dc.contributor.author	Forouzesh, Negin	en
dc.contributor.committeechair	Onufriev, Alexey	en
dc.contributor.committeemember	Zhang, Liqing	en
dc.contributor.committeemember	Raghvendra, Sharath	en
dc.contributor.committeemember	Bevan, David R.	en
dc.contributor.committeemember	Gohlke, Holger	en
dc.contributor.department	Computer Science	en
dc.date.accessioned	2021-12-25T07:00:11Z	en
dc.date.available	2021-12-25T07:00:11Z	en
dc.date.issued	2020-07-02	en
dc.description.abstract	Atomistic modeling and simulation methods facilitate biomedical research from many respects, including structure-based drug design. The ability of these methods to address biologically relevant problems is largely determined by the accuracy of the treatment of complex solvation effects in target biomolecules surrounded by water. The implicit solvent model – which treats solvent as a continuum with the dielectric and non-polar properties of water – offers a good balance between accuracy and speed. Simple and efficient, generalized Born (GB) model has become a widely used implicit solvent responsible for the estimation of key electrostatic interactions. The main goal of this research is to improve the accuracy of protein-ligand binding calculations in the implicit solvent framework. To address the problem (1) GBNSR6, an accurate yet efficient flavor of GB, has been thoroughly explored in the context of protein-ligand binding, (2) a global multidimensional optimization pipeline is developed to find the optimal dielectric boundary made of atomic and water probe radii specifically for protein-ligand binding calculations using GBNSR6. The pipeline includes (3) two novel post-processing steps for optimum robustness analysis and optimization landscape visualization. In the final step of this research, (4) accuracy gain the optimal dielectric boundary can bring in practice is explored on binding benchmarks, including the SARS-CoV-2 spike receptor-binding domain and the human ACE2 receptor.	en
dc.description.abstractgeneral	Drug discovery is one of the most challenging tasks in biological sciences as it takes about 10-15 years and $1.5-2 billion on average to discover a new drug. Therefore, efforts to speed up this process or lower its costs are highly valuable. Computer-aided drug design (CADD) plays a crucial role in the early stage of drug discovery. In CADD, computational approaches are used in order to discover, develop, and analyze drugs and similar biologically active molecules, such as proteins. Proteins are an important class of biological macromolecules that perform their functionality mainly through interactions with other molecules, for example, binding to small molecules so-called ligands. Thorough understanding of protein-ligand interactions is central to comprehending biology at the molecular level. In this study, we introduce and analyze a computational model used for protein-ligand binding free energy calculations. A global multidimensional optimization pipeline is developed to find the optimal parameters of the model,˘aparticularly˘athose parameters involved in the dielectric boundary. In order to examine the robustness of the optimal model to unavoidable perturbations and uncertainties, virtually inevitable in any complex system being optimized, a novel robustness metric is introduced. Finally, the robust optimal model is tested on protein-ligand benchmarks, including a complex related to the novel coronavirus. Results demonstrate relatively higher accuracy in terms of binding free energy calculations compared to reference models.	en
dc.description.degree	Doctor of Philosophy	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:26933	en
dc.identifier.uri	http://hdl.handle.net/10919/107266	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Free Energy Calculation	en
dc.subject	Molecular Simulation	en
dc.subject	Drug Discovery	en
dc.title	Efficient Biomolecular Computations Towards Applications in Drug Discovery	en
dc.type	Dissertation	en
thesis.degree.discipline	Computer Science and Applications	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Doctor of Philosophy	en

Efficient Biomolecular Computations Towards Applications in Drug Discovery

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