The simultaneous identification of proteins as well as their conformations in a biological system would greatly enhance our understanding of cellular mechanisms and disease. As an emerging technique, native proteomics analyzes proteins in their native states, facilitating the acquisition of protein stoichiometry, post-translational modifications (PTMs), and interactions with ligands. However, revealing protein conformations at the proteome scale remains a significant challenge. In this study, we propose an AI-assisted native proteomics method that integrates a protein structure prediction (PSP) module with top-down proteomics (TDP) and native mass spectrometry (nMS) to acquire both proteome identities and conformations. First, protein sequences are obtained using the TDP method, while the protein solvent-accessible surface area is measured by nMS. These data are integrated with the PSP module to acquire protein conformations under experimental conditions. This approach was applied to delineate the conformations of ribosomal proteins pre- and post-assembly. Results revealed that most ribosomal proteins with intrinsically disordered regions exhibit multiple conformational ensembles in the monomer state. Protein-drug interaction (PDI) experiments show that the preferred conformation during binding events may differ from its conformation within the complex, highlighting the importance of acquiring protein "dark" conformations during drug development. By enabling high-throughput proteome identification and conformational characterization, this method bridges the gap between structural biology and conventional proteomics technologies.

The properties of biological materials like proteins and nucleic acids are largely determined by their primary sequence. Certain segments in the sequence strongly influence specific functions, but identifying these segments, or so-called motifs, is challenging due to the complexity of sequential data. While deep learning (DL) models can accurately capture sequence-property relationships, the degree of nonlinearity in these models limits the assessment of monomer contributions to a property─a critical step in identifying key motifs. Recent advances in explainable AI (XAI) offer attention and gradient-based methods for estimating monomeric contributions. However, these methods are primarily applied to classification tasks, such as binding site identification, where they achieve limited accuracy (40-45%) and rely on qualitative evaluations. To address these limitations, we introduce a DL model with interpretable steps, enabling direct tracing of monomeric contributions. Inspired by the masking technique commonly used in vision and natural language processing domains, we propose a new metric ( I ) for quantitative analysis on datasets mainly containing distinct properties of anticancer peptides (ACP), antimicrobial peptides (AMP), and collagen. Our model exhibits 22% higher explainability than the gradient and attention-based state-of-the-art models, recognizes critical motifs (RRR, RRI, and RSS) that significantly destabilize ACPs, and identifies motifs in AMPs that are 50% more effective in converting non-AMPs to AMPs. These findings highlight the potential of our model in guiding mutation strategies for designing protein-based biomaterials.

G-quadruplexes are noncanonical DNA structures rather ubiquitous in the human genome, which are thought to play a crucial role in the development of the majority of cancers. Here, we present a novel coarse-grained approach in modeling G-quadruplexes that accounts for their structural flexibility. We apply it to study the polymeric properties of G-quadruplex multimers, with and without crowder molecules, to mimic in vivo conditions. We find that, contrary to some suggestions found in the literature, long G-quadruplex multimers are rather flexible polymeric macromolecules, with a local persistence length comparable to monomer size, exhibiting a chain stiffness variation profile consistent with a real polymer in good solvent. Moreover, in a crowded environment (up to 10% volume fraction), we report that G-quadruplex multimers exhibit an increased propensity for coiling, with a corresponding decrease in the measured chain stiffness.

Biological macromolecules are dynamic entities that transition between various conformational states, often playing a vital role in biological functions. Their inherent flexibility spans a broad range of timescales. Motions occurring within the microsecond to millisecond range are especially important, as they are integral to processes such as enzyme catalysis, folding, ligand binding, and allostery. NMR Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion measurements are the preferred method for characterizing macromolecular motions at atomic resolution. However, it is still uncertain whether the functional motions of multiple residues in macromolecules need to be coordinated and/or synchronized within the protein matrix in order to perform the desired function. Here, we illustrate an unbiased method to analyze NMR relaxation dispersion and identify dynamic clusters of residues that fluctuate on similar timescales within proteins. The method requires relaxation dispersion data for backbone amides or side-chain methyl groups, which are globally fitted using the Bloch-McConnell equations for each pair of residues. The goodness of the pairwise fitting serves as a metric to construct two-dimensional synchronous dynamics (SyncDyn) maps, allowing us to identify residue clusters whose dynamics are influenced by ligand binding. We applied our method to the catalytic subunit of the cAMP-dependent protein kinase A (PKAC) and the T17A mutant of ribonuclease A (RNAse A). The SyncDyn maps for PKA-C showed distinct clusters of residues located in critical allosteric sites. Nucleotide binding activates the movement of residues at the interface between the two lobes and also those distal to the active site. In the case of RNAse A, the SyncDyn maps show that residues fluctuating with the same time scale are interspersed in both lobes of the enzyme. Overall, our approach eliminates arbitrary manual selection of residues for dynamic clustering and objectively identifies all possible residue pairs that fluctuate synchronously, i.e. on the same timescale.

