Normal mode analysis of biomolecular structures: functional mechanisms of membrane proteins

Computational analysis and protein engineering of artificial PDZ domain/self-binding peptide fusion biomacromolecular system with molecular switch functionality

Self-binding peptides (SBPs) are peptide-dependent intramolecular interactions described by our group, which conceptualize the short peptide segment within a monomeric protein as able to perform biological function by its dynamic and reversible binding to a cognate domain partner in the same monomer. Previously, we offered evidences that the SBPs represent a new biomolecular dynamic phenomenon that works across folding and binding, and also proposed the SBPs as potential drug targets for pharmacological intervention. Here, we attempted to model and design artificial protein systems containing SBP with molecular switch functionality by using computational peptidology strategies and protein engineering methods. In the procedure, the free decapeptide ligands were fused to the C-terminal tail of human CAL PDZ domain through a flexible polypeptide linker, thus resulting in a number of PDZ/SBP fusion biomacromolecular systems. The intramolecular binding event of SBP to PDZ was observed in the fusion systems as well as between the free decapeptide and PDZ. We carefully examined linker effects on the intramolecular binding event and systematically optimized the length and composition of linker to improve binding. Computational analysis suggested dynamics, but not thermodynamics, primarily responsible for the binding of SBP to PDZ. The linker plays an important role, as it restrains the SBP within a small local spatial region nearby the binding site of PDZ. The term effective concentration (EC) was defined to characterize the high local concentration of SBP at a small region due to the linker restriction, which improves the binding probability of SBP to PDZ from a dynamics point of view. The CAL PDZ/SBP(iCAL36-K-6) fusion protein system with poly(G)8 linker can work as designed well, where the SBP(iCAL36-K-6) dynamically binds to/unbinds from PDZ in a regulatable manner by externally controlling its C-terminal deamidation/amidation, thus exhibiting a typical molecular switch functionality.

Dissecting Large-Scale Structural Transitions in Membrane Transporters Using Advanced Simulation Technologies

Membrane transporters are integral membrane proteins that act as gatekeepers of the cell, controlling fundamental processes such as recruitment of nutrients and expulsion of waste material. At a basic level, transporters operate using the "alternating access model," in which transported substances are accessible from only one side of the membrane at a time. This model usually involves large-scale structural changes in the transporter, which often cannot be captured using unbiased, conventional molecular simulation techniques. In this article, we provide an overview of some of the major simulation techniques that have been applied to characterize the structural dynamics and energetics involved in the transition of membrane transporters between their functional states. After briefly introducing each technique, we discuss some of their advantages and limitations and provide some recent examples of their application to membrane transporters.

The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives

Protein allostery is a fundamental biological regulatory mechanism that allows communication between distant locations within a protein, modifying its function in response to signals. Experimental techniques, such as NMR spectroscopy and cryo-electron microscopy (cryo-EM), are critical validation tools for computational predictions and provide valuable insights into dynamic conformational changes. Combining these approaches has greatly improved our understanding of classical conformational allostery and complex dynamic coupling mechanisms. Recent advances in machine learning and enhanced sampling methods have broadened the scope of allostery research, identifying cryptic allosteric sites and directing new drug discovery approaches. Despite progress, bridging static structural data with dynamic functional states remains challenging. This review underscores the importance of combining experimental and computational approaches to comprehensively understand protein allostery and its diverse applications in biology and medicine.

An accelerated molecular dynamics study for investigating protein pathways using the bond-boost hyperdynamics method

Molecular dynamics (MD) simulation is an important tool for understanding protein dynamics and the thermodynamic properties of proteins. However, due to the high computational cost of MD simulations, it is still challenging to explore a wide conformational space. To solve this problem, a variety of accelerated MD (aMD) schemes have been proposed over the past few decades. The bond-boost method (BBM) is one of such aMD schemes, which expedites escape events from energy basins by adding a bias potential based on changes in bond length. In this paper, we present a new methodology based on the BBM for accelerating the conformational transition of proteins. In our modified BBM, the bias potential is constructed using the dihedral angle and hydrogen bond, which are more suitable variables to monitor the conformational change in proteins. Additionally, we have developed an efficient algorithm compatible with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) package. The method is validated with the conformational change of ribose binding protein and adenylate kinase by comparing the conventional and accelerated MD simulation results. Based on the aMD results, the characteristics of the proteins are investigated by monitoring the conformational transition pathways. Moreover, the free energy landscape calculated using umbrella sampling confirms all the states identified by the aMD simulation are the free energy minima, and the system makes transitions following the path indicated by the free energy landscape. Our efficient approach is expected to play a key role in investigating transition pathways in a wide range of protein simulations.

Structural and Thermodynamic Impact of Oncogenic Mutations on the Nucleosome Core Particle

The nucleosome core particle (NCP) is essential for chromatin structure and function, serving as the fundamental unit of eukaryotic chromatin. Oncogenic mutations in core histones disrupt chromatin dynamics, altering DNA repair and transcription processes. Here, we investigate the molecular consequences of two mutations-H2BE76K and H4R92T-using 36 μs of all-atom molecular dynamics simulations and experimental biophysical assays. These mutations destabilize the H2B-H4 interface by disrupting critical salt bridges and hydrogen bonds, reducing binding free energy at this interface. Principal component analysis reveals altered helix conformations and increased interhelical distances in mutant systems. Thermal stability assays (TSA) and differential scanning calorimetry (DSC) confirm that these mutations lower the dimer dissociation temperature and reduce enthalpy compared to the wild type. Taken together, our results elucidate how these mutations compromise nucleosome stability and propose mechanisms through which they could modulate chromatin accessibility and gene dysregulation in cancer.

Impact of Replicas and Simulation Length on In Silico Behaviors of a Protein Domain

Molecular dynamics (MD) simulations are immensely valuable for studying protein structure, function and dynamics. Their ability to capture atomic-level behavior of molecules and describe their evolution over time makes it a powerful synergistic tool for biochemistry, structural biology and other life sciences. To advance research and knowledge on reasonable timescales, researchers must optimize the amount of useful information extracted from simulation data while often frugally managing computational resources. Often, this involves balancing the length of MD trajectories with the number of replicas of a given system, with the aim of maximizing sampling of the conformational landscape. However, identifying this balance is not always intuitive, and the lack of standards among researchers can produce large variability in results and predictions from MD measurements. Here, we investigate the variability in MD results when simulation length and replica numbers are varied. Using a 231-amino acid domain, we compare measurements from independent trajectories to a benchmark trajectory of 3, 1000-ns replicates. We perform these simulations on 27 protein-ligand complexes, allowing us to compare ligand-specific rankings of complexes across independent replicas. Our results reveal that some MD measurements are accurately ranked by single trajectories, while others are not. We uncover similar variability in the effects of trajectory lengths on measurements. Our findings suggest that a one-size-fits-all approach to MD simulations is not necessarily the best approach, and depending on the intended measurements and research question, it may be advantageous sometimes to prioritize longer trajectories over multiple replicas. This work provides important considerations for researchers while designing simulation studies.

Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics

The advent of accurate methods to predict the fold of proteins initiated by AlphaFold2 is rapidly changing our understanding of proteins and helping their design. However, these methods are mainly trained on protein structures determined with X-ray diffraction, where the protein is packed in crystals at often cryogenic temperatures. They can therefore only reliably cover well-folded parts of proteins that experience few, if any, conformational changes. Experimentally, solution nuclear magnetic resonance (NMR) is the experimental method of choice to gain insight into protein dynamics at near physiological conditions. Computationally, methods such as molecular dynamics (MD) simulations and Normal Mode Analysis (NMA) allow the estimation of a protein's intrinsic flexibility based on a single protein structure. This work addresses, on a large scale, the relationships for proteins between the AlphaFold2 pLDDT metric, the observed dynamics in solution from NMR metrics, interpreted MD simulations, and the computed dynamics with NMA from single AlphaFold2 models and NMR ensembles. We observe that these metrics agree well for rigid residues that adopt a single well-defined conformation, which are clearly distinct from residues that exhibit dynamic behavior and adopt multiple conformations. This direct order/disorder categorisation is reflected in the correlations observed between the parameters, but becomes very limited when considering only the likely dynamic residues. The gradations of dynamics observed by NMR in flexible protein regions are therefore not represented by these computational approaches. Our results are interactively available for each protein from https://bio2byte.be/af_nmr_nma/.

