Mechanism of Substrate Translocation in an Alternating Access Transporter.

Transporters shuttle molecules across cell membranes by alternating among distinct conformational states. Fundamental questions remain about how transporters transition between states and how such structural rearrangements regulate substrate translocation. Here, we capture the translocation process by crystallography and unguided molecular dynamics simulations, providing an atomic-level description of alternating access transport. Simulations of a SWEET-family transporter initiated from an outward-open, glucose-bound structure reported here spontaneously adopt occluded and inward-open conformations. Strikingly, these conformations match crystal structures, including our inward-open structure. Mutagenesis experiments further validate simulation predictions. Our results reveal that state transitions are driven by favorable interactions formed upon closure of extracellular and intracellular "gates" and by an unfavorable transmembrane helix configuration when both gates are closed. This mechanism leads to tight allosteric coupling between gates, preventing them from opening simultaneously. Interestingly, the substrate appears to take a "free ride" across the membrane without causing major structural rearrangements in the transporter.

A Zn-dependent structural transition of SOD1 modulates its ability to undergo phase separation.

The misfolding and mutation of Cu/Zn superoxide dismutase (SOD1) is commonly associated with amyotrophic lateral sclerosis (ALS). SOD1 can accumulate within stress granules (SGs), a type of membraneless organelle, which is believed to form via liquid-liquid phase separation (LLPS). Using wild-type, metal-deficient, and different ALS disease mutants of SOD1 and computer simulations, we report here that the absence of Zn leads to structural disorder within two loop regions of SOD1, triggering SOD1 LLPS and amyloid formation. The addition of exogenous Zn to either metal-free SOD1 or to the severe ALS mutation I113T leads to the stabilization of the loops and impairs SOD1 LLPS and aggregation. Moreover, partial Zn-mediated inhibition of LLPS was observed for another severe ALS mutant, G85R, which shows perturbed Zn-binding. By contrast, the ALS mutant G37R, which shows reduced Cu-binding, does not undergo LLPS. In addition, SOD1 condensates induced by Zn-depletion exhibit greater cellular toxicity than aggregates formed by prolonged incubation under aggregating conditions. Overall, our work establishes a role for Zn-dependent modulation of SOD1 conformation and LLPS properties that may contribute to amyloid formation.

Atomistic simulations reveal impacts of missense mutations on the structure and function of SynGAP1.

De novo mutations in the synaptic GTPase activating protein (SynGAP) are associated with neurological disorders like intellectual disability, epilepsy, and autism. SynGAP is also implicated in Alzheimer's disease and cancer. Although pathogenic variants are highly penetrant in neurodevelopmental conditions, a substantial number of them are caused by missense mutations that are difficult to diagnose. Hence, in silico mutagenesis was performed for probing the missense effects within the N-terminal region of SynGAP structure. Through extensive molecular dynamics simulations, encompassing three 150-ns replicates for 211 variants, the impact of missense mutations on the protein fold was assessed. The effect of the mutations on the folding stability was also quantitatively assessed using free energy calculations. The mutations were categorized as potentially pathogenic or benign based on their structural impacts. Finally, the study introduces wild-type-SynGAP in complex with RasGTPase at the inner membrane, while considering the potential effects of mutations on these key interactions. This study provides structural perspective to the clinical assessment of SynGAP missense variants and lays the foundation for future structure-based drug discovery.

Interaction networks within disease-associated GαS variants characterized by an integrative biophysical approach.