In living systems, dynamic biomacromolecular assemblies are driven and regulated by energy dissipative chemical reaction networks, enabling various autonomous functions. Inspired by this biological principle, we report a chemically-fueled phase transition of a poly(N-isopropylacrylamide) (PNIPAAm)-based polymer bearing viologen units (P(NIPAAm-V)), wherein redox changes drive coil-to-globule phase transitions. Upon the addition of a reducing agent, viologen moieties in P(NIPAAm-V) are converted into their reduced state, resulting in enhanced hydrophobicity and polymer aggregation. Coexistence of a platinum catalyst couples these redox-driven structural changes to hydrogen evolution, which oxidizes the viologen radicals, thus restoring the polymer chains to their hydrated random coil state. As a result, transient polymer assemblies form and subsequently disassemble upon depletion of the reducing agent, leading to a temporally controlled out-of-equilibrium phase transition. Moreover, by tuning the platinum concentration and reaction temperature, we achieve precise control of both the size and lifetime of these assemblies. Notably, viologen moieties constitute only about 1% of the polymer repeating units, underscoring that chemically-fueled phase transition is efficient strategy for dynamically regulating molecular assemblies. These findings demonstrate that chemically-fueled phase transitions in redox-responsive polymers offer a promising blueprint for designing dynamic, biomimetic materials capable of spatiotemporally regulated structural transformations.

One of cancer's well-known hallmarks is DNA damage, yet it's intriguing that DNA damage has been explored as a therapeutic strategy against cancer. Tyrosyl-DNA phosphodiesterase 1, involved in DNA repair from topoisomerase I inhibitors, a chemotherapy class for cancer treatment. Inhibiting TDP1 can increase unresolved Top1 cleavage complexes in cancer cells, inducing DNA damage and cell death. TDP1's catalytic activity depends on His263 and His493 residues. Using molecular simulation, structure-based drug design, and free energy calculation, we identified potential drugs against TDP1. A multi-step screening of medicinal plant compound databases (North Africa, East Africa, Northeast Africa, and South Africa) identified the top four candidates. Docking scores for top hits 1-4 were -7.76, -7.37, -7.35, and -7.24 kcal/mol. Top hit 3 exhibited the highest potency, forming a strong bonding network with both His263 and His493 residues. All-atoms simulations showed consistent dynamics for top hits 1-4, indicating stability and potential for efficient interaction with interface residues. Minimal fluctuations in residue flexibility suggest these compounds can stabilize internal flexibility upon binding. The binding free energies of -35.11, -36.70, -31.38, and -23.85 kcal/mol were calculated for the top hit 1-4 complexes. Furthermore, the chosen compounds demonstrate outstanding ADMET characteristics, such as excellent water solubility, effective gastrointestinal absorption, and the absence of hepatotoxicity. Cytotoxicity analysis revealed top hit 2 higher probability of activity against 24 cancer cell lines. Our findings suggest that these compounds (top hits 1-4) hold promise for innovative drug therapies, suitable for both in vivo and in vitro experiments.

Hidradenitis suppurativa (HS) is a chronic inflammatory skin condition of the terminal hair follicle, which can present in sporadic, familial, or syndromic forms. The exact pathogenesis of HS remains elusive, posing a challenge for the development of effective treatments. Among the various immunological mechanisms, the NLRP3 inflammasome is thought to contribute to the pathogenesis of HS, releasing cytokines such as IL-1β and IL-18 which initiates and exacerbates inflammation. Consequently, targeting NLRP3 offers a potential strategy for mitigating inflammation in HS-affected skin.

