Pallab Dutta

Externally imposed perturbations can subtly reshape protein structure and mechanics prior to the emergence of global unfolding signatures. Delineating locally sensitive zones, however, can pose immense challenges. Subjecting a small folded protein to isotropic pressure spanning 0.001–10 kbar, we develop a simplified and interpretable computational framework to characterize local responses embedded within its conformational ensemble. Pairwise mechanical stiffness descriptors are treated as high-dimensional mechanical fingerprints and projected using linear discriminant analysis (LDA) to identify the pressure ranges that maximally separate conformational populations. To attribute the discriminative mechanical changes to specific residues, we combine the LDA model with SHapley Additive exPlanations, yielding a ranked set of locally responsive sites. While the overall rigidity landscape of the protein shows only modest pressure dependence, the top-ranked residues exhibit pronounced, pressure-specific alterations in solvent association. Radial distribution functions and tetrahedral order metrics reveal significant reorganization of hydration structure around the leading hotspot across the LDA-inferred transition region, whereas low-ranked sites display weak or nonspecific hydration changes. Together, these results connect global thermodynamic perturbation to localized mechanical and hydration responses and suggest a practical route for prioritizing mutation sites and mechanistic hypotheses in perturbed biomolecular systems.

Membrane protein folding in the viscous microenvironment of a lipid bilayer is an inherently slow process that challenges experiments and computational efforts alike. The folding kinetics is moreover associated with topological modulations of the biological milieu. Studying such structural changes in membrane-embedded proteins and understanding the associated topological signatures in membrane leaflets, therefore, remain relatively unexplored. Herein, we first aim to estimate the free energy barrier and the minimum free energy path (MFEP) connecting the membrane-embedded fully and partially inserted states of the bacteriorhodopsin fragment. To achieve this, we have considered independent sets of simulations from membrane-mimicking and membrane-embedded environments, respectively. An autoencoder model is used to elicit state-distinguishable collective variables for the system utilizing membrane-mimicking simulations. Our in-house Expectation Maximized Molecular Dynamics algorithm is initially used to deduce the barrier height between the two membrane-embedded states. Next, we develop the Geometry Optimized Local Direction search as a post-processing algorithm to identify the MFEP and the corresponding peptide conformations from the autoencoder-projected trajectories. Finally, we apply a graph attention neural network (GAT) model to learn the membrane surface topology as a function of the associated peptide structure, supervised by the membrane-embedded simulations. The resultant GAT model is then utilized to predict the membrane leaflet topology for the peptide structures along MFEP, obtained from membrane-mimicking simulations. The combined framework is expected to be useful in capturing key phenomena accompanying folding transitions in membranes. We discuss opportunities and avenues for further development.

Computer simulations are increasingly used to access thermo‐kinetic information underlying structural transformation of protein kinases. Such information are necessary to probe their roles in disease progression and interactions with drug targets. However, the investigations are frequently challenged by forbiddingly high computational expense, and by the lack of standard protocols for the design of low dimensional physical descriptors that encode system features important for transitions. Here, we consider the demarcating characteristics of the different states of Abelson tyrosine kinase associated with distinct catalytic activity to construct a set of physically meaningful, orthogonal collective variables that preserve the slow modes of the system. Independent sampling of each metastable state is followed by the estimation of global partition function along the appropriate physical descriptors using the modified Expectation Maximized Molecular Dynamics method. The resultant free energy barriers are in excellent agreement with experimentally known rate‐limiting dynamics and activation energy computed with conventional enhanced sampling methods. We discuss possible directions for further development and applications.

Biophysical processes often encounter high energy transition states that lie in regions of the free energy landscape (FEL) inaccesible to conventional molecular dynamics simulations. Various enhanced sampling methods have been developed to handle the inherent quasi-nonergodicity, either by adding a biasing potential to the underlying Hamiltonian or by forcing the transitions with parallel tempering. However, when attempting to probe systems of increasing complexity with limited computational resources, there arises an imminent need for fast and efficient FEL exploration with sufficient accuracy. Herein, we present a computationally efficient algorithm based on statistical inference for fast estimation of key features in the two-dimensional FEL. Unlike conventional enhanced sampling methods, this newly developed method avoids direct sampling of high free energy states. Rather, the transition states connecting metastable regions of comparable free energies are estimated using Bayesian likelihood maximization. Furthermore, the method incorporates a tunable self-feedback mechanism with classical molecular dynamics for preventing unnecessary sampling that no more effectively contributes to the underlying distributions of metastable states. We have applied this novel protocol in three independent case studies and compared the results against a conventional method. We conclude with the scope of further developments for improved accuracy of the new method and its generalization toward estimation of features in more complex FELs.