Simulating Protein Structures, Complexes, And Dynamics

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We develop, implement, and apply computational methods to study the structure, dynamics, and functional mechanisms of biomolecules. Over the last year, we have worked in three main areas: i) refinement of a continuum model to represent molecular interactions in solutions, the predictive power of which was demonstrated in the prediction of peptide structures from primary sequences, ii) development of a self-guided multiscale method for modeling many-protein systems in realistic crowded environments, and iii) use of ab-initio quantum chemistry to investigate the geometry and energetics of bioactive compounds. This quantum mechanical approach is particularly useful in elucidating the transition states of chemical reactions that cannot be probed experimentally. The mechanistic understanding provides a firm structural/theoretical basis for controlling the outcome of chemical reactions, e.g., the ratio of possible products (two patents granted). We have begun to develop and apply pattern-recognition techniques, including artificial neural networks (ANN), to identify non-obvious behaviors in biological systems. We are using ANN in two projects with potential impact in translational science: to predict the physiological effects of opioid analgesics from interactions at the receptor level, and to reverse-engineer nanostructures that interact with membranes and proteins in specific ways. This approach has implications in basic and applied research, including precision medicine. Opioid analgesics used to treat chronic pain lead to addiction and have other serious side effects, including potential respiratory arrest. We are carrying out dynamics simulations to study the roles of agonists and antagonists of mu and delta opioid receptors in activating G-protein. We are combining computational modeling techniques developed for small molecules and for macromolecules to propose chemical substitutions that may lead to a deeper understanding of the effects of both opioid-like and non-opioid compounds. We are focusing our attention on a new series of compounds recently found to have G-protein biased properties. These properties are emerging as essential for avoiding side effects, such as respiratory depression. Using computer simulations, we have identified the residues that potentiate the beta-arrestin-2 bias to the cannabinoid receptor 1 (CB1R) antagonists. We have also provided a molecular rationale to design and synthesize biased antagonists that can have a better anti-diabetic and anti-obesity efficacy. A manuscript is in preparation. We have studied gold nanoparticles in serum and in cell media to optimize strategies for drug delivery and imaging. We have used a recently developed multiscaling technique to realistically represent biological media, an approach that speeds up both Monte Carlo and molecular dynamics simulations of multiprotein-multiparticle solutions. With this method, we were able to carry out systematic studies of ultrasmall nanostructures of different designs and have demonstrated that they can be rationally designed to interact with biological matter in specific ways. We are currently using the same approach to predict binding modes, affinities, and kinetics in nanoparticle-protein binding, and to assess whether protein function can be modulated. We have carried out quantum chemical calculations to elucidate the fluorination mechanism of diaryliodonium salts at the atomic level. An understanding of this process is essential in the development of novel 18F-labeled PET probes for brain imaging. Ongoing studies of fluorination include the elucidation of the hydrocarbon fluorination catalyzed by CoF3 as well as the trifluoromethylation mechanism of aryliodonium salts with CuCF3. These studies will provide insight into the efficient synthesis of 11C-labeled fluoroform, e.g. (11C)CHF3, for PET imaging. Male contraceptives have been pursued worldwide, but all clinical trials by the World Health Organization have failed at different stages because of long-term health concerns due to their hormonal basis. This project is centered on finding inhibitors of the Gonadotropin Regulated Testicular RNA Helicase (GRTH/DDX25), a novel protein discovered by our IRP collaborators, which may lead to the development of a non-hormonal contraceptive. Using a variety of molecular modeling techniques, we have developed a reliable model of DDX25 that we have used to develop a series of pharmacophores. We are now using the most promising ones to design a macro-cyclic peptide inhibitor of DDX25 activity. We have synthesized and tested two specific peptides that display the beneficial properties predicted computationally. We have developed a collaboration with structural biologists (NMR Center at Carnegie Mellon University) to characterize the peptide experimentally. This project is also the basis for new methodological developments for reverse-engineering of cyclic peptides. We are investigating the structure and energetics of polymethylated 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) ligand complexed with lanthanide ions. Quantum chemical calculations are being carried out to determine the structural requirements that modulate the thermochemical stability of one isomer over the other. These complexes find application in magnetic resonance imaging and protein-structure studies. We have modeled a major ternary complex that contains a protein recently discovered by one of our IRP collaborators and shown to be a key player in itch sensation associated with eczema, psoriasis, hepatic cholestasis, uremia, and some neuropathies. Using a battery of experimental and computer modeling tools, we were able to design an antibody to inhibit the protein and prevent itch in mice. We proposed a rather general experimental-modeling approach to find inhibitors of complexes with limited structural information based on cross-linking mass spectrometry (XL-MS). We are now applying the same technique to a series of chaperones/co-chaperones (multiprotein complexes and aggregates, where Hsp90 plays the central role) involved in the folding and regulation of protein function. We use known protein structures and homology modeling to interpret the structure-function effects of mutations, including those observed in patients. We are working with NIDDK to study protein-RNA interfaces using computational analyses and experimental verification. Also with NIDDK, we modeled the mouse MLH3 protein to help interpret the elimination of repeat expansions in a mouse stem cell model of the Fragile X-related disorders. In another study, we helped characterize HLA alleles and amino acid residues associated with scleroderma. We have expanded the capabilities of our MemExp software, which is used world-wide to recover distributions of effective lifetimes from kinetics data. The analysis of time-correlated single-photon counting experiments has been improved. With colleagues at Florida State University, we published a paper interpreting the nanosecond-timescale dynamics and conformational heterogeneity in human GCK regulation and disease. Version 6.0 of MemExp was made available.