In this study we used the docking, molecular dynamics simulation and binding free energy approaches to identify the potent inhibitor of NLRP3 by screening the African phytocompounds and traditional Chinese medicine databases.

Our virtual drug screening analysis identified two lead compounds from each database, characterized by high docking scores such as SA-21676268 (-8.135 kcal/mol), SA-167673 (-10.251 kcal/mol), EA-45360194 (-10.376 kcal/mol), EA-46881231 (-10.011 kcal/mol), NEA-44258150 (-9.856 kcal/mol), NEA-135926572 (-7.662 kcal/mol), NA-163089376 (-9.237 kcal/mol), NA-440735 (-8.826 kcal/mol), TCM-392442 (-10.438 kcal/mol), and TCM-10043097 (-9.046 kcal/mol) which highlighted the strong binding affinity as compared to the control NP3-146 drug (-5.09 kcal/mol). Moreover, the values of dissociation constant further validated the strong binding affinity between the identified lead compounds and NLRP3. The dynamic stability and strong bonding energies of the lead compounds-NLRP3 complexes were confirmed by the molecular dynamic simulation and binding free energy calculation. The analysis of ADMET properties for all compounds indicated high intestinal absorption, water solubility, absence of hepatotoxicity, and skin sensitivity.

In conclusion, our molecular simulations and binding free energy calculations confirmed the strong affinity of these lead compounds for NLRP3 as compared to the control drug, highlighting their potential as part of a combinatorial therapeutic strategy for HS to effectively reduce disease-related inflammation.

Point mutation D54K in the human N-terminal caspase recruitment domain (CARD) of nucleotide-binding oligomerization domain -1 (NOD1) abrogates an imperative downstream interaction with receptor-interacting protein kinase (RIPK2) that entails combating bacterial infections and inflammatory dysfunction. Here, we addressed the molecular details concerning conformational changes and interaction patterns (monomeric-dimeric states) of D54K by signature-based molecular dynamics simulation. Initially, the sequence analysis prioritized D54K as a pathogenic mutation, among other variants, based on a sequence signature. Since the mutation is highly conserved, we derived the distant ortholog to predict the sequence and structural similarity between native and mutant. This analysis showed the utility of 33 communal core residues associated with structural-functional preservation and variations, concurrently served to infer the cryptic hotspots Cys39, Glu53, Asp54, Glu56, Ile57, Leu74, and Lys78 determining the inter helical fold forming homodimers for putative receptor interaction. Subsequently, the atomistic simulations with free energy (MM/PB(GB)SA) calculations predicted structural alteration that takes place in the N-terminal mutant CARD where coils changed to helices (45 α3- L4-α4-L6- α683) in contrast to native (45T2-L4-α4-L6-T483). Likewise, the C-terminal helices 93T1-α7105 connected to the loops distorted compared to native 93α6-L7105 may result in conformational misfolding that promotes functional regulation and activation. These structural perturbations of D54K possibly destabilize the flexible adaptation of critical homotypic NOD1CARD-CARDRIPK2 interactions (α4Asp42-Arg488α5 and α6Phe86-Lys471α4) is consistent with earlier experimental reports. Altogether, our findings unveil the conformational plasticity of mutation-dependent immunomodulatory response and may aid in functional validation exploring clinical investigation on CARD-regulated immunotherapies to prevent systemic infection and inflammation.

Although nuclear magnetic resonance (NMR) spectroscopy is powerful for protein dynamics investigations, the anisotropy of internal motions has been difficult to analyze with NMR. In principle, NMR order parameters for multiple bond vectors fixed on the same plane can reveal the anisotropy of internal motions. We investigated the anisotropic dynamics of protein asparagine (Asn) and glutamine (Gln) side chain NH2 groups using 2H and 15N NMR relaxation rates. Hindered rotations about the C-N bond causing chemical exchange between the two hydrogens of Asn/Gln NH2 groups are far slower than 2H relaxation. Using the 2H and 15N relaxation data at two magnetic fields, we determined two order parameters and 2H quadrupolar coupling constants for each NH2 group of ubiquitin. Our data clearly illuminate the heterogeneous dynamic properties of the protein side chain NH2 groups with different degrees of the motional anisotropy, which depends strongly on the local environment.

