@article {152899, title = {The alteration of structural network upon transient association between proteins studied using graph theory.}, journal = {Proteins}, volume = {93}, year = {2025}, month = {2025 Jan}, pages = {217-225}, abstract = {

Proteins such as enzymes perform their function by predominant non-covalent bond interactions between transiently interacting units. There is an impact on the overall structural topology of the protein, albeit transient nature of such interactions, that enable proteins to deactivate or activate. This aspect of the alteration of the structural topology is studied by employing protein structural networks, which are node-edge representative models of protein structure, reported as a robust tool for capturing interactions between residues. Several methods have been optimized to collect meaningful, functionally relevant information by studying alteration of structural networks. In this article, different methods of comparing protein structural networks are employed, along with spectral decomposition of graphs to study the subtle impact of protein-protein interactions. A detailed analysis of the structural network of interacting partners is performed across a dataset of around 900 pairs of bound complexes and corresponding unbound protein structures. The variation in network parameters at, around, and far away from the interface are analyzed. Finally, we present interesting case studies, where an allosteric mechanism of structural impact is understood from communication-path detection methods. The results of this analysis are beneficial in understanding protein stability, for future engineering, and docking studies.

}, keywords = {Algorithms, Allosteric Regulation, Databases, Protein, Models, Molecular, Protein Binding, Protein Conformation, Protein Interaction Mapping, Protein Interaction Maps, Proteins}, issn = {1097-0134}, doi = {10.1002/prot.26606}, author = {Prabantu, Vasam Manjveekar and Tandon, Himani and Sandhya, Sankaran and Sowdhamini, Ramanathan and Srinivasan, Narayanaswamy} } @article {161980, title = {Structure-guided engineering of protein stability through core hydrophobicity.}, journal = {Protein Sci}, volume = {34}, year = {2025}, month = {2025 Dec}, pages = {e70360}, abstract = {

Engineering thermostable proteins is advantageous for industrial and biomedical applications, where improved thermal stability can enhance conformational integrity, prolong functional half-life, and increase catalytic efficiency at elevated temperatures. We explored modifying the length of buried hydrocarbon chains to improve protein thermal stability. By optimizing the hydrophobic core through targeted amino acid substitutions, we aimed to minimize internal voids and improve core packing. To achieve this, we developed an algorithm that replaces buried hydrophobic residues with longer or bulkier hydrophobic side chains. The algorithm calculates the free energy of unfolding (ΔG) for each substitution, selecting only significantly stabilizing configurations. Functionally important residues and contact networks were excluded from mutation to preserve protein function. We applied the method to several proteins from the beta-grasp fold family. For experimental validation, we chose NEDD8, a beta-grasp protein with poor solubility and low thermal stability. Two subtle substitutions predicted by our algorithm increased NEDD8{\textquoteright}s thermal stability by 1.7 kcal/mol and raised its melting point by 17{\textdegree}C. MD simulations and NMR spectroscopy revealed reduced conformational fluctuations and increased stabilizing interactions, such as hydrogen bonding and electrostatic contacts. Functional assays confirmed that the substitutions did not perturb NEDD8{\textquoteright}s global fold or interactions with cofactors and enzymes. These results highlight the effectiveness of tuning buried hydrophobic residues to enhance protein stability without compromising function. This strategy could serve as a general framework for designing robust therapeutic proteins and enzymes for industrial or biomedical applications.

}, keywords = {Algorithms, Amino Acid Substitution, Hydrophobic and Hydrophilic Interactions, Molecular Dynamics Simulation, NEDD8 Protein, Protein Conformation, Protein Engineering, Protein Stability, Thermodynamics}, issn = {1469-896X}, doi = {10.1002/pro.70360}, author = {Ravichandran, Aravind and Puri, Anindita and Bhate, Suhas H and Habibullah, Batul Ismail and Singh, Gajendra and Das, Ranabir} } @article {152898, title = {BharatSim: An agent-based modelling framework for India.}, journal = {PLoS Comput Biol}, volume = {20}, year = {2024}, month = {2024 Dec}, pages = {e1012682}, abstract = {

BharatSim is an open-source agent-based modelling framework for the Indian population. It can simulate populations at multiple scales, from small communities to states. BharatSim uses a synthetic population created by applying statistical methods and machine learning algorithms to survey data from multiple sources, including the Census of India, the India Human Development Survey, the National Sample Survey, and the Gridded Population of the World. This synthetic population defines individual agents with multiple attributes, among them age, gender, home and work locations, pre-existing health conditions, and socio-economic and employment status. BharatSim{\textquoteright}s domain-specific language provides a framework for the simulation of diverse models. Its computational core, coded in Scala, supports simulations of a large number of individual agents, up to 50 million. Here, we describe the design and implementation of BharatSim, using it to address three questions motivated by the COVID-19 pandemic in India: (i) When can schools be safely reopened given specified levels of hybrid immunity?, (ii) How do new variants alter disease dynamics in the background of prior infections and vaccinations? and (iii) How can the effects of varied non-pharmaceutical interventions (NPIs) be quantified for a model Indian city? Through its India-specific synthetic population, BharatSim allows disease modellers to address questions unique to this country. It should also find use in the computational social sciences, potentially providing new insights into emergent patterns in social behaviour.

