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.

Protein folding can be viewed as the origami engineering of biology resulting from the long process of evolution. Even decades after its recognition, research efforts worldwide focus on demystifying molecular factors that underlie protein structure−function relationships; this is particularly relevant in the era of proteopathic disease. A complex co-occurrence of different physicochemical factors such as temperature, pressure, solvent, cosolvent, macromolecular crowding, confinement, and mutations that represent realistic biological environments are known to modulate the folding process and protein stability in unique ways. In the current review, we have contextually summarized the substantial efforts in unveiling individual effects of these perturbative factors, with major attention toward bottomup approaches. Moreover, we briefly present some of the biotechnological applications of the insights derived from these studies over various applications including pharmaceuticals, biofuels, cryopreservation, and novel materials. Finally, we conclude by summarizing the challenges in studying the combined effects of multifactorial perturbations in protein folding and refer to complementary advances in experiment and computational techniques that lend insights to the emergent challenges.

Computer simulations are increasingly used to access thermokinetic 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 mean-ingful, 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.

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