J
Jay W. Ponder
Researcher at Washington University in St. Louis
Publications - 79
Citations - 13526
Jay W. Ponder is an academic researcher from Washington University in St. Louis. The author has contributed to research in topics: Multipole expansion & Molecular dynamics. The author has an hindex of 40, co-authored 76 publications receiving 12300 citations. Previous affiliations of Jay W. Ponder include Yale University & Harvard University.
Papers
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Book ChapterDOI
Force fields for protein simulations
Jay W. Ponder,David A. Case +1 more
TL;DR: The chapter focuses on a general description of the force fields that are most commonly used at present and gives an indication of the directions of current research that may yield better functions in the near future.
Journal ArticleDOI
Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequences for different structural classes.
TL;DR: The preliminary tests reported here make it appear likely that templates prepared from the currently known core structures will be able to discriminate between these structures, and an algorithm to supply a list of permitted sequences of internal residues compatible with a known core structure is developed.
Journal ArticleDOI
Polarizable Atomic Multipole Water Model for Molecular Mechanics Simulation
Pengyu Ren,Jay W. Ponder +1 more
TL;DR: In this paper, a new classical empirical potential is proposed for water, which uses a polarizable atomic multipole description of electrostatic interactions, and a modified version of Thole's interaction model is used to damp induction at short range.
Journal ArticleDOI
Current Status of the AMOEBA Polarizable Force Field
Jay W. Ponder,Chuanjie Wu,Pengyu Ren,Vijay S. Pande,John D. Chodera,Michael J. Schnieders,Imran S. Haque,David L. Mobley,Daniel S. Lambrecht,Robert A. DiStasio,Martin Head-Gordon,Gary N. I. Clark,Margaret E. Johnson,Teresa Head-Gordon +13 more
TL;DR: It is shown that the AMOEBA force field is in fact a significant improvement over fixed charge models for small molecule structural and thermodynamic observables in particular, although further fine-tuning is necessary to describe solvation free energies of drug-like small molecules, dynamical properties away from ambient conditions, and possible improvements in aromatic interactions.
Journal ArticleDOI
An efficient newton‐like method for molecular mechanics energy minimization of large molecules
TL;DR: In this paper, a variant of the Truncated Newton nonlinear optimization procedure is proposed for potential energy minimization of large molecular systems, which shows particular promise for large molecular system.