Activation of G proteins through nucleotide exchange initiates intracellular signaling cascades essential for life processes. Under normal conditions, nucleotide exchange is regulated by the formation of G protein-G protein-coupled receptor complexes. Single point mutations in the Gα subunit of G proteins bypass this interaction, leading to loss of function or constitutive gain of function, which is closely linked with the onset of multiple diseases. Despite the recognized significance of Gα mutations in disease pathology, structural information for most variants is lacking, potentially due to inherent protein dynamics that pose challenges for crystallography. To address this, we leveraged an integrative spectroscopic and computational approach to structurally characterize seven of the most frequently observed and clinically relevant mutations in the stimulatory Gα subunit, GαS. A previously proposed allosteric model of Gα activation linked structural changes in the nucleotide-binding pocket with functionally important changes in interactions between switch regions. We investigated this allosteric connection in GαS by integrating data from variable temperature CD spectroscopy, which measured changes in global protein structure and stability, and molecular dynamics simulations, which observed changes in interaction networks between GαS switch regions. Additionally, saturation-transfer difference NMR spectroscopy was applied to observe changes in nucleotide interactions with residues within the nucleotide binding site. These data have enabled testing of predictions regarding how mutations in GαS result in loss or gain of function and evaluation of proposed structural mechanisms. The integration of experimental and computational data allowed us to propose a more nuanced classification of mechanisms underlying GαS gain-of-function and loss-of-function mutations.

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

A novel mRNA multi-epitope vaccine of Acinetobacter baumannii based on multi-target protein design in immunoinformatic approach.

Acinetobacter baumannii is a gram-negative bacillus prevalent in nature, capable of thriving under various environmental conditions. As an opportunistic pathogen, it frequently causes nosocomial infections such as urinary tract infections, bacteremia, and pneumonia, contributing to increased morbidity and mortality in clinical settings. Consequently, developing novel vaccines against Acinetobacter baumannii is of utmost importance. In our study, we identified 10 highly conserved antigenic proteins from the NCBI and UniProt databases for epitope mapping. We subsequently screened and selected 8 CTL, HTL, and LBL epitopes, integrating them into three distinct vaccines constructed with adjuvants. Following comprehensive evaluations of immunological and physicochemical parameters, we conducted molecular docking and molecular dynamics simulations to assess the efficacy and stability of these vaccines. Our findings indicate that all three multi-epitope mRNA vaccines designed against Acinetobacter baumannii are promising; however, further animal studies are required to confirm their reliability and effectiveness.

Cellular Phosphate Sensing and Anion Binding by an Azacrown-Calixpyrrole Hybrid.

A hybrid receptor-sensor for anions originating from the merging of positively charged ammonium moieties for electrostatic attraction/stronger binding of azacrowns with directionality of calixpyrrole hydrogen bond donors for selectivity is investigated. As demonstrated this hybrid receptor-sensor shows a remarkable selectivity for orthophosphate even in the presence of other phosphates and anions found in cellular materials (Kassoc H2PO4 ->H2P2O7 2->AMP-≫ADP2- or ATP3- over halides, nitrate, or hydrogen sulfate; all Na+ salts in water) but also cellular polyphosphate or phospholipids. This selectivity is harnessed in a real-time monitoring of cell lysis by lysozyme, which releases orthophosphate and other phosphates and anions from the cells. This sensitive (LOD 0.4 µM) fluorescence-based microscale method compares favorably with the state-of-the-art techniques but can easily be practiced in a high-throughput screening (HTS) manner. The anion binding and selectivity in aqueous solutions were investigated by NMR and put in context with phosphate binding of the parent calix[4]pyrrole. The microscopic understanding of anion binding by the hybrid receptor was then obtained from a combination of density functional theory (DFT), classical molecular dynamics (MD) with explicit water solvation, and ab initio MD (AIMD) simulations. Correlating the NMR and fluorescence binding data with studies of solvation of the receptor, phosphate anion, and the resulting complex confirms the binding is largely driven by entropic component (TΔS) associated with receptor and anion desolvation.

Comparative molecular dynamics simulations provided insights into the mechanisms of cold-adaption of alginate lyases from the PL7 family.

