MD-TASK: a software suite for analyzing molecular dynamics trajectories

AlloViz: A tool for the calculation and visualisation of protein allosteric communication networks

Allostery, the presence of functional interactions between distant parts of proteins, is a critical concept in the field of biochemistry and molecular biology, particularly in the context of protein function and regulation. Understanding the principles of allosteric regulation is essential for advancing our knowledge of biology and developing new therapeutic strategies. This paper presents AlloViz, an open-source Python package designed to quantitatively determine, analyse, and visually represent allosteric communication networks on the basis of molecular dynamics (MD) simulation data. The software integrates well-known techniques for understanding allosteric properties simplifying the process of accessing, rationalising, and representing protein allostery and communication routes. It overcomes the inefficiency of having multiple methods with heterogeneous implementations and showcases the advantages of using MD simulations and multiple replicas to obtain statistically sound information on protein dynamics; it also enables the calculation of "consensus-like" scores aggregating methods that consider multiple structural aspects of allosteric networks. We demonstrate the features of AlloViz on two proteins: β-arrestin 1, a key player for regulating G protein-coupled receptor (GPCR) signalling, and the protein tyrosine phosphatase 1B, an important pharmaceutical target for allosteric inhibitors. The software includes comprehensive documentation and examples, tutorials, and a user-friendly graphical interface.

In-silico screening of missense nsSNPs in Delta-opioid receptor protein and their restoring tendency on MCRT interaction; focusing on dynamic nature

Delta-opioid receptor protein (OPRD1) is one of the potential targets for treating pain. The presently available opioid agonists are known to cause unnecessary side effects. To discover a novel opioid agonist, our research group has synthesized a chimeric peptide MCRT and proved its potential activity through in vivo analysis. Non-synonymous SNPs (nsSNPs) missense mutations affect the functionality and stability of proteins leading to diseases. The current research was focused on understanding the role of MCRT in restoring the binding tendency of OPRD1 nsSNPs missense mutations on dynamic nature in comparison with Deltorphin-II and morphiceptin. The deleterious effects of nsSNPs were analyzed using various bioinformatics tools for predicting structural, functional, and oncogenic influence. The shortlisted nine nsSNPs were predicted for allergic reactions, domain changes, post-translation modification, multiple sequence alignment, secondary structure, molecular dynamic simulation (MDS), and peptide docking influence. Further, the docked complex of three shortlisted deleterious nsSNPs was analyzed using an MDS study, and the highly deleterious shortlisted nsSNP A149T was further analyzed for higher trajectory analysis. MCRT restored the binding tendency influence caused by nsSNPs on the dynamics of stability, functionality, binding affinity, secondary structure, residues connection, motion, and folding of OPRD1 protein.

Exploring Plant-Based Compounds as Alternatives for Targeting Enterococcus faecalis in Endodontic Therapy: A Molecular Docking Approach

Endodontic infections pose significant challenges in dental practice due to their persistence and potential complications. Among the causative agents, Enterococcus faecalis stands out for its ability to form biofilms and develop resistance to conventional antibiotics, leading to treatment failures and recurrent infections. The urgent need for alternative treatments arises from the growing concern over antibiotic resistance and the limitations of current therapeutic options in combating E. faecalis-associated endodontic infections. Plant-based natural compounds offer a promising avenue for exploration, given their diverse bioactive properties and potential as sources of novel antimicrobial agents. In this study, molecular docking and dynamics simulations are employed to explore the interactions between SrtA, a key enzyme in E. faecalis, and plant-based natural compounds. Analysis of phytocompounds through molecular docking unveiled several candidates with binding energies surpassing that of the control drug, ampicillin, with pinocembrin emerging as the lead compound due to its strong interactions with key residues of SrtA. Comparative analysis with ampicillin underscored varying degrees of structural similarity among the study compounds. Molecular dynamics simulations provided deeper insights into the dynamic behavior and stability of protein-ligand complexes, with pinocembrin demonstrating minimal conformational changes and effective stabilization of the N-terminal region. Free energy landscape analysis supported pinocembrin's stabilizing effects, further corroborated by hydrogen bond analysis. Additionally, physicochemical properties analysis highlighted the drug-likeness of pinocembrin and glabridin. Overall, this study elucidates the potential anti-bacterial properties of selected phytocompounds against E. faecalis infections, with pinocembrin emerging as a promising lead compound for further drug development efforts, offering new avenues for combating bacterial infections and advancing therapeutic interventions in endodontic practice.

Identification of a Cryptic Pocket in Methionine Aminopeptidase-II Using Adaptive Bandit Molecular Dynamics Simulations and Markov State Models

Methionine aminopeptidase-II (MetAP-II) is a metalloprotease, primarily responsible for the cotranslational removal of the N-terminal initiator methionine from the nascent polypeptide chain during protein synthesis. MetAP-II has been implicated in angiogenesis and endothelial cell proliferation and is therefore considered a validated target for cancer therapeutics. However, there is no effective drug available against MetAP-II. In this study, we employ Adaptive Bandit molecular dynamics simulations to investigate the structural dynamics of the apo and ligand-bound MetAP-II. Our results focus on the dynamic behavior of the disordered loop that is not resolved in most of the crystal structures. Further analysis of the conformational flexibility of the disordered loop reveals a hidden cryptic pocket that is predicted to be potentially druggable. The network analysis indicates that the disordered loop region has a direct signaling route to the active site. These findings highlight a new way to target MetAP-II by designing inhibitors for the allosteric site within this disordered loop region.

