Production of Distinct Fibrillar, Oligomeric, and Other Aggregation States from Network Models of Multibody Interaction.

Protein aggregation can produce a wide range of states, ranging from fibrillar structures and oligomers to unstructured and semistructured gel phases. Recent work has shown that many of these states can be recapitulated by relatively simple, topological models specified in terms of multibody interaction energies, providing a direct connection between aggregate intermolecular forces and aggregation products. Here, we examine a low-dimensional network Hamiltonian model (NHM) based on four basic multibody interactions found in any aggregate system.

Comparative analysis of thermal adaptations of extremophilic prolyl oligopeptidases.

Prolyl oligopeptidases from psychrophilic, mesophilic, and thermophilic organisms found in a range of natural environments are studied using a combination of protein structure prediction, atomistic molecular dynamics, and trajectory analysis to determine how the S9 protease family adapts to extreme thermal conditions. We compare our results with hypotheses from the literature regarding structural adaptations that allow proteins to maintain structure and function at extreme temperatures, and we find that, in the case of prolyl oligopeptidases, only a subset of proposed adaptations are employed for maintaining stability. The catalytic and propeller domains are highly structured, limiting the range of mutations that can be made to enhance hydrophobicity or form disulfide bonds without disrupting the formation of necessary secondary structure.

Comparative Modeling and Analysis of Extremophilic D-Ala-D-Ala Carboxypeptidases.

Understanding the molecular adaptations of organisms to extreme environments requires a comparative analysis of protein structure, function, and dynamics across species found in different environmental conditions. Computational studies can be particularly useful in this pursuit, allowing exploratory studies of large numbers of proteins under different thermal and chemical conditions that would be infeasible to carry out experimentally. Here, we perform such a study of the MEROPS family S11, S12, and S13 proteases from psychophilic, mesophilic, and thermophilic bacteria.

Mutation Effects on Structure and Dynamics: Adaptive Evolution of the SARS-CoV-2 Main Protease.

The main protease of SARS-CoV-2 (M) plays a critical role in viral replication; although it is relatively conserved, M has nevertheless evolved over the course of the COVID-19 pandemic. Here, we examine phenotypic changes in clinically observed variants of M, relative to the originally reported wild-type enzyme. Using atomistic molecular dynamics simulations, we examine effects of mutation on protein structure and dynamics.

Network Hamiltonian Models for Unstructured Protein Aggregates, with Application to γD-Crystallin.

Network Hamiltonian models (NHMs) are a framework for topological coarse-graining of protein-protein interactions, in which each node corresponds to a protein, and edges are drawn between nodes representing proteins that are noncovalently bound. Here, this framework is applied to aggregates of γD-crystallin, a structural protein of the eye lens implicated in cataract disease. The NHMs in this study are generated from atomistic simulations of equilibrium distributions of wild-type and the cataract-causing variant W42R in solution, performed by Wong, E.

Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures.

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail-an effect that is especially acute for topological representations such as protein structure networks (PSNs).

A cyclic peptide inhibitor of the SARS-CoV-2 main protease.

This paper presents the design and study of a first-in-class cyclic peptide inhibitor against the SARS-CoV-2 main protease (M). The cyclic peptide inhibitor is designed to mimic the conformation of a substrate at a C-terminal autolytic cleavage site of M. The cyclic peptide contains a [4-(2-aminoethyl)phenyl]-acetic acid (AEPA) linker that is designed to enforce a conformation that mimics a peptide substrate of M.

Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease.

The SARS-CoV-2 main protease (M) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant neutral drift and selection pressure, with new M mutations arising over time. Identification and structural characterization of M variants is thus critical for robust inhibitor design.

Sequence characterization and molecular modeling of clinically relevant variants of the SARS-CoV-2 main protease.

The SARS-CoV-2 main protease (M ) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant selection pressure, with new M mutations arising over time. Identification and structural characterization of M variants is thus critical for robust inhibitor design.