Allostery and missense mutations as intermittently linked promising aspects of modern computational drug discovery

Allosteric modulation of Plasmodium falciparum Isoleucyl tRNA synthetase by South African natural compounds

Targeting Plasmodium falciparum (Pf) aminoacyl tRNA synthetases is a viable strategy to overcome malaria parasite multi-drug resistance. Here, we focused on Pf Isoleucyl tRNA synthetase (PfIleRS) to identify potential allosteric inhibitors from 1019 South African Natural Compounds (SANC). Eleven potential hits, which passed ADMET and PAINS, were selected based on their docking binding affinity which was higher for PfIleRS than for human IleRS. Molecular dynamics simulations revealed that the compounds, particularly SANC456, commonly induced considerable changes in the global conformation and dynamics of PfIleRS, suggesting potential allosteric modulatory effects. Importantly, all 11 SANC hits reduced the binding affinity of the nucleotide AMP molecule by at least 25%. Some SANC ligand-bound systems (SANC456, SANC1095, and SANC1104) significantly increased the distance between the AMP and Ile ligands. Possible explanations for these changes were explored using three dynamic residue network centrality metrics. Betweenness centrality identified a major allosteric pathway in holo PfIleRS spanning the entire protein length. In contrast, SANC382, SANC456, SANC522, SANC806 and SANC1095 ligand-bound systems exhibited delta BC pathways (SANC-protein minus holo-protein), induced by the ligands, extending from their respective pockets into the active site. Additionally, eigenvector centrality revealed two important residue clusters either side of the holo active site which became altered in the ligand-bound systems, indicating possible allosteric activity. Lastly, many SANC systems showed decreased closeness centrality of zinc finger and active site residues, including the HYGH and KMSKR motifs. We believe that the compounds identified in this study as potential allosteric inhibitors have strong translational potential and warrant further investigation through in vitro and in vivo experiments. Overall, they hold promise as starting points for the development of new and effective antimalarial therapies, particularly against multidrug-resistant Plasmodium parasites.

Dynamic Allostery: Evolution's Double-Edged Sword in Protein Function and Disease

Allostery is a core mechanism in biology that allows proteins to communicate and regulate activity over long structural distances. While classical models of allostery focus on conformational changes triggered by ligand binding, dynamic allostery-where protein function is modulated through alterations in thermal fluctuations without major conformational shifts-has emerged as a critical evolutionary mechanism. This review explores how evolution leverages dynamic allostery to fine-tune protein function through subtle mutations at distal sites, preserving core structural architecture while dramatically altering functional properties. Using a combination of computational approaches including Dynamic Flexibility Index (DFI), Dynamic Coupling Index (DCI), and vibrational density of states (VDOS) analysis, we demonstrate that functional adaptations in proteins often involve "hinge-shift" mechanisms, where redistribution of rigid and flexible regions modulates collective motions without changing the overall fold. This evolutionary principle is a double-edged sword: the same mechanisms that enable functional innovation also create vulnerabilities that can be exploited in disease states. Disease-associated variants frequently occur at positions highly coupled to functional sites despite being physically distant, forming Dynamic Allosteric Residue Couples (DARC sites). We demonstrate applications of these principles in understanding viral evolution, drug resistance, and capsid assembly dynamics. Understanding dynamic allostery provides critical insights into protein evolution and offers new avenues for therapeutic interventions targeting allosteric regulation.