Extended Sampling of Macromolecular Conformations from Uniformly Distributed Points on Multidimensional Normal Mode Hyperspheres

Proteins are dynamic entities that adopt diverse conformations, which play a pivotal role in their function. Understanding these conformations is essential, and protein collective motions, particularly those captured by normal mode (NM) and their linear combinations, provide a robust means for conformational sampling. This work introduces a novel approach to obtaining a uniformly oriented set of a given number of lowest frequency NM combined vectors and generating harmonically equidistant restrained structures along them. They are all thus uniformly located on concentric hyperspheres, systematically covering the defined NM space fully. The generated structures are further relaxed with standard molecular dynamics (MD) simulations to explore the conformational space. The efficiency of the approach we termed "distributed points Molecular Dynamics using Normal Modes" (dpMDNM) was assessed by applying it to hen egg-white lysozyme and human cytochrome P450 3A4 (CYP3A4). To this purpose, we compared our new approach with other methods and analyzed the sampling of existing experimental structures. Our results demonstrate the efficacy of dpMDNM in extensive conformational sampling, particularly as more NMs are considered. Ensembles generated by dpMDNM exhibited a broad coverage of the experimental structures, providing valuable insights into the functional aspects of lysozyme and CYP3A4. Furthermore, dpMDNM also covered lysozyme structures with relatively elevated energies corresponding to transient states not easily obtained by standard MD simulations, in conformity with nuclear magnetic resonance structural indications. This method offers an efficient and rational framework for comprehensive protein conformational sampling, contributing significantly to our understanding of protein dynamics and function.

Complementing Adiabatic and Nonadiabatic Methods To Understand Internal Conversion Dynamics in Porphyrin Derivatives

We analyze the internal conversion dynamics within the Qy and Qx excited states of both bare and functionalized porphyrins, which are known to exhibit significantly different time constants experimentally. Through the integration of two complementary approaches, static calculation of per-mode reorganization energies and nonadiabatic molecular dynamics, we achieve a comprehensive understanding of the factors determining the different behavior of the two molecules. We identify the key normal and essential modes responsible for the population transfer between excited states and discuss the efficacy of different statistical and nonstatistical analyses in providing a full physics-based description of the phenomenon.

In silico design, modelling and molecular mechanisms of Axl receptor tyrosine kinase inhibitors

A kinase domain from receptor tyrosine kinases (RTKs) regulate intracellular communications to control cellular metabolic activities. Some of the malignant cells have upregulated and overexpressed RTKs which are responsible for angiogenesis in many metastatic cancers. Axl RTK is present in most of the eukaryotic cells and all metastatic cancer cells have overexpressed Axl tyrosine kinase to trigger uncontrolled growth and angiogenesis in the malignant cells. The upregulated kinases can be inhibited in its active and inactive states in the presence of small organic molecule inhibitors. Kinase inhibitors have been discovered to arrest the signal transduction pathways in the malignant cells as a therapy and cure for cancer. In this work, small molecule databases were screened using the pharmacophore features of a macrocyclic inhibitor (7YS) taken as reference from the crystal structure of Axl kinase domain. Pharmacophore based virtual screening of small molecule libraries (CHEMBL32, ChemDiv, Chemspace, Mcule, MolProt, PubChem and Zinc), followed by molecular docking, molecular dynamics simulations, binding energies from MM-PBSA calculations and trajectory analysis as principal component analysis were studied. The molecular basis for the binding of macrocyclic inhibitor, ATP and seven screened hit molecules bound at Axl kinase domain in two different modes at catalytic and regulatory sites was analyzed.

The C-terminal self-binding helical peptide of human estrogen-related receptor γ can be druggably targeted by a novel class of rationally designed peptidic antagonists

Orphan nuclear estrogen-related receptor γ (ERRγ) has been recognized as a potential therapeutic target for cancer, inflammation and metabolic disorder. The ERRγ contains a regulatory AF2 helical tail linked C-terminally to its ligand-binding domain (LBD), which is a self-binding peptide (SBP) and serves as molecular switch to dynamically regulate the receptor alternation between active and inactive states by binding to and unbinding from the AF2-binding site on ERRγ LBD surface, respectively. Traditional ERRγ modulators are all small-molecule chemical ligands that can be classified into agonists and inverse agonists in terms of their action mechanism; the agonists stabilize the AF2 in ABS site with an agonist conformation, while the inverse agonists lock the AF2 out of the site to largely abolish ERRγ transcriptional activity. Here, a class of ERRγ peptidic antagonists was described to compete with native AF2 for the ABS site, thus blocking the active state of AF2 binding to ERRγ LBD domain. Self-inhibitory peptide was derived from the SBP-covering AF2 region and we expected it can rebind potently to the ABS site by reducing its intrinsic disorder and entropy cost upon the rebinding. Hydrocarbon stapling was employed to do so, which employed an all-hydrocarbon bridge across the [i, i + 4]-anchor residue pair in the N-terminal, middle or C-terminal region of the self-inhibitory peptide. As might be expected, it is revealed that the stapled peptides are good binders of ERRγ LBD domain and can effectively compete with the native AF2 helical tail for ERRγ ABS site, which exhibit a basically similar binding mode with AF2 to the site and form diverse noncovalent interactions with the site, thus conferring stability and specificity to the domain-peptide complexes.

Application of immunoinformatics to develop a novel and effective multiepitope chimeric vaccine against Variovorax durovernensis

Bloodstream infections pose a significant public health challenge caused by resistant bacteria such as Variovorax durovernensis, a recently reported Gram-negative bacterium, worsening the burden on healthcare systems. The design of a vaccine using chimeric peptides derived from a representative V. durovernensis strain holds significant promise for preventing disease onset. The current study aimed to employ reverse vaccinology (RV) approaches such as the retrieval of V. durovernensis proteomics data, removal of redundant proteins by CD-HIT, filtering of non-homologous proteins to humans and essential proteins, identification of outer membrane (OM) proteins by CELLO and PSORTb. Following these steps immunoinformatic approaches were applied, such as epitope prediction by IEDB, vaccine design using linkers and adjuvant and analysis of antigenicity, allergenicity, safety and stability. Among the 4208 nonredundant proteins, an OmpA family protein (A0A940EKP4) was designated a potential candidate for the development of a multiepitope vaccine construct. Upon analysis of OM protein, six immunodominant (B cell) epitopes were found on the basis of the chimeric construct following the prediction of CTL stands cytotoxic T lymphocyte and HTL stands helper T lymphocyte epitopes. To ensure comprehensive population coverage globally, the CTL and HTL coverage rates were 58.18 % and 46.56 %, respectively, and 77.23 % overall. By utilizing EAAAK, GPGPG, and AAY linkers, Cholera toxin B subunit adjuvants, and appropriate epitopes were smoothly incorporated into a chimeric vaccine effectively triggering both adaptive and innate immune responses. For example, the administered antigen showed a peak in counts on the fifthday post injection and then gradually declined until the fifteenth day. Elevated levels of several antibodies (IgG + IgM > 700,000; IgM > 600,000; IgG1 + IgG2; IgG1 > 500,000) were observed as decreased in the antigen concentration. Molecular dynamics simulations carried out via iMODS revealed strong correlations between residue pairs, highlighting the stability of the docked complex. The designed vaccine has promising potential in eliciting specific immunogenic responses, thereby facilitating future research for vaccine development against V. durovernensis.

