How water’s properties are encoded in its molecular structure and energies E Brini, CJ Fennell, M Fernandez-Serra, B Hribar-Lee, M Luksic, KA Dill Chemical reviews 117 (19), 12385-12414, 2017 | 395 | 2017 |
Systematic coarse-graining methods for soft matter simulations–a review E Brini, EA Algaer, P Ganguly, C Li, F Rodríguez-Ropero, ... Soft Matter 9 (7), 2108-2119, 2013 | 389 | 2013 |
Conditional reversible work method for molecular coarse graining applications E Brini, V Marcon, NFA van der Vegt Physical Chemistry Chemical Physics 13 (22), 10468-10474, 2011 | 100 | 2011 |
Grid-based backbone correction to the ff12SB protein force field for implicit-solvent simulations A Perez, JL MacCallum, E Brini, C Simmerling, KA Dill Journal of chemical theory and computation 11 (10), 4770-4779, 2015 | 79 | 2015 |
Chemically transferable coarse-grained potentials from conditional reversible work calculations E Brini, NFA Van der Vegt The Journal of Chemical Physics 137 (15), 2012 | 71 | 2012 |
Protein storytelling through physics E Brini, C Simmerling, K Dill Science 370 (6520), eaaz3041, 2020 | 67 | 2020 |
Blind protein structure prediction using accelerated free-energy simulations A Perez, JA Morrone, E Brini, JL MacCallum, KA Dill Science advances 2 (11), e1601274, 2016 | 65 | 2016 |
Shape governs the motion of chemically propelled janus swimmers F Lugli, E Brini, F Zerbetto The Journal of Physical Chemistry C 116 (1), 592-598, 2012 | 57 | 2012 |
Thermodynamic transferability of coarse-grained potentials for polymer–additive systems E Brini, CR Herbers, G Deichmann, NFA van der Vegt Physical Chemistry Chemical Physics 14 (34), 11896-11903, 2012 | 33 | 2012 |
NMR‐assisted protein structure prediction with MELDxMD JC Robertson, R Nassar, C Liu, E Brini, KA Dill, A Perez Proteins: Structure, Function, and Bioinformatics 87 (12), 1333-1340, 2019 | 22 | 2019 |
Computing ligands bound to proteins using MELD-accelerated MD C Liu, E Brini, A Perez, KA Dill Journal of chemical theory and computation 16 (10), 6377-6382, 2020 | 16 | 2020 |
Predicting protein dimer structures using MELD× MD E Brini, D Kozakov, KA Dill Journal of chemical theory and computation 15 (5), 3381-3389, 2019 | 15 | 2019 |
Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4 S Kotelnikov, A Alekseenko, C Liu, M Ignatov, D Padhorny, E Brini, ... Journal of computer-aided molecular design 34, 179-189, 2020 | 14 | 2020 |
Accelerating protein folding molecular dynamics using inter-residue distances from machine learning servers R Nassar, E Brini, S Parui, C Liu, GL Dignon, KA Dill Journal of Chemical Theory and Computation 18 (3), 1929-1935, 2022 | 12 | 2022 |
Modeling beta‐sheet peptide‐protein interactions: Rosetta FlexPepDock in CAPRI rounds 38‐45 A Khramushin, O Marcu, N Alam, O Shimony, D Padhorny, E Brini, KA Dill, ... Proteins: Structure, Function, and Bioinformatics 88 (8), 1037-1049, 2020 | 11 | 2020 |
ClusPro in rounds 38 to 45 of CAPRI: Toward combining template‐based methods with free docking D Padhorny, KA Porter, M Ignatov, A Alekseenko, D Beglov, S Kotelnikov, ... Proteins: Structure, Function, and Bioinformatics 88 (8), 1082-1090, 2020 | 9 | 2020 |
Adapting the semi-explicit assembly solvation model for estimating water-cyclohexane partitioning with the SAMPL5 molecules E Brini, SS Paranahewage, CJ Fennell, KA Dill Journal of computer-aided molecular design 30, 1067-1077, 2016 | 7 | 2016 |
Accelerating Molecular Dynamics Enrichments of High-Affinity Ligands for Proteins C Liu, E Brini, KA Dill Journal of Chemical Theory and Computation 18 (1), 374-379, 2021 | 2 | 2021 |
Determining protein structures using accelerated md simulations and noisy data R Nassar, A Perez, JC Robertson, C Liu, E Brini, KA Dill Biophysical Journal 118 (3), 141a, 2020 | 1 | 2020 |
Energy-based clustering of protein structures T Ruggiero, E Brini Biophysical Journal 123 (3), 57a, 2024 | | 2024 |