Protein structure predication is a field of computational molecular modeling with an enormous potential for improvement. Side-chain geometry prediction is a critical component of this process that is crucial for computational protein structure predication as well as crystallographers in refining experimentally determined protein crystal structures. The cornerstone of side-chain geometry prediction are side-chain rotamer libraries, usually obtained through exhaustive statistical analysis of existing protein structures. Little is known, however, about the driving forces leading to the preference or suitability of one rotamer over another. Construction of 3D hydropathic interaction maps for nearly 30,000 tyrosines extracted from the PDB reveals their environments, in terms of hydrophobic and polar (collectively “hydropathic”) interactions. Using a unique 3D similarity metric, these environments were clustered with k-means. In the ϕ, ψ region (–200° < ϕ < –155°; –205° < ψ < –160°) representing 631 tyrosines, clustering reduced the set to 14 unique hydropathic environments, with most diversity arising from favorable hydrophobic interactions. Polar interactions for tyrosine include ubiquitous hydrogen bonding with the phenolic OH and a handful of unique environments surrounding the backbone. The memberships of all but one of the 14 environments are dominated by a single χ1/χ2 rotamer. Each tyrosine residue attempts to fulfill its hydropathic valence. Structural water molecules are thus used in a variety of roles throughout protein structure. A second project involves elucidating the 3D structure of CRIP1a, a cannabinoid 1 receptor (CB1R) binding protein that could provide information for designing small molecules targeting the CRIP1a-CB1R interaction. The CRIP1a protein was produced in high purity. Crystallization experiments failed, both with and without the last 9 or 12 amino acid peptide of the CB1R C-terminus. Attempts were made to use NMR for structure determination; however, the protein precipitated out during data acquisition. A model was thus built computationally to which the CB1R C-terminus peptide was docked. HINT was used in selecting optimum models and analyzing interactions involved in the CRIP1a-CB1R complex. The final model demonstrated key putative interactions between CRIP1a and CB1R while also predicting highly flexible areas of the CRIP1a possibly contributing to the difficulties faced during crystallization.

Hydropathic molecular modeling is a computer-aided molecular design technique for obtaining, representing, and understanding the properties and interactions of biomacromolecular complexes in the biological environment. Hydropathic INTeraction (HINT) is a novel empirical force field to calculate the free energy of intermolecular interaction based on experimentally determined partition coefficients (log Po/w). It includes all the expected interactions between molecules such as hydrogen bonding, hydrophobic, electrostatic, acid-base, and Coulombic interactions, entropy, solvation and others. HINT tools were used to determine, evaluate, and analyze protein-ligand interactions in different research projects: 1) We used these tools to discover small molecule inhibitors of PsaA, a potential target for Streptococcus pneumoniae. We screened and scored potential molecules to obtain hits. After the growth conditions for both the wild type and PsaA mutant of S. pneumoniae were optimized, we then tested our hits. A few compounds passed through the three-stage assay protocol and confirmed the inhibition of PsaA with MICs between 125-250 μM. 2) The SAR of C-3 and C-5 pyrrole-based antitubulin agents at the colchicine-binding site with explicitly solvated models was performed. After docking with GOLD at the colchicine site, post-docking scoring and evaluation were performed with HINT. The total HINT score correlates with binding and activity; similarly, the significance of individual functional groups, protein residues and interactions amongst a collection of compounds can be quantitated. The possibility of water-mediated interactions in a way solvent accessible part of the pocket was considered by subjecting molecular models to MD simulations. Several water molecules were identified to be contributing to the binding and were confirmed by HINT scoring. Finally, using hydropathic molecular modeling tools helped us to understand, evaluate, analyze, and improve protein-ligand interactions in different biological systems.

