan TryggAbstract:Background Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.
-
OPLS discriminant analysis combining the strengths of pls da and simca classification
Journal of Chemometrics, 2006Co-Authors: Max Bylesjo, Jeremy K Nicholson, Elaine Holmes, Mattias Rantalainen, Olivier Cloarec, Johan TryggAbstract:The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classific ...