Differential glycosylation in mutant vitamin D-binding protein decimates the binding stability of vitamin D

Vitamin D (VD) is produced by the skin upon exposure to sunlight or is obtained from dietary sources. Several risk factors are associated with VD deficiency including mutations and post-translational modifications in its transport protein known as vitamin D binding protein (VDBP) or GC-globulin. The two common single nucleotide polymorphisms rs7041 and rs4588 create three major isoforms of VDBP, including GC-1F also called wild type, GC1S, and GC-2. The 3D models for both GC-1F and GC-2 were constructed in their glycosylated states to decipher the effect of these mutations on the overall conformational changes and VD-binding affinity. The binding affinities were estimated using the Molecular Mechanics Poison-Boltzmann surface area (MM-PBSA) method and conformational changes were investigated after free energy landscapes estimations. Total free energies suggest that GC-1F exhibits stronger affinity (ΔE = -116.09 kJ/mol) than GC-2 (ΔE = -95 kJ/mol) variant with VD. The GC-1F isoforms had more streamlined motion compared to GC-2 isoforms, predicting a trade-off between cross-talk residues that significantly impacts protein structural stability. The data suggest that glycation at Thr418 plays a vital role in the overall VDBP-VD affinity by stabilizing the N-T loop that holds the domain I (VD-pocket) and domain III intact. The loss of glycation in GC-2 has a pivotal role in the inter-domain conformational stability of VDBP, which may ultimately affect VD transportation and maturation. These findings describe a novel mechanism in how mutations distant from the VD-active site change the overall conformational of the VDBP and abrogate the VDBP-VD interaction.Communicated by Ramaswamy H. Sarma.

Epitope Identification of an mGlu5 Receptor Nanobody Using Physics-Based Molecular Modeling and Deep Learning Techniques

The world has witnessed a revolution in therapeutics with the development of biological medicines such as antibodies and antibody fragments, notably nanobodies. These nanobodies possess unique characteristics including high specificity and modulatory activity, making them promising candidates for therapeutic applications. Identifying their binding mode is essential for their development. Experimental structural techniques are effective to get such information, but they are expensive and time-consuming. Here, we propose a computational approach, aiming to identify the epitope of a nanobody that acts as an agonist and a positive allosteric modulator at the rat metabotropic glutamate receptor 5. We employed multiple structure modeling tools, including various artificial intelligence algorithms for epitope mapping. The computationally identified epitope was experimentally validated, confirming the success of our approach. Additional dynamics studies provided further insights on the modulatory activity of the nanobody. The employed methodologies and approaches initiate a discussion on the efficacy of diverse techniques for epitope mapping and later nanobody engineering.

Application of Computational Techniques in Antibody Fc-Fused Molecule Design for Therapeutics

Since the advent of hybridoma technology in the year 1975, it took a decade to witness the first approved monoclonal antibody Orthoclone OKT39 (muromonab-CD3) in the year 1986. Since then, continuous strides have been made to engineer antibodies for specific desired effects. The engineering efforts were not confined to only the variable domains of the antibody but also included the fragment crystallizable (Fc) region that influences the immune response and serum half-life. Engineering of the Fc fragment would have a profound effect on the therapeutic dose, antibody-dependent cell-mediated cytotoxicity as well as antibody-dependent cellular phagocytosis. The integration of computational techniques into antibody engineering designs has allowed for the generation of testable hypotheses and guided the rational antibody design framework prior to further experimental evaluations. In this article, we discuss the recent works in the Fc-fused molecule design that involves computational techniques. We also summarize the usefulness of in silico techniques to aid Fc-fused molecule design and analysis for the therapeutics application.

Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families

Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.

Fungi Tryptophan Synthases: What Is the Role of the Linker Connecting the α and β Structural Domains in Hemileia vastatrix TRPS? A Molecular Dynamics Investigation

Tryptophan synthase (TRPS) is a complex enzyme responsible for tryptophan biosynthesis. It occurs in bacteria, plants, and fungi as an αββα heterotetramer. Although encoded by independent genes in bacteria and plants, in fungi, TRPS is generated by a single gene that concurrently expresses the α and β entities, which are linked by an elongated peculiar segment. We conducted 1 µs all-atom molecular dynamics simulations on Hemileia vastatrix TRPS to address two questions: (i) the role of the linker segment and (ii) the comparative mode of action. Since there is not an experimental structure, we started our simulations with homology modeling. Based on the results, it seems that TRPS makes use of an already-existing tunnel that can spontaneously move the indole moiety from the α catalytic pocket to the β one. Such behavior was completely disrupted in the simulation without the linker. In light of these results and the αβ dimer's low stability, the full-working TRPS single genes might be the result of a particular evolution. Considering the significant losses that Hemileia vastatrix causes to coffee plantations, our next course of action will be to use the TRPS to look for substances that can block tryptophan production and therefore control the disease.

Elucidation of bezlotoxumab binding specificity to toxin B in Clostridioides difficile

C. difficile or Clostridioides difficile infection (CDI) is currently one of the major causes of epidemics worldwide. Toxin B from Clostridioides difficile toxin B (TcdB) infection is the main target protein inhibiting CDI recurrence. Clinical research suggested that bezlotoxumab's (Bez) efficiency is significantly reduced in neutralizing the B2 strain compared to the B1 strain. The monoclonal antibody (mAb) functions by binding to the epitope 1 and 2 regions in the combined repetitive oligopeptide (CROP) domain. Some binding residues are distinctively different between B1 and B2 strains. In this work, we aimed to elucidate and compare insights into the interaction of toxins B1 and B2 in complex with Bez by using all-atom molecular dynamics (MD) simulations and binding free energy calculations. The predicted ΔGbinding values suggested that the antibody (Ab) could bind to toxin B1 significantly better than B2, supported by higher salt bridge and hydrogen bonding (H-bonding) interactions, as well as the number of contact residues between the two focused proteins. The toxin B1 residues important for binding with Bez were E1878, T1901, E1902, F1905, N1941, V1946, N2031, T2032, E2033, V2076, V2077, and E2092. The lower susceptibility of Bez towards toxin B2 was primarily due to a change of residue E2033 from glutamate to alanine (A2033) and the loss of E1878 and E1902 contributions, as determined by the intermolecular interaction changes from the dynamic residue interaction network (dRIN) analysis. The obtained data strengthen our understanding of Bez/toxin B binding.

