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Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism. Although this decision must be made based on multifaceted factors, a major one is the suitability of pathways for the endogenous metabolism of a host organism, in part because the efficacy of heterologous biosynthesis is affected by competing endogenous pathways. To address this point, we developed an open-access web server called MRE (metabolic route explorer) that systematically searches for promising heterologous pathways by considering competing endogenous reactions in a given host organism. MRE utilizes reaction Gibbs free energy information. However, 25% of the reactions do not have accurate estimations or cannot be estimated. To address this issue, we developed a method called FC (fingerprint contribution) to provide a more accurate and complete estimation of the reaction free energy. To rationally design a productive heterologous biosynthesis system, it is essential to consider the suitability of foreign reactions for the specific endogenous metabolic infrastructure of a host. For a given pair of starting and desired compounds in a given chassis organism, MRE ranks biosynthesis routes from the perspective of the integration of new reactions into the endogenous metabolic system. For each promising heterologous biosynthesis pathway, MRE suggests actual enzymes for foreign metabolic reactions and generates information on competing endogenous reactions for the consumption of metabolites. The URL of MRE is http://www.cbrc.kaust.edu.sa/mre/. Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems. Here, we introduce a machine learning method, referred to as fingerprint contribution (FC), with chemical fingerprint-based features for the prediction of the Gibbs free energy of biochemical reactions. From a large pool of 2D fingerprint-based features, this method systematically selects a small number of relevant ones and uses them to construct a regularized linear model. FC is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx.

Thesis (Ph. D.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains viii, 113 p. : ill. (some col.). Includes abstract. Includes bibliographical references.

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Thesis (Ph.D.)--Drexel University, 2007. / Includes abstract and vita. Includes bibliographical references (leaves 148-150).

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 105-110).

In this thesis we develop a molecular modeling toolkit that models the conformation space of proteins and allows easy prototyping of algorithms on the conformation space models, by extending an existing molecular modeling tool. Our toolkit creates a factor graph to represent the conformation space model and links it with an inference framework. This enables execution of statistical inference tasks and implementation of algorithms that work on graphical models. As an application of our toolkit, we study estimating free energy changes after mutations. We show that it is possible to represent molecular dynamics trajectories using graphical models and free energy perturbation calculations can be done efficiently on these models.

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. / Doctor of Philosophy / 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.