Alginate is an important polysaccharide that is abundant in the marine environments, including the Polar Regions, and bacterial alginate lyases play key roles in its degradation. Many reported alginate lyases show characteristics of cold-adapted enzymes, including relatively low temperature optimum of activities (Topt) and low thermal stabilities. However, the cold-adaption mechanisms of alginate lyases remain unclear. Here, we studied the cold-adaptation mechanisms of alginate lyases by comparing four members of the PL7 family from different environments: AlyC3 from the Arctic ocean (Psychromonas sp. C-3), AlyA1 from the temperate ocean (Zobellia galactanivorans), PA1167 from the human pathogen (Pseudomonas aeruginosa PAO1), and AlyQ from the tropic ocean (Persicobacter sp. CCB-QB2). Sequence comparison and comparative molecular dynamics (MD) simulations revealed two main strategies of cold adaptation. First, the Arctic AlyC3 and temperate AlyA1 increased the flexibility of the loops close to the catalytic center by introducing insertions at these loops. Second, the Arctic AlyC3 increased the electrostatic attractions with the negatively charged substrate by introducing a high portion of positively charged lysine at three of the insertions mentioned above. Furthermore, our study also revealed that the root mean square fluctuation (RMSF) increased greatly when the temperature was increased to Topt or higher, suggesting the RMSF increase temperature as a potential indicator of the cold adaptation level of the PL7 family. This study provided new insights into the cold-adaptation mechanisms of bacterial alginate lyases and the marine carbon cycling at low temperatures.

Design of Cryptococcus neoformans multi-epitope vaccine based on immunoinformatics method.

Cryptococcus neoformans is a widely distributed opportunistic pathogenic fungus. While C. neoformans commonly infects immunocompromised individuals, it can also affect those who are immunocompetent. Transmission of C. neoformans primarily occurs through the respiratory tract, leading to the development of meningitis. The mortality rate of Cryptococcal meningitis is high, and treatment options are limited. Cryptococcus neoformans infections pose a significant public health threat and currently lack targeted and effective response strategies. This study aimed to screen T lymphocyte (cytotoxic T lymphocyte and helper T lymphocyte) and B lymphocyte epitopes derived from four C. neoformans antigens and develop two multi-epitope vaccines by combining them with various adjuvants. Molecular docking results demonstrated that the vaccines bind stably to Toll-like receptor 4 ( and induce innate immunity. The credibility of the molecular docking results was validated through subsequent molecular dynamics simulations. Furthermore, the results of immune simulation analyses underscored the multi-epitope vaccine's capability to effectively induce robust humoral and cellular immune responses within the host organism. These two vaccines have demonstrated theoretical efficacy against C. neoformans infection as indicated by computer analysis. Nevertheless, additional experimental validation is essential to substantiate the protective efficacy of the vaccines.

A multi-epitope Cryptococcus neoformans vaccine covering the most common A and D phenotypes was designed using bioinformatics methods.

The effects of physical morphologies and strain rate on piezoelectric potential of boron nitride nanotubes: a molecular dynamics simulation.

The growing demand for self-powered systems and the slow progress in energy storage devices have led to the emergence of piezoelectric materials as a promising solution for energy harvesting. This study aims to investigate the effects of chirality, length, and strain rate on the piezoelectric potential of boron nitride nanotubes (BNNTs) through molecular dynamics simulation. Accurate data and guidance are provided to explain the piezoelectricity of chiral nanotubes, as the piezoelectric potentials of these nanotubes have previously remained unclear. The present study focuses on calculating the effect of these parameters based on the atomic model. The observed results stem from the frequencies and internal deformations, as the axial frequencies and deformations exhibit more substantial modifications compared to transverse directions. The piezoelectricity was found to depend on chirality, with the order of BNNT piezoelectricity sufficiency being in the sequence of zigzag > chirality > armchair configurations. The length of the BNNTs was also found to influence piezoelectricity, while the strain rate had no effect. The results also indicate that BNNTs can generate power in the milliwatts range, which is adequate for low-power electronic devices and Internet of Things applications. This research provides valuable insights into the piezoelectricity of chiral nanotubes and offers guidance for designing efficient energy harvesting devices.

Advancing DBD Plasma Chemistry: Insights into Reactive Nitrogen Species such as NO2, N2O5, and N2O Optimization and Species Reactivity through Experiments and MD Simulations.

