A Case Study of the Glycoside Hydrolase Enzyme Mechanism Using an Automated QM-cluster Model Building Toolkit

Glycoside hydrolase enzymes are important for hydrolyzing the β-1,4 glycosidic bond in polysaccharides for deconstruction of carbohydrates. The two-step retaining reaction mechanism was explored with different sized QM-cluster models built by the Residue Interaction Network ResidUe Selector (RINRUS) software using both the wild-type protein and its E217Q mutant. The first step is the glycosylation, in which the acidic residue 217 donates a proton to the glycosidic oxygen leading to bond cleavage. In the subsequent deglycosylation step, one water molecule migrates into the active site and attacks the anomeric carbon. Residue interaction-based QM-cluster models lead to reliable structural and energetic results for proposed glycoside hydrolase mechanisms. The free energies of activation for glycosylation in the largest QM-cluster models were predicted to be 19.5 and 31.4 kcal mol for the wild-type protein and its E217Q mutant, which agree with experimental trends that mutation of the acidic residue Glu217 to Gln will slow down the reaction, and are higher in free energy than the deglycosylation transition states (13.8 and 25.5 kcal mol for the wild-type protein and its mutant, respectively). For the mutated protein, glycosylation led to a low-energy product. This thermodynamic sink may correspond to the intermediate state which was isolated in the X-ray crystal structure. Hence, the glycosylation is validated to be the rate-limiting step in both the wild-type and mutated enzyme. The E217Q mutation led to a higher glycosylation activation free energy that also agrees with experimental observation that mutation of E217 will slow down the reaction, but not deactivate catalysis.

Structural models of SARS-CoV-2 Omicron variant in complex with ACE2 receptor or antibodies suggest altered binding interfaces

There is enormous ongoing interest in characterizing the binding properties of the SARS-CoV-2 Omicron Variant of Concern (VOC) (B.1.1.529), which continues to spread towards potential dominance worldwide. To aid these studies, based on the wealth of available structural information about several SARS-CoV-2 variants in the Protein Data Bank (PDB) and a modeling pipeline we have previously developed for tracking the ongoing global evolution of SARS-CoV-2 proteins, we provide a set of computed structural models (henceforth models) of the Omicron VOC receptor-binding domain (omRBD) bound to its corresponding receptor Angiotensin-Converting Enzyme (ACE2) and a variety of therapeutic entities, including neutralizing and therapeutic antibodies targeting previously-detected viral strains. We generated bound omRBD models using both experimentally-determined structures in the PDB as well as machine learning-based structure predictions as starting points. Examination of ACE2-bound omRBD models reveals an interdigitated multi-residue interaction network formed by omRBD-specific substituted residues (R493, S496, Y501, R498) and ACE2 residues at the interface, which was not present in the original Wuhan-Hu-1 RBD-ACE2 complex. Emergence of this interaction network suggests optimization of a key region of the binding interface, and positive cooperativity among various sites of residue substitutions in omRBD mediating ACE2 binding. Examination of neutralizing antibody complexes for Barnes Class 1 and Class 2 antibodies modeled with omRBD highlights an overall loss of interfacial interactions (with gain of new interactions in rare cases) mediated by substituted residues. Many of these substitutions have previously been found to independently dampen or even ablate antibody binding, and perhaps mediate antibody-mediated neutralization escape (e.g., K417N). We observe little compensation of corresponding interaction loss at interfaces when potential escape substitutions occur in combination. A few selected antibodies (e.g., Barnes Class 3 S309), however, feature largely unaltered or modestly affected protein-protein interfaces. While we stress that only qualitative insights can be obtained directly from our models at this time, we anticipate that they can provide starting points for more detailed and quantitative computational characterization, and, if needed, redesign of monoclonal antibodies for targeting the Omicron VOC Spike protein. In the broader context, the computational pipeline we developed provides a framework for rapidly and efficiently generating retrospective and prospective models for other novel variants of SARS-CoV-2 bound to entities of virological and therapeutic interest, in the setting of a global pandemic.

