Low molecular weight heparin and synthetic mimetics such as fondaparinux show different binding kinetics, protease specificity, and clinical effects. A combination of allosteric and template-mediated bridging mechanisms have been proposed to explain the differences in rate acceleration and specificity. The difficulty in working with heterogeneous heparin species has rendered a crystallographic interpretation of the differences in antithrombin activation between mimetics and natural heparin inaccessible. In this study, we examine the allosteric changes in antithrombin caused by binding fondaparinux, enoxaparin and depolymerized natural heparins using millisecond hydrogen deuterium exchange mass spectrometry (TRESI-HDX MS) and relate these conformational changes to complex stability in the gas phase using collision induced unfolding (CIU). This exploration reveals that in addition to the dynamic changes caused by fondaparinux, long chain heparins reduce structural flexibility proximal to Arg393, the cleavable residue in the reactive centre loop of the protein. These local changes in protein dynamics are associated with an increase in overall complex stability that increases with heparin chain length. Ultimately, these results shed light on the molecular mechanisms underlying differences in activity and specificity between heparin mimetics and natural heparins.

In this study, we utilize Protein Residue Networks (PRNs), constructed using Local Spatial Pattern (LSP) alignment, to explore the dynamic behavior of Catabolite Activator Protein (CAP) upon the sequential binding of cAMP. We employed the Degree Centrality of these PRNs to investigate protein dynamics on a sub-nanosecond time scale, hypothesizing that it would reflect changes in CAP\'s entropy related to its thermal motions. We show that the binding of the first cAMP led to an increase in stability in the Cyclic-Nucleotide Binding Domain A (CNBD-A) and destabilization in CNBD-B, agreeing with previous reports explaining the negative cooperativity of cAMP binding in terms of an entropy-driven allostery. LSP-based PRNs also allow for the study of Betweenness Centrality, another graph-theoretical characteristic of PRNs, providing insights into global residue connectivity within CAP. Using this approach, we were able to correctly identify amino acids that were shown to be critical in mediating allosteric interactions in CAP. The agreement between our studies and previous experimental reports validates our method, particularly with respect to the reliability of Degree Centrality as a proxy for entropy related to protein thermal dynamics. Because LSP-based PRNs can be easily extended to include dynamics of small organic molecules, polynucleotides, or other allosteric proteins, the methods presented here mark a significant advancement in the field, positioning them as vital tools for a fast, cost-effective, and accurate analysis of entropy-driven allostery and identification of allosteric hotspots.

Amyotrophic lateral sclerosis (ALS) is a serious neurodegenerative disorder affecting nerve cells in the brain and spinal cord that is caused by mutations in the superoxide dismutase 1 (SOD1) enzyme. ALS-related mutations cause misfolding, dimerisation instability, and increased formation of aggregates. The underlying allosteric mechanisms, however, remain obscure as far as details of their fundamental atomistic structure are concerned. Hence, this gap in knowledge limits the development of novel SOD1 inhibitors and the understanding of how disease-associated mutations in distal sites affect enzyme activity.
We combined microsecond-scale based unbiased molecular dynamics (MD) simulation with network analysis to elucidate the local and global conformational changes and allosteric communications in SOD1 Apo (unmetallated form), Holo, Apo_CallA (mutant and unmetallated form), and Holo_CallA (mutant form) systems. To identify hotspot residues involved in SOD1 signalling and allosteric communications, we performed network centrality, community network, and path analyses.
Structural analyses showed that unmetallated SOD1 systems and cysteine mutations displayed large structural variations in the catalytic sites, affecting structural stability. Inter- and intra H-bond analyses identified several important residues crucial for maintaining interfacial stability, structural stability, and enzyme catalysis. Dynamic motion analysis demonstrated more balanced atomic displacement and highly correlated motions in the Holo system. The rationale for structural disparity observed in the disulfide bond formation and R143 configuration in Apo and Holo systems were elucidated using distance and dihedral probability distribution analyses.
Our study highlights the efficiency of combining extensive MD simulations with network analyses to unravel the features of protein allostery.

Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many computational models have been developed for accurate allosteric site prediction. Most of these models focus on designing a general rule that can be applied to pockets of proteins from various families. In this study, we present a new approach using the concept of Learning to Rank (LTR). The LTR model ranks pockets based on their relevance to allosteric sites, that is, how well a pocket meets the characteristics of known allosteric sites. After the training and validation on two datasets, the Allosteric Database (ASD) and CASBench, the LTR model was able to rank an allosteric pocket in the top three positions for 83.6% and 80.5% of test proteins, respectively. The model outperforms other common machine learning models with higher F1 scores (0.662 in ASD and 0.608 in CASBench) and Matthews correlation coefficients (0.645 in ASD and 0.589 in CASBench). The trained model is available on the PASSer platform (https://passer.smu.edu) to aid in drug discovery research.

