MDM-TASK-web: MD-TASK and MODE-TASK web server for analyzing protein dynamics

Mechanistic Insight Into the Conformational Changes of Cas8 Upon Binding to Different PAM Sequences in the Transposon-Encoded Type I-F CRISPR-Cas System

The INTEGRATE system is a gene-editing approach that offers advantages over the widely used CRISPR-Cas9 system. It does not introduce double strand breaks in the target DNA but rather integrates the desired DNA sequence directly into it. The first step in the integration process is PAM recognition, which is critical to understanding and optimizing the system. Experimental testing revealed varying integration efficiencies of different PAM mutants, and computational simulations were carried out to gain mechanistic insight into the conformational changes of Cas8 during PAM recognition. Our results showed that the interaction between Arg246 and guanine at position (-1) of the target strand is critical for PAM recognition. We found that unfavorable interactions in the 5'-AC-3' PAM mutant disrupted this interaction and may be responsible for its 0% integration efficiency. Additionally, we discovered that PAM sequences not only initiate the integration process but also regulate it through an allosteric mechanism that connects the N-terminal domain and the helical bundle of Cas8. This allosteric regulation was present in all PAMs tested, even those with lower integration efficiencies, such as 5'-TC-3' and 5'-AC-3'. We identified the Cas8 residues that are involved in this regulation. Our findings provide valuable insights into PAM recognition mechanisms in the INTEGRATE system and can help improve the gene-editing technology.

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.

Using residue interaction networks to understand protein function and evolution and to engineer new proteins

Residue interaction networks (RINs) provide graph-based representations of interaction networks within proteins, providing important insight into the factors driving protein structure, function, and stability relationships. There exists a wide range of tools with which to perform RIN analysis, taking into account different types of interactions, input (crystal structures, simulation trajectories, single proteins, or comparative analysis across proteins), as well as formats, including standalone software, web server, and a web application programming interface (API). In particular, the ability to perform comparative RIN analysis across protein families using "metaRINs" provides a valuable tool with which to dissect protein evolution. This, in turn, highlights hotspots to avoid (or target) for in vitro evolutionary studies, providing a powerful framework that can be exploited to engineer new proteins.

Shedding light on cancer immunology at the molecular level: A quantum biochemistry study of representative PD-1/PD-L1 conformations

BACKGROUND

Programmed death 1 (PD-1) binding to PD-L1 is a potent mechanism used by immunogenic tumors to evade the immune system and the immune checkpoint PD-1PD-L1 has emerged as a promising target in the search for new drugs to improve cancer treatment. The crystallographic structure of humanPD-1humanPD-L1 shed light on the molecular characterization of this system and allowed computational studies to be carried out to characterize structural behaviors.

METHODS

This study demonstrated the importance of analyzing the flexibility of protein systems through molecular dynamics simulations (MDS) and its impacts on the interaction energy obtained through quantum biochemistry.

RESULTS

The computational results obtained provide a description of the flexibility and energetic profile of the PD-1PD-L1 contact surface using representative conformations from MDS. Variations of up to 50 % in the total interaction energy values were detected depending on the scrutinized conformation, which can be mainly attributed to the flexibility of the CC' loop, FG loop and ASP85-GLN91 of PD-1 and the MET58-LYS62 segment of PD-L1. Quantum biochemistry revealed the three hot spots in PD-L1: ARG113L-ARG125L > ILE54L-VAL76L > ALA18L-ASP26L; and two energetic hot spots in PD-1: ALA125-ARG139 > VAL63-GLN88. Nonetheless, VAL63-GLN88 and GLY124-ARG139 exhibit significant variation in interaction energy between different conformations, while ARG113L-ARG125L is the only hot spot with high energetic fluctuation on the PD-L1 surface.

CONCLUSION

This is the first application of MDS coupled to dimensionality reduction and density functional theory (DFT) demonstrating new structural and energetic features that might be useful in discovering/designing more potent PD-1PD-L1 inhibitors.

