Allosteric pockets and dynamic residue network hubs of falcipain 2 in mutations including those linked to artemisinin resistance

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.

Three Decades of Targeting Falcipains to Develop Antiplasmodial Agents: What have we Learned and What can be Done Next?

Malaria is a devastating infectious disease that affects large swathes of human populations across the planet's tropical regions. It is caused by parasites of the genus Plasmodium, with Plasmodium falciparum being responsible for the most lethal form of the disease. During the intraerythrocytic stage in the human hosts, malaria parasites multiply and degrade hemoglobin (Hb) using a battery of proteases, which include two cysteine proteases, falcipains 2 and 3 (FP-2 and FP-3). Due to their role as major hemoglobinases, FP-2 and FP-3 have been targeted in studies aiming to discover new antimalarials and numerous inhibitors with activity against these enzymes, and parasites in culture have been identified. Nonetheless, cross-inhibition of human cysteine cathepsins remains a serious hurdle to overcome for these compounds to be used clinically. In this article, we have reviewed key functional and structural properties of FP-2/3 and described different compound series reported as inhibitors of these proteases during decades of active research in the field. Special attention is also paid to the wide range of computer-aided drug design (CADD) techniques successfully applied to discover new active compounds. Finally, we provide guidelines that, in our understanding, will help advance the rational discovery of new FP-2/3 inhibitors.

Investigation of Multi-Subunit Mycobacterium tuberculosis DNA-Directed RNA Polymerase and Its Rifampicin Resistant Mutants

Emerging Mycobacterium tuberculosis (Mtb) resistant strains have continued to limit the efficacies of existing antitubercular therapies. More specifically, mutations in the RNA replicative machinery of Mtb, RNA polymerase (RNAP), have been widely linked to rifampicin (RIF) resistance, which has led to therapeutic failures in many clinical cases. Moreover, elusive details on the underlying mechanisms of RIF-resistance caused by Mtb-RNAP mutations have hampered the development of new and efficient drugs that are able to overcome this challenge. Therefore, in this study we attempt to resolve the molecular and structural events associated with RIF-resistance in nine clinically reported missense Mtb RNAP mutations. Our study, for the first time, investigated the multi-subunit Mtb RNAP complex and findings revealed that the mutations commonly disrupted structural-dynamical attributes that may be essential for the protein's catalytic functions, particularly at the βfork loop 2, β'zinc-binding domain, the β' trigger loop and β'jaw, which in line with previous experimental reports, are essential for RNAP processivity. Complementarily, the mutations considerably perturbed the RIF-BP, which led to alterations in the active orientation of RIF needed to obstruct RNA extension. Consequentially, essential interactions with RIF were lost due to the mutation-induced repositioning with corresponding reductions in the binding affinity of the drug observed in majority of the mutants. We believe these findings will significantly aid future efforts in the discovery of new treatment options with the potential to overcome antitubercular resistance.

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.

Insights into the Allosteric Effect of SENP1 Q597A Mutation on the Hydrolytic Reaction of SUMO1 via an Integrated Computational Study

Small ubiquitin-related modifier (SUMO)-specific protease 1 (SENP1) is a cysteine protease that catalyzes the cleavage of the C-terminus of SUMO1 for the processing of SUMO precursors and deSUMOylation of target proteins. SENP1 is considered to be a promising target for the treatment of hepatocellular carcinoma (HCC) and prostate cancer. SENP1 Gln597 is located at the unstructured loop connecting the helices α4 to α5. The Q597A mutation of SENP1 allosterically disrupts the hydrolytic reaction of SUMO1 through an unknown mechanism. Here, extensive multiple replicates of microsecond molecular dynamics (MD) simulations, coupled with principal component analysis, dynamic cross-correlation analysis, community network analysis, and binding free energy calculations, were performed to elucidate the detailed mechanism. Our MD simulations showed that the Q597A mutation induced marked dynamic conformational changes in SENP1, especially in the unstructured loop connecting the helices α4 to α5 which the mutation site occupies. Moreover, the Q597A mutation caused conformational changes to catalytic Cys603 and His533 at the active site, which might impair the catalytic activity of SENP1 in processing SUMO1. Moreover, binding free energy calculations revealed that the Q597A mutation had a minor effect on the binding affinity of SUMO1 to SENP1. Together, these results may broaden our understanding of the allosteric modulation of the SENP1-SUMO1 complex.