The molecular mechanism of the effects of the anti-neuropathic ligands on the modulation of the Sigma-2 receptor: An in-silico study

Neuropathic pain (NP) is a prevalent medical condition that lacks an effective treatment. Recently, the Sigma-2 receptor (S2R) has been proposed as a potential therapeutic target for NP. Some highly-selective S2R ligands (UKH1114, CM398, and YTD) have shown promising results in vivo, but the molecular interaction between the S2R and these ligands has been scarcely investigated. This work explores changes in the S2R upon interaction with the three mentioned ligands using in silico approaches. The results indicated that the ICL1, H1, ICL2, and ECL are the most dynamic regions of S2R in all systems. Binding interaction analysis identified amino acids with significant contribution to the binding free energy. Notably, the UKH1114-S2R simulation trajectory revealed that small alterations in the ICL1, H1, ICL2, and ECL form a new stable opening in the S2R, linking the occluded S2R binding pocket to the endoplasmic reticulum lumen, providing more evidence for the assumptions about the EBP and S2R mechanism of function. Further, the agreement between the membrane parameters in our study and experimental values confirms the validity of the MD simulations. Overall, this study provides new insights into the interaction between anti-NP ligands and the S2R.

Xport-A functions as a chaperone by stabilizing the first five transmembrane domains of rhodopsin-1

Rhodopsin-1 (Rh1), the main photosensitive protein of Drosophila, is a seven-transmembrane domain protein, which is inserted co-translationally in the endoplasmic reticulum (ER) membrane. Biogenesis of Rh1 occurs in the ER, where various chaperones interact with Rh1 to aid in its folding and subsequent transport from the ER to the rhabdomere, the light-sensing organelle of the photoreceptors. Xport-A has been proposed as a chaperone/transport factor for Rh1, but the exact molecular mechanism for Xport-A activity upon Rh1 is unknown. Here, we propose a model where Xport-A functions as a chaperone during the biogenesis of Rh1 in the ER by stabilizing the first five transmembrane domains (TMDs) of Rh1.

Mechanism of Fluoride Ion Encapsulation by Magnesium Ions in a Bacterial Riboswitch

Riboswitches sense various ions in bacteria and activate gene expression to synthesize proteins that help maintain ion homeostasis. The crystal structure of the aptamer domain (AD) of the fluoride riboswitch shows that the F- ion is encapsulated by three Mg2+ ions bound to the ligand-binding domain (LBD) located at the core of the AD. The assembly mechanism of this intricate structure is unknown. To this end, we performed computer simulations using coarse-grained and all-atom RNA models to bridge multiple time scales involved in riboswitch folding and ion binding. We show that F- encapsulation by the Mg2+ ions bound to the riboswitch involves multiple sequential steps. Broadly, two Mg2+ ions initially interact with the phosphate groups of the LBD using water-mediated outer-shell coordination and transition to a direct inner-shell interaction through dehydration to strengthen their interaction with the LBD. We propose that the efficient binding mode of the third Mg2+ and F- is that they form a water-mediated ion pair and bind to the LBD simultaneously to minimize the electrostatic repulsion between three Mg2+ bound to the LBD. The tertiary stacking interactions among the LBD nucleobases alone are insufficient to stabilize the alignment of the phosphate groups to facilitate Mg2+ binding. We show that the stability of the whole assembly is an intricate balance of the interactions among the five phosphate groups, three Mg2+, and the encapsulated F- ion aided by the Mg2+ solvated water. These insights are helpful in the rational design of RNA-based ion sensors and fast-switching logic gates.

Temperature as a modulator of allosteric motions and crosstalk in mesophilic and thermophilic enzymes

Mesophilic and thermophilic enzyme counterparts are often studied to understand how proteins function under harsh conditions. To function well outside of standard temperature ranges, thermophiles often tightly regulate their structural ensemble through intra-protein communication (via allostery) and altered interactions with ligands. It has also become apparent in recent years that the enhancement or diminution of allosteric crosstalk can be temperature-dependent and distinguish thermophilic enzymes from their mesophilic paralogs. Since most studies of allostery utilize chemical modifications from pH, mutations, or ligands, the impact of temperature on allosteric function is comparatively understudied. Here, we discuss the biophysical methods, as well as critical case studies, that dissect temperature-dependent function of mesophilic-thermophilic enzyme pairs and their allosteric regulation across a range of temperatures.

Identification of PPA1 inhibitor candidates for potential repurposing in cancer medicine