In this study, three novel derivatives of benzo[b]thiophene-2-carbaldehyde (BTAP1, BTAP2, and BTAP3) were successfully synthesized and comprehensively characterized using spectroscopic techniques including FTIR, UV-VIS, 1HNMR, and 13CNMR. Thermal analysis through TGA and DTA demonstrated remarkable thermal stability with a maximum threshold at 270 °C. Spectroscopic investigations revealed π → π* transitions in all compounds, attributed to the conjugated system comprising benzothiophene rings connected to bromophenyl/ aminophenyl/phenol rings via α, β-unsaturated ketone bridges. Quantum chemical calculations indicated varying molecular stabilities, with BTAP3 exhibiting the highest energy gap (ΔE = 3.59 eV) and global hardness (η = 1.8), while BTAP2 showed enhanced reactivity potential with the lowest energy gap (ΔE = 3.22 eV) and highest global softness (S = 0.62). Virtual screening and molecular docking studies identified protein target 4JVW as the most favorable interaction partner, with binding energies of -8.0, -7.5, and -7.6 kcal/mol for BTAP1, BTAP2, and BTAP3, respectively. Molecular dynamics simulations revealed stable protein-ligand complexes, characterized by minimal RMSD and RMSF fluctuations, optimal Rg values, sustained hydrogen bonding networks and favorable solvent accessible surface area values. MMGBSA analysis highlighted the significance of Coulombic, van der Waals, and lipophilic interactions in complex stabilization. Toxicological evaluations demonstrated favorable safety profiles with minimal ocular and dermal irritation potential compared to allicin, coupled with low lethal dose values. These findings collectively position these novel compounds as promising candidates for pharmaceutical applications, warranting further investigation into their therapeutic potential.

Multicomponent traditional medicine prescriptions are widely used in Ethiopia for disease treatment. However, inconsistencies across practitioners, cultures, and locations have hindered the development of reliable therapeutic medicines. Systematic analysis of traditional medicine data is crucial for identifying consistent and reliable medicinal materials. In this study, we compiled and analyzed a dataset of 505 prescriptions, encompassing 567 medicinal materials used for treating 106 diseases. Using association rule mining, we identified significant associations between diseases and medicinal materials. Notably, wound healing-the most frequently treated condition-was strongly associated with Rumex abyssinicus Jacq., showing a high support value. This association led to further in silico and network analysis of R. abyssinicus Jacq. compounds, revealing 756 therapeutic targets enriched in various KEGG pathways and biological processes. The Random-Walk with Restart (RWR) algorithm applied to the CODA PPI network identified these targets as linked to diseases such as cancer, inflammation, and metabolic, immune, respiratory, and neurological disorders. Many hub target genes from the PPI network were also directly associated with wound healing, supporting the traditional use of R. abyssinicus Jacq. for treating wounds. In conclusion, this study uncovers significant associations between diseases and medicinal materials in Ethiopian traditional medicine, emphasizing the therapeutic potential of R. abyssinicus Jacq. These findings provide a foundation for further research, including in vitro and in vivo studies, to explore and validate the efficacy of traditional and natural product-derived medicines.

Accurately predicting mutation-caused binding free energy changes (ΔΔGs) on protein interactions is crucial for understanding how genetic variations affect interactions between proteins and other biomolecules, such as proteins, DNA/RNA, and ligands, which are vital for regulating numerous biological processes. Developing computational approaches with high accuracy and efficiency is critical for elucidating the mechanisms underlying various diseases, identifying potential biomarkers for early diagnosis, and developing targeted therapies. This review provides a comprehensive overview of recent advancements in predicting the impact of mutations on protein interactions across different interaction types, which are central to understanding biological processes and disease mechanisms, including cancer. We summarize recent progress in predictive approaches, including physicochemical-based, machine learning, and deep learning methods, evaluating the strengths and limitations of each. Additionally, we discuss the challenges related to the limitations of mutational data, including biases, data quality, and dataset size, and explore the difficulties in developing accurate prediction tools for mutation-induced effects on protein interactions. Finally, we discuss future directions for advancing these computational tools, highlighting the capabilities of advancing technologies, such as artificial intelligence to drive significant improvements in mutational effects prediction.

