Applications of molecular modeling techniques in the design of xanthine based adenosine receptor antagonists and the development of the protein function annotation method SALSA

of Dissertation In the field of molecular modeling, theoretical and computational methods are used to study biological structures, dynamics and interactions. Using functional site predictors to identify the binding sites in 3D protein structures provides information that can be used in the fields of drug design and protein function annotation. This dissertation is divided in two parts, each describes the application of molecular modeling techniques in the development of antitumor immunotherapies to target the human A2A adenosine receptor and the development of an automated program, Structurally Aligned Local Sites of Activity (SALSA), to assign function based on local sites of alignment and chemical properties. Cancer cells undergo tumor hypoxia in the earlier stages of growth. Hypoxic tumors generally have poor prognosis and can become resistant to traditional radiation and chemotherapy treatments. In the tumor microenvironment, an overaccumulation of adenosine is produced through the hypoxia-adenosinergic tissue-protecting mechanism that activates A2A adenosine receptors (A2AAR) on the surface of surrounding cells, leading to the protection of cancerous tissues. Targeting the A2AAR with antagonists will disrupt A2AAR signaling, thus preventing the protection of tumor cells. Molecular modeling techniques were applied to the design and optimization of xanthine-based A2AAR antagonists. In silico docking studies, using the programs AutoDock 4.0 and Schrodinger’s GLIDE, yielded similar and favorable binding poses to identify a lead compound. The lead compound was converted to a PEG derivative tested in two in vitro functional binding assays to confirm efficacy. The lead compound performed better than a previously known antagonist. In 2000, the Protein Structure Initiative (PSI) was launched to make the 3D structures of proteins easily attainable from the knowledge of their corresponding DNA sequences. The PSI project generated a large set of 3D structures named structural genomics proteins. To date, there are 12,900 structural genomics (SG) protein structures reported in the Protein Data Bank. These SG proteins were annotated (assigned function) using sequence or structural similarity matching tools. Unfortunately, a number of these SG proteins are listed with unknown, putative and hypothetical function. The computational method, Structurally Aligned Local Sites of Activity (SALSA) is able to reliably assign function to these SG proteins. The strategy of SALSA is to define functional subclasses of characterized members from a protein superfamily. Functional site predictors identify active site residues to create unique signatures for each subclass. Incorporating multiple structure alignment tools, a local spatial pattern of the active site residues is established for each functional subclass and this can be used to sort a superfamily according to biochemical function. An automated version of SALSA, SALSA-DT, utilizes Delaunay triangulation to better match the local active sites of 3D protein structures for faster structural alignments within the superfamily. The application of the SALSA and SALSA-DT methods applied to the ribulose phosphate binding barrel (RPBB) superfamily, successfully sorts the superfamily and 27 SG proteins are evaluated. Additionally, SALSA revealed more information about the structural architecture of the active sites within the superfamily.