Development of a Machine Learning Potential to Study the Structure and Thermodynamics of Nickel Nanoclusters

Machine learning (ML) potentials such as the Gaussian approximation potential (GAP) have demonstrated impressive capabilities in mapping structure to properties across diverse systems. Here, we introduce a GAP model for low-dimensional Ni nanoclusters and demonstrate its flexibility and effectiveness in capturing the energetics, structural diversity, and thermodynamic properties of Ni nanoclusters across a broad size range. Through a systematic approach encompassing model development, validation, and application, we evaluate the model's efficacy in representing energetics and configurational features in low-dimensional regimes while also examining its extrapolative nature to vastly different spatiotemporal regimes. Our analysis and discussion shed light on the data quality required to effectively train such models. Trajectories from large-scale MD simulations using the GAP model analyzed with data-driven models like graph neural networks reveal intriguing insights into the size-dependent phase behavior and thermomechanical stability characteristics of porous Ni nanoparticles. Overall, our work underscores the potential of ML models, which coupled with data-driven approaches serve as versatile tools for studying low-dimensional systems and complex material dynamics.

Exploration of Cryptic Pockets Using Enhanced Sampling Along Normal Modes: A Case Study of KRAS G12D

Identification of cryptic pockets has the potential to open new therapeutic opportunities by discovering ligand binding sites that remain hidden in static apo structures of a target protein. Moreover, allosteric cryptic pockets can become valuable for designing target-selective ligands when the natural ligand binding sites are conserved in variants of a protein. For example, before an allosteric cryptic pocket was discovered, KRAS was considered undruggable due to its smooth surface and conservation of the GDP/GTP binding pocket across the wild type and oncogenic isoforms. Recent identification of the Switch-II cryptic pocket in the KRASG12C mutant and FDA approval of anticancer drugs targeting this site underscores the importance of cryptic pockets in solving pharmaceutical challenges. Here, we present a newly developed approach for the exploration of cryptic pockets using weighted ensemble molecular dynamics simulations with inherent normal modes as progress coordinates applied to the wild type KRAS and the G12D mutant. We performed extensive all-atomic simulations (>400 μs) with and without several cosolvents (xenon, ethanol, benzene), and analyzed trajectories using three distinct methods to search for potential binding pockets. These methods have been applied as a proof-of-concept to KRAS and have shown they can predict known cryptic binding sites. Furthermore, we performed ligand-binding simulations of a known inhibitor (MRTX1133) to shed light on the nature of cryptic pockets in KRASG12D and the role of conformational selection vs induced-fit mechanism in the formation of these cryptic pockets.

Investigating the Effect of GLU283 Protonation State on the Conformational Heterogeneity of CCR5 by Molecular Dynamics Simulations

CCR5 is a class A GPCR and serves as one of the coreceptors facilitating HIV-1 entry into host cells. This receptor has vital roles in the immune system and is involved in the pathogenesis of different diseases. Various studies were conducted to understand its activation mechanism, including structural studies in which inactive and active states of the receptor were determined in complex with various binding partners. These determined structures provided opportunities to perform molecular dynamics (MD) simulations and to analyze conformational changes observed in the protein structures. The atomic-level dynamic studies allow us to explore the effects of ionizable residues on the receptor. Here, our aim was to investigate the conformational changes in CCR5 when it forms a complex with either the inhibitor maraviroc (MRV), an approved anti-HIV drug, or HIV-1 envelope protein GP120, and compare these changes to the receptor's apo form. In our simulations, we considered both ionized and protonated states of ionizable binding site residue GLU2837.39 in CCR5 as the protonation state of this residue was considered ambiguously in previous studies. Our molecular simulations results suggested that in fact, the change in the protonation state of GLU2837.39 caused interaction profiles to be different between CCR5 and its binding partners, GP120 or MRV. We observed that when the protonated state of GLU2837.39 was considered in complex with the envelope protein GP120, there were substantial structural changes in CCR5, indicating that it adopts a more active-like conformation. On the other hand, CCR5 in complex with MRV always adopted an inactive conformation regardless of the protonation state. Hence, the CCR5 coreceptor displays conformational heterogeneity not only depending on its binding partner but also influenced by the protonation state of the binding site binding site residue GLU2837.39. This outcome is also in accordance with some studies showing that GP120 binding could activate signaling pathways. This outcome could also have significant implications for discovering novel CCR5 inhibitors as anti-HIV drugs using in silico methods such as molecular docking, as it may be necessary to consider the protonated state of GLU2837.39.

Analysis of mapping atomic models to coarse-grained resolution

Low-resolution coarse-grained (CG) models provide significant computational and conceptual advantages for simulating soft materials. However, the properties of CG models depend quite sensitively upon the mapping, M, that maps each atomic configuration, r, to a CG configuration, R. In particular, M determines how the configurational information of the atomic model is partitioned between the mapped ensemble of CG configurations and the lost ensemble of atomic configurations that map to each R. In this work, we investigate how the mapping partitions the atomic configuration space into CG and intra-site components. We demonstrate that the corresponding coordinate transformation introduces a nontrivial Jacobian factor. This Jacobian factor defines a labeling entropy that corresponds to the uncertainty in the atoms that are associated with each CG site. Consequently, the labeling entropy effectively transfers configurational information from the lost ensemble into the mapped ensemble. Moreover, our analysis highlights the possibility of resonant mappings that separate the atomic potential into CG and intra-site contributions. We numerically illustrate these considerations with a Gaussian network model for the equilibrium fluctuations of actin. We demonstrate that the spectral quality, Q, provides a simple metric for identifying high quality representations for actin. Conversely, we find that neither maximizing nor minimizing the information content of the mapped ensemble results in high quality representations. However, if one accounts for the labeling uncertainty, Q(M) correlates quite well with the adjusted configurational information loss, Îmap(M), that results from the mapping.

Delineation of the CENP-LN sub-complex dissociation mechanism upon multisite phosphorylation during mitosis

Phosphorylation is the most prevalent form of regulation in cells, organizing virtually all cellular functions, including survival, motility, differentiation, proliferation, and metabolism. This regulatory function has been largely conserved from the primitive single-cell to the more complex multicellular organisms. More than a third of proteins in eukaryotes are phosphorylated, and essentially every class of protein undergoes regulation by phosphorylation. A decline in the cellular level of CENP-L and CENP-N (components of the constitutive centromere associated network) has earlier been reported and linked to cyclin-dependent kinase (CDK) phosphorylation upon transition into mitosis. Given the importance of posttranslational modifications in cell cycle regulation, mechanistic comprehension of the impact of phosphorylation on both proteins (CENP-L and CENP-N) is of high significance. Through the application of diverse computational analytical techniques, including atomistic molecular dynamics simulations, the mechanism of kinetochore mis-localization and dissociation of the CENP-LN sub-complex in mitosis was delineated. We showed that the phosphorylation of both components of the sub-complex induces global conformational destabilizing effects on the proteins, combined with changes in the electrostatic potential and increase in steric clashes around the protein-protein interaction interface. This, consistent with earlier experimental reports, suggest that the multisite phosphorylation of the CENP-LN sub-complex plays a crucial role in the regulation of cell division.Communicated by Ramaswamy H. Sarma.

Structural shifts in TolC facilitate Efflux-Mediated β-lactam resistance

Efflux-mediated β-lactam resistance is a major public health concern, reducing the effectiveness of β-lactam antibiotics against many bacteria. Structural analyses show the efflux protein TolC in Gram-negative bacteria acts as a channel for antibiotics, impacting bacterial susceptibility and virulence. This study examines β-lactam drug efflux mediated by TolC using experimental and computational methods. Molecular dynamics simulations of drug-free TolC reveal essential movements and key residues involved in TolC opening. A whole-gene-saturation mutagenesis assay, mutating each TolC residue and measuring fitness effects under β-lactam selection, is performed. Here we show the TolC-mediated efflux of three antibiotics: oxacillin, piperacillin, and carbenicillin. Steered molecular dynamics simulations identify general and drug-specific efflux mechanisms, revealing key positions at TolC's periplasmic entry affecting efflux motions. Our findings provide insights into TolC's structural dynamics, aiding the design of new antibiotics to overcome bacterial efflux mechanisms.