This study aims to fine-tune the plasma composition with a particular emphasis on reactive nitrogen species (RNS) including nitrogen dioxide (NO2), dinitrogen pentoxide (N2O5), and nitrous oxide (N2O), produced by a self-constructed cylindrical dielectric barrier discharge (CDBD). We demonstrated the effective manipulation of the plasma chemical profile by optimizing electrical properties, including the applied voltage and frequency, and by adjusting the nitrogen and oxygen ratios in the gas mixture. Additionally, quantification of these active species was achieved using Fourier transform infrared spectroscopy. The study further extends to exploring the aerosol polymerization of acrylamide (AM) into polyacrylamide (PAM), serving as a model reaction to evaluate the reactivity of different plasma-generated species, highlighting the significant role of NO2 in achieving high polymerization yields. Complementing our experimental data, molecular dynamics (MD) simulations, based on the ReaxFF reactive force field potential, explored the interactions between reactive oxygen species, specifically hydroxyl radicals (OH) and hydrogen peroxide (H2O2), with water molecules. Understanding these interactions, combined with the optimization of plasma chemistry, is crucial for enhancing the effectiveness of DBD plasma in environmental applications like air purification and water treatment.

Determination of promising inhibitors for N-SH2 domain of SHP2 tyrosine phosphatase: an in silico study.

There are many genes that produce proteins related to diseases and these proteins can be targeted with drugs as a potential therapeutic approach. Recent advancement in drug discovery techniques have created new opportunities for treating variety of diseases by targeting disease-related proteins. Structure-based drug discovery is a faster and more cost-effective approach than traditional methods. SHP2 phosphatase, encoded by the PTPN11 gene, has been the focus of much attention due to its involvement in many types of diseases. The biological function of SHP2 is enabled mostly by protein-protein interaction through its SH2 domains. In this study, we report the identification of a potential small molecule inhibitor for the N-SH2 domain of SHP2 by structure-based drug discovery approach. We utilized molecular docking studies, followed by molecular dynamics simulations and MM/PBSA calculations, to analyze compounds retrieved from the Broad's Drug Repurposing Hub and ZINC15 databases. We selected 10 hit compounds with the best docking scores from the libraries and examined their binding properties in the N-SH2 domain. We found that compound CID 60838 (Irinotecan) was the most suitable compound with a binding free energy value of - 64.45 kcal/mol and significant interactions with the target residues in the domain.

Review of Recent Computational Research on the Adsorption of PFASs with a Variety of Substrates.

The widespread use and impervious nature of per- and polyfluorinated alkyl substances (PFASs) is leading to potentially harmful exposure in numerous environments. One avenue to explore remediation of PFAS-contaminated environments involves investigating how well PFASs adsorb onto various substrates. In the current review, we focus on summarizing recent computational research, largely involving density functional theory (DFT) and molecular dynamics (MD), into the adsorption and interaction of PFASs with a variety of substrates with an aim to provide insight and inspire further research that may lead to solutions to this critical problem that impacts the environment and human health.

Revealing the Interaction Mechanism between Mycobacterium tuberculosis GyrB and Novobiocin, SPR719 through Binding Thermodynamics and Dissociation Kinetics Analysis.

With the rapid emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb), various levels of resistance against existing anti-tuberculosis (TB) drugs have developed. Consequently, the identification of new anti-TB targets and drugs is critically urgent. DNA gyrase subunit B (GyrB) has been identified as a potential anti-TB target, with novobiocin and SPR719 proposed as inhibitors targeting GyrB. Therefore, elucidating the molecular interactions between GyrB and its inhibitors is crucial for the discovery and design of efficient GyrB inhibitors for combating multidrug-resistant TB. In this study, we revealed the detailed binding mechanisms and dissociation processes of the representative inhibitors, novobiocin and SPR719, with GyrB using classical molecular dynamics (MD) simulations, tau-random acceleration molecular dynamics (τ-RAMD) simulations, and steered molecular dynamics (SMD) simulations. Our simulation results demonstrate that both electrostatic and van der Waals interactions contribute favorably to the inhibitors' binding to GyrB, with Asn52, Asp79, Arg82, Lys108, Tyr114, and Arg141 being key residues for the inhibitors' attachment to GyrB. The τ-RAMD simulations indicate that the inhibitors primarily dissociate from the ATP channel. The SMD simulation results reveal that both inhibitors follow a similar dissociation mechanism, requiring the overcoming of hydrophobic interactions and hydrogen bonding interactions formed with the ATP active site. The binding and dissociation mechanisms of GyrB with inhibitors novobiocin and SPR719 obtained in our work will provide new insights for the development of promising GyrB inhibitors.