Inorganic pyrophosphatase 1 (PPA1) is pivotal to cellular metabolism as it facilitates the hydrolysis of PPi-a by-product of various metabolic processes that influence cell growth and differentiation. Overexpression of PPA1 enzyme has been linked to diminished patient survival and was shown to influence tumor cell dynamics, thereby positioning it as a potential therapy target for a variety of cancers including colorectal cancer, diffuse large B-cell lymphoma, and lung adenocarcinoma. Despite this therapeutic promise, there are no known inhibitors of PPA1 as of today. In this study, we searched for potential PPA1 inhibitors using a molecular docking screen of 30 470 compounds with a history of clinical trials and/or US Food and Drug Administration approval. We specifically targeted the active pocket that coincides with the established catalytic domain. Our screen identified promising hits, which we further subjected to ADMET (absorption, distribution, metabolism, excretion, and toxicity) filtering. Subsequent molecular dynamics (MD) analyses were conducted on devazepide, quinotolast, and tarazepide-the three substances that successfully navigated all filters. MD analyses reinforced the stability of the protein-ligand complexes and confirmed ligand binding, as substantiated by our root mean square deviation, radius of gyration and secondary structures of proteins analyses. Furthermore, Molecular Mechanics Poisson-Boltzmann Surface Area calculations post-MD identified devazepide and quinotolast as showing higher binding affinities; being supported by principal component analysis, free energy landscape, and dynamic cross-correlation matrix results. Overall, our study reveals devazepide and quinotolast as potential candidates for PPA1 inhibition which could be considered for repurposing studies that need further experimental validation. These results not only reveal a potential for clinical repurposing for PPA1 inhibition but they also offer valuable insights into the development of future compounds for targeting the crucial PPA1 enzyme.

A multi-tier computational screening framework to effectively search the mutational space of SARS-CoV-2 receptor binding motif to identify mutants with enhanced ACE2 binding abilities

SARS-CoV-2 gained crucial mutations at the receptor binding domain (RBD) that often changed the course of the pandemic leading to new waves with increased case fatality. Variants are observed with enhanced transmission and immune invasion abilities. Thus, predicting future variants with enhanced transmission ability is a problem of utmost research interest. Here, we have developed a multi-tier exhaustive SARS-CoV-2 mutation screening platform combining MM/GBSA, extensive molecular dynamics simulations, and steered molecular dynamics to identify RBD mutants with enhanced ACE2 binding capability. We have identified four RBM mutations (F490K, S494K, G504F, and the P499L) with significantly higher ACE2 binding abilities than wild-type RBD. Compared to wild-type RBD, they all form stable complexes with more hydrogen bonds and salt-bridge interactions with ACE2. Our simulation data suggest that these mutations allosterically alter the packing of the RBM interface of the RBD-ACE2 complex. As a result, the rupture force required to break the RBD-ACE2 contacts is significantly higher for these mutants.

Probing conformational landscapes of binding and allostery in the SARS-CoV-2 omicron variant complexes using microsecond atomistic simulations and perturbation-based profiling approaches: hidden role of omicron mutations as modulators of allosteric signaling and epistatic relationships

In this study, we systematically examine the conformational dynamics, binding and allosteric communications in the Omicron BA.1, BA.2, BA.3 and BA.4/BA.5 spike protein complexes with the ACE2 host receptor using molecular dynamics simulations and perturbation-based network profiling approaches. Microsecond atomistic simulations provided a detailed characterization of the conformational landscapes and revealed the increased thermodynamic stabilization of the BA.2 variant which can be contrasted with the BA.4/BA.5 variants inducing a significant mobility of the complexes. Using the dynamics-based mutational scanning of spike residues, we identified structural stability and binding affinity hotspots in the Omicron complexes. Perturbation response scanning and network-based mutational profiling approaches probed the effect of the Omicron mutations on allosteric interactions and communications in the complexes. The results of this analysis revealed specific roles of Omicron mutations as conformationally plastic and evolutionary adaptable modulators of binding and allostery which are coupled to the major regulatory positions through interaction networks. Through perturbation network scanning of allosteric residue potentials in the Omicron variant complexes performed in the background of the original strain, we characterized regions of epistatic couplings that are centered around the binding affinity hotspots N501Y and Q498R. Our results dissected the vital role of these epistatic centers in regulating protein stability, efficient ACE2 binding and allostery which allows for accumulation of multiple Omicron immune escape mutations at other sites. Through integrative computational approaches, this study provides a systematic analysis of the effects of Omicron mutations on thermodynamics, binding and allosteric signaling in the complexes with ACE2 receptor.

Elastic network models and molecular dynamic simulations reveal the molecular basis of allosteric regulation in ubiquitin-specific protease 7 (USP7)

Ubiquitin-specific protease 7 (USP7) is one of the most abundant deubiquitinases and plays an important role in various malignant tumors. However, the molecular mechanisms underlying USP7's structures, dynamics, and biological significance are yet to be investigated. In this study, we constructed the full-length models of USP7 in both the extended and compact state, and applied elastic network models (ENM), molecular dynamics (MD) simulations, perturbation response scanning (PRS) analysis, residue interaction networks as well as allosteric pocket prediction to investigate allosteric dynamics in USP7. Our analysis of intrinsic and conformational dynamics revealed that the structural transition between the two states is characterized by global clamp motions, during which the catalytic domain (CD) and UBL4-5 domain exhibit strong negative correlations. The PRS analysis, combined with the analysis of disease mutations and post-translational modifications (PTMs) further highlighted the allosteric potential of the two domains. The residue interaction network based on MD simulations captured an allosteric communication path which starts at CD domain and ends at UBL4-5 domain. Moreover, we identified a pocket at the TRAF-CD interface as a high-potential allosteric site for USP7. Overall, our studies not only provide molecular insights into the conformational changes of USP7, but also aid in the design of allosteric modulators that target USP7.

PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles

Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.

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.

Modeling the role of charged residues in thermophilic proteins by rotamer and dynamic cross correlation analysis

Discerning the determinants of protein thermostability is very important both from the theoretical and applied perspective. Different lines of evidence seem to indicate that a dynamical network of salt bridges/charged residues plays a fundamental role in the thermostability of enzymes. In this work, we applied measures of dynamic variance, like the Gini coefficients, Kullback-Leibler (KL) divergence and dynamic cross correlation (DCC) coefficients to compare the behavior of 3 pairs of homologous proteins from the thermophilic bacterium Thermus thermophilus and mesophilic Escherichia coli. Molecular dynamic (MD) simulations of these proteins were performed at 303 K and 363 K. In the characterization of their side chain rotamer distributions, the corresponding Gini coefficients and KL-divergence both revealed significant correlations with temperature. Similarly, a DCC analysis revealed a higher trend to de-correlate the movement of charged residues at higher temperatures in the thermophilic proteins, when compared with their mesophilic homologues. These results highlight the importance of dynamic electrostatic network interactions for the thermostability of enzymes.