Transfer learning towards predicting viral missense mutations: A case study on SARS-CoV-2

Understanding viral evolution and predicting future mutations are crucial for overcoming drug resistance and developing long-lasting treatments. Previously, we established machine learning (ML) models using dynamic residue network (DRN) metric data and leveraging a vast amount of existing mutation data from the SARS-CoV-2 main protease (Mpro). Here, we sought to assess the generalizability and robustness of the current models across other SARS-CoV-2 proteins. To achieve this, for the first time, we employed a transfer learning (TL) approach, allowing us to determine the extent to which Mpro trained models could be applied to other SARS-CoV-2 proteins. The TL results were highly promising, with artificial neural network (ANN) and random forest (RF) correlation coefficients for Mpro closely matching those of NSP10, NSP16, and PLpro. The ANN |R| value for Mpro was 0.564, while NSP10, NSP16, and PLpro had values of 0.533, 0.527, and 0.464, respectively. Similarly, the RF |R| value for Mpro was 0.673, compared to 0.457, 0.460, and 0.437 for NSP10, NSP16, and PLpro, respectively. Interestingly, we did not observe a strong correlation for the spike (S) protein monomer and its domains. The low p-values that are associated with the correlation |R| values show that the linear correlations between predicted and actual mutation frequencies are statistically significant. This indicates that TL may generalize well across structurally related viral proteins using DRN-derived ML model from Mpro. Overall, we aim to develop a universal ML model for predicting missense mutation frequencies in viral proteins, and this study lays the foundation for that goal.

A new view of missense mutations in α-mannosidosis using molecular dynamics conformational ensembles

The mutation of remote positions on enzyme scaffolds and how these residue changes can affect enzyme catalysis is still far from being fully understood. One paradigmatic example is the group of lysosomal storage disorders, where the enzyme activity of a lysosomal enzyme is abolished or severely reduced. In this work, we analyze molecular dynamics simulation conformational ensembles to unveil the molecular features controlling the deleterious effects of the 43 reported missense mutations in the human lysosomal α-mannosidase. Using residue descriptors for protein dynamics, their coupling with the active site, and their impact on protein stability, we have assigned the contribution of each of the missense mutations into protein stability, protein dynamics, and their connectivity with the active site. We demonstrate here that the use of conformational ensembles is a powerful approach not only to better understand missense mutations at the molecular level but also to revisit the missense mutations reported in lysosomal storage disorders in order to aid the treatment of these diseases.

Impact of African-Specific ACE2 Polymorphisms on Omicron BA.4/5 RBD Binding and Allosteric Communication Within the ACE2-RBD Protein Complex

Severe acute respiratory symptom coronavirus 2 (SARS-CoV-2) infection occurs via the attachment of the spike (S) protein's receptor binding domain (RBD) to human ACE2 (hACE2). Natural polymorphisms in hACE2, particularly at the interface, may alter RBD-hACE2 interactions, potentially affecting viral infectivity across populations. This study identified the effects of six naturally occurring hACE2 polymorphisms with high allele frequency in the African population (S19P, K26R, M82I, K341R, N546D and D597Q) on the interaction with the S protein RBD of the BA.4/5 Omicron sub-lineage through post-molecular dynamics (MD), inter-protein interaction and dynamic residue network (DRN) analyses. Inter-protein interaction analysis suggested that the K26R variation, with the highest interactions, aligns with reports of enhanced RBD binding and increased SARS-CoV-2 susceptibility. Conversely, S19P, showing the fewest interactions and largest inter-protein distances, agrees with studies indicating it hinders RBD binding. The hACE2 M82I substitution destabilized RBD-hACE2 interactions, reducing contact frequency from 92 (WT) to 27. The K341R hACE2 variant, located distally, had allosteric effects that increased RBD-hACE2 contacts compared to WThACE2. This polymorphism has been linked to enhanced affinity for Alpha, Beta and Delta lineages. DRN analyses revealed that hACE2 polymorphisms may alter the interaction networks, especially in key residues involved in enzyme activity and RBD binding. Notably, S19P may weaken hACE2-RBD interactions, while M82I showed reduced centrality of zinc and chloride-coordinating residues, hinting at impaired communication pathways. Overall, our findings show that hACE2 polymorphisms affect S BA.4/5 RBD stability and modulate spike RBD-hACE2 interactions, potentially influencing SARS-CoV-2 infectivity-key insights for vaccine and therapeutic development.

