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

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.

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.

Decoding Allosteric Effects of Missense Variations in Drug Metabolism: Afrocentric CYP3A4 Alleles Explored

There is growing research on the allosteric behaviour of proteins, including studies on allosteric mutations that contribute to human diseases and the development of allosteric drugs. Allostery also plays a key role in drug metabolism, an essential factor in drug development. However, population specific variations, particularly in 3D protein structures, remain understudied. This study focuses on CYP3A4, a key enzyme responsible for metabolizing over 50% of FDA-approved drugs and often linked to adverse drug reactions. Given the vast genetic diversity of Africa, we investigated 13 CYP3A4 alleles from African populations using post-molecular dynamics analyses, with 12 being single variations and one containing a double variation. Except for one, all allele variations were located away from the active site, suggesting allosteric effects. Our comparative analyses of reference and variant structures, through hydrogen bond interactions, dynamic residue network analysis and substrate channel dynamics, revealed notable differences at both global and residue levels. The *32-I335T variant showed the largest changes compared to the reference structure, while *3-M445T (near normal metabolizer) exhibited the least change, with other variants falling in between. The *32-I335T variant showed a distorted conformation in the radius of gyration, a distinct kink in the I helix with specific hydrogen bonds and altered channel patterns. The *12-L373F variant, associated with reduced metabolism of midazolam and quinine, showed increased rigidity in its vicinity, potentially interfering with catalytic activity. Our findings align with clinical and wet lab data, suggesting that our approaches could be applied to analyse variants without clinical evidence.

Enhancing the understandings on SARS-CoV-2 main protease (Mpro) mutants from molecular dynamics and machine learning

While star drugs like Paxlovid have shown remarkable performance in combating SARS-CoV-2, we still face serious challenges such as viral mutants and resistance. In this study, we employ a computational framework combining molecular dynamics (MD) simulations, enhanced sampling techniques, and machine learning (ML) approaches to systematically investigate the molecular mechanisms underlying drug resistance in SARS-CoV-2 main protease (Mpro) mutants. Specifically, based on the accuracy of the analytical structures and the advantages of MD simulation, we deeply analyze the influence of mutants on drug resistance and its intrinsic function from the dynamic dimension. The relevant data for Mpro with different states are compared and analyzed to consolidate the understanding of mutant effect. Through the free energy perturbation method, the absolute binding free energy diagrams of Mpro mutants and Nirmatrelvir are provided, which is meaningful to the design, comparison and optimization of the new-generation inhibitors. The interaction pattern between Mpro mutants and substrate is unraveled with the AlphaFold3 model, effectively filling the deficiency of experiments. Moreover, ML model is used to explore the differentiated synergetic pathways with the important dual mutants. The critical sites in the protein network are provided, which emphasizes on the importance and urgency of in-depth research on similar systems.

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.

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.

The workshops on computational applications in secondary metabolite discovery (CAiSMD)

We report the outcomes of the second session of the free online open-access workshop "Computational Applications in Secondary Metabolite Discovery (CAiSMD) 2022" that took place from 09 to 11 March 2022. The first session was held from 08 to 10 March 2021 and drew the attention of many early career scientists from academia and industry. The 23 invited speakers of this year's workshop also came from academia and industry and 222 registered participants from five continents (Africa, Asia, Europe, South, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites or natural products as drug candidates and drug leads. For three days, the participants of this online workshop discussed modern computer-based approaches for exploring NP discovery in the "omics" age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. These were followed by oral presentations during which much interaction between the speakers and the audience was observed. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) upon submission of an abstract. The final program available on the workshop website (https://indiayouth.info/index.php/caismd) comprised three keynote lectures, 14 oral presentations, two round table discussions, and four hands-on sessions. This meeting report also references internet resources for computational biology around secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community.

The "violin model": Looking at community networks for dynamic allostery

Allosteric regulation of proteins continues to be an engaging research topic for the scientific community. Models describing allosteric communication have evolved from focusing on conformation-based descriptors of protein structural changes to appreciating the role of internal protein dynamics as a mediator of allostery. Here, we explain a "violin model" for allostery as a contemporary method for approaching the Cooper-Dryden model based on redistribution of protein thermal fluctuations. Based on graph theory, the violin model makes use of community network analysis to functionally cluster correlated protein motions obtained from molecular dynamics simulations. This Review provides the theory and workflow of the methodology and explains the application of violin model to unravel the workings of protein kinase A.

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.