Investigating the binding affinity of andrographolide against human SARS-CoV-2 spike receptor-binding domain through docking and molecular dynamics simulations

SARS-CoV-2 is a positive-sense single-stranded RNA virus that causes a deadly coronavirus disease (COVID-19) in humans. The infection of SARS-CoV-2 in humans involves a viral surface spike glycoprotein containing the receptor-binding domain (RBD). The interactions of SARS-CoV-2 with the host angiotensin-converting enzyme 2 (ACE2) receptor are mediated by RBD. It binds to the host ACE2 and influences viral replication and disease pathogenesis. Therefore, targeting the RBD to prevent SARS-CoV-2 infections is of utmost importance. In this study, we used docking and molecular dynamics simulations to understand the binding effect of andrographolide on the SARS-CoV-2 spike protein. During docking, a strong binding affinity was observed between the ligand and the target receptor protein. MD results demonstrated higher conformational fluctuations in the ligand-free protein compared to the bound form. Several residues in the active sites make conformational rearrangements for the S protein to interact with the ligand. While RBD experiences conformational transition to gain more stability upon binding with the ligand. This binding is strengthened via several non-covalent interactions that make the complex structure more stable with higher binding affinity. Overall findings of the study may shed some valuable insights concerning the development of potential therapeutics in the strategies for COVID-19 prevention.

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.

Wordom update 2: A user-friendly program for the analysis of molecular structures and conformational ensembles

We present the second update of Wordom, a user-friendly and efficient program for manipulation and analysis of conformational ensembles from molecular simulations. The actual update expands some of the existing modules and adds 21 new modules to the update 1 published in 2011. The new adds can be divided into three sets that: 1) analyze atomic fluctuations and structural communication; 2) explore ion-channel conformational dynamics and ionic translocation; and 3) compute geometrical indices of structural deformation. Set 1 serves to compute correlations of motions, find geometrically stable domains, identify a dynamically invariant core, find changes in domain-domain separation and mutual orientation, perform wavelet analysis of large-scale simulations, process the output of principal component analysis of atomic fluctuations, perform functional mode analysis, infer regions of mechanical rigidity, analyze overall fluctuations, and perform the perturbation response scanning. Set 2 includes modules specific for ion channels, which serve to monitor the pore radius as well as water or ion fluxes, and measure functional collective motions like receptor twisting or tilting angles. Finally, set 3 includes tools to monitor structural deformations by computing angles, perimeter, area, volume, β-sheet curvature, radial distribution function, and center of mass. The ring perception module is also included, helpful to monitor supramolecular self-assemblies. This update places Wordom among the most suitable, complete, user-friendly, and efficient software for the analysis of biomolecular simulations. The source code of Wordom and the relative documentation are available under the GNU general public license at http://wordom.sf.net.

Domain motions, dimerization, and membrane interactions of the murine guanylate binding protein 2

Guanylate-binding proteins (GBPs) are a group of GTPases that are induced by interferon-[Formula: see text] and are crucial components of cell-autonomous immunity against intracellular pathogens. Here, we examine murine GBP2 (mGBP2), which we have previously shown to be an essential effector protein for the control of Toxoplasma gondii replication, with its recruitment through the membrane of the parasitophorous vacuole and its involvement in the destruction of this membrane likely playing a role. The overall aim of our work is to provide a molecular-level understanding of the mutual influences of mGBP2 and the parasitophorous vacuole membrane. To this end, we performed lipid-binding assays which revealed that mGBP2 has a particular affinity for cardiolipin. This observation was confirmed by fluorescence microscopy using giant unilamellar vesicles of different lipid compositions. To obtain an understanding of the protein dynamics and how this is affected by GTP binding, mGBP2 dimerization, and membrane binding, assuming that each of these steps are relevant for the function of the protein, we carried out standard as well as replica exchange molecular dynamics simulations with an accumulated simulation time of more than 30 μs. The main findings from these simulations are that mGBP2 features a large-scale hinge motion in its M/E domain, which is present in each of the studied protein states. When bound to a cardiolipin-containing membrane, this hinge motion is particularly pronounced, leading to an up and down motion of the M/E domain on the membrane, which did not occur on a membrane without cardiolipin. Our prognosis is that this up and down motion has the potential to destroy the membrane following the formation of supramolecular mGBP2 complexes on the membrane surface.

Neospongodes atlantica, a potential case of an early biological introduction in the Southwestern Atlantic

Soft corals (Anthozoa: Octocorallia) are discreet components in the Southwestern Atlantic reef communities. In Brazil, the native octocoral shallow-reef fauna is mostly represented by gorgonians. Consequently, except for the nephtheid Neospongodes atlantica, most of the known soft corals from this region are considered non-indigenous. Hitherto, the monotypic genus Neospongodes, which was proposed in the early 1900s, has been considered to be endemic to the Northeastern Brazilian coast. Herein, based on in situ records, we show that N. atlantica is a substrate generalist that has been probably expanding its distribution by dominating extensive shallow and mesophotic sandy and reef bottoms, generally outcompeting other reef benthic organisms, including Brazilian endemic species. Based on previously unidentified museum specimens, new records, and a broad literature review, we provide the most comprehensive modelling of the potential distribution of this species in the Southwestern Atlantic. Based on molecular inference supported by in-depth morphological analysis, the probable non-indigenous and, therefore, ancient introduction of N. atlantica in Brazilian waters is discussed. Finally, these results support that Neospongodes and the Indo-Pacific Stereonephthya are synonyms, which led us to propose the latter as taxonomically invalid.

