Yingkai Zhang, Ph.D. - US grants

With support from the Major Research Instrumentation (MRI) Program, the Department of Chemistry at New York University will acquire large-scale parallel computational resources for biological and materials modeling. This equipment will enhance research in a number of areas including a) the application of novel conformational sampling tools to protein structure prediction; b) modeling of DNA polymerase mechanisms; c) studies of metalloenzyme mechanisms; d) analysis of protein-ligand binding; e) accurate treatment of hydrogen-bond dynamics in supramolecular complexes; f) materials design for proton-exchange membranes; g) computationally aided design of novel RNAs; and h) development of linear scaling electronic structure algorithms.

A cluster of fast, modern computer workstations is vital to serving the computing needs of active research departments. Such a "computer network" also serves as a development environment for new theoretical codes and algorithms, provides state-of-the-art graphics and visualization facilities, and supports research in state-of-the-art applications of parallel processing. These studies will have a significant impact in a wide number of areas, including biochemistry and materials science.

Yingkai Zhang of New York University is supported by the Theoretical and Computational Chemistry program within the Chemistry Division in a co-funding arrangement with the Molecular Biophysics program within Molecular and Cellular Biosciences for research involving the development and application of novel theoretical and computational methods to understand the inner workings of metalloenzymes. The investigation is focusing on mononuclear non-heme iron (II) enzymes, including peptide deformylase and alpha-ketoglutarate dioxygenases. The theoretical approaches combine density functional theory and QM/MM methods which allow for accurate modeling of the metal active site while properly including the effects of the protein environment. This work is having a broad impact in the field of biomolecular modeling and is being carried out in conjunction with the establishment of a new computational biology graduate program at NYU.

DESCRIPTION (provided by applicant): Our long term goal is to develop and apply computational methods to provide novel mechanistic insights into catalysis and regulation of histone modifying enzymes, and to facilitate the rational design of enzyme sub-type specific modulators for probing epigenetic pathways and therapeutic use. Reversible histone acetylation has emerged as a vital regulator in a multitude of essential epigenetic processes. The enzymes responsible for this essential post-translational modification are histone acetyl transferases (HATs) and histone deacetylases (HDACs) that add and remove acetyl groups to and from target lysine residues, respectively. The aberrant activity of these enzymes has been implicated in numerous human diseases, notably cancer, and quite a few HATs and HDACs have been established as important drug targets. Our theoretical approaches will center on Born-Oppenheimer ab initio QM/MM molecular dynamics simulations, a state-of-the-art computational approach to simulate enzyme reactions which allow for accurate modeling of the chemistry at the enzyme active site while properly including dynamics and effects of protein environment. The specific aims are: 1. Characterize the catalytic mechanism for HATs and rational redesign of tGcn5 for its improved efficiency. Aim 2: Elucidate inner workings of sirtuins, a novel family o class III histone deacetylases. Aim 3: Advance ab initio QM/MM methods. The successful completion of the proposed research will provide a detailed mechanistic understanding for HATs and sirtuins for the first time. This will stimulate further mechanistic studies of these important enzymes, and facilitate the development of novel mechanism-based modulators for probing acetylation dependent epigenetic pathways and for therapeutic use. Meanwhile, our methodology development efforts will significantly advance this computational tour de force to simulate enzyme reactions, and help establish it as an equal partner to experimental approaches in this important field of enzymology.

DESCRIPTION (provided by applicant): This proposal is directed at developing novel transferable non-bonded force fields to model zinc metalloproteins and to design zinc enzyme inhibitors. Zinc proteins play essential roles in many biological processes, and there is an increasing appreciation of their biological and medical importance. For example, zinc-dependent histone deacetylases (HDACs) play a critical role in transcriptional repression and gene silencing, and are among the most attractive targets for the development of new therapeutics against cancer and various other diseases. Thus, robust computational approaches are greatly needed to help characterize the structure and dynamics of zinc metalloproteins, and to facilitate the design and ranking of zinc enzyme inhibitors. However, progress along this direction has been very much impeded mainly due to the lack of transferable pairwise force fields to adequately describe zinc coordination. The current dominant view is that such a force field may not be possible and that it would be necessary to go beyond the pairwise non-bonded model for reasonable description of the zinc coordination. In our preliminary studies, we have discovered a novel practical strategy to overcome this inherent challenge, which is to design short-long effective functions (SLEF) to treat electrostatic interactions between the zinc ion and all other atoms. Our preliminary results indicated that this SLEF approach is very promising to adequately model flexible zinc coordination. Here we propose to develop SLEF force fields to simulate zinc metalloproteins, and to develop SLEF scoring functions for docking ligands into zinc enzymes. The developed SLEF patches to the AMBER and Autodock softwares as well as tutorials and test sets will be made freely available to the public.