Neutral mutational drift is an important source of biological diversity that remains underexploited in fundamental studies of protein biophysics. This study uses a synthetic transcriptional circuit to study neutral drift in protein tyrosine phosphatase 1B (PTP1B), a mammalian signaling enzyme for which conformational changes are rate limiting. Kinetic assays of purified mutants indicate that catalytic activity, rather than thermodynamic stability, guides enrichment under neutral drift, where neutral or mildly activating mutations can mitigate the effects of deleterious ones. In general, mutants show a moderate activity-stability tradeoff, an indication that minor improvements in the activity of PTP1B do not require concomitant losses in its stability. Multiplexed sequencing of large mutant pools suggests that substitutions at allosterically influential sites are purged under biological selection, which enriches for mutations located outside of the active site. Findings indicate that the positional dependence of neutral mutations within drifting populations can reveal the presence of allosteric networks and illustrate an approach for using synthetic transcriptional systems to explore these mutations in regulatory enzymes.

Direct structural and dynamic characterization of protein conformers in solution is highly desirable but currently impractical. Herein, we developed a single molecule gold plasmonic nanopore system for observation of protein allostery, enabling us to monitor translocation dynamics and conformation transition of proteins by ion current detection and SERS spectrum measurement, respectively. Allosteric transition of calmodulin (CaM) was elaborately probed by the nanopore system. Two conformers of CaM were well-resolved at a single-molecule level using both the ion current blockage signal and the SERS spectra. The collected SERS spectra provided structural evidence to confirm the interaction between CaM and the gold plasmonic nanopore, which was responsible for the different translocation behaviors of the two conformers. SERS spectra revealed the amino acid residues involved in the conformational change of CaM upon calcium binding. The results demonstrated that the excellent spectral characterization furnishes a single-molecule nanopore technique with an advanced capability of direct structure analysis.

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

The binding of the type 1 fimbrial adhesin FimH to mannosylated receptors is allosterically regulated to enhance the fitness of uropathogenic Escherichia coli (UPEC) during urinary tract infection (UTI). Mutations in the two FimH domains (pilin and lectin) located outside the mannose binding pocket have been shown to influence mannose binding affinity, yet the details of the allostery mechanism are not fully elucidated. Here we characterised different FimH conformational states (termed low-affinity tense and high-affinity relaxed conformations) of natural FimH variants using molecular dynamics (MD) simulation techniques and report key structural dynamics differences between them. The clinically dominant FimH30 variant from the pandemic multidrug resistant E. coli ST131 lineage contains an R166H mutation that weakens FimH interdomain interactions and allows enhanced mannose interactions with pre-existing high-affinity relaxed conformations. When expressed in an isogenic ST131 strain background, FimH30 mediated high human cell adhesion and invasion, and enhanced biofilm formation over other variants. Collectively, our computational and experimental findings support a model of FimH protein allostery that is mediated by shifts in the pre-existing conformational equilibrium of FimH, additional to the sequential step-wise process of structural perturbations transmitted from one site to another within the protein. Importantly, it is the first study to shed light into how natural mutations in a clinically dominant FimH variant influence the protein\'s conformational landscape optimising its function for ST131 fitness at intestinal and extraintestinal niches.

The human high-temperature requirement A2 (HtrA2) mitochondrial protease is critical for cellular proteostasis, with mutations in this enzyme closely associated with the onset of neurodegenerative disorders. HtrA2 forms a homotrimeric structure, with each subunit composed of protease and PDZ (PSD-95, DLG, ZO-1) domains. Although we had previously shown that successive ligand binding occurs with increasing affinity, and it has been suggested that allostery plays a role in regulating catalysis, the molecular details of how this occurs have not been established. Here, we use cysteine-based chemistry to generate subunits in different conformational states along with a protomer mixing strategy, biochemical assays, and methyl-transverse relaxation optimized spectroscopy-based NMR studies to understand the role of interprotomer allostery in regulating HtrA2 function. We show that substrate binding to a PDZ domain of one protomer increases millisecond-to-microsecond timescale dynamics in neighboring subunits that prime them for binding substrate molecules. Only when all three PDZ-binding sites are substrate bound can the enzyme transition into an active conformation that involves significant structural rearrangements of the protease domains. Our results thus explain why when one (or more) of the protomers is fixed in a ligand-binding-incompetent conformation or contains the inactivating S276C mutation that is causative for a neurodegenerative phenotype in mouse models of Parkinson\'s disease, transition to an active state cannot be formed. In this manner, wild-type HtrA2 is only active when substrate concentrations are high and therefore toxic and unregulated proteolysis of nonsubstrate proteins can be suppressed.

Studies of protein allostery increasingly reveal an involvement of the back and forth order-disorder transitions in this mechanism of protein activity regulation. Here, we investigate the allosteric mechanisms mediated by structural disorder using the structure-based statistical mechanical model of allostery (SBSMMA) that we have previously developed. We show that SBSMMA accounts for the energetics and causality of allosteric communication underlying dimerization of the BirA biotin repressor, activation of the sortase A enzyme, and inhibition of the Rac1 GTPase. Using the SBSMMA, we also show that introducing structural order or disorder in various regions of esterases can originate tunable allosteric modulation of the catalytic triad. On the basis of obtained results, we propose that operating with the order-disorder continuum allows one to establish an allosteric control scale for achieving desired modulation of the protein activity.