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.

Insights into the molecular mechanism of triazolopyrimidinone derivatives effects on the modulation of α1β2γ2 subtype of GABAA receptor: An in silico approach

Due to the importance of benzodiazepine drugs in clinical practice, such as the treatment of anxiety disorders, depression, and insomnia and the side effects of classical benzodiazepines, the study of new benzodiazepine agonists has received much attentions. In this work, we used in silico methods to explore the molecular mechanism of 1,2,4-triazolo [1,5--a] pyrimidinone derivatives in the modulation of α₁β₂γ₂ subtype of GABA_A receptor. To this aim, molecular docking, molecular dynamics simulation (MD), post-MD analysis, binding free energy calculation, and prediction of ADME properties were performed. Results showed that all new compounds have a better binding affinity for the Benzodiazepine (BZD) site of the receptor than diazepam and compound 4c had the highest affinity among them. Moreover, a good agreement was observed between the calculated ΔG_binding and experimental IC₅₀ values. Also, we noticed that residues in loop regions (particularly loop C and D-F in α₁ and γ₂ subunits, respectively) forming BZD binding site, take part in forming several H-bonds between the agonists and the receptor. Ser205, Thr207, Tyr160, and His102 of α₁ subunit and Thr207 of γ₂ subunit are mainly involved in forming H-bonds. Also, the orientation of agonists in the BZD binding site leads to π-π interactions with hydrophobic residues in loops A-F. Based on the DCCM analysis, the correlated motions in the γ₂ subunit residues are greater than those of α₁ subunit residues. Further, predicted ADME results indicated that all agonists meet the criteria. The triplicate MD simulation showed the reproducibility of the results and strengthened the study. Our results provide a comprehensive insight into the receptor-agonist interactions and clues for designing future BZD agonists.

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.

SARS-CoV-2 and MERS-CoV Spike Protein Binding Studies Support Stable Mimic of Bound 9-O-Acetylated Sialic Acids

Many disease-causing viruses target sialic acids (Sias), a class of nine-carbon sugars known to coat the surface of many cells, including those in the lungs. Human beta coronaviridae, known for causing respiratory tract diseases, often bind Sias, and some preferentially bind to those with 9-O-Ac-modification. Currently, co-binding of SARS-CoV-2, a beta coronavirus responsible for the COVID-19 pandemic, to human Sias has been reported and its preference towards α2-3-linked Neu5Ac has been shown. Nevertheless, O-acetylated Sias-protein binding studies are difficult to perform, due to the ester lability. We studied the binding free energy differences between Neu5,9Ac2α2-3GalβpNP and its more stable 9-NAc mimic binding to SARS-CoV-2 spike protein using molecular dynamics and alchemical free energy simulations. We identified multiple Sia-binding pockets, including two novel sites, with similar binding affinities to those of MERS-CoV, a known co-binder of sialic acid. In our binding poses, 9-NAc and 9-OAc Sias bind similarly, suggesting an experimentally reasonable mimic to probe viral mechanisms.

Decreased Interfacial Dynamics Caused by the N501Y Mutation in the SARS-CoV-2 S1 Spike:ACE2 Complex

Coronavirus Disease of 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has resulted in a massive health crisis across the globe, with some genetic variants gaining enhanced infectivity and competitive fitness, and thus significantly aggravating the global health concern. In this regard, the recent SARS-CoV-2 alpha, beta, and gamma variants (B.1.1.7, B.1.351, and P.1 lineages, respectively) are of great significance in that they contain several mutations that increase their transmission rates as evident from clinical reports. By the end of March 2021, these variants were accounting for about two-thirds of SARS-CoV-2 variants circulating worldwide. Specifically, the N501Y mutation in the S1 spike receptor binding domain (S1-RBD) of these variants have been reported to increase its affinity for ACE2, although the basis for this is not entirely clear yet. Here, we dissect the mechanism underlying the increased binding affinity of the N501Y mutant for ACE2 using molecular dynamics (MD) simulations of the available ACE2-S1-RBD complex structure (6M0J) and show a prolonged and stable interfacial interaction of the N501Y mutant S1-RBD with ACE2 compared to the wild type S1-RBD. Additionally, we find that the N501Y mutant S1-RBD displays altered dynamics that likely aids in its enhanced interaction with ACE2. By elucidating a mechanistic basis for the increased affinity of the N501Y mutant S1-RBD for ACE2, we believe that the results presented here will aid in developing therapeutic strategies against SARS-CoV-2 including designing of therapeutic agents targeting the ACE2-S1-RBD interaction.

Computer-Aided and AILDE Approaches to Design Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a pivotal enzyme in tocopherol and plastoquinone synthesis and a potential target for novel herbicides. Thirty-five pyridine derivatives were selected to establish a Topomer comparative molecular field analysis (Topomer CoMFA) model to obtain correlation information between HPPD inhibitory activity and the molecular structure. A credible and predictive Topomer CoMFA model was established by "split in two R-groups" cutting methods and fragment combinations (q² = 0.703, r² = 0.957, ONC = 6). The established model was used to screen out more active compounds and was optimized through the auto in silico ligand directing evolution (AILDE) platform to obtain potential HPPD inhibitors. Twenty-two new compounds with theoretically good HPPD inhibition were obtained by combining the high-activity contribution substituents in the existing molecules with the R-group search via Topomer search. Molecular docking results revealed that most of the 22 fresh compounds could form stable π-π interactions. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction and drug-like properties made 9 compounds potential HPPD inhibitors. Molecular dynamics simulation indicated that Compounds Y12 and Y14 showed good root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values and stability. According to the AILDE online verification, 5 new compounds with potential HPPD inhibition were discovered as HPPD inhibitor candidates. This study provides beneficial insights for subsequent HPPD inhibitor design.