Nanomechanical collective vibration of SARS-CoV-2 spike proteins

The development of effective therapeutics against COVID-19 requires a thorough understanding of the receptor recognition mechanism of the SARS-CoV-2 spike (S) protein. Here the multidomain collective dynamics on the trimer of the spike protein has been analyzed using normal mode analysis (NMA). A common nanomechanical profile was identified in the spike proteins of SARS-CoV-2 and its variants. The profile involves collective vibrations of the receptor-binding domain (RBD) and the N-terminal domain (NTD), which may mediate the physical interaction process. Quantitative analysis of the collective modes suggests a nanomechanical property involving large-scale conformational changes, which explains the difference in receptor binding affinity among different variants. These results support the use of intrinsic global dynamics as a valuable perspective for studying the allosteric and functional mechanisms of the S protein. This approach also provides a low-cost theoretical toolkit for screening potential pathogenic mutations and drug targets.

Micro-second time-resolved X-ray single-molecule internal motions of SARS-CoV-2 spike variants

Single-molecule intramolecular dynamics were successfully measured for three variants of SARS-CoV-2 spike protein, alpha: B.1.1.7, delta: B.1.617, and omicron: B.1.1.529, with a time resolution of 100 μs using X-rays. The results were then compared with respect to the magnitude and directions of motions for the three variants. The largest 3-D intramolecular movement was observed for the omicron variant irrespective of ACE2 receptor binding. A more detailed analysis of the intramolecular motions revealed that the distribution state of intramolecular motion for the three variants was completely different with and without ACE2 receptor binding. The molecular dynamics for the trimeric spike protein of the omicron variant increased when ACE2 binding occurred. At that time, the diffusion constant increased from 71.0 [mrad2/ms] to 91.1 [mrad2/ms].

Anisotropic Network Analysis of Open/Closed HCN4 Channel Advocates Asymmetric Subunit Cooperativity in cAMP Modulation of Gating

Hyperpolarization-activated cyclic nucleotide-modulated (HCN) channels are opened in an allosteric manner by membrane hyperpolarization and cyclic nucleotides such as cAMP. Because of conflicting reports from experimental studies on whether cAMP binding to the four available binding sites in the channel tetramer operates cooperatively in gating, we employ here a computational approach as a promising route to examine ligand-induced conformational changes after binding to individual sites. By combining an elastic network model (ENM) with linear response theory (LRT) for modeling the apo-holo transition of the cyclic nucleotide-binding domain (CNBD) in HCN channels, we observe a distinct pattern of cooperativity matching the "positive-negative-positive" cooperativity reported from functional studies. This cooperativity pattern is highly conserved among HCN subtypes (HCN4, HCN1), but only to a lesser extent visible in structurally related channels, which are only gated by voltage (KAT1) or cyclic nucleotides (TAX4). This suggests an inherent cooperativity between subunits in HCN channels as part of a ligand-triggered gating mechanism in these channels.

Immunoinformatics and Reverse Vaccinology Approach for the Identification of Potential Vaccine Candidates against Vandammella animalimors

Vandammella animalimorsus is a Gram-negative and non-motile bacterium typically transmitted to humans through direct contact with the saliva of infected animals, primarily through biting, scratches, or licks on fractured skin. The absence of a confirmed post-exposure treatment of V. animalimorsus bacterium highlights the imperative for developing an effective vaccine. We intended to determine potential vaccine candidates and paradigm a chimeric vaccine against V. animalimorsus by accessible public data analysis of the strain by utilizing reverse vaccinology. By subtractive genomics, five outer membranes were prioritized as potential vaccine candidates out of 2590 proteins. Based on the instability index and transmembrane helices, a multidrug transporter protein with locus ID A0A2A2AHJ4 was designated as a potential candidate for vaccine construct. Sixteen immunodominant epitopes were retrieved by utilizing the Immune Epitope Database. The epitope encodes the strong binding affinity, nonallergenic properties, non-toxicity, high antigenicity scores, and high solubility revealing the more appropriate vaccine construct. By utilizing appropriate linkers and adjuvants alongside a suitable adjuvant molecule, the epitopes were integrated into a chimeric vaccine to enhance immunogenicity, successfully eliciting both adaptive and innate immune responses. Moreover, the promising physicochemical features, the binding confirmation of the vaccine to the major innate immune receptor TLR-4, and molecular dynamics simulations of the designed vaccine have revealed the promising potential of the selected candidate. The integration of computational methods and omics data has demonstrated significant advantages in discovering novel vaccine targets and mitigating vaccine failure rates during clinical trials in recent years.

Normal Mode Analysis Elicits Conformational Shifts in Proteins at Both Proximal and Distal Regions to the Phosphosite Stemming from Single-Site Phosphorylation

Phosphorylation, a fundamental biochemical switch, intricately regulates protein function and signaling pathways. Our study employs extensive computational structural analyses on a curated data set of phosphorylated and unphosphorylated protein pairs to explore the multifaceted impact of phosphorylation on protein conformation. Using normal mode analysis (NMA), we investigated changes in protein flexibility post-phosphorylation, highlighting an enhanced level of structural dynamism. Our findings reveal that phosphorylation induces not only local changes at the phosphorylation site but also extensive alterations in distant regions, showcasing its far-reaching influence on protein structure-dynamics. Through in-depth case studies on polyubiquitin B and glycogen synthase kinase-3 beta, we elucidate how phosphorylation at distinct sites leads to variable structural and dynamic modifications, potentially dictating functional outcomes. While phosphorylation largely preserves the residue motion correlation, it significantly disrupts low-frequency global modes, presenting a dualistic impact on protein dynamics. We also explored alterations in the total accessible surface area (ASA), emphasizing region-specific changes around phosphorylation sites. This study sheds light on phosphorylation-induced conformational changes, dynamic modulation, and surface accessibility alterations, leveraging an integrated computational approach with RMSD, NMA, and ASA, thereby contributing to a comprehensive understanding of cellular regulation and suggesting promising avenues for therapeutic interventions.

Scalable computation of anisotropic vibrations for large macromolecular assemblies

The Normal Mode Analysis (NMA) is a standard approach to elucidate the anisotropic vibrations of macromolecules at their folded states, where low-frequency collective motions can reveal rearrangements of domains and changes in the exposed surface of macromolecules. Recent advances in structural biology have enabled the resolution of megascale macromolecules with millions of atoms. However, the calculation of their vibrational modes remains elusive due to the prohibitive cost associated with constructing and diagonalizing the underlying eigenproblem and the current approaches to NMA are not readily adaptable for efficient parallel computing on graphic processing unit (GPU). Here, we present eigenproblem construction and diagonalization approach that implements level-structure bandwidth-reducing algorithms to transform the sparse computation in NMA to a globally-sparse-yet-locally-dense computation, allowing batched tensor products to be most efficiently executed on GPU. We map, optimize, and compare several low-complexity Krylov-subspace eigensolvers, supplemented by techniques such as Chebyshev filtering, sum decomposition, external explicit deflation and shift-and-inverse, to allow fast GPU-resident calculations. The method allows accurate calculation of the first 1000 vibrational modes of some largest structures in PDB ( > 2.4 million atoms) at least 250 times faster than existing methods.

A Fresh Look at the Normal Mode Analysis of Proteins: Introducing Allosteric Co-Vibrational Modes

We propose a new way of utilizing normal modes to study protein conformational transitions. Instead of considering individual modes independently, we show that a weighted mixture of low-frequency vibrational modes can reveal dynamic information about the conformational mechanism in more detail than any single mode can. The weights in the mixed mode, termed the allosteric covibrational mode, are determined using a simple model where the conformational transition is viewed as a perturbation of the coupled harmonic oscillator associated with either of the two conformations. We demonstrate our theory in a biologically relevant example of high pharmaceutical interest involving the V617F mutation of Janus 2 tyrosine kinase (JAK2).

