Protein structure networks provide insight into active site flexibility in esterase/lipases from the carnivorous plant Drosera capensis

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. Using a combination of protein structure prediction, atomistic molecular dynamics, and trajectory analysis, we examine both conserved features and trends across thermal groups. Our findings suggest a number of hypotheses for experimental investigation.

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

The main protease of SARS-CoV-2 (Mpro) plays a critical role in viral replication; although it is relatively conserved, Mpro has nevertheless evolved over the course of the COVID-19 pandemic. Here, we examine phenotypic changes in clinically observed variants of Mpro, relative to the originally reported wild-type enzyme. Using atomistic molecular dynamics simulations, we examine effects of mutation on protein structure and dynamics. In addition to basic structural properties such as variation in surface area and torsion angles, we use protein structure networks and active site networks to evaluate functionally relevant characters related to global cohesion and active site constraint. Substitution analysis shows a continuing trend toward more hydrophobic residues that are dependent on the location of the residue in primary, secondary, tertiary, and quaternary structures. Phylogenetic analysis provides additional evidence for the impact of selective pressure on mutation of Mpro. Overall, these analyses suggest evolutionary adaptation of Mpro toward more hydrophobicity and a less-constrained active site in response to the selective pressures of a novel host environment.

A cold‐active esterase enhances mesophilic properties through Mn 2+ binding

A key aspect of adaptation to cold environments is the production of cold-active enzymes by psychrophilic organisms. These enzymes not only have high activity at low temperatures, but also exhibit remarkable structural flexibility and thermolability. In this context, the role of metal ions has been little explored, and the few available studies seem to suggest that metal binding counteracts structural flexibility. This article reports an investigation into the role of the binding of manganese ion (Mn²⁺ ) in the thermal adaptation of an esterase (M-Est) of the GDSx family, identified in the genome of the Antarctic bacterium Marinomonas sp. ef1. M-Est is specific for esters containing acetate groups and turned out to be a highly thermolabile cold-active enzyme, with a catalysis optimum temperature of 5 °C and a melting temperature of 31.7 °C. A combination of biochemical and computational analyses, including molecular dynamics simulations, revealed that M-Est binds Mn²⁺ ions via a single binding site located on the surface of the enzyme, close to the active site. Although the interaction between M-Est and Mn²⁺ induces only local conformational changes involving the active site, quite surprisingly they trigger an improvement in both thermal stability and catalytic efficiency under mild temperature conditions. These results, together with the conservation of the Mn²⁺ binding site among psychrophilic and psychrotolerant homologues, suggest that Mn²⁺ binding may be a useful, albeit atypical, strategy to mitigate the detrimental effects of temperature on true cold-active enzymes.

An Overview into Polyethylene Terephthalate (PET) Hydrolases and Efforts in Tailoring Enzymes for Improved Plastic Degradation

Plastic or microplastic pollution is a global threat affecting ecosystems, with the current generation reaching as much as 400 metric tons per/year. Soil ecosystems comprising agricultural lands act as microplastics sinks, though the impact could be unexpectedly more far-reaching. This is troubling as most plastic forms, such as polyethylene terephthalate (PET), formed from polymerized terephthalic acid (TPA) and ethylene glycol (EG) monomers, are non-biodegradable environmental pollutants. The current approach to use mechanical, thermal, and chemical-based treatments to reduce PET waste remains cost-prohibitive and could potentially produce toxic secondary pollutants. Thus, better remediation methods must be developed to deal with plastic pollutants in marine and terrestrial environments. Enzymatic treatments could be a plausible avenue to overcome plastic pollutants, given the near-ambient conditions under which enzymes function without the need for chemicals. The discovery of several PET hydrolases, along with further modification of the enzymes, has considerably aided efforts to improve their ability to degrade the ester bond of PET. Hence, this review emphasizes PET-degrading microbial hydrolases and their contribution to alleviating environmental microplastics. Information on the molecular and degradation mechanisms of PET is also highlighted in this review, which might be useful in the future rational engineering of PET-hydrolyzing enzymes.

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). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

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

The SARS-CoV-2 main protease (Mpro) 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 Mpro mutations arising over time. Identification and structural characterization of Mpro variants is thus critical for robust inhibitor design. Here we report sequence analysis, structure predictions, and molecular modeling for seventy-nine Mpro variants, constituting all clinically observed mutations in this protein as of April 29, 2020. Residue substitution is widely distributed, with some tendency toward larger and more hydrophobic residues. Modeling and protein structure network analysis suggest differences in cohesion and active site flexibility, revealing patterns in viral evolution that have relevance for drug discovery.

The Droserasin 1 PSI: A Membrane-Interacting Antimicrobial Peptide from the Carnivorous Plant Drosera capensis

The Droserasins, aspartic proteases from the carnivorous plant Drosera capensis, contain a 100-residue plant-specific insert (PSI) that is post-translationally cleaved and independently acts as an antimicrobial peptide. PSIs are of interest not only for their inhibition of microbial growth, but also because they modify the size of lipid vesicles and strongly interact with biological membranes. PSIs may therefore be useful for modulating lipid systems in NMR studies of membrane proteins. Here we present the expression and biophysical characterization of the Droserasin 1 PSI (D1 PSI.) This peptide is monomeric in solution and maintains its primarily α-helical secondary structure over a wide range of temperatures and pH values, even under conditions where its three disulfide bonds are reduced. Vesicle fusion assays indicate that the D1 PSI strongly interacts with bacterial and fungal lipids at pH 5 and lower, consistent with the physiological pH of D. capensis mucilage. It binds lipids with a variety of head groups, highlighting its versatility as a potential stabilizer for lipid nanodiscs. Solid-state NMR spectra collected at a field strength of 36 T, using a unique series-connected hybrid magnet, indicate that the peptide is folded and strongly bound to the membrane. Molecular dynamics simulations indicate that the peptide is stable as either a monomer or a dimer in a lipid bilayer. Both the monomer and the dimer allow the passage of water through the membrane, albeit at different rates.

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

The SARS-CoV-2 main protease (M ^pro ) 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 ^pro mutations arising over time. Identification and structural characterization of M ^pro variants is thus critical for robust inhibitor design. Here we report sequence analysis, structure predictions, and molecular modeling for seventy-nine M ^pro variants, constituting all clinically observed mutations in this protein as of April 29, 2020. Residue substitution is widely distributed, with some tendency toward larger and more hydrophobic residues. Modeling and protein structure network analysis suggest differences in cohesion and active site flexibility, revealing patterns in viral evolution that have relevance for drug discovery.

Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods

Simulations of intrinsically disordered proteins (IDPs) pose numerous challenges to comparative analysis, prominently including highly dynamic conformational states and a lack of well-defined secondary structure. Machine learning (ML) algorithms are especially effective at discriminating among high-dimensional inputs whose differences are extremely subtle, making them well suited to the study of IDPs. In this work, we apply various ML techniques, including support vector machines (SVM) and clustering, as well as related methods such as principal component analysis (PCA) and protein structure network (PSN) analysis, to the problem of uncovering differences between configurational data from molecular dynamics simulations of two variants of the same IDP. We examine molecular dynamics (MD) trajectories of wild-type amyloid beta (Aβ₁₋₄₀) and its “Arctic” variant (E22G), systems that play a central role in the etiology of Alzheimer's disease. Our analyses demonstrate ways in which ML and related approaches can be used to elucidate subtle differences between these proteins, including transient structure that is poorly captured by conventional metrics.