Derivation of Fixed Partial Charges for Amino Acids Accommodating a Specific Water Model and Implicit Polarization

Cerutti, David S.; Rice, Julia E.; Swope, William C.; Case, David A.

doi:10.1021/jp311851r

Cited by 106 publications

(221 citation statements)

References 46 publications

(113 reference statements)

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“…In particular, the r 0 distance for N η −O δ/ε pairs increased by 6%. 159 This new force field remains a work in progress; in its current state, Amber ff13α still shows a tendency to significantly overestimate the strength of salt bridge interactions. 52 Nevertheless, it is clear that any solution to this problem is likely to involve the Lennard-Jones parameters.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Role of Electrostatic Interactions in Binding of Peptides and Intrinsically Disordered Proteins to Their Folded Targets. 1. NMR and MD Characterization of the Complex between the c-Crk N-SH3 Domain and the Peptide Sos

et al. 2014

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Intrinsically disordered proteins (IDPs) often rely on electrostatic interactions to bind their structured targets. To obtain insight into the mechanism of formation of the electrostatic encounter complex, we investigated the binding of the peptide Sos (PPPVPPRRRR), which serves as a minimal model for an IDP, to the c-Crk N-terminal SH3 domain. Initially, we measured ¹⁵N relaxation rates at two magnetic field strengths and determined the binding shifts for the complex of Sos with wild-type SH3. We have also recorded a 3 μs molecular dynamics (MD) trajectory of this complex using the Amber ff99SB*-ILDN force field. The comparison of the experimental and simulated data shows that MD simulation consistently overestimates the strength of salt bridge interactions at the binding interface. The series of simulations using other advanced force fields also failed to produce any satisfactory results. To address this issue, we have devised an empirical correction to the Amber ff99SB*-ILDN force field whereby the Lennard-Jones equilibrium distance for the nitrogen-oxygen pair across the Arg-to-Asp and Arg-to-Glu salt bridges has been increased by 3%. Implementing this correction resulted in a good agreement between the simulations and the experiment. Adjusting the strength of salt bridge interactions removed a certain amount of strain contained in the original MD model, thus improving the binding of the hydrophobic N-terminal portion of the peptide. The arginine-rich C-terminal portion of the peptide, freed from the effect of the overstabilized salt bridges, was found to interconvert more rapidly between its multiple conformational states. The modified MD protocol has also been successfully used to simulate the entire binding process. In doing so, the peptide was initially placed high above the protein surface. It then arrived at the correct bound pose within ∼2 Å of the crystallographic coordinates. This simulation allowed us to analyze the details of the dynamic binding intermediate, i.e., the electrostatic encounter complex. However, an experimental characterization of this transient, weakly populated state remains out of reach. To overcome this problem, we designed the double mutant of c-Crk N-SH3 in which mutations Y186L and W169F abrogate tight Sos binding and shift the equilibrium toward the intermediate state resembling the electrostatic encounter complex. The results of the combined NMR and MD study of this engineered system will be reported in the next part of this paper.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Role of Electrostatic Interactions in Binding of Peptides and Intrinsically Disordered Proteins to Their Folded Targets. 1. NMR and MD Characterization of the Complex between the c-Crk N-SH3 Domain and the Peptide Sos

et al. 2014

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“…As the GA solutions conserve both the total charge Q and the dipole moment μz, we can expect that the s⃗1 and s⃗2 vectors correspond to the vectors u⃗1 = (nX nC nH) and u⃗2 = (nXzX nCzC nHzH), eqs 16 and 17, in which case the third vector s⃗3 should be collinear with the cross product u⃗3 = u⃗1 × u⃗2, along which the GA solutions are distributed. Unlike the s⃗1 and s⃗2 vectors, the u⃗1 and u⃗2 vectors are generally not orthogonal, but their orthogonality can be achieved by appropriately shifting the coordinate origin: (25) (26) (27) where z0 is the coordinate of the new origin along the z axis. As expected, the set of the three orthogonal vectors u⃗i: (28) numerically matches, after normalization, with the eigenbasis of the corresponding covariance matrix of the GA solutions Σ and the eigenbasis of the LS sum Hessian matrix H (Table S4 in the Supporting Information): (29) Thus, analysis of the covariance matrix provides a convenient and general method to understand the nature of the premature convergence of the small-population GA point charge optimizations that yields highly dispersed suboptimal solutions.…”