Global Analysis of Plasmodium falciparum Dihydropteroate Synthase Variants Associated with Sulfadoxine Resistance Reveals Variant Distribution and Mechanisms of Resistance: A Computational-Based Study

The continual rise in sulfadoxine (SDX) resistance affects the therapeutic efficacy of sulfadoxine-pyrimethamine; therefore, careful monitoring will help guide its prolonged usage. Mutations in Plasmodium falciparum dihydropteroate synthase (Pfdhps) are being surveilled, based on their link with SDX resistance. However, there is a lack of continuous analyses and data on the potential effect of molecular markers on the Pfdhps structure and function. This study explored single-nucleotide polymorphisms (SNPs) in Pfdhps that were isolated in Africa and other countries, highlighting the regional distribution and its link with structure. In total, 6336 genomic sequences from 13 countries were subjected to SNPs, haplotypes, and structure-based analyses. The SNP analysis revealed that the key SDX resistance marker, A437G, was nearing fixation in all countries, peaking in Malawi. The mutation A613S was rare except in isolates from the Democratic Republic of Congo and Malawi. Molecular docking revealed a general loss of interactions when comparing mutant proteins to the wild-type protein. During MD simulations, SDX was released from the active site in mutants A581G and A613S before the end of run-time, whereas an unstable binding of SDX to mutant A613S and haplotype A437A/A581G/A613S was observed. Conformational changes in mutant A581G and the haplotypes A581G/A613S, A437G/A581G, and A437G/A581G/A613S were seen. The radius of gyration revealed an unfolding behavior for the A613S, K540E/A581G, and A437G/A581G systems. Overall, tracking such mutations by the continuous analysis of Pfdhps SNPs is encouraged. SNPs on the Pfdhps structure may cause protein-drug function loss, which could affect the applicability of SDX in preventing malaria in pregnant women and children.

Viral proteases as therapeutic targets

Some medically important viruses-including retroviruses, flaviviruses, coronaviruses, and herpesviruses-code for a protease, which is indispensable for viral maturation and pathogenesis. Viral protease inhibitors have become an important class of antiviral drugs. Development of the first-in-class viral protease inhibitor saquinavir, which targets HIV protease, started a new era in the treatment of chronic viral diseases. Combining several drugs that target different steps of the viral life cycle enables use of lower doses of individual drugs (and thereby reduction of potential side effects, which frequently occur during long term therapy) and reduces drug-resistance development. Currently, several HIV and HCV protease inhibitors are routinely used in clinical practice. In addition, a drug including an inhibitor of SARS-CoV-2 main protease, nirmatrelvir (co-administered with a pharmacokinetic booster ritonavir as Paxlovid®), was recently authorized for emergency use. This review summarizes the basic features of the proteases of human immunodeficiency virus (HIV), hepatitis C virus (HCV), and SARS-CoV-2 and discusses the properties of their inhibitors in clinical use, as well as development of compounds in the pipeline.

Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions

The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes.

Allosteric Hotspots in the Main Protease of SARS-CoV-2

Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph-theoretical methods: Bond-to-bond propensity, which has been previously successful in identifying allosteric sites in extensive benchmark data sets without a priori knowledge, and Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. Using statistical bootstrapping, we score the highest ranking sites against random sites at similar distances, and we identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.

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.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Identification of Aloe-derived natural products as prospective lead scaffolds for SARS-CoV-2 main protease (Mpro) inhibitors

In the past two years, the COVID-19 pandemic has caused over 5 million deaths and 250 million infections worldwide. Despite successful vaccination efforts and emergency approval of small molecule therapies, a diverse range of antivirals is still needed to combat the inevitable resistance that will arise from new SARS-CoV-2 variants. The main protease of SARS-CoV-2 (M^pro) is an attractive drug target due to the clinical success of protease inhibitors against other viruses, such as HIV and HCV. However, in order to combat resistance, various chemical scaffolds need to be identified that have the potential to be developed into potent inhibitors. To this end, we screened a high-content protease inhibitor library against M^proin vitro, in order to identify structurally diverse compounds that could be further developed into antiviral leads. Our high-content screening efforts retrieved 27 hits each with > 50% inhibition in our M^pro FRET assay. Of these, four of the top inhibitor compounds were chosen for follow-up due to their potency and drugability (Lipinski's rules of five criteria): anacardic acid, aloesin, aloeresin D, and TCID. Further analysis via dose response curves revealed IC₅₀ values of 6.8 μM, 38.9 μM, 125.3 μM, and 138.0 μM for each compound, respectively. Molecular docking studies demonstrated that the four inhibitors bound at the catalytic active site of M^pro with varying binding energies (-7.5 to -5.6 kcal/mol). Furthermore, M^pro FRET assay kinetic studies demonstrated that M^pro catalysis is better represented by a sigmoidal Hill model than the standard Michaelis-Menten hyperbola, indicating substantial cooperativity of the active enzyme dimer. This result suggests that the dimerization interface could be an attractive target for allosteric inhibitors. In conclusion, we identified two closely-related natural product compounds from the Aloe plant (aloesin and aloeresin D) that may serve as novel scaffolds for M^pro inhibitor design and additionally confirmed the strongly cooperative kinetics of M^pro proteolysis. These results further advance our knowledge of structure-function relationships in M^pro and offer new molecular scaffolds for inhibitor design.

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.

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.