about
P185
Markov state models of biomolecular conformational dynamicsToward Automated Benchmarking of Atomistic Force Fields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data ArchiveA Simple Method for Automated Equilibration Detection in Molecular Simulations.Modeling error in experimental assays using the bootstrap principle: understanding discrepancies between assays using different dispensing technologies.Time step rescaling recovers continuous-time dynamical properties for discrete-time Langevin integration of nonequilibrium systems.Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics.Statistically optimal analysis of samples from multiple equilibrium states.Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design.The social network (of protein conformations)Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation.The molten globule state is unusually deformable under mechanical force.Hypoxia Induces Production of L-2-Hydroxyglutarate.Ensembler: Enabling High-Throughput Molecular Simulations at the Superfamily ScaleLimitations of constant-force-feedback experiments.Systematic improvement of a classical molecular model of waterMechanistically distinct cancer-associated mTOR activation clusters predict sensitivity to rapamycin.Estimation and validation of Markov models.Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge.L-2-Hydroxyglutarate production arises from noncanonical enzyme function at acidic pHOpenMM 7: Rapid development of high performance algorithms for molecular dynamics.A water-mediated allosteric network governs activation of Aurora kinase A.Treating entropy and conformational changes in implicit solvent simulations of small moleculesSpectral Rate Theory for Two-State Kinetics.On the use of orientational restraints and symmetry corrections in alchemical free energy calculationsThe ribosome modulates nascent protein folding.Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint.On the Use of Experimental Observations to Bias Simulated Ensembles.Systematic errors in isothermal titration calorimetry: concentrations and baselines.MOPED: method for optimizing physical energy parameters using decoys.Approaches for calculating solvation free energies and enthalpies demonstrated with an update of the FreeSolv database.Introduction to the special issue: Data Part 2: Experimental Data.Quantitative self-assembly prediction yields targeted nanomedicines.A dynamic mechanism for allosteric activation of Aurora kinase A by activation loop phosphorylation.Uncertainty estimation.Biomolecular Simulations Under Realistic Macroscopic Salt Conditions.Splitting probabilities as a test of reaction coordinate choice in single-molecule experiments.Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo.Toward Learned Chemical Perception of Force Field Typing RulesToward Learned Chemical Perception of Force Field Typing RulesToward Learned Chemical Perception of Force Field Typing Rules
P50
Q26827934-02BFC75E-E59C-467E-A615-1C5981DE3404Q27301284-D90653D7-827C-4FEB-A8B4-7A26E3A4AC1CQ30353235-9D264E2E-C2EB-402D-9372-C06AD59D43E7Q30367791-979F28C1-D1AE-4D8C-A261-6DDA359F264FQ30407945-D3212806-D4F1-44C9-A528-3910B2E9DDF8Q30409188-52B82B2C-2D91-4ECA-B343-FEB39E4720D1Q30438437-1B081FAE-5D1E-4191-864D-BE5B4AF212DCQ34011410-49404AAD-CB7C-4CC2-9663-E905D7C2FA2FQ35164737-7AF2F2EC-20E4-45E8-89F4-8F4D9C9D112DQ35546784-97FC7131-121D-4E49-9D54-83B1735C0592Q35844982-09CAD690-F603-464F-8238-051C18C50BB0Q35925050-D1CDC245-8E53-4E76-BE10-8A1BA2BF8F14Q36060332-E7E0D7F2-CD4D-43AE-BDA2-632BCF4BCF47Q36317972-A48F6085-CB13-4F75-AEDB-2E258CAB3D6EQ37164266-072EB774-42B2-4CBF-B3D5-71DEF909517DQ37217474-614AC2D6-B9C1-426A-B059-71C9A0B1445AQ38168112-348882F5-19AB-480C-9B26-0720CF7C3D6BQ38813544-AD25BF80-0CA2-4C05-8145-D9F116901BCFQ41056037-7E906A34-3B4B-41E6-8D9E-D56E83550901Q41327186-A45AB9D3-CF35-4405-B15B-BEF7366DE842Q41654097-8B1DF6E9-8E8B-42E5-BBCF-9524C306A2F2Q41870076-F76544C8-0CB2-4255-8D42-8FB0F7BFB261Q41886237-29C455F0-8E28-4878-9504-F7D185549B62Q42106721-7A958349-3D17-4249-915C-B6C002D8AEA7Q42714513-A31D6A74-6A76-441B-9713-723B3BDC4840Q43065567-49EF6F81-B4A7-4503-AEC2-03C8BB13F5A7Q44810418-87C3DFF6-C105-48B8-8CE0-4D719B407E69Q45332007-FDBA9539-F0D4-4193-9CB4-2941FB017667Q46021752-4C05ED6F-7718-428C-9AD4-EF1A9934F808Q46105073-638D8672-D78A-4C1C-95B7-6A15D9E5BABDQ46157145-E156FCFE-65D7-4711-807F-4922A90E95ECQ48102709-D1400A4F-9B7F-4725-A1BA-EE9FD5AD71B1Q49713465-34341EE8-7BDD-4F62-BAE0-2E36062A0726Q52881572-BEC162A9-0C27-4889-843C-3E97D70441A2Q54116342-E9CDD1DE-621F-4EE3-B349-2F2ECB8C8059Q54984405-2CAB32F6-549C-4C75-9320-1CCF577B1426Q55476262-63A74C90-1914-41A7-8393-03AF02DCFA6CQ57457375-8D9095ED-D2D9-43CB-A2BF-1DDD99E3D24BQ57457378-6F8EEBA6-C8C5-496D-A604-572A8C6B383AQ57457383-F93A130F-7356-41CC-B08F-FED08EE34C71
P50
description
computational biologist
@en
hulumtues
@sq
onderzoeker
@nl
հետազոտող
@hy
name
John D Chodera
@ast
John D Chodera
@es
John D Chodera
@sl
John D. Chodera
@en
type
label
John D Chodera
@ast
John D Chodera
@es
John D Chodera
@sl
John D. Chodera
@en
altLabel
John Chodera
@en
John Damon Chodera
@en
prefLabel
John D Chodera
@ast
John D Chodera
@es
John D Chodera
@sl
John D. Chodera
@en
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6506773320
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nnEg7_8AAAAJ
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0000-0003-0542-119X