This work was aimed at developing novel tools that utilize HINT, an empirical forcefield capable of quantitating both hydrophobic and hydrophilic (hydropathic) interactions, for implementation in theoretical biology and drug discovery/design. The role of hydrophobicity in determination of macromolecular structure and formation of complexes in biological molecules is undeniable and has been the subject of research across several decades. Hydrophobicity is introduced, with a review of its history and contemporary theories. This is followed by a description of various methods that quantify this all-pervading phenomenon and their use in protein folding and contemporary drug design projects – including a detailed overview of the HINT forcefield. The specific aim of this dissertation is to introduce our attempts at developing new methods for use in the study of antibacterial drug resistance and antiviral drug discovery. Multidrug efflux is commonly regarded as a fast growing problem in the field of medicine. Several species of microbes are known to have developed resistance against almost all classes of antibiotics by various modes-of-action, which include multidrug transporters (a.k.a. efflux pumps). These proteins are present in both gram-positive and gram-negative bacteria and extrude molecules of various classes. They protect the efflux pump-expressing bacterium from harmful effects of exogenous agents by simply evacuating the latter. Perhaps the best characterized mechanism amongst these is that of the AcrA-AcrB-TolC efflux pump. Data is available in literature and perhaps also in proprietary databases available with pharmaceutical companies, characterizing this pump in terms of the minimum inhibitory concentration ratios (MIC ratios) for various antibiotics. We procured a curated dataset of 32 β-lactam and 12 antibiotics of other classes from this literature. Initial attempts at studying the MIC ratios of β-lactam antibiotics as a function of their three dimensional topology via 3D-quantitative structure activity relationship (3D-QSAR) technology yielded seemingly good models. However, this methodology is essentially designed to address single receptor-ligand interactions. Molecules being transported by the efflux pump must undoubtedly be involved in multiple interactions with the same. Notably, such methods require a pharmacophoric overlap of ligands prior to the generation of models, thereby limiting their applicability to a set of structurally-related compounds. Thus, we designed a novel method that takes various interactions between antibiotic agents and the AcrA-AcrB-TolC pump into account in conjunction with certain properties of the drugs. This method yielded mathematical models that are capable of predicting high/low efflux with significant efficiency (>93% correct). The development of this method, along with the results from its validation, is presented herein. A parallel aim being pursued by us is to discover inhibitors for hemagglutinin-neuraminidase (HN) of human parainfluenza virus type 3 (HPIV3) by in silico screening. The basis for targeting HN is explored, along with commentary on the methodology adopted during this effort. This project yielded a moderate success rate of 34%, perhaps due to problems in the computational methodology utilized. We highlight one particular problem – that of emulating target flexibility – and explore new avenues for overcoming this obstacle in the long run. As a starting point towards enhancing the tools available to us for virtual screening in general (and for discovering antiviral compounds in specific), we explored the compatibility between sidechain rotamer libraries and the HINT scoring function. A new algorithm was designed to optimize amino acid residue sidechains, if provided with the backbone coordinates, by generating sidechain positions using the Dunbrack and Cohen backbone-dependent rotamer library and scoring them with the HINT scoring function. This rotamer library was previously used by its developers previously to design a very successful sidechain optimization algorithm called SCWRL. Output structures from our algorithm were compared with those from SCWRL and showed extraordinary similarities as well as significant differences, which are discussed herein. This successful implementation of HINT in our sidechain optimization algorithm establishes the compatibility between this forcefield and sidechain rotamer libraries. Future aims in this project include enhancement of our current algorithm and the design of a new algorithm to explore partial induced-fit in targets aimed at improving current docking methodology. This work shows significant progress towards the implementation of our hydropathic force field in theoretical modeling of biological systems in order to enhance our ability to understand atomistic details of inter- and intramolecular interactions which must form the basis for a wide variety of biological phenomena. Such efforts are key to not only to understanding the said phenomena, but also towards a solid basis for efficient drug design in the future.

Computationally driven drug design/discovery efforts generally rely on accurate assessment of the forces that guide the molecular recognition process. HINT (Hydropathic INTeraction) is a natural force field, derived from experimentally determined partition coefficients that quantifies all non-bonded interactions in the biological environment, including hydrogen bonding, electrostatic and hydrophobic interactions, and the energy of desolvation. The overall goal of this work is to apply the HINT-based atomic level description of molecular systems to biologically important proteins, to better understand their biochemistry – a key step in exploiting them for therapeutic purposes. This dissertation discusses the results of three diverse projects: i) structural modeling of human sphingosine kinase 2 (SphK2, a novel anticancer target) and binding mode determination of an isoform selective thiazolidine-2,4-dione (TZD) analog; ii) structural modeling of human cytomegalorvirus (HCMV) alkaline nuclease (AN) UL98 (a novel antiviral target) and subsequent virtual screening of its active site; and iii) explicit treatment of interfacial waters during protein-protein docking process using HINT-based computational tools. SphK2 is a key regulator of the sphingosine-rheostat, and its upregulation /overexpression has been associated with cancer development. We report structural modeling studies of a novel TZD-analog that selectively inhibits SphK2, in a HINT analysis that identifies the key structural features of ligand and protein binding site responsible for isoform selectivity. The second aim was to build a three-dimensional structure of a novel HCMV target – AN UL98, to identify its catalytically important residues. HINT analysis of the interaction of 5’ DNA end at its active site is reported. A parallel aim to perform in silico screening with a site-based pharmacophore model, identified several novel hits with potentially desirable chemical features for interaction with UL98 AN. The majority of current protein-protein docking algorithms fail to account for water molecules involved in bridging interactions between partners, mediating and stabilizing their association. HINT is capable of reproducing the physical and chemical properties of such waters, while accounting for their energetic stabilizing contributions. We have designed a solvated protein-protein docking protocol that explicitly models the Relevant bridging waters, and demonstrate that more accurate results are obtained when water is not ignored.