ArticleJournal of chemical theory and computation2016
Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules.
Article in Journal of chemical theory and computation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 304 papers.
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Who cites it
304 citing papers in PubMed.
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- SplitSeek-Pro: accurate prediction of splittable sites on protein structures.Nature communications · 2026Article
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- MotifLeadDB: A Hierarchical Structural Data Set for Congeneric Ligand Binding Activity Change.Journal of chemical information and modeling · 2026Article
- Mechanistic design of cell-penetrating disruptors for phospho-dependent TACC3-CHC interaction.Structure (London, England : 1993) · 2026Article
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- Development of Antagonist Peptides of the Crk/CrkL SH2 Domains by Modifying the Peripheral Amino Acid Residues of the YXXP Motif.Biochemistry · 2026Article
- VUStruct: A compute pipeline for high throughput and personalized structural biology.PLoS computational biology · 2026Article
- Hybrid Supercharged Antibodies: A Rational Approach to Boost Immunoassay Sensitivity via Controlled Nanoparticle Adsorption.Langmuir : the ACS journal of surfaces and colloids · 2026Article
- Observation of redistribution for local conformational dynamics in cross-reactive antibody design.Journal of biochemistry · 2026Article
244 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking have been parametrized using existing macromolecular structural data; this contrasts molecular mechanics force fields which are largely optimized using small-molecule data. In this study, we describe an integrated method that enables optimization of a biomolecular modeling energy function simultaneously against small-molecule thermodynamic data and high-resolution macromolecular structural data. We use this approach to develop a next-generation Rosetta energy function that utilizes a new anisotropic implicit solvation model, and an improved electrostatics and Lennard-Jones model, illustrating how energy functions can be considerably improved in their ability to describe large-scale energy landscapes by incorporating both small-molecule and macromolecule data. The energy function improves performance in a wide range of protein structure prediction challenges, including monomeric structure prediction, protein-protein and protein-ligand docking, protein sequence design, and prediction of the free energy changes by mutation, while reasonably recapitulating small-molecule thermodynamic properties.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.