Understanding the P-Loop Conformation in the Determination of Inhibitor Selectivity Toward the Hepatocellular Carcinoma-Associated Dark Kinase STK17B

As a member of the death-associated protein kinase family of serine/threonine kinases, the STK17B has been associated with diverse diseases such as hepatocellular carcinoma. However, the conformational dynamics of the phosphate-binding loop (P-loop) in the determination of inhibitor selectivity profile to the STK17B are less understood. Here, a multi-microsecond length molecular dynamics (MD) simulation of STK17B in the three different states (ligand-free, ADP-bound, and ligand-bound states) was carried out to uncover the conformational plasticity of the P-loop. Together with the analyses of principal component analysis, cross-correlation and generalized correlation motions, secondary structural analysis, and community network analysis, the conformational dynamics of the P-loop in the different states were revealed, in which the P-loop flipped into the ADP-binding site upon the inhibitor binding and interacted with the inhibitor and the C-lobe, strengthened the communication between the N- and C-lobes. These resulting interactions contributed to inhibitor selectivity profile to the STK17B. Our results may advance our understanding of kinase inhibitor selectivity and offer possible implications for the design of highly selective inhibitors for other protein kinases.

Deciphering Isoniazid Drug Resistance Mechanisms on Dimeric Mycobacterium tuberculosis KatG via Post-molecular Dynamics Analyses Including Combined Dynamic Residue Network Metrics

Resistance mutations in Mycobacterium tuberculosis (Mtb) catalase peroxidase protein (KatG), an essential enzyme in isoniazid (INH) activation, reduce the sensitivity of Mtb to first-line drugs, hence presenting challenges in tuberculosis (TB) management. Thus, understanding the mutational imposed resistance mechanisms remains of utmost importance in the quest to reduce the TB burden. Herein, effects of 11 high confidence mutations in the KatG structure and residue network communication patterns were determined using extensive computational approaches. Combined traditional post-molecular dynamics analysis and comparative essential dynamics revealed that the mutant proteins have significant loop flexibility around the heme binding pocket and enhanced asymmetric protomer behavior with respect to wild-type (WT) protein. Heme contact analysis between WT and mutant proteins identified a reduction to no contact between heme and residue His270, a covalent bond vital for the heme-enabled KatG catalytic activity. Betweenness centrality calculations showed large hub ensembles with new hubs especially around the binding cavity and expanded to the dimerization domain via interface in the mutant systems, providing possible compensatory allosteric communication paths for the active site as a result of the mutations which may destabilize the heme binding pocket and the loops in its vicinity. Additionally, an interesting observation came from Eigencentrality hubs, most of which are located in the C-terminal domain, indicating relevance of the domain in the protease functionality. Overall, our results provide insight toward the mechanisms involved in KatG-INH resistance in addition to identifying key regions in the enzyme functionality, which can be used for future drug design.

Autopromotion of K-Ras4B Feedback Activation Through an SOS-Mediated Long-Range Allosteric Effect

The Ras-specific guanine nucleotide exchange factors Son of Sevenless (SOS) regulates Ras activation by converting inactive GDP-bound to active GTP-bound states. The catalytic activity of Ras is further allosterically regulated by GTP-Ras bound to a distal site through a positive feedback loop. To address the mechanism underlying the long-range allosteric activation of the catalytic K-Ras4B by an additional allosteric GTP-Ras through SOS, we employed molecular dynamics simulation of the K-Ras4BG13D•SOScat complex with and without an allosteric GTP-bound K-Ras4BG13D. We found that the binding of an allosteric GTP-K-Ras4BG13D enhanced the affinity between the catalytic K-Ras4BG13D and SOScat, forming a more stable conformational state. The peeling away of the switch I from the nucleotide binding site facilitated the dissociation of GDP, thereby contributing to the increased nucleotide exchange rate. The community networks further showed stronger edge connection upon allosteric GTP-K-Ras4BG13D binding, which represented an increased interaction between catalytic K-Ras4BG13D and SOScat. Moreover, GTP-K-Ras4BG13D binding transmitted allosteric signaling pathways though the Cdc25 domain of SOS that enhanced the allosteric regulatory from the K-Ras4BG13D allosteric site to the catalytic site. This study may provide an in-depth mechanism for abnormal activation and allosteric regulation of K-Ras4BG13D.