Section: Covariance Matrix Analysis Of Ga Resultsmentioning

confidence: 99%

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

2015

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Evolutionary methods, such as genetic algorithms (GAs), provide powerful tools for optimization of the force field parameters, especially in the case of simultaneous fitting of the force field terms against extensive reference data. However, GA fitting of the nonbonded interaction parameters that includes point charges has not been explored in the literature, likely due to numerous difficulties with even a simpler problem of the least-squares fitting of the atomic point charges against a reference molecular electrostatic potential (MEP), which often demonstrates an unusually high variation of the fitted charges on buried atoms. Here, we examine the performance of the GA approach for the least-squares MEP point charge fitting, and show that the GA optimizations suffer from a magnified version of the classical buried atom effect, producing highly scattered yet correlated solutions. This effect can be understood in terms of the linearly independent, natural coordinates of the MEP fitting problem defined by the eigenvectors of the least-squares sum Hessian matrix, which are also equivalent to the eigenvectors of the covariance matrix evaluated for the scattered GA solutions. GAs quickly converge with respect to the high-curvature coordinates defined by the eigenvectors related to the leading terms of the multipole expansion, but have difficulty converging with respect to the low-curvature coordinates that mostly depend on the buried atom charges. The performance of the evolutionary techniques dramatically improves when the point charge optimization is performed using the Hessian or covariance matrix eigenvectors, an approach with a significant potential for the evolutionary optimization of the fixed-charge biomolecular force fields.

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“…14 and Cerutti et al 15 (the latter work first calibrated the partial charges). The generated system can be solvated and/or combined with previously generated macromolecular topologies.…”

Section: Discussionmentioning

confidence: 99%

“…12,13 This means that the force fields should be re-parameterized using more accurate charges, followed by re-calibration of the van der Waals parameters. 14,15 With these improvements it might be possible to further improve the accuracy of free energy calculations of current fixed charge force fields, rather than switching to polarizable force fields that are both computationally expensive and difficult to parameterize. 13 These advances have been made possible both by faster computers, and because methods for free energy calculations have improved to the level where the precision (but not necessarily accuracy) of calculated solvation free energies now rivals experimental measurements.…”

Section: 245mentioning

confidence: 99%

Automatic GROMACS Topology Generation and Comparisons of Force Fields for Solvation Free Energy Calculations

2014

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Free energy calculation has long been an important goal for molecular dynamics simulation and force field development, but historically it has been challenged both by limited performance, accuracy, and creation of topologies for arbitrary small molecules.This has made it difficult to systematically compare different sets of parameters to improve existing force fields, but in the last few years several authors have developed increasingly automated procedures to generate parameters for force fields such as Amber, CHARMM, and OPLS. Here, we present a new framework that enables fully automated generation of GROMACS topologies for any of these force fields and an automated setup for parallel adaptive optimization of high-throughput free energy calculation by adjusting lambda point placement on the fly. We have calculated solvation free energies of * To whom correspondence should be addressed † Royal Institute of Technology ‡ Stockholm University 1 50 different small molecules using the GAFF, OPLS-AA and CGenFF force fields and four different water models, and by including the often neglected polarization costs we show that the common charge models are somewhat underpolarized.

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Derivation of Fixed Partial Charges for Amino Acids Accommodating a Specific Water Model and Implicit Polarization

Cited by 106 publications

References 46 publications

Role of Electrostatic Interactions in Binding of Peptides and Intrinsically Disordered Proteins to Their Folded Targets. 1. NMR and MD Characterization of the Complex between the c-Crk N-SH3 Domain and the Peptide Sos

Role of Electrostatic Interactions in Binding of Peptides and Intrinsically Disordered Proteins to Their Folded Targets. 1. NMR and MD Characterization of the Complex between the c-Crk N-SH3 Domain and the Peptide Sos

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

Automatic GROMACS Topology Generation and Comparisons of Force Fields for Solvation Free Energy Calculations

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