Statistical Mechanics in Chemistry and Biology (SMCB)

Schedule: 10.07.2025 (Thursday) at 3:00 PM (IST)

Duration: 1 hour, followed by discussion

Talk title: EVOLVE: A Statistical Mechanics-Guided Machine Learning Method for Protein Mutation Prediction and Evolutionary Phase Exploration

Abstract: 

Statistical Mechanics not only provides exact microscopic expressions for thermodynamic quantities like entropy and free energy but also offers one of its most profound contributions—the fluctuation–dissipation theorem, connecting spontaneous equilibrium fluctuations to system responses under external perturbations. For example, energy fluctuations relate to specific heat, volume fluctuations to compressibility, and number density fluctuations to susceptibility. [1] For timedependent phenomena, similar principles underlie key results such as Einstein’s celebrated relation between diffusion and mobility. These response functions often undergo dramatic changes, or tend to diverge near critical points, signalling the onset of phase transitions and therefore, motivating efforts to

identify suitable order parameters to capture early-warning for systems across natural and social sciences. [2] Staying grounded in fundamentals, the first part of this seminar will be dedicated to students, introducing the Landau theory of phase transitions. This framework describes how free energy landscapes, as a function of a relevant order parameter and associated response functions, characterize distinct phases and identify phase transition points. [2,3] Modern extensions of Landau theory to finitetime dynamical phase transitions further broaden its scope to far-from-equilibrium, time-dependent phenomena, even enabling the detection of critical transition time. [4] Within this framework, I’ll introduce the Mutational Response Function (MRF)—aptly quantifying viral protein’s mutational entropy fluctuations. [5] MRF captures sharp transitions that mark the emergence of SARS-CoV-2

Variants of Concern (VOC) and Variants Under Monitoring (VUM), as defined by the WHO, and this evolutionary transition has been extensively validated across viral genomic and proteomic datasets spanning multiple variants. In the second part of this seminar, I’ll show how mutational entropy helps spatially identify mutation hotspots, and thus, functionally critical residues in viral and bacterial proteins. Building on this, we develop a statistical-mechanics-guided, ancestral-sequence-based machine learning framework to forecast future mutations of a target protein with implications for pathogenicity and transmissibility. Integrating all such sequence-space analysis modules recently, we

have developed EVOLVE, a web tool enabling researchers to explore prospective mutation sites and their collective behaviour. [6] EVOLVE streamlines data upload and analysis with a user-friendly interface and comprehensive tutorials. Access EVOLVE free at https://evolve-iiserkol.com.

References:

1. Biman Bagchi, Statistical Mechanics for Chemistry and Materials Science, CRC Press, Taylor and Francis.

2. L.D. Landau and E.M. Lifshitz. Statistical Physics. Vol. 5. Elsevier.

3. L. D. Landau 1937 On the theory of phase transitions. I Zh. Eksp. Teor. Fiz. 11, 19.

4. J. Meibohm and M. Esposito* 2022 Finite-time dynamical phase transition in nonequilibrium relaxation Phys. Rev. Lett. 128 110603.

5. Satyam Sangeet, Raju Sarkar, Saswat K. Mohanty, Susmita Roy*. 2022 Quantifying Mutational Response to Track the Evolution of SARS-CoV-2 Spike Variants: Introducing a Statistical-Mechanics-Guided Machine Learning Method. J. Phys. Chem. B. 126, 7895–7905.

6. Satyam Sangeet, Anushree. Sinha, Madhav. B. Nair, Arpita Mahata, Raju Sarkar, Susmita Roy*. 2025 EVOLVE: A Web Platform for AI-based Protein Mutation Prediction and Evolutionary Phase Exploration, J. Chem. Inf. Model. 2025, 65, 4293-4310.