Dissecting mutational allosteric effects in alkaline phosphatases associated with different Hypophosphatasia phenotypes: An integrative computational investigation

Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the ALPL mutations showed a significant difference between the control, mild and severe HPP phenotypes. Molecular dynamics simulations coupled with protein structure network analysis were employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and the developed machine learning model suggested that the topological network parameters could serve as a robust indicator of severe mutations. The results indicated that the severity of disease-associated mutations is often linked with mutation-induced modulation of allosteric communications in the protein. This study suggested that ALPL mutations associated with mild and more severe HPPs can exert markedly distinct effects on the protein stability and long-range network communications. By linking the disease phenotypes with dynamic and allosteric molecular signatures, the proposed integrative computational approach enabled to characterize and quantify the allosteric effects of ALPL mutations and role of allostery in the pathogenesis of HPPs.

Tetracycline Derivatives Inhibit Plasmodial Cysteine Protease Falcipain-2 through Binding to a Distal Allosteric Site

Allosteric inhibitors regulate enzyme activity from remote and usually specific pockets. As they promise an avenue for less toxic and safer drugs, the identification and characterization of allosteric inhibitors has gained great academic and biomedical interest in recent years. Research on falcipain-2 (FP-2), the major papain-like cysteine hemoglobinase of Plasmodium falciparum, might benefit from this strategy to overcome the low selectivity against human cathepsins shown by active site-directed inhibitors. Encouraged by our previous finding that methacycline inhibits FP-2 noncompetitively, here we assessed other five tetracycline derivatives against this target and characterized their inhibition mechanism. As previously shown for methacycline, tetracycline derivatives inhibited FP-2 in a noncompetitive fashion, with K_i values ranging from 121 to 190 μM. A possible binding to the S' side of the FP-2 active site, similar to that described by X-ray crystallography (PDB: 6SSZ) for the noncompetitive inhibitor E-chalcone 48 (EC48), was experimentally discarded by kinetic analysis using a large peptidyl substrate spanning the whole active site. By combining lengthy molecular dynamics (MD) simulations that allowed methacycline to diffuse from solution to different FP-2 surface regions and free energy calculations, we predicted the most likely binding mode of the ligand. Of note, the proposed binding pose explains the low differences in K_i values observed for the tested tetracycline derivatives and the calculated binding free energies match the experimental values. Overall, this study has implications for the design of novel allosteric inhibitors against FP-2 and sets the basis for further optimization of the tetracycline scaffold to produce more potent and selective inhibitors.

Harnessing Reversed Allosteric Communication: A Novel Strategy for Allosteric Drug Discovery

Allostery is a fundamental and extensive mechanism of intramolecular signal transmission. Allosteric drugs possess several unique pharmacological advantages over traditional orthosteric drugs, including greater selectivity, better physicochemical properties, and lower off-target toxicity. However, owing to the complexity of allosteric regulation, experimental approaches for the development of allosteric modulators are traditionally serendipitous. Recently, the reversed allosteric communication theory has been proposed, providing a feasible tool for the unbiased detection of allosteric sites. Herein, we review the latest research on the reversed allosteric communication effect using the examples of sirtuin 6, epidermal growth factor receptor, 3-phosphoinositide-dependent protein kinase 1, and Related to A and C kinases (RAC) serine/threonine protein kinase B and recapitulate the methodologies of reversed allosteric communication strategy. The novel reversed allosteric communication strategy greatly expands the horizon of allosteric site identification and allosteric mechanism exploration and is expected to accelerate an end-to-end framework for drug discovery.

Novel dynamic residue network analysis approaches to study allosteric modulation: SARS-CoV-2 Mpro and its evolutionary mutations as a case study

The rational search for allosteric modulators and the allosteric mechanisms of these modulators in the presence of mutations is a relatively unexplored field. Here, we established novel in silico approaches and applied them to SARS-CoV-2 main protease (Mpro) as a case study. First, we identified six potential allosteric modulators. Then, we focused on understanding the allosteric effects of these modulators on each of its protomers. We introduced a new combinatorial approach and dynamic residue network (DRN) analysis algorithms to examine patterns of change and conservation of critical nodes, according to five independent criteria of network centrality. We observed highly conserved network hubs for each averaged DRN metric on the basis of their existence in both protomers in the absence and presence of all ligands (persistent hubs). We also detected ligand specific signal changes. Using eigencentrality (EC) persistent hubs and ligand introduced hubs we identified a residue communication path connecting the allosteric binding site to the catalytic site. Finally, we examined the effects of the mutations on the behavior of the protein in the presence of selected potential allosteric modulators and investigated the ligand stability. One crucial outcome was to show that EC centrality hubs form an allosteric communication path between the allosteric ligand binding site to the active site going through the interface residues of domains I and II; and this path was either weakened or lost in the presence of some of the mutations. Overall, the results revealed crucial aspects that need to be considered in rational computational drug discovery.