Structural insights into the role of deleterious mutations at the dimeric interface of Toll-like receptor interferon-β related adaptor protein

Toll-like receptors (TLRs) are major players in the innate immune system-recognizing pathogens and differentiating self/non-self components of immunity. These proteins are present either on the plasma membrane or endosome and recognize pathogens at their extracellular domains. They are characterized by a single transmembrane helix and an intracellular toll-interleukin-1 receptor (TIR) domain. Few TIRs directly invoke downstream signaling, while others require other TIR domains of adaptors like TIR domain-containing adaptor-inducing interferon-β (TRIF) and TRIF-related adaptor molecule (TRAM). On recognizing pathogenic lipopolysaccharides, TLR4 dimerises and interacts with the intracellular TRAM dimer through the TIR domain to recruit a downstream signaling adaptor (TRIF). We have performed an in-depth study of the structural effect of two mutations (P116H and C117H) at the dimeric interface of the adaptor TRAM, which are known to abrogate downstream signaling. We modeled the structure and performed molecular dynamics studies in order to decipher the structural basis of this effect. We observed that these mutations led to an increased radius of gyration of the complex and resulted in several changes to the interaction energy values when compared against the wild type (WT) and positive control mutants. We identified highly interacting residues as hubs in the WT dimer, and a few such hubs that were lost in the mutant dimers. Changes in the protein residue path, hampering the information flow between the crucial A86/E87/D88/D89 and T155/S156 sites, were observed for the mutants. Overall, we show that such residue changes can have subtle but long-distance effects, impacting the signaling path allosterically.

Repurposing DrugBank compounds as potential Plasmodium falciparum class 1a aminoacyl tRNA synthetase multi-stage pan-inhibitors with a specific focus on mitomycin

Plasmodium falciparum aminoacyl tRNA synthetases (PfaaRSs) are potent antimalarial targets essential for proteome fidelity and overall parasite survival in every stage of the parasite's life cycle. So far, some of these proteins have been singly targeted yielding inhibitor compounds that have been limited by incidences of resistance which can be overcome via pan-inhibition strategies. Hence, herein, for the first time, we report the identification and in vitro antiplasmodial validation of Mitomycin (MMC) as a probable pan-inhibitor of class 1a (arginyl(A)-, cysteinyl(C), isoleucyl(I)-, leucyl(L), methionyl(M), and valyl(V)-) PfaaRSs which hypothetically may underlie its previously reported activity on the ribosomal RNA to inhibit protein translation and biosynthesis. We combined multiple in silico structure-based discovery strategies that first helped identify functional and druggable sites that were preferentially targeted by the compound in each of the plasmodial proteins: Ins1-Ins2 domain in Pf-ARS; anticodon binding domain in Pf-CRS; CP1-editing domain in Pf-IRS and Pf-MRS; C-terminal domain in Pf-LRS; and CP-core region in Pf-VRS. Molecular dynamics studies further revealed that MMC allosterically induced changes in the global structures of each protein. Likewise, prominent structural perturbations were caused by the compound across the functional domains of the proteins. More so, MMC induced systematic alterations in the binding of the catalytic nucleotide and amino acid substrates which culminated in the loss of key interactions with key active site residues and ultimate reduction in the nucleotide-binding affinities across all proteins, as deduced from the binding energy calculations. These altogether confirmed that MMC uniformly disrupted the structure of the target proteins and essential substrates. Further, MMC demonstrated IC50 < 5 μM against the Dd2 and 3D7 strains of parasite making it a good starting point for malarial drug development. We believe that findings from our study will be important in the current search for highly effective multi-stage antimalarial drugs.

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papainlike endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

MDiGest: A Python package for describing allostery from molecular dynamics simulations

Many biological processes are regulated by allosteric mechanisms that communicate with distant sites in the protein responsible for functionality. The binding of a small molecule at an allosteric site typically induces conformational changes that propagate through the protein along allosteric pathways regulating enzymatic activity. Elucidating those communication pathways from allosteric sites to orthosteric sites is, therefore, essential to gain insights into biochemical processes. Targeting the allosteric pathways by mutagenesis can allow the engineering of proteins with desired functions. Furthermore, binding small molecule modulators along the allosteric pathways is a viable approach to target reactions using allosteric inhibitors/activators with temporal and spatial selectivity. Methods based on network theory can elucidate protein communication networks through the analysis of pairwise correlations observed in molecular dynamics (MD) simulations using molecular descriptors that serve as proxies for allosteric information. Typically, single atomic descriptors such as α-carbon displacements are used as proxies for allosteric information. Therefore, allosteric networks are based on correlations revealed by that descriptor. Here, we introduce a Python software package that provides a comprehensive toolkit for studying allostery from MD simulations of biochemical systems. MDiGest offers the ability to describe protein dynamics by combining different approaches, such as correlations of atomic displacements or dihedral angles, as well as a novel approach based on the correlation of Kabsch-Sander electrostatic couplings. MDiGest allows for comparisons of networks and community structures that capture physical information relevant to allostery. Multiple complementary tools for studying essential dynamics include principal component analysis, root mean square fluctuation, as well as secondary structure-based analyses.