Designing of a multi-epitopes based vaccine against Haemophilius parainfluenzae and its validation through integrated computational approaches

Haemophilus parainfluenzae is a Gram-negative opportunist pathogen within the mucus of the nose and mouth without significant symptoms and has an ability to cause various infections ranging from ear, eye, and sinus to pneumonia. A concerning development is the increasing resistance of H. parainfluenzae to beta-lactam antibiotics, with the potential to cause dental infections or abscesses. The principal objective of this investigation is to utilize bioinformatics and immuno-informatic methodologies in the development of a candidate multi-epitope Vaccine. The investigation focuses on identifying potential epitopes for both B cells (B lymphocytes) and T cells (helper T lymphocytes and cytotoxic T lymphocytes) based on high non-toxic and non-allergenic characteristics. The selection process involves identifying human leukocyte antigen alleles demonstrating strong associations with recognized antigenic and overlapping epitopes. Notably, the chosen alleles aim to provide coverage for 90% of the global population. Multi-epitope constructs were designed by using suitable linker sequences. To enhance the immunological potential, an adjuvant sequence was incorporated using the EAAAK linker. The final vaccine construct, comprising 344 amino acids, was achieved after the addition of adjuvants and linkers. This multi-epitope Vaccine demonstrates notable antigenicity and possesses favorable physiochemical characteristics. The three-dimensional conformation underwent modeling and refinement, validated through in-silico methods. Additionally, a protein-protein molecular docking analysis was conducted to predict effective binding poses between the multi-epitope Vaccine and the Toll-like receptor 4 protein. The Molecular Dynamics (MD) investigation of the docked TLR4-vaccine complex demonstrated consistent stability over the simulation period, primarily attributed to electrostatic energy. The docked complex displayed minimal deformation and enhanced rigidity in the motion of residues during the dynamic simulation. Furthermore, codon translational optimization and computational cloning was performed to ensure the reliability and proper expression of the multi-Epitope Vaccine. It is crucial to emphasize that despite these computational validations, experimental research in the laboratory is imperative to demonstrate the immunogenicity and protective efficacy of the developed vaccine. This would involve practical assessments to ascertain the real-world effectiveness of the multi-epitope Vaccine.

Surveying the energy landscape of coarse-grained mappings

Simulations of soft materials often adopt low-resolution coarse-grained (CG) models. However, the CG representation is not unique and its impact upon simulated properties is poorly understood. In this work, we investigate the space of CG representations for ubiquitin, which is a typical globular protein with 72 amino acids. We employ Monte Carlo methods to ergodically sample this space and to characterize its landscape. By adopting the Gaussian network model as an analytically tractable atomistic model for equilibrium fluctuations, we exactly assess the intrinsic quality of each CG representation without introducing any approximations in sampling configurations or in modeling interactions. We focus on two metrics, the spectral quality and the information content, that quantify the extent to which the CG representation preserves low-frequency, large-amplitude motions and configurational information, respectively. The spectral quality and information content are weakly correlated among high-resolution representations but become strongly anticorrelated among low-resolution representations. Representations with maximal spectral quality appear consistent with physical intuition, while low-resolution representations with maximal information content do not. Interestingly, quenching studies indicate that the energy landscape of mapping space is very smooth and highly connected. Moreover, our study suggests a critical resolution below which a "phase transition" qualitatively distinguishes good and bad representations.

Convergence and equilibrium in molecular dynamics simulations

Molecular dynamics is a powerful tool that has been long used for the simulation of biomolecules. It complements experiments, by providing detailed information about individual atomic motions. But there is an essential and often overlooked assumption that, left unchecked, could invalidate any results from it: is the simulated trajectory long enough, so that the system has reached thermodynamic equilibrium, and the measured properties are converged? Previous studies showed mixed results in relation to this assumption. This has profound implications, as the resulting simulated trajectories may not be reliable in predicting equilibrium properties. Yet, this is precisely what most molecular dynamics studies do. So the question arises: are these studies even valid?Here, we present a thorough analysis of up to a hundred microseconds long trajectories, of several system with varying size, to probe the convergence of different structural, dynamical and cumulative properties, and elaborate on the relevance of the concept of equilibrium, and its physical and biological meaning. The results show that properties with the most biological interest tend to converge in multi-microsecond trajectories, although other properties-like transition rates to low probability conformations-may require more time.

How alginate lyase produces quasi-monodisperse oligosaccharides: A normal-mode-based docking and molecular dynamics simulation study

Oligosaccharide degradation products of alginate (AOS) hold significant potential in diverse fields, including pharmaceuticals, health foods, textiles, and agricultural production. Enzymatic alginate degradation is appealing due to its mild conditions, predictable activity, high yields, and controllability. However, the alginate degradation often results in a complex mixture of oligosaccharides, necessitating costly purification to isolate highly active oligosaccharides with a specific degree of polymerization (DP). Addressing this, our study centers on the alginate lyase AlyB from Vibrio Splendidus OU02, which uniquely breaks down alginate into mono-distributed trisaccharides. This enzyme features a polysaccharide lyase family 7 domain (PL-7) and a CBM32 carbohydrate-binding module connected by a helical structure. Through normal-mode-based docking and all-atom molecular simulations, we demonstrate that AlyB's substrate and product specificities are influenced by the spatial conformation of the catalytic pocket and the flexibility of its structure. The helically attached CBM is pivotal in releasing trisaccharides, which is crucial for avoiding further degradation. This study sheds light on AlyB's specificity and efficiency and contributes to the evolving field of enzyme design for producing targeted oligosaccharides, with significant implications for various bioindustries.

Oliveira AS, Mulholland AJ, Serapian SA, Colombo G. Decrypting Allostery in Membrane-Bound K-Ras4B Using Complementary In Silico Approaches Based on Unbiased Molecular Dynamics Simulations

Protein functions are dynamically regulated by allostery, which enables conformational communication even between faraway residues, and expresses itself in many forms, akin to different "languages": allosteric control pathways predominating in an unperturbed protein are often unintuitively reshaped whenever biochemical perturbations arise (e.g., mutations). To accurately model allostery, unbiased molecular dynamics (MD) simulations require integration with a reliable method able to, e.g., detect incipient allosteric changes or likely perturbation pathways; this is because allostery can operate at longer time scales than those accessible by plain MD. Such methods are typically applied singularly, but we here argue their joint application─as a "multilingual" approach─could work significantly better. We successfully prove this through unbiased MD simulations (∼100 μs) of the widely studied, allosterically active oncotarget K-Ras4B, solvated and embedded in a phospholipid membrane, from which we decrypt allostery using four showcase "languages": Distance Fluctuation analysis and the Shortest Path Map capture allosteric hotspots at equilibrium; Anisotropic Thermal Diffusion and Dynamical Non-Equilibrium MD simulations assess perturbations upon, respectively, either superheating or hydrolyzing the GTP that oncogenically activates K-Ras4B. Chosen "languages" work synergistically, providing an articulate, mutually coherent, experimentally consistent picture of K-Ras4B allostery, whereby distinct traits emerge at equilibrium and upon GTP cleavage. At equilibrium, combined evidence confirms prominent allosteric communication from the membrane-embedded hypervariable region, through a hub comprising helix α5 and sheet β5, and up to the active site, encompassing allosteric "switches" I and II (marginally), and two proposed pockets. Upon GTP cleavage, allosteric perturbations mostly accumulate on the switches and documented interfaces.

Potential Antidiabetic Activity of β-sitosterol from Zingiber roseum Rosc. via Modulation of Peroxisome Proliferator-activated Receptor Gamma (PPARγ)

AIM

To evaluate the antidiabetic potential of β-sitosterol from Zingiber roseum.

BACKGROUND

Diabetes mellitus is a cluster of metabolic disorders, and 90% of diabetic patients are affected with Type II diabetes (DM2). For the treatment of DM2, thiazolidinedione drugs (TZDs) were proposed, but recent studies have shown that TZDs have several detrimental effects, such as weight gain, kidney enlargement (hypertrophy), fluid retention, increased risk of bone fractures, and potential harm to the liver (hepatotoxicity). That is why a new molecule is needed to treat DM2.

OBJECTIVE

The current research aimed to assess the efficacy of β-Sitosterol from methanolic extract of Zingiber roseum in managing diabetes via PPARγ modulation.