Free Energy Evaluation of Cavity Formation in Metastable Liquid Based on Stochastic Thermodynamics.

Nucleation is a fundamental and general process at the initial stage of first-order phase transition. Although various models based on the classical nucleation theory (CNT) have been proposed to explain the energetics and kinetics of nucleation, detailed understanding at nanoscale is still required. Here, in view of the homogeneous bubble nucleation, we focus on cavity formation, in which evaluation of the size dependence of free energy change is the key issue. We propose the application of a formula in stochastic thermodynamics, the Jarzynski equality, for data analysis of molecular dynamics (MD) simulation to evaluate the free energy of cavity formation. As a test case, we performed a series of MD simulations with a Lennard-Jones (LJ) fluid system. By applying an external spherical force field to equilibrated LJ liquid, we evaluated the free energy change during cavity growth as the Jarzynski's ensemble average of required works. A fairly smooth free energy curve was obtained as a function of bubble radius in metastable liquid of mildly negative pressure conditions.

The role of S477N mutation in the molecular behavior of SARS-CoV-2 spike protein: An in-silico perspective.

The attachment of SARA-CoV-2 happens between ACE2 and the receptor binding domain (RBD) on the spike protein. Mutations in this domain can affect the binding affinity of the spike protein for ACE2. S477N, one of the most common mutations reported in the recent variants, is located in the RBD. Today's computational approaches in biology, especially during the SARS-CoV-2 pandemic, assist researchers in predicting a protein's behavior in contact with other proteins in more detail. In this study, we investigated the interactions of the S477N-hACE2 in silico to find the impact of this mutation on its binding affinity for ACE2 and immunity responses using dynamics simulation, protein-protein docking, and immunoinformatics methods. Our computational analysis revealed an increased binding affinity of N477 for ACE2. Four new hydrogen and hydrophobic bonds in the mutant RBD-ACE2 were formed (with S19 and Q24 of ACE2), which do not exist in the wild type. Also, the protein spike structure in this mutation was associated with an increase in stabilization and a decrease in its fluctuations at the atomic level. N477 mutation can be considered as the cause of increased escape from the immune system through MHC-II.

Bioinformatics insights into CENP-T and CENP-W protein-protein interaction disruptive amino acid substitution in the CENP-T-W complex.

Kinetochores are multi-protein assemblies present at the centromere of the human chromosome and play a crucial role in cellular mitosis. The CENP-T and CENP-W chains form a heterodimer, which is an integral part of the inner kinetochore, interacting with the linker DNA on one side and the outer kinetochore on the other. Additionally, the CENP-T-W dimer interacts with other regulatory proteins involved in forming inner kinetochores. The specific roles of different amino acids in the CENP-W at the protein-protein interaction (PPI) interface during the CENP-T-W dimer formation remain incompletely understood. Since cell division goes awry in diseases like cancer, this CENP-T-W partnership is a potential target for new drugs that could restore healthy cell division. We employed molecular docking, binding free energy calculations, and molecular dynamics (MD) simulations to investigate the disruptive effects of amino acids substitutions in the CENP-W chain on CENP-T-W dimer formation. By conducting a molecular docking study and analysing hydrogen bonding interactions, we identified key residues in CENP-W (ASN-46, ARG-53, LEU-83, SER-86, ARG-87, and GLY-88) for further investigation. Through site-directed mutagenesis and subsequent binding free energy calculations, we refined the selection of mutant. We chose four mutants (N46K, R53K, L83K, and R87E) of CENP-W to assess their comparative potential in forming CENP-T-W dimer. Our analysis from 250 ns long revealed that the substitution of LEU83 and ARG53 residues in CENP-W with the LYS significantly disrupts the formation of CENP-T-W dimer. In conclusion, LEU83 and ARG53 play a critical role in CENP-T and CENP-W dimerization which is ultimately required for cellular mitosis. Our findings not only deepen our understanding of cell division but also hint at exciting drug-target possibilities.

