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Computational prediction of protein small molecule interfaces using ROSETTA

dc.creatorKaufmann, Kristian Wallace
dc.description.abstractProtein small molecule docking has focused on the modeling of small molecule flexibility and scoring of small molecules binding to fixed protein structures due to the inherent complexity of incorporating protein degrees of freedom. Recent developments in modeling of protein folding have opened the possibility of including protein degrees of freedom in small molecule protein interface modeling. ROSETTA, a protein modeling suite, has performed at the forefront of protein modeling in recent years. Its combination of knowledge based discrete sampling and knowledge based energy functions have pushed protein modeling to sub-angstrom accuracy. In the dissertation existing ROSETTA sampling protocols and energy functions are discussed along with previous applications of Rosetta to a variety of protein modeling tasks including de Novo protein folding, comparative modeling, protein docking, and ligand docking with rigid small molecules. Expansion of ROSETTALIGAND to allowing simultaneous sampling of protein binding site flexibility and small molecule flexibility using a parallel knowledge based approach to both sampling and scoring is detailed. In a benchmark of small molecule docking the new method recovered a native-like binding mode in 9 of 10 cases when docked back into the parent crystal structure, while in 7 of 11 cases the protocol recovered a native-like binding mode when docked to a structure of the same protein crystallized with a different small molecule. The value of specializing energy scoring functions to specific ligand families is examined in the context of PDZ. Specialized energy functions are shown to improve prediction of binding energies upon mutation within PDZ domains and to predict specificity of peptides binding to the domains. We dock ligands to comparative models built by Rosetta and models from the 8th Critical Assessment of Structure Prediction. We find that 60% of cases produce a native-like model among the top ranked models indicating that comparative models can be used in predictions. Finally, an advanced case study in modeling a small molecule protein interface is described using serotonin bound to the serotonin transporter.
dc.subjecthomology modeling
dc.subjectvirtual screening
dc.subjectdrug design
dc.titleComputational prediction of protein small molecule interfaces using ROSETTA
dc.contributor.committeeMemberDr. Randy D. Blakely
dc.contributor.committeeMemberDr. Michael P. Stone
dc.contributor.committeeMemberDr. Brian O. Bachmann
dc.type.materialtext University
dc.contributor.committeeChairDr. Jens Meiler

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