Abstract: Protein-protein interactions (PPIs) are central factors in all cellular signaling and gene regulation protein networks, and their misregulation has been associated with a variety of diseases, notably cancer. Inevitably, many PPIs are biologically compelling targets for drug discovery. However, PPIs feature large, flat binding surfaces, lacking the tight-binding cavities that define typical drug targets. Accordingly, many PPIs pose a fundamental thermodynamic challenge to the development of conventional small molecule modulators. A promising PPI inhibitor discovery strategy is to use miniature protein domain mimetics (PDMs) to reproduce the key interface contacts utilized by nature. PDMs are advantageous as medium-sized molecules with high surface complementarity and a broader set of contact points than typical small molecules, but are still limited because?by definition?only a portion of the total PPI binding energy is captured in the interaction. The binding affinity of the synthetic domains is often lower than the cognate full-length proteins. Targeted covalent inhibition is an orthogonal therapeutic approach traditionally employed to enhance binding affinities of small molecules, but the approach has a potential drawback as the high reactivity of typical covalent warheads may lead to nonspecific interactions and toxicity. Here we propose to develop computational methods for a new design strategy that will leverage the strengths of these two methods?PDMs and covalent inhibition? while simultaneously mitigating their respective limitations. The focus of the effort is to rationally discover potent inhibitors that will non-covalently recognize and then covalently target protein- protein binding interfaces with exquisite specificity.

Abstract The overall goal of my research program is to develop and apply computational tools to facilitate the rational design of modulators of important cellular pathways for therapeutic use. Protein-protein interactions (PPIs) are central factors in cellular signaling and gene regulation networks. Their misregulation is associated with a variety of diseases, including cancer, neurodegenerative disease, autoimmune disease, and diabetes. Inevitably, many PPIs are biologically compelling targets for drug discovery. But despite a few notable successes, most PPIs have not been successfully targeted and remain undruggable. The fundamental challenge derives from their intrinsic structural features: the binding surfaces of many PPIs are generally large in area, flat, and dynamic. PPIs are often transient and involve multivalent contacts. Currently, one most promising PPI inhibitor discovery strategy is to use miniature protein domain mimetics (PDMs) to reproduce the key interface contacts utilized by nature. PDMs are advantageous as medium-sized molecules with high surface complementarity and a broader set of contact points than typical small molecules, but are still limited because?by definition?only a portion of the total PPI binding energy is captured in the interaction. The binding affinity of the synthetic domains is often lower than the cognate full-length proteins. On the other hand, targeted covalent inhibition is an orthogonal therapeutic approach fit to overcome the fundamental binding limitations at PPIs, but has a well-known drawback: the high reactivity of typical covalent warheads leads to nonspecific inhibition, and toxicity. Here we aim to develop computational methods for a new design strategy that will leverage the strengths of these two methods?PDMs and covalent inhibition?while simultaneously mitigating their respective limitations. The focus of the effort is to rationally discover potent inhibitors that will non-covalently recognize and then covalently target protein-protein binding interfaces with exquisite specificity. Furthermore, our development of robust scoring functions by integrating multitask machine learning and molecular modeling would significantly accelerate the rational drug discovery process. The planned work builds on our recent advances in three state-of-the-art computational approaches: AlphaSpace for fragment- centric topographical mapping of PPI interfaces; ab initio QM/MM molecular dynamics for modeling covalent inhibition; and a novel delta-machine learning strategy to simultaneously improve scoring, docking and screening performance of a protein-ligand scoring function. Our design efforts will result in highly specific and potent modulators of a variety of therapeutically important but previously undruggable PPI interfaces, providing new leads for drug development.