A general expression for the statistical error in a diffusion coefficient obtained from a solid-state molecular-dynamics simulation.

Analysis of the mean squared displacement of species k , r k 2 , as a function of simulation time t constitutes a powerful method for extracting, from a molecular-dynamics (MD) simulation, the tracer diffusion coefficient, D k * . The statistical error in D k * is seldom considered, and when it is done, the error is generally underestimated. In this study, we examined the statistics of r k 2 t curves generated by solid-state diffusion by means of kinetic Monte Carlo sampling. Our results indicate that the statistical error in D k * depends, in a strongly interrelated way, on the simulation time, the cell size, and the number of relevant point defects in the simulation cell. Reducing our results to one key quantity-the number of k particles that have jumped at least once-we derive a closed-form expression for the relative uncertainty in D k * . We confirm the accuracy of our expression through comparisons with self-generated MD diffusion data. With the expression, we formulate a set of simple rules that encourage the efficient use of computational resources for MD simulations.

Exploring TEAD2 as a drug target for therapeutic intervention of cancer: A multi-computational case study.

Transcriptional enhanced associate domain (TEAD) is a family of transcription factors that plays a significant role during embryonic developmental processes, and its dysregulation is responsible for tumour progression. TEAD is considered as druggable targets in various diseases, namely cancer, cardiovascular diseases and neurodegenerative disorders. Previous structural studies revealed the importance of the central hydrophobic pocket of TEAD as a potential target for small-molecule inhibitors and demonstrated flufenamic acid (FLU) (a COX-2 enzyme inhibitor) to bind and inhibit TEAD2 functions. However, to date, no drug candidates that bind specifically to TEAD2 with high selectivity and efficacy have been developed or proposed. Within this framework, we present here a case study where we have identified potential TEAD2 inhibitor candidates by integrating multiple computational approaches. Among the candidates, the top two ranked compounds ZINC95969481 (LG1) which is a fused pyrazole derivative and ZINC05203789 (LG2), a fluorene derivative resulted in much favourable binding energy scores than the reference ligand, FLU. The drug likeliness of the best compounds was also evaluated in silico to ensure the bioavailability of these compounds particularly LG1 as compared to FLU thus providing a strong rationale for their development as leads against TEAD. Molecular dynamics simulations results highlighted the role of key residues contributing to favourable interactions in TEAD2-LG1 complex with much favourable interaction and binding free energy values with respect to the reference compound. Altogether, this study provides a starting platform to be more exploited by future experimental research towards the development of inhibitors against TEAD, a persuasive strategy for therapeutic intervention in cancer treatment.

Sodium-Dependent Neutral Amino Acid Transporter 2 Can Serve as a Tertiary Carrier for l-Type Amino Acid Transporter 1-Utilizing Prodrugs.

Membrane transporters are the key determinants of the homeostasis of endogenous compounds in the cells and their exposure to drugs. However, the substrate specificities of distinct transporters can overlap. In the present study, the interactions of l-type amino acid transporter 1 (LAT1)-utilizing prodrugs with sodium-coupled neutral amino acid transporter 2 (SNAT2) were explored. The results showed that the cellular uptake of LAT1-utilizing prodrugs into a human breast cancer cell line, MCF-7 cells, was mediated via SNATs as the uptake was increased at higher pH (8.5), decreased in the absence of sodium, and inhibited in the presence of unselective SNAT-inhibitor, (α-(methylamino)isobutyric acid, MeAIB). Moreover, docking the compounds to a SNAT2 homology model (inward-open conformation) and further molecular dynamics simulations and the subsequent trajectory and principal component analyses confirmed the chemical features supporting the interactions of the studied compounds with SNAT2, which was found to be the main SNAT expressed in MCF-7 cells.