Functional insight into Cordyceps militaris sugar transporters by structure modeling, network analysis and allosteric regulation

Insights into the structures, functions and dynamics of Cordyceps militaris (C. militaris) sugar transporters are necessary for understanding their versatile metabolic capability for fungal growth. The sequence-function relationship study of 85 C. militaris sugar transporters showed that there is a gap between phylogenetic-based subfamily classification and their functions. Beyond protein sequences, structural modeling and principal component analysis of the structural ensemble revealed the different folds of the Car and Org subfamilies. Performing channel detection and network analysis found that the Alp and Hex subfamilies can be specifically distinguished from others by the betweenness of channel residues. Signature dynamics analysis further suggested that the Hex subfamily demonstrates different dynamics, with high flexibility at the H1 region in TM11. Furthermore, the H1 region as an allosteric site was examined by network parameter calculations that guided allosteric pathways between this region and the channel cavity. Together with gene expression data of C. militaris, e.g., Hex06741 in the Hex subfamily, it was promisingly expressed when sugar utilization was altered. This work demonstrates an in silico framework for investigating C. militaris sugar transporters as an example case study of the allosteric activity of the Hex subfamily and can facilitate sugar transporter engineering design that can further optimize the preferable sugar utilization and fermentation process of C. militaris.

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.

Effect of phosphorylation on the structural dynamics, thermal stability of human dopamine transporter: A simulation study using normal modes, molecular dynamics and Markov State Model

The Human Dopamine Transporter (hDAT) plays an essential role in modulating the Influx/Efflux of dopamine, and it is involved in the mechanism of certain neurodegenerative diseases such as Parkinson's disease. Several studies have reported important states for Dopamine transport: outward-facing open state (OFo), the outward-facing closed state (OFc), the holo-occluded state closed (holo), and the inward-facing open state (IFo). Furthermore, experimental assays have shown that different phosphorylation conditions in hDAT can affect the rate of dopamine absorption. We present a protocol using hybrid simulation methods to study the conformational dynamics and stability of states of hDAT under different phosphorylation sites. With this protocol, we explored the conformational space of hDAT, identified the states, and evaluated the free energy differences and the transition probabilities between them in each of the phosphorylation cases. We also presented the conformational changes and correlated them with those described in the literature. There is a thesis/hypothesis that the phosphorylation condition corresponding to NP-333 system (where all sites Ser/Thr from residue 2 to 62 and 254 to 613 are phosphorylated, except residue 333) would decrease the rate of dopamine transport from the extracellular medium to the intracellular medium by hDAT as previously described in the literature by Lin et al., 2003. Our results corroborated this thesis/hypothesis and the data reported. It is probably due to the affectation/changes/alteration of the conformational dynamics of this system that makes the intermediate states more likely and makes it difficult to initial states associated with the uptake of dopamine in the extracellular medium, corroborating the experimental results. Furthermore, our results showed that just single phosphorylation/dephosphorylation could alter intrinsic protein motions affecting the sampling of one or more states necessary for dopamine transport. In this sense, the modification of phosphorylation influences protein movements and conformational preferences, affecting the stability of states and the transition between them and, therefore, the transport.

Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks

Protein Structure Networks (PSNs) are a well-known mathematical model for estimation and analysis of the three-dimensional protein structure. Investigating the topological architecture of PSNs may help identify the crucial amino acid residues for protein stability and protein-protein interactions, as well as deduce any possible mutational effects. But because proteins go through conformational changes to give rise to essential biological functions, this has to be done dynamically over time. The most effective method to describe protein dynamics is molecular dynamics simulation, with the most popular software programs for manipulating simulations to infer interaction networks being RING, MD-TASK, and NAPS. Here, we compare the computational approaches used by these three tools-all of which are accessible as web servers-to understand the pathogenicity of missense mutations and talk about their potential applications as well as their advantages and disadvantages.

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.