Advances in structure-based allosteric drug design

The identification of allosteric binding sites forms a critical connection between structural and computational biology, substantially advancing the discovery of allosteric drugs. However, the prevailing strategies for allosteric drug development predominantly rely on high-throughput screening, which suffers from high failure rates due to a limited understanding of allosteric mechanisms. This review collects insights from case studies on allosteric mechanisms, protein structure databases and computation algorithm developments, aiming to enhance our comprehension of allostery and guide more effective allosteric drug development. A crucial element in this area is the integration of structural biology with computational biology, which is vital for translating three-dimensional structural datasets into available drug discovery knowledge. These datasets and AI algorithms underpin the establishment of the allosteric binding site identification leading to structure-activity relationships (SARs) and are fueling the development of computational algorithms tailored for allosteric proteins, thereby driving forward the field of allosteric drug discovery.

Revealing SARS-CoV-2 Mpro mutation cold and hot spots: Dynamic residue network analysis meets machine learning

Deciphering the effect of evolutionary mutations of viruses and predicting future mutations is crucial for designing long-lasting and effective drugs. While understanding the impact of current mutations on protein drug targets is feasible, predicting future mutations due to natural evolution of viruses and environmental pressures remains challenging. Here, we leveraged existing mutation data during the evolution of the SARS-CoV-2 protein drug target main protease (Mpro) to test the predictive power of dynamic residue network (DRN) analysis in identifying mutation cold and hot spots. We conducted molecular dynamics simulations on the Mpro of SARS-CoV-2 (Wuhan strain) and calculated eight DRN metrics (averaged BC, CC, DC, EC, ECC, KC, L, PR), each of which identifies a unique network feature within the protein. The sets of residues with the highest and lowest values for each metric, comprising potential cold and hot spots, were compared to published biochemical analyses and per residue mutation frequencies observed across five SARS-CoV-2 lineages, encompassing a total of 191,878 sequences. Individual DRN metrics displayed only modest power to predict the mutation frequency of individual residues. However, integrating the eight DRN metrics with additional structural and sequence-derived metrics allowed us to develop machine learning models which significantly improved the prediction of residue mutation frequency. While further refinements should enhance accuracy, we demonstrated a robust method to understand pathogen evolution. This approach can also guide the development of long-lasting drugs by targeting functional residues located in and near active site, and allosteric sites, that are less prone to mutations.

Amyloid-Driven Allostery

The fields of allostery and amyloid-related pathologies, such as Parkinson's disease (PD), have been extensively explored individually, but less is known about how amyloids control allostery. Recent advancements have revealed that amyloids can drive allosteric effects in both intrinsically disordered proteins, such as alpha-synuclein (αS), and multi-domain signaling proteins, such as protein kinase A (PKA). Amyloid-driven allostery plays a central role in explaining the mechanisms of gain-of-pathological-function mutations in αS (e.g. E46K, which causes early PD onset) and loss-of-physiological-function mutations in PKA (e.g. A211D, which predisposes to tumors). This review highlights allosteric effects of disease-related mutations and how they can cause exposure of amyloidogenic regions, leading to amyloids that are either toxic or cause aberrant signaling. We also discuss multiple potential modulators of these allosteric effects, such as MgATP and kinase substrates, opening future opportunities to improve current pharmacological interventions against αS and PKA-related pathologies. Overall, we show that amyloid-driven allosteric models are useful to explain the mechanisms underlying disease-related mutations.