METHODS

Zingiber roseum was extracted using methanol, and GC-MS was employed to analyze the extract. Through homology modeling, PPARγ structure was predicted. Molecular docking, MD simulation, free binding energies, QSAR, ADMET, and bioactivity and toxicity scores were all used during the in-depth computer-based research.

RESULTS

Clinically, agonists of synthetic thiazolidinedione (TZDs) have been used therapeutically to treat DM2, but these TZDs are associated with significant risks. Hence, GC-MS identified phytochemicals to search for a new PPAR-γ agonist. Based on the in-silico investigation, β-sitosterol was found to have a higher binding affinity (-8.9 kcal/mol) than standard drugs. MD simulations and MMGBSA analysis also demonstrated that β-sitosterol bound to the PPAR-γ active site stably.

CONCLUSION

It can be concluded that β-sitosterol from Z. roseum attenuates Type-II diabetes by modulating PPARγ activity.

A Structure-Based Allosteric Modulator Design Paradigm

Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.

Investigation on the molecular, electronic and spectroscopic properties of rosmarinic acid: an intuition from an experimental and computational perspective

Various drugs such as corticosteroids, salbutamol, and β2 agonist are available for the treatment of asthma an inflammatory disease and its symptoms, although the ingredient and the mode of action of these drugs are not clearly elucidated. Hence this research aimed at carrying out improved scientific research with respect to the use of natural product rosmarinic acid which poses minima, side effects. Herein, we first carried out extraction, isolation, and spectroscopic (FT-IR, 1H-NMR and 13C-NMR) investigation, followed by molecular modeling analysis on the naturally occurring rosmarinic acid extracted from Rosmarinus officinalis. A detailed comparison of the experimental and theoretical vibrational analysis has been carried out using five DFT functionals: BHANDH, HSEH1PBE, M06-2X, MPW3PBE and THCTHHYB with the basis set 6-311++G (d, p) to investigate into the structural, reactivity, and stability of the isolated compound. Frontier molecular orbital analysis and appropriate quantum descriptors were calculated. Results showed that the compound was more stable at M06-2X and more reactive at HSEH1PBE with an energy gap of 6.43441 eV and 3.8047 eV, respectively, which was later affirmed by the global quantum reactivity parameters. From natural bond orbital analysis, π* → π* is the major contributor to electron transition with the summation perturbation energy of 889.57 kcal/mol, while π → π* had the perturbation energy totaling of 145.3 kcal/mol. Geometry analysis shows BHANDH to have lower bond length values and lesser deviation from 120° in carbon-carbon angle. The potency of the title molecule as an asthma drug was tested via a molecular docking approach and the binding score of -8.2 kcal/mol was observed against -7.0 of salbutamol standard drug, suggesting romarinic acid as a potential natural organic treatment for asthma.Communicated by Ramaswamy H. Sarma.

Structural and pKa Estimation of the Amphipathic HR1 in SARS-CoV-2: Insights from Constant pH MD, Linear vs. Nonlinear Normal Mode Analysis

A comprehensive understanding of molecular interactions and functions is imperative for unraveling the intricacies of viral protein behavior and conformational dynamics during cellular entry. Focusing on the SARS-CoV-2 spike protein (SARS-CoV-2 sp), a Principal Component Analysis (PCA) on a subset comprising 131 A-chain structures in presence of various inhibitors was conducted. Our analyses unveiled a compelling correlation between PCA modes and Anisotropic Network Model (ANM) modes, underscoring the reliability and functional significance of low-frequency modes in adapting to diverse inhibitor binding scenarios. The role of HR1 in viral processing, both linear Normal Mode Analysis (NMA) and Nonlinear NMA were implemented. Linear NMA exhibited substantial inter-structure variability, as evident from a higher Root Mean Square Deviation (RMSD) range (7.30 Å), nonlinear NMA show stability throughout the simulations (RMSD 4.85 Å). Frequency analysis further emphasized that the energy requirements for conformational changes in nonlinear modes are notably lower compared to their linear counterparts. Using simulations of molecular dynamics at constant pH (cpH-MD), we successfully predicted the pKa order of the interconnected residues within the HR1 mutations at lower pH values, suggesting a transition to a post-fusion structure. The pKa determination study illustrates the profound effects of pH variations on protein structure. Key results include pKa values of 9.5179 for lys-921 in the D936H mutant, 9.50 for the D950N mutant, and a slightly higher value of 10.49 for the D936Y variant. To further understand the behavior and physicochemical characteristics of the protein in a biologically relevant setting, we also examine hydrophobic regions in the prefused states of the HR1 protein mutants D950N, D936Y, and D936H in our study. This analysis was conducted to ascertain the hydrophobic moment of the protein within a lipid environment, shedding light on its behavior and physicochemical properties in a biologically relevant context.

Computing Free Energies of Fold-Switching Proteins Using MELD x MD

Some proteins are conformational switches, able to transition between relatively different conformations. To understand what drives them requires computing the free-energy difference ΔGAB between their stable states, A and B. Molecular dynamics (MD) simulations alone are often slow because they require a reaction coordinate and must sample many transitions in between. Here, we show that modeling employing limited data (MELD) x MD on known endstates A and B is accurate and efficient because it does not require passing over barriers or knowing reaction coordinates. We validate this method on two problems: (1) it gives correct relative populations of α and β conformers for small designed chameleon sequences of protein G; and (2) it correctly predicts the conformations of the C-terminal domain (CTD) of RfaH. Free-energy methods like MELD x MD can often resolve structures that confuse machine-learning (ML) methods.

Unveiling the Anthelminthic Potential of Merremia vitifolia Stem through in Vitro and in Silico Approach

This study aimed to assess the anthelmintic activity of methanol extracts from Merremia vitifolia stems using a combination approach encompassing experimental, in vitro, and in silico evaluations. Despite the well-recognized pharmacological properties of M. vitifolia, its potential as an anthelmintic agent remained unexplored. This plant's anthelmintic potential was assessed on adult earthworms (Pheretima posthuma), revealing a dose-dependent reduction in spontaneous motility leading to paralysis and eventual mortality. The most effective dose of M. vitifolia (200 mg/ml) for anthelmintic effects on Pheretima posthuma was identified. Complementary in silico investigations were also conducted, employing Autodock PyRx 0.8 for docking studies of reported M. vitifolia compounds. Notably, quercetin emerged as a promising candidate with superior binding energies against β-tubulin (-8.3 Kcal/mol). Moreover, this comprehensive research underlines the anthelmintic potential of Merremia vitifolia stem extract and highlights quercetin as a noteworthy compound for further investigation in the quest for novel anthelmintic agents.

Scipion-EM-ProDy: A Graphical Interface for the ProDy Python Package within the Scipion Workflow Engine Enabling Integration of Databases, Simulations and Cryo-Electron Microscopy Image Processing

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common theme has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows, together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.

Normal mode analysis and comparative study of intrinsic dynamics of alcohol oxidase enzymes from GMC protein family

Glucose-Methanol-Choline (GMC) family enzymes are very important in catalyzing the oxidation of a wide range of structurally diverse substrates. Enzymes that constitute the GMC family, share a common tertiary fold but < 25% sequence identity. Cofactor FAD, FAD binding signature motif, and similar structural scaffold of the active site are common features of oxidoreductase enzymes of the GMC family. Protein functionality mainly depends on protein three-dimensional structures and dynamics. In this study, we used the normal mode analysis method to search the intrinsic dynamics of GMC family enzymes. We have explored the dynamical behavior of enzymes with unique substrate catabolism and active site characteristics from different classes of the GMC family. Analysis of individual enzymes and comparative ensemble analysis of enzymes from different classes has shown conserved dynamic motion at FAD binding sites. The present study revealed that GMC enzymes share a strong dynamic similarity (Bhattacharyya coefficient >90% and root mean squared inner product >52%) despite low sequence identity across the GMC family enzymes. The study predicts that local deformation energy between atoms of the enzyme may be responsible for the catalysis of different substrates. This study may help that intrinsic dynamics can be used to make meaningful classifications of proteins or enzymes from different organisms.Communicated by Ramaswamy H. Sarma.