Insights into the Ligand Binding to Bromodomain-Containing Protein 9 (BRD9): A Guide to the Selection of Potential Binders by Computational Methods

The estimation of the binding of a set of molecules against BRD9 protein was carried out through an in silico molecular dynamics-driven exhaustive analysis to guide the identification of potential novel ligands. Starting from eight crystal structures of this protein co-complexed with known binders and one apo form, we conducted an exhaustive molecular docking/molecular dynamics (MD) investigation. To balance accuracy and an affordable calculation time, the systems were simulated for 100 ns in explicit solvent. Moreover, one complex was simulated for 1 µs to assess the influence of simulation time on the results. A set of MD-derived parameters was computed and compared with molecular docking-derived and experimental data. MM-GBSA and the per-residue interaction energy emerged as the main indicators for the good interaction between the specific binder and the protein counterpart. To assess the performance of the proposed analysis workflow, we tested six molecules featuring different binding affinities for BRD9, obtaining promising outcomes. Further insights were reported to highlight the influence of the starting structure on the molecular dynamics simulations evolution. The data confirmed that a ranking of BRD9 binders using key parameters arising from molecular dynamics is advisable to discard poor ligands before moving on with the synthesis and the biological tests.

Ssl2/TFIIH function in transcription start site scanning by RNA Polymerase II in Saccharomyces cerevisiae

In Saccharomyces cerevisiae, RNA Polymerase II (Pol II) selects transcription start sites (TSS) by a unidirectional scanning process. During scanning, a preinitiation complex (PIC) assembled at an upstream core promoter initiates at select positions within a window ~40-120 basepairs downstream. Several lines of evidence indicate that Ssl2, the yeast homolog of XPB and an essential and conserved subunit of the general transcription factor (GTF) TFIIH, drives scanning through its DNA-dependent ATPase activity, therefore potentially controlling both scanning rate and scanning extent (processivity). To address questions of how Ssl2 functions in promoter scanning and interacts with other initiation activities, we leveraged distinct initiation-sensitive reporters to identify novel ssl2 alleles. These ssl2 alleles, many of which alter residues conserved from yeast to human, confer either upstream or downstream TSS shifts at the model promoter ADH1 and genome-wide. Specifically, tested ssl2 alleles alter TSS selection by increasing or narrowing the distribution of TSSs used at individual promoters. Genetic interactions of ssl2 alleles with other initiation factors are consistent with ssl2 allele classes functioning through increasing or decreasing scanning processivity but not necessarily scanning rate. These alleles underpin a residue interaction network that likely modulates Ssl2 activity and TFIIH function in promoter scanning. We propose that the outcome of promoter scanning is determined by two functional networks, the first being Pol II activity and factors that modulate it to determine initiation efficiency within a scanning window, and the second being Ssl2/TFIIH and factors that modulate scanning processivity to determine the width of the scanning widow.

Molecular insights into phosphorylation-induced allosteric conformational changes in β2-adrenergic receptor

The large conformational flexibility of G protein-coupled receptors (GPCRs) has been a puzzle in structural and pharmacological studies for the past few decades. Apart from structural rearrangements induced by ligands, enzymatic phosphorylations by GPCR kinases (GRKs) at the carboxy-terminal tail (C-tail) of a GPCR also makes conformational alterations to the transmembrane helices and facilitates the binding of one of its transducer proteins named β-arrestin. Phosphorylation-induced conformational transition of the receptor that causes specific binding to β-arrestin but prevents the association of other transducers such as G proteins lacks atomistic understanding and is elusive to experimental studies. Using microseconds of all-atom conventional and Gaussian accelerated molecular dynamics (GaMD) simulations, we investigate the allosteric mechanism of phosphorylation induced-conformational changes in β2-adrenergic receptor, a well-characterized GPCR model system. Free energy profiles reveal that the phosphorylated receptor samples a new conformational state in addition to the canonical active state corroborating with recent nuclear magnetic resonance experimental findings. The new state has a smaller intracellular cavity that is likely to accommodate β-arrestin better than G protein. Using contact map and inter-residue interaction energy calculations, we found the phosphorylated C-tail adheres to the cytosolic surface of the transmembrane domain of the receptor. Transfer entropy calculations show that the C-tail residues drive the correlated motions of TM residues, and the allosteric signal is relayed via several residues at the cytosolic surface. Our results also illustrate how the redistribution of inter-residue nonbonding interaction couples with the allosteric communication from the phosphorylated C-tail to the transmembrane. Atomistic insight into phosphorylation-induced β-arrestin specific conformation is therapeutically important to design drugs with higher efficacy and fewer side effects. Our results therefore open novel opportunities to fine-tune β-arrestin bias in GPCR signaling.