Comparative molecular dynamics analyses on PIK3CA hotspot mutations with PI3Kα specific inhibitors and ATP

PI3K pathway is heavily emphasized in cancer where PIK3CA, which encodes for the p110α subunit of PI3Kα, presents itself as the second most common mutated gene. A lot of effort has been put in developing PI3K inhibitors, opening promising avenues for the treatment of cancer. Among these, PI3Kα specific inhibitor alpelisib was approved by FDA for breast cancer and other α-isoform specific inhibitors such as inavolisib and serabelisib reached clinical trials. However, the mode of action of these inhibitors on mutated PI3Kα and how they interact with mutant structures has not been fully elucidated yet. In this study, we are revealing the calculated interactions and binding affinities of these inhibitors within the context of PIK3CA hotspot mutations (E542K, E545K and H1047R) by employing molecular dynamics (MD) simulations. We performed principal component analysis to understand the motions of the protein complex during our simulations and also checked the correlated motions of all amino acids. Binding affinity calculations with MM-PBSA confirmed the consistent binding of alpelisib across mutations and revealed relatively higher affinities for inavolisib towards wild-type and H1047R mutant structures in comparison to other inhibitors. On the other hand, E542K mutation significantly impaired the interaction of inavolisib and serabelisib with PI3Kα. We also investigated the structural relationship of the natural ligand ATP with PI3Kα, and interestingly realized a significant reduction in binding affinity for the mutants, with potentially unexpected implications on the mechanisms that render these mutations oncogenic. Moreover, correlated motions of all residues were generally higher for ATP except the H1047R mutation which exhibited a distinguishable reduction. The results presented here could be guiding for pre-clinical and clinical studies of personalized medicine where individual mutations are a strong consideration point.

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.

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.

SenseNet, a tool for analysis of protein structure networks obtained from molecular dynamics simulations

Computational methods play a key role for investigating allosteric mechanisms in proteins, with the potential of generating valuable insights for innovative drug design. Here we present the SenseNet (“Structure ENSEmble NETworks”) framework for analysis of protein structure networks, which differs from established network models by focusing on interaction timelines obtained by molecular dynamics simulations. This approach is evaluated by predicting allosteric residues reported by NMR experiments in the PDZ2 domain of hPTP1e, a reference system for which previous computational predictions have shown considerable variance. We applied two models based on the mutual information between interaction timelines to estimate the conformational influence of each residue on its local environment. In terms of accuracy our prediction model is comparable to the top performing model published for this system, but by contrast benefits from its independence from NMR structures. Our results are complementary to experimental data and the consensus of previous predictions, demonstrating the potential of our new analysis tool SenseNet. Biochemical interpretation of our model suggests that allosteric residues in the PDZ2 domain form two distinct clusters of contiguous sidechain surfaces. SenseNet is provided as a plugin for the network analysis software Cytoscape, allowing for ease of future application and contributing to a system of compatible tools bridging the fields of system and structural biology.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

Novel mutation in hexokinase 2 confers resistance to 2-deoxyglucose by altering protein dynamics

Glucose is central to many biological processes, serving as an energy source and a building block for biosynthesis. After glucose enters the cell, hexokinases convert it to glucose-6-phosphate (Glc-6P) for use in anaerobic fermentation, aerobic oxidative phosphorylation, and the pentose-phosphate pathway. We here describe a genetic screen in Saccharomyces cerevisiae that generated a novel spontaneous mutation in hexokinase-2, hxk2^G238V, that confers resistance to the toxic glucose analog 2-deoxyglucose (2DG). Wild-type hexokinases convert 2DG to 2-deoxyglucose-6-phosphate (2DG-6P), but 2DG-6P cannot support downstream glycolysis, resulting in a cellular starvation-like response. Curiously, though the hxk2^G238V mutation encodes a loss-of-function allele, the affected amino acid does not interact directly with bound glucose, 2DG, or ATP. Molecular dynamics simulations suggest that Hxk2^G238V impedes sugar binding by altering the protein dynamics of the glucose-binding cleft, as well as the large-scale domain-closure motions required for catalysis. These findings shed new light on Hxk2 dynamics and highlight how allosteric changes can influence catalysis, providing new structural insights into this critical regulator of carbohydrate metabolism. Given that hexokinases are upregulated in some cancers and that 2DG and its derivatives have been studied in anti-cancer trials, the present work also provides insights that may apply to cancer biology and drug resistance.

Glucose fuels many of the energy-production processes required for normal cell growth. Before glucose can participate in these processes, it must first be chemically modified by proteins called hexokinases. To better understand how hexokinases modify glucose—and how mutations in hexokinase genes might confer drug resistance—we evolved resistance in yeast to a toxic hexokinase-binding molecule called 2DG. We discovered a mutation in the hexokinase gene that confers 2DG resistance and reduces the protein’s ability to modify glucose. Biochemical analyses and computer simulations of the hexokinase protein suggest that the mutation diminishes glucose binding by altering enzyme flexibility. This work shows how cells can evolve resistance to toxins via only modest changes to protein structures. Furthermore, because cancer-cell hexokinases are particularly active, 2DG has been studied as cancer chemotherapy. Thus, the insights this work provides might also apply to cancer biology.

