Biology subjects

Suresh, P.

Publications and source records attributed to Suresh, P..

3 recordsLinked to original sources

bioRxiv2018

qFit-ligand reveals widespread conformational heterogeneity of drug-like molecules in X-ray electron density maps

Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein-ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor-ligand complexes. We found that up to 29 % of a dataset of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand-receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multi-temperature crystallography could therefore augment the structure-based drug design toolbox.

biophysics↗

bioRxiv2017

Flex ddG: Rosetta ensemble-based estimation of changes in protein-protein binding affinity upon mutation

Computationally modeling changes in binding free energies upon mutation (interface {Delta}{Delta}G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using \"backrub\" to generate an ensemble of models, then applying torsion minimization, side chain repacking and averaging across this ensemble to estimate interface {Delta}{Delta}G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting non-linear reweighting model improved agreement with experimentally determined interface DDG values, but also highlights the necessity of future energy function improvements.

biophysics↗

bioRxiv2017

Extending Chemical Perturbations Of The Ubiquitin Fitness Landscape In A Classroom Setting

Although the primary protein sequence of ubiquitin (Ub) is extremely stable over evolutionary time, it is highly tolerant to mutation during selection experiments performed in the laboratory. We have proposed that this discrepancy results from the difference between fitness under laboratory culture conditions and the selective pressures in changing environments over evolutionary time scales. Building on our previous work (Mavor et al 2016), we used deep mutational scanning to determine how twelve new chemicals (3-Amino-1,2,4-triazole, 5-fluorocytosine, Amphotericin B, CaCl2, Cerulenin, Cobalt Acetate, Menadione, Nickel Chloride, p-fluorophenylalanine, Rapamycin, Tamoxifen, and Tunicamycin) reveal novel mutational sensitivities of ubiquitin residues. We found sensitization of Lys63 in eight new conditions. In total, our experiments have uncovered a sensitizing condition for every position in Ub except Ser57 and Gln62. By determining the Ubiquitin fitness landscape under different chemical constraints, our work helps to resolve the inconsistencies between deep mutational scanning experiments and sequence conservation over evolutionary timescales.\n\nBuilds onMavor D, Barlow KA, Thompson S, Barad BA, Bonny AR, Cario CL, Gaskins G, Liu Z, Deming L, Axen SD, Caceres E, Chen W, Cuesta A, Gate R, Green EM, Hulce KR, Ji W, Kenner LR, Mensa B, Morinishi LS, Moss SM, Mravic M, Muir RK, Niekamp S, Nnadi CI, Palovcak E, Poss EM, Ross TD, Salcedo E, See S, Subramaniam M, Wong AW, Li J, Thorn KS, Conchuir SO, Roscoe BP, Chow ED, DeRisi JL, Kortemme T, Bolon DN, Fraser JS. Determination of Ubiquitin Fitness Landscapes Under Different Chemical Stresses in a Classroom Setting. eLife. 2016.\n\nImpact StatementWe organized a project-based course that used deep mutational scanning in multiple chemical conditions to resolve the inconsistencies between tolerance to mutations in laboratory conditions and sequence conservation over evolutionary timescales.

bioinformatics↗