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Prediction of protein fitness from computational modeling is an area of active research in rational protein design. Here, we investigated whether protein fluctuations computed from molecular dynamics simulations can be used to predict the expression levels of SARS-CoV-2 receptor binding domain (RBD) mutants determined in the deep mutational scanning experiment of Starr et al. [Science (New York, N.Y.) 2022, 377, 420] Specifically, we performed more than 0.7 milliseconds of molecular dynamics (MD) simulations of 557 mutant RBDs in triplicate to achieve statistical significance under various simulation conditions. Our results show modest but significant anticorrelation in the range [-0.4, -0.3] between expression and RBD protein flexibility. A simple linear regression machine learning model achieved correlation coefficients in the range [0.7, 0.8], thus outperforming MD-based models, but required about 25 mutations at each residue position for training., (© 2024 Wiley Periodicals LLC.)

This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein-protein interactions. The descriptors can be used to predict various properties of interest, such as protein-protein binding affinities, or inhibitory concentrations (IC 50 ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC 50 of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk., (© 2022 Wiley Periodicals LLC.)

A rapid method for the calculation of the electrostatic energy of a system without a cutoff is described in which the computational time grows linearly with the number of particles or charges. The inverse of the distance is approximated as a polynomial, which is then transformed into a function whose terms involve individual particles, instead of particle pairs, by a partitioning of the double sum. In this way, the electrostatic energy that is determined by the interparticle interactions is obtained without explicit calculation of these interactions. For systems of positive charges positioned on a face-centered cubic lattice, the calculation of the energy by the new method is shown to be faster than the calculation of the exact energy, in many cases by an order of magnitude, and to be accurate to within 1-2%. The application of this method to increase the accuracy of conventional truncation-based calculations in condensed-phase systems is also demonstrated by combining the approximated long-range electrostatic interactions with the exact short-range interactions in a "hybrid" calculation. For a 20-A sphere of water molecules, the forces are shown to be six times as accurate using this hybrid method as those calculated with conventional truncation of the electrostatic energy function at 12 A. This is accomplished with a slight increase in speed, and with a sevenfold increase in speed relative to the exact all-pair calculation. Structures minimized with the hybrid function are shown to be closer to structures minimized with an exact all-pair electrostatic energy function than are those minimized with a conventional 13-A cutoff-based electrostatic energy function. Comparison of the energies and forces calculated with the exact method illustrate that the absolute errors obtained with standard truncation can be very large. The extension of the current method to other pairwise functions as well as to multibody functions, is described.

Parameters for the zinc ion have been developed in the self-consistent charge density functional tight-binding (SCC-DFTB) framework. The approach was tested against B3LYP calculations for a range of systems, including small molecules that contain the typical coordination environment of zinc in biological systems (cysteine, histidine, glutamic/aspartic acids, and water) and active site models for a number of enzymes such as alcohol dehydrogenase, carbonic anhydrase, and aminopeptidase. The SCC-DFTB approach reproduces structural and energetic properties rather reliably (e.g., total and relative ligand binding energies and deprotonation energies of ligands and barriers for zinc-assisted proton transfers), as compared with B3LYP/6-311+G** or MP2/6-311+G** calculations., (Copyright 2003 Wiley Periodicals, Inc. J Comput Chem 24: 565-581, 2003)

A description of the origin of my interest in and the development of molecular dynamics simulations of biomolecules is presented with a historical overview, including the role of my interactions with Shneior Lifson and his group in Israel. Some early applications of the methodology by members of my group are summarized, followed by a description of examples of recent applications and some discussion of possible future directions., (Copyright 2003 Wiley Periodicals, Inc. Biopolymers: 350-358, 2003)

Generation of the list of near-neighbor pairs of atoms not bonded to each other is a key feature of many programs for calculating the energy and energy derivatives for large molecules. Because this step can take a significant amount of CPU time, more efficient nonbonded list generation can speed up the energy calculations. In this article, a novel nonbonded list generation algorithm, BYCC, is introduced. It combines certain features of other algorithms and achieves more rapid nonbonded list generation; a factor of approximately 2.5 for a molecule of 5000 atoms with a cutoff in the 10 A range is obtained on Hewlett-Packard (HP) and Alpha processors, without greatly increasing memory requirements., (Copyright 2002 Wiley Periodicals, Inc.)

An essential element of implicit solvent models, such as the generalized Born method, is a knowledge of the volume associated with the individual atoms of the solute. Two approaches for determining atomic volumes for the generalized Born model are described; one is based on Voronoi polyhedra and the other, on minimizing the fluctuations in the overall volume of the solute. Volumes to be used with various parameter sets for protein and nucleic acids in the CHARMM force field are determined from a large set of known structures. The volumes resulting from the two different approaches are compared with respect to various parameters, including the size and solvent accessibility of the structures from which they are determined. The question of whether to include hydrogens in the atomic representation of the solute volume is examined. Copyright 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1857-1879, 2001