RING 3.0: fast generation of probabilistic residue interaction networks from structural ensembles

Residue interaction networks (RINs) are used to represent residue contacts in protein structures. Thanks to the advances in network theory, RINs have been proved effective as an alternative to coordinate data in the analysis of complex systems. The RING server calculates high quality and reliable non-covalent molecular interactions based on geometrical parameters. Here, we present the new RING 3.0 version extending the previous functionality in several ways. The underlying software library has been re-engineered to improve speed by an order of magnitude. RING now also supports the mmCIF format and provides typed interactions for the entire PDB chemical component dictionary, including nucleic acids. Moreover, RING now employs probabilistic graphs, where multiple conformations (e.g. NMR or molecular dynamics ensembles) are mapped as weighted edges, opening up new ways to analyze structural data. The web interface has been expanded to include a simultaneous view of the RIN alongside a structure viewer, with both synchronized and clickable. Contact evolution across models (or time) is displayed as a heatmap and can help in the discovery of correlating interaction patterns. The web server, together with an extensive help and tutorial, is available from URL: https://ring.biocomputingup.it/.

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.

Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0

Cryo-electron microscopy (cryoEM) has become a well established technique with the potential to produce structures of large and dynamic supramolecular complexes that are not amenable to traditional approaches for studying structure and dynamics. The size and low resolution of such molecular systems often make structural modelling and molecular dynamics simulations challenging and computationally expensive. This, together with the growing wealth of structural data arising from cryoEM and other structural biology methods, has driven a trend in the computational biophysics community towards the development of new pipelines for analysing global dynamics using coarse-grained models and methods. At the centre of this trend has been a return to elastic network models, normal mode analysis (NMA) and ensemble analyses such as principal component analysis, and the growth of hybrid simulation methodologies that make use of them. Here, this field is reviewed with a focus on ProDy, the Python application programming interface for protein dynamics, which has been developed over the last decade. Two key developments in this area are highlighted: (i) ensemble NMA towards extracting and comparing the signature dynamics of homologous structures, aided by the recent SignDy pipeline, and (ii) pseudoatom fitting for more efficient global dynamics analyses of large and low-resolution supramolecular assemblies from cryoEM, revisited in the CryoDy pipeline. It is believed that such a renewal and extension of old models and methods in new pipelines will be critical for driving the field forward into the next cryoEM revolution.

Slipknot or Crystallographic Error: A Computational Analysis of the Plasmodium falciparum DHFR Structural Folds

The presence of protein structures with atypical folds in the Protein Data Bank (PDB) is rare and may result from naturally occurring knots or crystallographic errors. Proper characterisation of such folds is imperative to understanding the basis of naturally existing knots and correcting crystallographic errors. If left uncorrected, such errors can frustrate downstream experiments that depend on the structures containing them. An atypical fold has been identified in P. falciparum dihydrofolate reductase (PfDHFR) between residues 20–51 (loop 1) and residues 191–205 (loop 2). This enzyme is key to drug discovery efforts in the parasite, necessitating a thorough characterisation of these folds. Using multiple sequence alignments (MSA), a unique insert was identified in loop 1 that exacerbates the appearance of the atypical fold-giving it a slipknot-like topology. However, PfDHFR has not been deposited in the knotted proteins database, and processing its structure failed to identify any knots within its folds. The application of protein homology modelling and molecular dynamics simulations on the DHFR domain of P. falciparum and those of two other organisms (E. coli and M. tuberculosis) that were used as molecular replacement templates in solving the PfDHFR structure revealed plausible unentangled or open conformations of these loops. These results will serve as guides for crystallographic experiments to provide further insights into the atypical folds identified.

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.

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

Continually emerging resistant strains of malarial parasites to current drugs present challenges. Understanding the underlying resistance mechanisms, especially those linked to allostery is, thus, highly crucial for drug design. This forms the main concern of the paper through a case study of falcipain 2 (FP-2) and its mutations, some of which are linked to artemisinin (ART) drug resistance. Here, we applied a variety of in silico approaches and tools that we developed recently, together with existing computational tools. This included novel essential dynamics and dynamic residue network (DRN) analysis algorithms. We identified six pockets demonstrating dynamic differences in the presence of some mutations. We observed striking allosteric effects in two mutant proteins. In the presence of M245I, a cryptic pocket was detected via a unique mechanism in which Pocket 2 fused with Pocket 6. In the presence of the A353T mutation, which is located at Pocket 2, the pocket became the most rigid among all protein systems analyzed. Pocket 6 was also highly stable in all cases, except in the presence of M245I mutation. The effect of ART linked mutations was more subtle, and the changes were at residue level. Importantly, we identified an allosteric communication path formed by four unique averaged BC hubs going from the mutated residue to the catalytic site and passing through the interface of three identified pockets. Collectively, we established and demonstrated that we have robust tools and a pipeline that can be applicable to the analysis of mutations.