Interactions of the male contraceptive target EPPIN with semenogelin-1 and small organic ligands

Novel male contraceptives will promote gender equality in sharing contraceptive responsibility. The sperm-associated protein epididymal protease inhibitor (EPPIN) is a promising target for non-hormonal male contraception. EPPIN interacts with the semen coagulum protein semenogelin-1 (SEMG1) on the sperm surface, leading to transient inhibition of sperm motility after ejaculation. Small organic molecules targeting EPPIN's SEMG1-binding are under development as male contraceptives. Here, we combined computational approaches to uncover key aspects underlying EPPIN binding to SEMG1 and small organic ligands. We generated a human EPPIN model showing a typical arrangement of the WFDC (Whey-acid four disulfide core)-type and Kunitz-type domains, connected by a hinge region. Determining the EPPIN model's intrinsic motion by molecular dynamics simulations and normal mode analysis revealed a conformation, presenting a binding pocket that accommodates SEMG1Glu229-Gln247, EP055, and EP012. EPPIN's residues Phe63 and Lys68 (WFDC domain), Asp71 (hinge region), and Asn113, Asn114, and Asn115 (Kunitz domain) were identified as hot spots for SEMG1, EP055, and EP012 binding. Moreover, hydrophobic and hydrophilic residues in the WFDC and Kunitz domains allow plasma membrane anchoring, orienting the EPPIN binding pocket to the solvent. Targeting EPPIN's essential residues for its biomolecular interactions may improve the rational design of EPPIN ligands as spermiostatic compounds.

The stem region of group A transferase is crucial for its specificity, and its alteration promotes heterologous Forssman synthase activity

Some stem region mutants of human blood group A transferase (hAT) possess Forssman synthase (FS) activity, but very little is known about the mechanisms responsible for this enzymatic crosstalk. We performed confocal microscopy and image analysis to determine whether different intra-Golgi localization was accountable for this acquired activity. We also performed structural modeling and mutational and normal mode analyses. We introduced new mutations in the stem region and tested its FS and AT activities. No differences in subcellular localization were found between hAT and FS-positive mutants. AlphaFold models of hAT and mFS (mouse Forssman synthase) showed that the hAT stem region has a tether-like stem region, while in mFS, it encircles its catalytic domain. In silico analysis of FS-positive mutants indicated that stem region mutations induced structural changes, decreasing interatomic interactions and mobility of hAT that correlated with FS activity. Several additional mutations introduced in that region also bestowed FS activity without altering the AT activity: hAT 37-55 aa substitution by mFS 34-52, 37-55 aa deletion, and missense mutations: S46P, Q278Y, and Q286M. Stem region structure, mobility, and interactions are crucial for hAT specificity. Moreover, stem region mutations can lead to heterologous Forssman activity without changes in the catalytic machinery.

Characterizing interactions in E-cadherin assemblages

Cadherin intermolecular interactions are critical for cell-cell adhesion and play essential roles in tissue formation and the maintenance of tissue structures. In this study, we focus on E-cadherin, a classical cadherin that connects epithelial cells, to understand how they interact in cis and trans conformations when attached to the same cell or opposing cells. We employ coevolutionary sequence analysis and molecular dynamics simulations to confirm previously known interaction sites as well as to identify new interaction sites. The sequence coevolutionary results yield a surprising result indicating that there are no strongly favored intermolecular interaction sites, which is unusual and suggests that many interaction sites may be possible, with none being strongly preferred over others. By using molecular dynamics, we test the persistence of these interactions and how they facilitate adhesion. We build several types of cadherin assemblages, with different numbers and combinations of cis and trans interfaces to understand how these conformations act to facilitate adhesion. Our results suggest that, in addition to the established interaction sites on the EC1 and EC2 domains, an additional plausible cis interface at the EC3-EC5 domain exists. Furthermore, we identify specific mutations at cis/trans binding sites that impair adhesion within E-cadherin assemblages.

Defensin-based therapeutic peptide design in attenuating V30M TTR-induced Familial Amyloid Polyneuropathy

In the present study, we aimed to formulate an effective therapeutic candidate against V30M mutant transthyretin (TTR) protein to hinder its pathogenic misfolding. Nicotiana alata Defensin 1 (NaD1) Antimicrobial Peptide (AMP) was availed due to its tendency to aggregate, which may compete for aggregation-prone regions of pathogenic TTR protein. Based on NaD1's potential to bind to V30M TTR, we proposed NaD1-derived tetra peptides: CKTE and SKIL to be initial therapeutic candidates. Based on their association with mutant TTR protein, CKTE tetra peptide showed considerable interaction and curative potential as compared to SKIL tetra peptide. Further analyses from discrete molecular dynamics simulation corroborate CKTE tetra peptide's effectiveness as a 'beta-sheet breaker' against V30M TTR. Various post-simulation trajectory analyses suggested that CKTE tetra peptide alters the structural dynamics of pathogenic V30M TTR protein, thereby potentially attenuating its beta-sheets and impeding its aggregation. Normal mode analysis simulation corroborated that V30M TTR conformation is altered upon its interaction with CKTE peptide. Moreover, simulated thermal denaturation findings suggested that CKTE-V30M TTR complex is more susceptible to simulated denaturation, relative to pathogenic V30M TTR; further substantiating CKTE peptide's potential to alter V30M TTR's pathogenic conformation. Moreover, the residual frustration analysis augmented CKTE tetra peptide's proclivity in reorienting the conformation of V30M TTR. Therefore, we predicted that the tetra peptide, CKTE could be a promising therapeutic candidate in mitigating the amyloidogenic detrimental effects of V30M TTR-mediated familial amyloid polyneuropathy (FAP).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03646-4.

Microsecond Motion of the Bacterial Transporter EmrE in Lipid Bilayers

The bacterial transporter EmrE is a homo-dimeric membrane protein that effluxes cationic polyaromatic substrates against the concentration gradient by coupling to proton transport. As the archetype of the small multidrug resistance family of transporters, EmrE structure and dynamics provide atomic insights into the mechanism of transport by this family of proteins. We recently determined high-resolution structures of EmrE in complex with a cationic substrate, tetra(4-fluorophenyl)phosphonium (F4-TPP+), using solid-state NMR spectroscopy and an S64V-EmrE mutant. The substrate-bound protein exhibits distinct structures at acidic and basic pH, reflecting changes upon binding or release of a proton from residue E14, respectively. To obtain insight into the protein dynamics that mediate substrate transport, here we measure 15N rotating-frame spin-lattice relaxation (R1ρ) rates of F4-TPP+-bound S64V-EmrE in lipid bilayers under magic-angle spinning (MAS). Using perdeuterated and back-exchanged protein and 1H-detected 15N spin-lock experiments under 55 kHz MAS, we measured 15N R1ρ rates site-specifically. Many residues show spin-lock field-dependent 15N R1ρ relaxation rates. This relaxation dispersion indicates the presence of backbone motions at a rate of about 6000 s-1 at 280 K for the protein at both acidic and basic pH. This motional rate is 3 orders of magnitude faster than the alternating access rate but is within the range estimated for substrate binding. We propose that these microsecond motions may allow EmrE to sample different conformations to facilitate substrate binding and release from the transport pore.

