You are here
Nilesh Banavali, Ph.D.
Nilesh Banavali, Ph.D.
Biological systems exist through a complex interplay of a diverse range of macromolecules performing specific functions. For each macromolecule, the gap between visualizing its three dimensional (3D) structure and understanding its functional properties needs to be traversed by a description of the free energy landscapes governing its activity. The function and assembly of large macromolecular complexes relies on molecular recognition of individual macromolecules by their partners and dynamic control of their interaction with substrates. The primary goal of our research is to use Molecular Mechanics (MM) based simulation methods to understand free energy landscapes describing chemical and conformational change in macromolecules and their complexes. We also aim to validate our computational predictions with structural and biochemical data through our collaborations with experimental groups. Our research is presently focused on the following areas:
Dynamic mechanisms behind indel mutations and their sequence dependence in nucleic acid replication:
Base pairing, or its reverse (base pair separation), is the main molecular mechanism involved in the three primary processes in the central dogma of biology: replication, transcription and translation. In nucleic acid replication, a single error in template base recognition, left uncorrected, can result in drastic malfunctions in all downstream processes. The sequence context of each base can have a large effect on its recognition by its pairing partner, and the specific protein environment also plays an important role in facilitating the required dynamic changes. We aim to establish a quantitative and atomistic understanding of such dynamic and molecular recognition processes involved in nucleic acid function. We have recently delineated the underlying 3D strand slippage mechanism by which a single base insertion or deletion (indel) mutation can occur in DNA strand extension, and identified the possibility of specific non-canonical interactions providing a direct mechanism for sequence dependence of indel mutations (Banavali, JACS, 2013). This work is being applied to understand the introduction of such mutations during strand extension by error-prone Y-family DNA polymerases in collaboration with Dr. Janice Pata at the Wadsworth Center.
Atomic-detail articulation of the detailed dynamics of biochemical reaction mechanisms:
We have recently developed a strategy called Altered Bonds and Iterative Topology Switching (ABITS) to obtain multiple iterative atomic-detail trajectories for biochemical reactions using standard biomacromolecular MM programs. The strategy assumes that the response of the environment to a chemical rearrangement depends more strongly on the structural details of the rearrangement as compared to the intrinsic energetics of the reaction. We are able to generate realistic trajectories for complex biochemical reactions by altering the standard harmonic bond energy term for each bond being formed or cleaved and treating this term as an external bias restraint function. Since the strategy uses MM energy functions, it is orders of magnitude faster than standard Quantum Mechanics/Molecular Mechanics (QM/MM) approaches. Its ability to rapidly explore different postulated mechanisms in 3D atomic detail is ideally suited for computational biochemists to use as a first step before QM/MM calculations, or for experimentalists to identify mutations that could modulate specific steps of the reactions. The strategy has been successfully applied to model multiple different reactions: (a) Proton transfer in an ammonia transporter, (b) Nucleic acid strand extension in both DNA and RNA polymerases, (c) Chromophore formation in Green Fluorescent Protein (GFP), (d) RNA Splicing by Group I intron, and (e) All three classes of protein splicing mechanisms used by inteins. To utilize its predictive abilities productively, and for experimental validation, this work is pursued in collaboration with local research labs who study these mechanisms through biochemical and structural approaches. Our experimental collaborators for this work are: Dr. Janice Pata at the Wadsworth Center (DNA strand extension), Dr. Joachim Jaeger (DNA and RNA strand extension), Dr. Chris Bystroff at RPI (GFP chromophore formation), Dr. Chunyu Wang at RPI (protein splicing), and Dr. Marlene Belfort at SUNY Albany (RNA and protein splicing).
Structure determination and inhibitor design:
In spite of continuing advances, structure determination of large macromolecular assemblies is still an extremely challenging problem for higher resolution techniques such as X-Ray crystallography or Nuclear Magnetic Resonance spectroscopy. Combination of these techniques with lower resolution techniques such as cryo-electron microscopy or even predictive computational methods can broaden the length scales addressed. We aim to develop protocols to combine experimental data at different resolutions with computational structure prediction strategies to obtain pseudo-atomic models for large macromolecular machinery. This work is being pursued in collaboration with Dr. Rajendra Agrawal at the Wadsworth Center to elucidate the detailed structure of the mammalian mitochondrial ribosome, and with Dr. Sam Bowser and Dr. Haixin Sui at the Wadsworth Center to generate structural models for various microtubule architectures. Computational structure prediction methods are also useful in inhibitor design by enabling rapid prediction of structures and interaction energies for a multitude of small molecules binding to a macromolecular binding site. We aim to use these methods to identify small molecule inhibitors for biomedically relevant enzymes. This work is being pursued in collaboration with Dr. Hongmin Li and Dr. Laura Kramer at the Wadsworth Center to identify novel classes of molecules that inhibit essential enzymes in Flaviviruses such as Dengue and West Nile Virus.
Our long range goals are to incorporate detailed atomic-scale understanding of chemical and conformational change into simplified predictive multi-scale models that scrutinize how macromolecular components interact and coordinate their activity to form functional nanoscale cellular machinery.