Transition-state (TS) theory has provided the theoretical framework to explain the enormous rate accelerations of chemical reactions by enzymes. Given that proteins display large ensembles of conformations, unique TSs would pose a huge entropic bottleneck for enzyme catalysis. To shed light on this question, we studied the nature of the enzymatic TS for the phosphoryl-transfer step in adenylate kinase by quantum-mechanics/molecular-mechanics calculations. We find a structurally wide set of energetically equivalent configurations that lie along the reaction coordinate and hence a broad transition-state ensemble (TSE). A conformationally delocalized ensemble, including asymmetric TSs, is rooted in the macroscopic nature of the enzyme. The computational results are buttressed by enzyme kinetics experiments that confirm the decrease of the entropy of activation predicted from such wide TSE. TSEs as a key for efficient enzyme catalysis further boosts a unifying concept for protein folding and conformational transitions underlying protein function.

Hydrogen exchange mass spectrometry (HXMS) is a powerful tool to understand protein folding pathways and energetics. However, HXMS experiments to date have used exchange conditions termed EX1 or EX2 which limit the information that can be gained compared to the more general EXX exchange regime. If EXX behavior could be understood and analyzed, a single HXMS timecourse on an intact protein could fully map its folding landscape without requiring denaturation. To address this challenge, we developed a numerical simulation method called NumSimEX that models EXX exchange for arbitrarily complex folding pathways. NumSimEx fits protein folding dynamics to experimental HXMS data by iteratively comparing the simulated and experimental timecourses, allowing for determination of both kinetic and thermodynamic protein folding parameters. After analytically verifying NumSimEX's accuracy, we demonstrated its power on HXMS data from beta-2 microglobulin (β2M), a protein involved in dialysis-related amyloidosis. In particular, using NumSimEX, we identified three-state kinetics that near-perfectly matched experimental observation. This proof-of-principle application of NumSimEX sets the stage for harnessing HXMS to expand our understanding of proteins currently excluded from traditional protein folding methods. NumSimEX is freely available at https://github.com/JaswalLab/NumSimEX_Public.

The relationship among protein structure, function, and dynamics is fundamental to biological activity, particularly in more complex biomolecular systems. Solid-state and solution-state NMR techniques offer powerful means to probe these dynamics across various time scales. However, standard assumptions about molecular motion are often challenged in phase-separated systems like phosphorylated heterochromatin protein 1 alpha (pHP1α), which exhibit both solid- and solution-like characteristics. This study investigates the nanosecond molecular motions in pHP1α liquid-liquid phase separation (LLPS) using relaxation in hetNOE-filtered HSQC signals. By systematically analyzing motions captured by hetNOE-filtered HSQC and conventional HSQC, we characterize the global dynamics site-specifically in pHP1α LLPS. Our findings reveal ∼15 ns motion in the pHP1α LLPS system, suggesting the coexistence of different dynamic phases, and support previous observations on its role in chromatin organization. This work contributes to the expanding literature on phase-separated biomolecular behavior, with implications for understanding the molecular basis of chromatin compaction and genomic stability.

Frameshifting is an essential mechanism employed by many viruses including coronaviruses to produce viral proteins from a compact RNA genome. It is facilitated by specific RNA folds in the frameshift element (FSE), which has emerged as an important therapeutic target. For SARS-CoV-2, a specific 3-stem pseudoknot has been identified to stimulate frameshifting. However, prior studies and our RNA-As-Graphs analysis coupled to chemical reactivity experiments revealed other folds, including a different pseudoknot. Although structural plasticity has been proposed to play a key role in frameshifting, paths between different FSE RNA folds have not been yet identified. Here, we capture atomic-level transition pathways between two key FSE pseudoknots by transition path sampling coupled to Markov State Modeling and our BOLAS free energy method. We reveal multiple transition paths within a heterogeneous, multihub conformational landscape. A shared folding mechanism involves RNA stem unpairing followed by a 5'-chain end release. Significantly, this pseudoknot transition critically tunes the tension through the RNA spacer region and places the viral RNA in the narrow ribosomal channel. Our work further explains the role of the alternative pseudoknot in ribosomal pausing and clarifies why the experimentally captured pseudoknot is preferred for frameshifting. Our capturing of this large-scale transition of RNA secondary and tertiary structure highlights the complex pathways of biomolecules and the inherent multifarious aspects that viruses developed to ensure virulence and survival. This enhanced understanding of viral frameshifting also provides insights to target key transitions for therapeutic applications. Our methods are generally applicable to other large-scale biomolecular transitions.

