Harold A. Scheraga
Cornell University
1160 Papers
25.6K Citations
Harold A. Scheraga is an academic researcher from Cornell University. The author has contributed to research in topics: Protein structure & Protein folding. The author has an hindex of 120, co-authored 1152 publications. Previous affiliations of Harold A. Scheraga include University of Gdańsk & National University of San Luis.
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Papers
PMFF: Development of a Physics-Based Molecular Force Field for Protein Simulation and Ligand Docking.
Sungbo Hwang,Chang Joon Lee,Sehan Lee,Songling Ma,Young Mook Kang,Kwang-Hwi Cho,Su Yeon Kim,Oh Young Kwon,Chang No Yoon,Young Kee Kang,Jeong Hyeok Yoon,Ky Youb Nam,Seong Gon Kim,Youngyong In,Han Ha Chai,William E. Acree,J. Andrew. Grant,Kenneth D. Gibson,Mu Shik Jhon,Harold A. Scheraga,Kyoung Tai No +20 more
TL;DR: The physics-based molecular force field (PMFF) as discussed by the authors was developed by integrating a set of potential energy functions in which each term in an intermolecular potential energy function is derived based on experimental values, such as the dipole moments, lattice energy, proton transfer energy, and X-ray crystal structures.
Implementation of a Serial Replica Exchange Method in a Physics-Based United-Residue (UNRES) Force Field.
TL;DR: This version of SREM worked for Ala(10) which is a simple system but failed to reproduce the thermodynamic results as well as regular REM on the more complex 1GAB protein, so SREM can be applied to the temperature-independent but not to theTemperature-dependent UNRES force field.
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Like‐charged residues at the ends of oligoalanine sequences might induce a chain reversal
Joanna Makowska,Joanna Makowska,Adam Liwo,Adam Liwo,Wioletta Żmudzińska,Agnieszka Lewandowska,Lech Chmurzyński,Harold A. Scheraga +7 more
TL;DR: The tendency to form a more or less pronounced chain reversal is observed and it seems to be stable in all three peptides, caused by screening of the nonpolar core from the solvent by the hydrated charged residues.
Use of decoys to optimize an all-atom force field including hydration.
TL;DR: The method is applied to optimize the parameters of a physics-based scoring function consisting of the all-atom ECEPP05 force field coupled with an implicit solvent model (a solvent-accessible surface area model) and the optimized force field is able to discriminate near-native from nonnative conformations of the six training proteins when used either for local energy minimization or for short Monte Carlo simulated annealing runs after localEnergy minimization.
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