Molecular Mechanisms of PTH/PTHrP Class B GPCR Signaling and Pharmacological Implications

The classical paradigm of G protein-coupled receptor (GPCR) signaling via G proteins is grounded in a view that downstream responses are relatively transient and confined to the cell surface, but this notion has been revised in recent years following the identification of several receptors that engage in sustained signaling responses from subcellular compartments following internalization of the ligand-receptor complex. This phenomenon was initially discovered for the parathyroid hormone (PTH) type 1 receptor (PTH1R), a vital GPCR for maintaining normal calcium and phosphate levels in the body with the paradoxical ability to build or break down bone in response to PTH binding. The diverse biological processes regulated by this receptor are thought to depend on its capacity to mediate diverse modes of cyclic adenosine monophosphate (cAMP) signaling. These include transient signaling at the plasma membrane and sustained signaling from internalized PTH1R within early endosomes mediated by PTH. Here we discuss recent structural, cell signaling, and in vivo studies that unveil potential pharmacological outputs of the spatial versus temporal dimension of PTH1R signaling via cAMP. Notably, the combination of molecular dynamics simulations and elastic network model-based methods revealed how precise modulation of PTH signaling responses is achieved through structure-encoded allosteric coupling within the receptor and between the peptide hormone binding site and the G protein coupling interface. The implications of recent findings are now being explored for addressing key questions on how location bias in receptor signaling contributes to pharmacological functions, and how to drug a difficult target such as the PTH1R toward discovering nonpeptidic small molecule candidates for the treatment of metabolic bone and mineral diseases.

Perspective on the Effect of Membrane Mimetics on Dynamic Properties of Integral Membrane Proteins

Integral membrane proteins are embedded into cell membranes by spanning the width of the lipid bilayer. They play an essential role in important biological functions for the survival of living organisms. Their functions include the transportation of ions and molecules across the cell membrane and initiating signaling pathways. The dynamic behavior of integral membrane proteins is very important for their function. Due to the complex behavior of integral membrane proteins in the cell membrane, studying their structural dynamics using biophysical approaches is challenging. Here, we concisely discuss challenges and recent advances in technical and methodological aspects of biophysical approaches for gleaning dynamic properties of integral membrane proteins to answer pertinent biological questions associated with these proteins.

Mechanism of Allosteric Modulation of Nicotinamide Phosphoribosyltransferase to Elevate Cellular NAD. Biochemistry 2023;62:923-933. [PMID: 36746631 DOI: 10.1021/acs.biochem.2c00655]

In aging and disease, cellular nicotinamide adenine dinucleotide (NAD+) is depleted by catabolism to nicotinamide (NAM). NAD+ supplementation is being pursued to enhance human healthspan and lifespan. Activation of nicotinamide phosphoribosyltransferase (NAMPT), the rate-limiting step in NAD+ biosynthesis, has the potential to increase the salvage of NAM. Novel NAMPT-positive allosteric modulators (N-PAMs) were discovered in addition to the demonstration of NAMPT activation by biogenic phenols. The mechanism of activation was revealed through the synthesis of novel chemical probes, new NAMPT co-crystal structures, and enzyme kinetics. Binding to a rear channel in NAMPT regulates NAM binding and turnover, with biochemical observations being replicated by NAD+ measurements in human cells. The mechanism of action of N-PAMs identifies, for the first time, the role of the rear channel in the regulation of NAMPT turnover coupled to productive and nonproductive NAM binding. The tight regulation of cellular NAMPT via feedback inhibition by NAM, NAD+, and adenosine 5'-triphosphate (ATP) is differentially regulated by N-PAMs and other activators, indicating that different classes of pharmacological activators may be engineered to restore or enhance NAD+ levels in affected tissues.

Phylogenetic, structural, functional characterisation and effect of exogenous spermidine on rice (Oryza sativa) HAK transporters under salt stress

The HAK (High-affinity K+ ) family members mediate K+ transport that confers normal plant growth and resistance against unfavourable environmental conditions. Rice (Oryza sativa L.) HAK transporters have been extensively investigated for phylogenetic analyses with other plants species with very few of them functionally characterised. But very little information is known about their evolutionary aspects, overall structural, functional characterisation, and global expression pattern of the complete HAK family members in response to salt stress. In this study, 27 rice transporters were phylogenetically clustered with different dicot and monocot family members. Subsequently, the exon-intron structural patterns, conserved motif analyses, evolutionary divergence based different substitution matrix, orthologous-paralogous relationships were studied elaborately. Structural characterisations included a comparative study of secondary and tertiary structure, post-translational modifications, correspondence analyses, normal mode analyses, K+ /Na+ binding affinities of each of the OsHAK gene members. Global expression profile under salt stress showed clade-specific expression pattern of the proteins. Additionally, five OsHAK genes were chosen for further expression analyses in root and shoot tissues of two rice varieties during short-term salinity in the presence and absence of exogenous spermidine. All the information can be used as first-hand data for dissecting the administrative role of rice HAK transporters under various abiotic stresses.

Toward Reliable and Insightful Entropy Calculations on Flexible Molecules

The absolute entropy of a flexible molecule can be approximated by the sum of a rigid-rotor-harmonic-oscillator (RRHO) entropy and a Gibbs-Shannon entropy associated to the Boltzmann distribution for the occupation of the conformational energy levels. Herein, we show that such partitioning, which has received renewed interest, leads to accurate entropies of single molecules of increasing size provided that the conformational part is estimated by means of a set of discretization and expansion techniques that are able to capture the significant correlation effects among the torsional motions. To ensure a reliable entropy estimation, we rely on extensive sampling as that produced by classical molecular dynamics simulations on the microsecond time scale, which is currently affordable for small- and medium-sized molecules. According to test calculations, the gas-phase entropy of simple organic molecules is predicted with a mean unsigned error of 0.9 cal/(mol K) when the RRHO entropies are computed at the B3LYP-D3/cc-pVTZ level. Remarkably, the same protocol gives small errors [<1 cal/(mol K)] for the extremely flexible linear alkane molecules (C_nH_2n+2, n = 14, 16, and 18). Similarly, we obtain well-converged entropies for a more challenging test of drug molecules, which exhibit more pronounced correlation effects. We also perform equivalent entropy calculations on a 76 amino acid protein, ubiquitin, by taking advantage of the cutoff-dependent formulation of an expansion technique (correlation-consistent multibody local approximation, CC-MLA), which incorporates genuine correlation effects among the neighboring dihedral angles. Moreover, we show that insightful descriptors of the coupled torsional motions can be obtained with the CC-MLA approach.

Evolutionary adaptation of the protein folding pathway for secretability

Secretory preproteins of the Sec pathway are targeted post-translationally and cross cellular membranes through translocases. During cytoplasmic transit, mature domains remain non-folded for translocase recognition/translocation. After translocation and signal peptide cleavage, mature domains fold to native states in the bacterial periplasm or traffic further. We sought the structural basis for delayed mature domain folding and how signal peptides regulate it. We compared how evolution diversified a periplasmic peptidyl-prolyl isomerase PpiA mature domain from its structural cytoplasmic PpiB twin. Global and local hydrogen-deuterium exchange mass spectrometry showed that PpiA is a slower folder. We defined at near-residue resolution hierarchical folding initiated by similar foldons in the twins, at different order and rates. PpiA folding is delayed by less hydrophobic native contacts, frustrated residues and a β-turn in the earliest foldon and by signal peptide-mediated disruption of foldon hierarchy. When selected PpiA residues and/or its signal peptide were grafted onto PpiB, they converted it into a slow folder with enhanced in vivo secretion. These structural adaptations in a secretory protein facilitate trafficking.

Exploring the multiple conformational states of RNA genome through interhelical dynamics and network analysis

The structural variation of RNA is often very transient and can be easily missed in experiments. Molecular dynamics simulation studies along with network analysis can be an effective tool to identify prominent conformations of such dynamic biomolecular systems. Here we describe a method to effectively sample different RNA conformations at six different temperatures based on the changes in the interhelical orientations. This method gives the information about prominent states of the RNA as well as the probability of the existence of different conformations and their interconnections during the process of evolution. In the case of the SARS-CoV-2 genome, the change of prominent structures was found to be faster at 333 K as compared to higher temperatures due to the formation of the non-native base pairs. ΔΔG calculated between 288 K and 363 K are found to be 10.31 kcal/mol (88 nt) considering the contribution from the multiple states of the RNA which agrees well with the experimentally reported denaturation energy for E. coli α mRNA pseudoknot (∼16 kcal/mol, 112 nt) determined by calorimetry/UV hyperchromicity and human telomerase RNA telomerase (4.5-6.6 kcal/mol, 54 nt) determined by FRET analysis.