The influence of fullerene C60 on the mechanical and thermal properties of natural rubber was systematically investigated using coarse-grained molecular dynamics simulations. The tensile results demonstrate that systems with longer NR chains exhibit reduced tensile strength. Moreover, the addition of C60 nanoparticles significantly enhanced the mechanical properties, with Young's modulus, yield strength, and tensile strength increasing by approximately 24.03%, 23.21%, and 51.61%, respectively, at a C60 concentration of 0.2 phr under a strain rate of 1e-6. Furthermore, the mechanical response was found to be strain rate-dependent, with higher strain rates leading to increased Young's modulus, yield strength, and tensile strength. Therefore, excessively high strain rates should be avoided in simulations to ensure consistency with real conditions. In terms of thermal properties, the addition of C60 nanoparticles was shown to significantly improve the thermal conductivity of natural rubber, with the optimal enhancement of 17.17% achieved at an inclusion level of approximately 0.1 phr. A comprehensive microstructural analysis, including mean square displacement, radial distribution function, coordination number, and interaction energy, revealed the reinforcement mechanisms of C60 on the mechanical and thermal properties of the nanocomposites. This study provides valuable insights into the rational design and fabrication of fullerene-reinforced natural rubber nanocomposites with superior mechanical and thermal performance.

In this study, coarse-grained molecular dynamics simulations were performed using the LAMMPS software. The system used the real unit system with periodic boundary conditions. The interatomic interactions were described using the lj/expand model. The simulations were conducted at a temperature of 300 K with a time step of 1 fs.

Understanding and predicting the diverse conformational states of membrane proteins is essential for elucidating their biological functions. Despite advancements in computational methods, accurately capturing these complex structural changes remains a significant challenge. Here, we introduce a computational approach to generate diverse and biologically relevant conformations of membrane proteins using a conditional diffusion model. Our approach integrates forward and backward diffusion processes, incorporating state classifiers and additional conditioners to control the generation gradient of conformational states. We specifically targeted the P-type ATPases, a critical family of membrane transporters, and constructed a comprehensive data set through a combination of experimental structures and molecular dynamics simulations. Our model, incorporating a graph neural network with specialized membrane constraints, demonstrates exceptional accuracy in generating a wide range of P-type ATPase conformations associated with different functional states. This approach represents a meaningful step forward in the computational generation of membrane protein conformations using AI and holds promise for studying the dynamics of other membrane proteins.

FcγRI plays a crucial role in the effector function of IgG antibodies, interacting with the lower hinge region of IgG1 with nanomolar affinity. Binding occurs specifically in domain 2 (D2) of the FcγRI ectodomain, while domain 3 (D3) is a flexible linker. The D3 domain is positioned away from the IgG binding site on the FcγRI and does not directly contact the Fc region. This study investigates the structural and functional properties of FcγRI D3 using 200 ns classical MD simulations of two models: (1) a full FcγRI ectodomain complex with Fc and (2) a truncated model excluding D3. Our findings suggest that the D3 ectodomain provides additional structural flexibility to the FcγRI-Fc complex without altering the C backbone motion or flexibility of the KHR binding motif in the FG loop. Critical residues involved in binding and contributing to complex stability were evaluated regarding changes in intramolecular interactions and destabilization tendency upon D3 truncation. Truncation did not significantly alter interactions around glycan-interacting residues in Fc chains or FcγRI-Fc binding interfaces. These findings provide valuable insights into the role of FcγRI D3 in modulating the structural dynamics of the FcγRI-Fc complex. While D3 does not directly contact Fc, its mobility and positioning may modulate the receptor's affinity, accessibility, and ability to bind IgG immune complexes. We suggest that a truncated FcγRI construct lacking the D3 domain may be a promising candidate for biosensor or capturing agents' development and optimization, offering improved performance in IgG capture assays without compromising critical binding interactions.