Regulation of Cholesterol Binding to the Receptor Patched1 by its interactions With the Ligand Sonic Hedgehog (Shh)

The Hedgehog (Hh) signaling pathway is essential in cell development and regeneration, which is activated by the ligand Sonic hedgehog (Shh). The binding of Shh to its receptor Patched1 (PTCH1) releases the inhibitory effect on the downstream protein Smoothened (SMO), a G-protein-coupled-receptor (GPCR) protein. Cholesterol was supposed to function as a secondary messenger between PTCH1 and SMO. However, the molecular mechanism of this regulation process is still unclear. Therefore, microsecond coarse-grained molecular dynamics simulations were performed to investigate the protein-lipid interactions of the PTCH1 monomer and dimer-Shh complex. It was observed that the binding of cholesterols to the monomer is more stable than that to the dimer-Shh complex. It is regulated by the enrichment of Ganglioside lipids around proteins and the conformation of Y446, a residue in the sterol-sensing domain (SSD). The regulation of Shh on the dynamics of PTCH1 was further analyzed to explore the allosteric communication pathways between the Shh and the SSD. Our study provides structural and dynamic details of an additional perspective on the regulation of Hh signaling pathway through the lipid micro-environments of PTCH1.

The Structural Basis of Mycobacterium tuberculosis RpoB Drug-Resistant Clinical Mutations on Rifampicin Drug Binding

Tuberculosis (TB), caused by the Mycobacterium tuberculosis infection, continues to be a leading cause of morbidity and mortality in developing countries. Resistance to the first-line anti-TB drugs, isoniazid (INH) and rifampicin (RIF), is a major drawback to effective TB treatment. Genetic mutations in the β-subunit of the DNA-directed RNA polymerase (rpoB) are reported to be a major reason of RIF resistance. However, the structural basis and mechanisms of these resistant mutations are insufficiently understood. In the present study, thirty drug-resistant mutants of rpoB were initially modeled and screened against RIF via a comparative molecular docking analysis with the wild-type (WT) model. These analyses prioritized six mutants (Asp441Val, Ser456Trp, Ser456Gln, Arg454Gln, His451Gly, and His451Pro) that showed adverse binding affinities, molecular interactions, and RIF binding hinderance properties, with respect to the WT. These mutant models were subsequently analyzed by molecular dynamics (MD) simulations. One-hundred nanosecond all-atom MD simulations, binding free energy calculations, and a dynamic residue network analysis (DRN) were employed to exhaustively assess the impact of mutations on RIF binding dynamics. Considering the global structural motions and protein-ligand binding affinities, the Asp441Val, Ser456Gln, and His454Pro mutations generally yielded detrimental effects on RIF binding. Locally, we found that the electrostatic contributions to binding, particularly by Arg454 and Glu487, might be adjusted to counteract resistance. The DRN analysis revealed that all mutations mostly distorted the communication values of the critical hubs and may, therefore, confer conformational changes in rpoB to perturb RIF binding. In principle, the approach combined fundamental molecular modeling tools for robust "global" and "local" level analyses of structural dynamics, making it well suited for investigating other similar drug resistance cases.

PSNtools for standalone and web-based structure network analyses of conformational ensembles

Structure graphs, in which interacting amino acids/nucleotides correspond to linked nodes, represent cutting-edge tools to investigate macromolecular function.

The graph-based approach defined as Protein Structure Network (PSN) was initially implemented in the Wordom software and subsequently in the webPSN server. PSNs are computed either on a molecular dynamics (MD) trajectory (PSN-MD) or on a single structure. In the latter case, information on atomic fluctuations is inferred from the Elastic Network Model-Normal Mode Analysis (ENM-NMA) (PSN-ENM). While Wordom performs both PSN-ENM and PSN-MD analyses but without output post-processing, the webPSN server performs only single-structure PSN-EMN but assisting the user in input setup and output analysis.

Here we release for the first time the standalone software PSNtools, which allows calculation and post-processing of PSN analyses carried out either on single structures or on conformational ensembles. Relevant unique and novel features of PSNtools are either comparisons of two networks or computations of consensus networks on sets of homologous/analogous macromolecular structures or conformational ensembles. Network comparisons and consensus serve to infer differences in functionally different states of the same system or network-based signatures in groups of bio-macromolecules sharing either the same functionality or the same fold.

In addition to the new software, here we release also an updated version of the webPSN server, which allows performing an interactive graphical analysis of PSN-MD, following the upload of the PSNtools output.

PSNtools, the auxiliary binary version of Wordom software, and the WebPSN server are freely available at http://webpsn.hpc.unimo.it/wpsn3.php.

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.

Gamma Carbonic Anhydrases from Hydrothermal Vent Bacteria: Cases of Alternating Active Site Due to a Long Loop with Proton Shuttle Residue

Accelerated CO2 sequestration uses carbonic anhydrases (CAs) as catalysts; thus, there is much research on these enzymes. The γ-CA from Escherichia coli (EcoCA-γ) was the first γ-CA to display an active site that switches between “open” and “closed” states through Zn2+ coordination by the proton-shuttling His residue. Here, we explored this occurrence in γ-CAs from hydrothermal vent bacteria and also the γ-CA from Methanosarcina thermophila (Cam) using molecular dynamics. Ten sequences were analyzed through multiple sequence alignment and motif analysis, along with three others from a previous study. Conservation of residues and motifs was high, and phylogeny indicated a close relationship amongst the sequences. All structures, like EcoCA-γ, had a long loop harboring the proton-shuttling residue. Trimeric structures were modeled and simulated for 100 ns at 423 K, with all the structures displaying thermostability. A shift between “open” and “closed” active sites was observed in the 10 models simulated through monitoring the behavior of the His proton-shuttling residue. Cam, which has two Glu proton shuttling residues on long loops (Glu62 and Glu84), also showed an active site switch affected by the first Glu proton shuttle, Glu62. This switch was thus concluded to be common amongst γ-CAs and not an isolated occurrence.