Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence.

Computational approaches can provide highly detailed insight into the molecular recognition processes that underlie drug binding, the assembly of protein complexes, and the regulation of biological functional processes. Classical simulation methods can bridge a wide range of length- and time-scales typically involved in such processes. Lately, automated learning and artificial intelligence methods have shown the potential to expand the reach of physics-based approaches, ushering in the possibility to model and even design complex protein architectures. The synergy between atomistic simulations and AI methods is an emerging frontier with a huge potential for advances in structural biology. Herein, we explore various examples and frameworks for these approaches, providing select instances and applications that illustrate their impact on fundamental biomolecular problems.

Conformational Behavior of SARS-Cov-2 Spike Protein Variants: Evolutionary Jumps in Sequence Reverberate in Structural Dynamic Differences.

SARS-CoV-2 has evolved rapidly in the first 3 years of pandemic diffusion. The initial evolution of the virus appeared to proceed through big jumps in sequence changes rather than through the stepwise accumulation of point mutations on already established variants. Here, we examine whether this nonlinear mutational process reverberates in variations of the conformational dynamics of the SARS-CoV-2 Spike protein (S-protein), the first point of contact between the virus and the human host. We run extensive microsecond-scale molecular dynamics simulations of seven distinct variants of the protein in their fully glycosylated state and set out to elucidate possible links between the mutational spectrum of the S-protein and the structural dynamics of the respective variant, at global and local levels. The results reveal that mutation-dependent structural and dynamic modulations mostly consist of increased coordinated motions in variants that acquire stability and in an increased internal flexibility in variants that are less stable. Importantly, a limited number of functionally important substructures (the receptor binding domain, in particular) share the same time of movements in all variants, indicating efficient preorganization for functional regions dedicated to host interactions. Our results support a model in which the internal dynamics of the S-proteins from different strains varies in a way that reflects the observed random and non-stepwise jumps in sequence evolution, while conserving the functionally oriented traits of conformational dynamics necessary to support productive interactions with host receptors.

AlphaFold predicted structure of the Hsp90-like domains of the neurodegeneration linked protein sacsin reveals key residues for ATPase activity.

The ataxia-linked protein sacsin has three regions of partial homology to Hsp90's N-terminal ATP binding domain. Although a crystal structure for this Hsp90-like domain has been reported the precise molecular interactions required for ATP-binding and hydrolysis are unclear and it is debatable whether ATP biding is compatible with these domains. Furthermore, the Identification of a sacsin domain(s) equivalent to the middle domain of Hsp90 has been elusive. Here we present the superimposition of an AlphaFold structure of sacsin with yeast Hsp90, which provides novel insights into sacsin's structure. We identify residues within the sacsin Hsp90-like domains that are required for ATP binding and hydrolysis, including the putative catalytic arginine residues equivalent to that of the Hsp90 middle domain. Importantly, our analysis allows comparison of the Hsp90 middle domain with corresponding sacsin regions and identifies a shorter lid segment, in the sacsin ATP-binding domains, than the one found in the N-terminal domain of Hsp90. Our results show how a realignment of residues in the lid segment of sacsin that are involved in ATP binding can better match equivalent residues seen in Hsp90, which we then corroborated using molecular dynamic simulations. We speculate, from a structural viewpoint, why some ATP competitive inhibitors of Hsp90 may not bind sacsin, while others would. Together our analysis supports the hypothesis that sacsin's function is ATP-driven and would be consistent with it having a role as a super molecular chaperone. We propose that the SR1 regions of sacsin be renamed as HSP-NRD (Hsp90 N-Terminal Repeat Domain; residues 84-324) and the fragment immediately after as HSP-MRD (Hsp90 Middle Repeat Domain; residues 325-518).