The Role of Methionine Residues in the Regulation of Liquid-Liquid Phase Separation

Membraneless organelles are non-stoichiometric supramolecular structures in the micron scale. These structures can be quickly assembled/disassembled in a regulated fashion in response to specific stimuli. Membraneless organelles contribute to the spatiotemporal compartmentalization of the cell, and they are involved in diverse cellular processes often, but not exclusively, related to RNA metabolism. Liquid-liquid phase separation, a reversible event involving demixing into two distinct liquid phases, provides a physical framework to gain insights concerning the molecular forces underlying the process and how they can be tuned according to the cellular needs. Proteins able to undergo phase separation usually present a modular architecture, which favors a multivalency-driven demixing. We discuss the role of low complexity regions in establishing networks of intra- and intermolecular interactions that collectively control the phase regime. Post-translational modifications of the residues present in these domains provide a convenient strategy to reshape the residue–residue interaction networks that determine the dynamics of phase separation. Focus will be placed on those proteins with low complexity domains exhibiting a biased composition towards the amino acid methionine and the prominent role that reversible methionine sulfoxidation plays in the assembly/disassembly of biomolecular condensates.

Regional Variant Analysis of Spike Glycoprotein Mutations of SARS-CoV-2 and Its Implications in COVID-19 Pandemic Control

Mutations in the spike glycoprotein have various impacts on the receptor binding, antibody in-teraction, and host range of SARS-CoV-2. As the interaction of spike glycoprotein with the human ACE2 receptor is the entry point of SARS-CoV-2 in human cells, mutations in the spike protein it-self contain numerous impacts on the pandemic. Here, we analysed all the mutations in the spike glycoprotein from123 strains isolated from Kerala, India. We also predicted the possible struc-tural relevance of the unique mutations based on topological analysis of the residue interaction network of the spike glycoprotein structure.

Molecular docking analysis on the interaction between bovine serum albumin and three commercial fluoroquinolones: Ciprofloxacin, enrofloxacin and pefloxacin

Fluoroquinolones are a family of broad spectrum, systemic antibacterial agents that have been used as therapy for infections in the respiratory and alimentary tract in animals. The pharmacodynamic of this class is widely described, predominantly to the commercial drugs ciprofloxacin (CIP), enrofloxacin (ENR), and pefloxacin (PEF). Bovine serum albumin (BSA) is the main endogenous carrier in the bovine bloodstream, being responsible for the biodistribution of different classes of molecules and drugs, including fluoroquinolones. The molecular features and interaction between BSA and fluoroquinolones are not fully described, thus, the present work enlightens the intimacy of the interaction of BSA with CIP, ENR, PEF through structural modeling and molecular docking calculation approaches. The role of key amino acid residues was assessed, indicating that the main protein binding pocket is composed by Trp-212 residue playing an important stabilization for the three fluoroquinolones through both hydrogen bonding and van der Waals forces, where reside the individual structural differences observed among the three fluoroquinolones and BSA. There is a descriptive protagonism of carboxyl group on the ENR interaction which traps the molecule and avoids the deep communication in the protein binding pocket, as well as the ligands CIP and PEF showed an interface amino acid residue interaction profile higher than 70%.

Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding

Residue interaction networks (RINs) describe a protein structure as a network of interacting residues. Central nodes in these networks, identified by centrality analyses, highlight those residues that play a role in the structure and function of the protein. However, little is known about the capability of such analyses to identify residues involved in the formation of macromolecular complexes. Here, we performed six different centrality measures on the RINs generated from the complexes of the SKEMPI 2 database of changes in protein–protein binding upon mutation in order to evaluate the capability of each of these measures to identify major binding residues. The analyses were performed with and without the crystallographic water molecules, in addition to the protein residues. We also investigated the use of a weight factor based on the inter-residue distances to improve the detection of these residues. We show that for the identification of major binding residues, closeness, degree, and PageRank result in good precision, whereas betweenness, eigenvector, and residue centrality analyses give a higher sensitivity. Including water in the analysis improves the sensitivity of all measures without losing precision. Applying weights only slightly raises the sensitivity of eigenvector centrality analysis. We finally show that a combination of multiple centrality analyses is the optimal approach to identify residues that play a role in protein–protein interaction.