| P50 | author | Vincent A. Voelz | Q50911020 |
| | Yunhui Ge | Q88003403 |
| P2860 | cites work | Potentials of mean force for protein structure prediction vindicated, formalized and generalized | Q21562146 |
| | SHIFTX2: significantly improved protein chemical shift prediction | Q24604571 |
| | Improved side-chain torsion potentials for the Amber ff99SB protein force field | Q24616749 |
| | Molecular simulation of ab initio protein folding for a millisecond folder NTL9(1-39) | Q24631041 |
| | Structural and energetic basis of allostery | Q26825355 |
| | Effective Born radii in the generalized Born approximation: the importance of being perfect | Q28221237 |
| | GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit | Q29615867 |
| | A test on peptide stability of AMBER force fields with implicit solvation | Q30369376 |
| | The design, structures and therapeutic potential of protein epitope mimetics. | Q30371623 |
| | Principles of protein structural ensemble determination | Q30397163 |
| | Generative probabilistic models extend the scope of inferential structure determination | Q30408069 |
| | Principles of protein folding--a perspective from simple exact models. | Q30417429 |
| | Statistically optimal analysis of samples from multiple equilibrium states | Q30438437 |
| | Inference of structure ensembles of flexible biomolecules from sparse, averaged data | Q30697040 |
| | Stabilizing capping motif for beta-hairpins and sheets | Q33581868 |
| | Precisely tuneable energy transfer system using peptoid helix-based molecular scaffold | Q33881451 |
| | Are Protein Force Fields Getting Better? A Systematic Benchmark on 524 Diverse NMR Measurements | Q34285601 |
| | COMPUTING: Screen Savers of the World Unite! | Q34670167 |
| | Rational design of topographical helix mimics as potent inhibitors of protein-protein interactions | Q35157579 |
| | Intrinsically disordered proteins in cellular signalling and regulation. | Q35510300 |
| | MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories. | Q35816082 |
| | Accelerating molecular simulations of proteins using Bayesian inference on weak information. | Q36103048 |
| | Computational methods to design cyclic peptides. | Q36122607 |
| | LapG, required for modulating biofilm formation by Pseudomonas fluorescens Pf0-1, is a calcium-dependent protease | Q36155321 |
| | De novo structure prediction and experimental characterization of folded peptoid oligomers. | Q36221874 |
| | A statistical thermodynamic model of the protein ensemble | Q36472751 |
| | Secondary structure bias in generalized Born solvent models: comparison of conformational ensembles and free energy of solvent polarization from explicit and implicit solvation | Q36736171 |
| | Acceleration of an aromatic Claisen rearrangement via a designed spiroligozyme catalyst that mimics the ketosteroid isomerase catalytic dyad | Q37731590 |
| | Peptoid polymers: a highly designable bioinspired material | Q38110623 |
| | Design, synthesis, and biological evaluation of diminutive forms of (+)-spongistatin 1: lessons learned | Q38279846 |
| | Computational and Experimental Evaluation of Designed β-Cap Hairpins Using Molecular Simulations and Kinetic Network Models | Q38725682 |
| | Bayesian energy landscape tilting: towards concordant models of molecular ensembles | Q38725972 |
| | Insights into Peptoid Helix Folding Cooperativity from an Improved Backbone Potential. | Q38941098 |
| | Computational screening and selection of cyclic peptide hairpin mimetics by molecular simulation and kinetic network models. | Q39205851 |
| | MSMBuilder2: Modeling Conformational Dynamics at the Picosecond to Millisecond Scale | Q39373045 |
| | Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics | Q39453313 |
| | Metainference: A Bayesian inference method for heterogeneous systems | Q40025086 |
| | The solution structure of (+)-spongistatin 1 in DMSO. | Q40068349 |
| | Molecular Simulation of Conformational Pre-Organization in Cyclic RGD Peptides | Q41291373 |
| | Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9. | Q41585818 |
| | Modeling intrinsically disordered proteins with bayesian statistics | Q42408893 |
| | Identification of slow molecular order parameters for Markov model construction | Q44985905 |
| | Control of porphyrin interactions via structural changes of a peptoid scaffold | Q47422858 |
| | Bayesian inference of conformational state populations from computational models and sparse experimental observables. | Q51033732 |
| | Inferential structure determination. | Q51969829 |
| | Peptoid conformational free energy landscapes from implicit-solvent molecular simulations in AMBER. | Q53078387 |
| | Kinetic network models of tryptophan mutations in β-hairpins reveal the importance of non-native interactions. | Q53281393 |
| | NMR-based protein potentials | Q84749920 |
| P4510 | describes a project that uses | Bayes' theorem | Q182505 |
| P433 | issue | 21 | |
| P921 | main subject | force field | Q1341441 |
| P304 | page(s) | 5610-5622 | |
| P577 | publication date | 2018-03-23 | |
| P1433 | published in | Journal of Physical Chemistry B | Q668669 |
| P1476 | title | Model Selection Using BICePs: A Bayesian Approach for Force Field Validation and Parameterization | |
| P478 | volume | 122 | |