ASM variants in the spotlight: A structure-based atlas for unraveling pathogenic mechanisms in lysosomal acid sphingomyelinase

Lysosomal acid sphingomyelinase (ASM), a critical enzyme in lipid metabolism encoded by the SMPD1 gene, plays a crucial role in sphingomyelin hydrolysis in lysosomes. ASM deficiency leads to acid sphingomyelinase deficiency, a rare genetic disorder with diverse clinical manifestations, and the protein can be found mutated in other diseases. We employed a structure-based framework to comprehensively understand the functional implications of ASM variants, integrating pathogenicity predictions with molecular insights derived from a molecular dynamics simulation in a lysosomal membrane environment. Our analysis, encompassing over 400 variants, establishes a structural atlas of missense variants of lysosomal ASM, associating mechanistic indicators with pathogenic potential. Our study highlights variants that influence structural stability or exert local and long-range effects at functional sites. To validate our predictions, we compared them to available experimental data on residual catalytic activity in 135 ASM variants. Notably, our findings also suggest applications of the resulting data for identifying cases suited for enzyme replacement therapy. This comprehensive approach enhances the understanding of ASM variants and provides valuable insights for potential therapeutic interventions.

MD simulations indicate Omicron P132H of SARS-CoV-2 Mpro is a potential allosteric mutant involved in modulating the dynamics of catalytic site entry loop

The SARS-CoV-2 main protease Mpro, essential for viral replication is an important drug target. It plays a critical role in processing viral polyproteins necessary for viral replication assembly. One of the predominant SARS-CoV-2 Mpro mutations of Omicron variant is Pro132His. Structurally, this mutation site is located ∼22 Å away from the catalytic site. The solved crystal structure of this mutant in complex with inhibitors as well as its reported catalytic efficiency did not show any difference with respect to the wild type. Thus, the mutation was concluded to be non-allosteric. Based on microsecond long MD simulation of the Pro132His mutant and wild type, we show that Pro132His mutation affects the conformational equilibrium with more population of conformational substates having open catalytic site, modulated by the dynamics of the catalytic site entry loop, implying the allosteric nature of this mutation. The structural analysis indicates that rearrangement of hydrogen bonds between His132 and adjacent residues enhances the dynamics of the linker, which in turn is augmented by the inherent dynamic flexibility of the catalytic pocket entry site due to the presence of charged residues. The altered dynamics leading to loss of secondary structures corroborate well with the reported compromised thermal stability.

Allosteric modulation of SHP2: Quest from known to unknown

Src homology-2 domain-containing protein tyrosine phosphatase-2 (SHP2) is a key regulatory factor in the cell cycle and its activating mutations play an important role in the development of various cancers, making it an important target for antitumor drugs. Due to the highly conserved amino acid sequence and positively charged nature of the active site of SHP2, it is difficult to discover inhibitors with high affinity for the catalytic site of SHP2 and sufficient cell permeability, making it considered an "undruggable" target. However, the discovery of allosteric regulation mechanisms provides new opportunities for transforming undruggable targets into druggable ones. Given the limitations of orthosteric inhibitors, SHP2 allosteric inhibitors have become a more selective and safer research direction. In this review, we elucidate the oncogenic mechanism of SHP2 and summarize the discovery methods of SHP2 allosteric inhibitors, providing new strategies for the design and improvement of SHP2 allosteric inhibitors.

Building Quantitative Bridges between Dynamics and Sequences of SARS-CoV-2 Main Protease and a Diverse Set of Thirty-Two Proteins

Proteases are major drug targets for many viral diseases. However, mutations can render several antiprotease drugs inefficient rapidly even though these mutations may not alter protein structures significantly. Understanding relations between quickly mutating residues, protease structures, and the dynamics of the proteases is crucial for designing potent drugs. Due to this reason, we studied relations between the evolutionary information on residues in the amino acid sequences and protein dynamics for SARS-CoV-2 main protease. More precisely, we analyzed three dynamical quantities (Schlitter entropy, root-mean-square fluctuations, and dynamical flexibility index) and their relation to the amino acid conservation extracted from multiple sequence alignments of the main protease. We showed that a quantifiable similarity can be built between a sequence-based quantity called Jensen-Shannon conservation and those three dynamical quantities. We validated this similarity for a diverse set of 32 different proteins, other than the SARS-CoV-2 main protease. We believe that establishing these kinds of quantitative bridges will have larger implications for all viral proteases as well as all proteins.

