Gopalakrishnan Bulusu

Bio: Gopalakrishnan Bulusu is an academic researcher from Tata Consultancy Services. The author has contributed to research in topics: Porphobilinogen & Hydroxymethylbilane Synthase. The author has an hindex of 12, co-authored 34 publications receiving 432 citations. Previous affiliations of Gopalakrishnan Bulusu include University of Hyderabad & Harvard University.

MD simulations of ligand-bound and ligand-free aptamer: Molecular level insights into the binding and switching mechanism of the add A-riboswitch

Abstract: Riboswitches are structural cis-acting genetic regulatory elements in 59 UTRs of mRNAs, consisting of an aptamer domain that regulates the behavior of an expression platform in response to its recognition of, and binding to, specific ligands. While our understanding of the ligand-bound structure of the aptamer domain of the adenine riboswitches is based on crystal structure data and is well characterized, understanding of the structure and dynamics of the ligand-free aptamer is limited to indirect inferences from physicochemical probing experiments. Here we report the results of 15-nsec-long explicit-solvent molecular dynamics simulations of the add A-riboswitch crystal structure (1Y26), both in the adenine-bound (CLOSED) state and in the adenine-free (OPEN) state. Root-mean-square deviation, root-mean-square fluctuation, dynamic cross-correlation, and backbone torsion angle analyses are carried out on the two trajectories. These, along with solvent accessible surface area analysis of the two average structures, are benchmarked against available experimental data and are shown to constitute the basis for obtaining reliable insights into the molecular level details of the binding and switching mechanism. Our analysis reveals the interaction network responsible for, and conformational changes associated with, the communication between the binding pocket and the expression platform. It further highlights the significance of a, hitherto unreported, noncanonical W:H trans base pairing between A73 and A24, in the OPEN state, and also helps us to propose a possibly crucial role of U51 in the context of ligand binding and ligand discrimination.

De novo design of new chemical entities for SARS-CoV-2 using artificial intelligence.

Abstract: Background: The novel coronavirus SARS-CoV-2 has severely affected the health and economy of several countries. Multiple studies are in progress to design novel therapeutics against the potential target proteins in SARS-CoV-2, including 3CL protease, an essential protein for virus replication. Materials & methods: In this study we employed deep neural network-based generative and predictive models for de novo design of small molecules capable of inhibiting the 3CL protease. The generative model was optimized using transfer learning and reinforcement learning to focus around the chemical space corresponding to the protease inhibitors. Multiple physicochemical property filters and virtual screening score were used for the final screening. Conclusion: We have identified 33 potential compounds as ideal candidates for further synthesis and testing against SARS-CoV-2.

Understanding the thermostability and activity of Bacillus subtilis lipase mutants: insights from molecular dynamics simulations.

Abstract: Improving the thermostability of industrial enzymes is an important protein engineering challenge. Point mutations, induced to increase thermostability, affect the structure and dynamics of the target protein in several ways and thus can also affect its activity. There appears to be no general rules for improving the thermostabilty of enzymes without adversely affecting their enzymatic activity. We report MD simulations, of wild type Bacillus subtilis lipase (WT) and its six progressively thermostable mutants (2M, 3M, 4M, 6M, 9M, and 12M), performed at different temperatures, to address this issue. Less thermostable mutants (LTMs), 2M to 6M, show WT-like dynamics at all simulation temperatures. However, the two more thermostable mutants (MTMs) show the required flexibility at appropriate temperature ranges and maintain conformational stability at high temperature. They show a deep and rugged free-energy landscape, confining them within a near-native conformational space by conserving noncovalent interactions, and thus protecting them from possible aggregation. In contrast, the LTMs having marginally higher thermostabilities than WT show greater probabilities of accessing non-native conformations, which, due to aggregation, have reduced possibilities of reverting to their respective native states under refolding conditions. Our analysis indicates the possibility of nonadditive effects of point mutations on the conformational stability of LTMs.

Accelerating De Novo Drug Design against Novel Proteins Using Deep Learning.

Abstract: In the world plagued by the emergence of new diseases, it is essential that we accelerate the drug design process to develop new therapeutics against them. In recent years, deep learning-based methods have shown some success in ligand-based drug design. Yet, these methods face the problem of data scarcity while designing drugs against a novel target. In this work, the potential of deep learning and molecular modeling approaches was leveraged to develop a drug design pipeline, which can be useful for cases where there is limited or no availability of target-specific ligand datasets. Inhibitors of the homologues of the target protein were screened at the active site of the target protein to create an initial target-specific dataset. Transfer learning was used to learn the features of the target-specific dataset. A deep predictive model was utilized to predict the docking scores of newly designed molecules. Both these models were combined using reinforcement learning to design new chemical entities with an optimized docking score. The pipeline was validated by designing inhibitors against the human JAK2 protein, where none of the existing JAK2 inhibitors were used for training. The ability of the method to reproduce existing molecules from the validation dataset and design molecules with better binding energy demonstrates the potential of the proposed approach.

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Abstract: Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview.

Abstract: With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the ava...

AMP-activated protein kinase: the current landscape for drug development.

Abstract: Since the discovery of AMP-activated protein kinase (AMPK) as a central regulator of energy homeostasis, many exciting insights into its structure, regulation and physiological roles have been revealed. While exercise, caloric restriction, metformin and many natural products increase AMPK activity and exert a multitude of health benefits, developing direct activators of AMPK to elicit beneficial effects has been challenging. However, in recent years, direct AMPK activators have been identified and tested in preclinical models, and a small number have entered clinical trials. Despite these advances, which disease(s) represent the best indications for therapeutic AMPK activation and the long-term safety of such approaches remain to be established.