}, keywords = {Algorithms, Computational Biology, Computer Simulation, COVID-19, Humans, India, Machine Learning, Pandemics, SARS-CoV-2, Systems Analysis}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1012682}, author = {Cherian, Philip and Kshirsagar, Jayanta and Neekhra, Bhavesh and Deshkar, Gaurav and Hayatnagarkar, Harshal and Kapoor, Kshitij and Kaski, Chandrakant and Kathar, Ganesh and Khandekar, Swapnil and Mookherjee, Saurabh and Ninawe, Praveen and Noronha, Riz Fernando and Ranka, Pranjal and Sinha, Vaibhhav and Vinod, Tina and Yadav, Chhaya and Gupta, Debayan and Menon, Gautam I} } @article {120168, title = {Integrative network analysis interweaves the missing links in cardiomyopathy diseasome.}, journal = {Sci Rep}, volume = {12}, year = {2022}, month = {2022 Nov 16}, pages = {19670}, abstract = {

Cardiomyopathies are progressive disease conditions that give rise to an abnormal heart phenotype and are a leading cause of heart failures in the general population. These are complex diseases that show co-morbidity with other diseases. The molecular interaction network in the localised disease neighbourhood is an important step toward deciphering molecular mechanisms underlying these complex conditions. In this pursuit, we employed network medicine techniques to systematically investigate cardiomyopathy{\textquoteright}s genetic interplay with other diseases and uncover the molecular players underlying these associations. We predicted a set of candidate genes in cardiomyopathy by exploring the DIAMOnD algorithm on the human interactome. We next revealed how these candidate genes form association across different diseases and highlighted the predominant association with brain, cancer and metabolic diseases. Through integrative systems analysis of molecular pathways, heart-specific mouse knockout data and disease tissue-specific transcriptomic data, we screened and ascertained prominent candidates that show abnormal heart phenotype, including NOS3, MMP2 and SIRT1. Our computational analysis broadens the understanding of the genetic associations of cardiomyopathies with other diseases and holds great potential in cardiomyopathy research.

}, keywords = {Algorithms, Animals, Cardiomyopathies, Heart, Humans, Mice, Phenotype}, issn = {2045-2322}, doi = {10.1038/s41598-022-24246-x}, author = {Chauhan, Pankaj Kumar and Sowdhamini, Ramanathan} } @article {869, title = {DOCKSCORE: a webserver for ranking protein-protein docked poses.}, journal = {BMC Bioinformatics}, volume = {16}, year = {2015}, month = {2015}, pages = {127}, abstract = {

BACKGROUND: Proteins interact with a variety of other molecules such as nucleic acids, small molecules and other proteins inside the cell. Structure-determination of protein-protein complexes is challenging due to several reasons such as the large molecular weights of these macromolecular complexes, their dynamic nature, difficulty in purification and sample preparation. Computational docking permits an early understanding of the feasibility and mode of protein-protein interactions. However, docking algorithms propose a number of solutions and it is a challenging task to select the native or near native pose(s) from this pool. DockScore is an objective scoring scheme that can be used to rank protein-protein docked poses. It considers several interface parameters, namely, surface area, evolutionary conservation, hydrophobicity, short contacts and spatial clustering at the interface for scoring.

RESULTS: We have implemented DockScore in form of a webserver for its use by the scientific community. DockScore webserver can be employed, subsequent to docking, to perform scoring of the docked solutions, starting from multiple poses as inputs. The results, on scores and ranks for all the poses, can be downloaded as a csv file and graphical view of the interface of best ranking poses is possible.

CONCLUSIONS: The webserver for DockScore is made freely available for the scientific community at: http://caps.ncbs.res.in/dockscore/ .

}, keywords = {Algorithms, Humans, Internet, Molecular Docking Simulation, Protein Binding, Protein Interaction Domains and Motifs, Proteins, ras GTPase-Activating Proteins, Software}, issn = {1471-2105}, doi = {10.1186/s12859-015-0572-6}, author = {Malhotra, Sony and Mathew, Oommen K and Sowdhamini, Ramanathan} }