Green and efficient one-pot three-component synthesis of novel drug-like furo[2,3–d]pyrimidines as potential active site inhibitors and putative allosteric hotspots modulators of both SARS-CoV-2 MPro and PLPro

In this paper, an environmentally benign, convenient, and efficient one-pot three-component reaction has been developed for the regioselective synthesis of novel 5-aroyl(or heteroaroyl)-6-(alkylamino)-1,3-dimethylfuro[2,3-d]pyrimidine-2,4(1H,3H)-diones (4a‒n) through the sequential condensation of aryl(or heteroaryl)glyoxal monohydrates (1a‒g), 1,3-dimethylbarbituric acid (2), and alkyl(viz. cyclohexyl or tert-butyl)isocyanides (3a or 3b) catalyzed by ultra-low loading ZrOCl₂•8H₂O (just 2 mol%) in water at 50 ˚C. After synthesis and characterization of the mentioned furo[2,3-d]pyrimidines (4a‒n), their multi-targeting inhibitory properties were investigated against the active site and putative allosteric hotspots of both SARS-CoV-2 main protease (M^Pro) and papain-like protease (PL^Pro) based on molecular docking studies and compare the attained results with various medicinal compounds which approximately in three past years were used, introduced, and or repurposed to fight against COVID-19. Furthermore, drug-likeness properties of the mentioned small heterocyclic frameworks (4a‒n) have been explored using in silico ADMET analyses. Interestingly, the molecular docking studies and ADMET-related data revealed that the novel series of furo[2,3-d]pyrimidines (4a‒n), especially 5-(3,4-methylendioxybenzoyl)-6-(cyclohexylamino)-1,3-dimethylfuro[2,3-d]pyrimidine-2,4(1H,3H)-dione (4g) as hit one is potential COVID-19 drug candidate, can subject to further in vitro and in vivo studies. It is worthwhile to note that the protein-ligand-type molecular docking studies on the human body temperature-dependent M^Pro protein that surprisingly contains zinc^II (Zn^II) ion between His41/Cys145 catalytic dyad in the active site, which undoubtedly can make new plans for designing novel SARS-CoV-2 M^Pro inhibitors, is performed for the first time in this paper, to the best of our knowledge.

Evolutionary progression of collective mutations in Omicron sub-lineages towards efficient RBD-hACE2: Allosteric communications between and within viral and human proteins

The interaction between the Spike (S) protein of SARS-CoV-2 and the human angiotensin converting enzyme 2 (hACE2) is essential for infection, and is a target for neutralizing antibodies. Consequently, selection of mutations in the S protein is expected to be driven by the impact on the interaction with hACE2 and antibody escape. Here, for the first time, we systematically characterized the collective effects of mutations in each of the Omicron sub-lineages (BA.1, BA.2, BA.3 and BA.4) on both the viral S protein receptor binding domain (RBD) and the hACE2 protein using post molecular dynamics studies and dynamic residue network (DRN) analysis. Our analysis suggested that Omicron sub-lineage mutations result in altered physicochemical properties that change conformational flexibility compared to the reference structure, and may contribute to antibody escape. We also observed changes in the hACE2 substrate binding groove in some sub-lineages. Notably, we identified unique allosteric communication paths in the reference protein complex formed by the DRN metrics betweenness centrality and eigencentrality hubs, originating from the RBD core traversing the receptor binding motif of the S protein and the N-terminal domain of the hACE2 to the active site. We showed allosteric changes in residue network paths in both the RBD and hACE2 proteins due to Omicron sub-lineage mutations. Taken together, these data suggest progressive evolution of the Omicron S protein RBD in sub-lineages towards a more efficient interaction with the hACE2 receptor which may account for the increased transmissibility of Omicron variants.