We present a calibration scheme to determine the conversion factors from a coarse-grained stochastic approximation Monte Carlo approach using the PRIME20 peptide interaction model to atomistic force-field interaction energies at full explicit aqueous solvation. The conversion from coarse-grained to atomistic structures was performed according to our previously established inverse coarse-graining protocol. We provide a physical energy scale for both the backbone hydrogen bonding interactions and the sidechain interactions by correlating the dimensionless energy descriptors of the PRIME20 model with the energies averaged over molecular dynamics simulations. The conversion factor for these interactions turns out to be around 2 kJ/mol for the backbone interactions, and zero for the sidechain interactions. We discuss these surprisingly small values in terms of their molecular interpretation.

Collective variables (CVs) describing slow degrees of freedom (DOFs) in biomolecular assemblies are crucial for analyzing molecular dynamics trajectories, creating Markov models and performing CV-based enhanced sampling simulations. While time-lagged independent component analysis (tICA) and its nonlinear successor, time-lagged autoencoder (tAE), are widely used, they often struggle to capture protein dynamics due to interference from random fluctuations along fast DOFs. To address this issue, we propose a novel approach integrating discrete wavelet transform (DWT) with dimensionality reduction techniques. DWT effectively separates fast and slow motion in protein simulation trajectories by decoupling high- and low-frequency signals. Based on the trajectory after filtering out high-frequency signals, which corresponds to fast motion, tICA and tAE can accurately extract CVs representing slow DOFs, providing reliable insights into protein dynamics. Our method demonstrates superior performance in identifying CVs that distinguish metastable states compared to standard tICA and tAE, as validated through analyses of conformational changes of alanine dipeptide and tripeptide and folding of CLN025. Moreover, we show that DWT can be used to improve the performance of a variety of CV-finding algorithms by combining it with Deep-tICA, a cutting-edge CV-finding algorithm, to extract CVs for enhanced-sampling calculations. Given its negligible computational cost and remarkable ability to screen fast motion, we propose DWT as a "free lunch" for CV extraction, applicable to a wide range of CV-finding algorithms.

Investigating the binding between proteins and aptamers, such as peptides or RNA molecules, is of crucial importance both for understanding the molecular mechanisms that regulate cellular activities and for therapeutic applications in several pathologies. Here, a new computational procedure, employing mainly docking, clustering analysis, and molecular dynamics simulations, was designed to estimate the binding affinities between a protein and some RNA aptamers, through the investigation of the dynamical behavior of the predicted molecular complex. Using the state-of-the-art software catRAPID, we computationally designed a set of RNA aptamers interacting with the TAR DNA-binding protein 43 (TDP-43), a protein associated with several neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). We thus devised a computational protocol to predict the RNA-protein molecular complex, so that the structural and dynamical behavior of such a complex can be investigated through extensive molecular dynamics simulation. We hypothesized that the coordinated and synchronized motion of the protein-binding residues, when in contact with RNA molecule, is a critical requisite in order to have a stable binding. Indeed, we calculated the motion covariance exhibited by the interface residues during molecular dynamics simulation: we tested the results against experimental measurements of binding affinity (in this case, the dissociation constant) for six RNA molecules, resulting in a linear correlation of about 0.9. Our findings suggest that the synchronized movement of interface residues plays a pivotal role in ensuring the stability within RNA-protein complexes, moreover providing insights into the contribution of each interface residue. This promising pipeline could thus contribute to the design of RNA aptamers interacting with proteins.

Achieving accurate characterization of protein structures in the gas phase continues to be a formidable challenge. To tackle this issue, the present study employs Molecular Dynamics (MD) simulations in tandem with enhanced sampling techniques (methods designed to efficiently explore protein conformations). The objective is to identify suitable structures of proteins by contrasting their calculated Collision Cross-Section (CCS) with those observed experimentally. Significant discrepancies were observed between the initial MD-simulated and experimentally measured CCS values through Ion Mobility-Mass Spectrometry (IMS-MS). To bridge this gap, we employed two distinct enhanced sampling methods, Harmonic Biasing Potential and Adaptive Biasing Force, which help the proteins overcome energy barriers to adopt more compact configurations. These techniques leverage the radius of gyration as a reaction coordinate (guiding parameter), guiding the system toward compressed states that potentially match experimental configurations more closely. The guiding forces are only employed to overcome existing barriers and are removed to allow the protein to naturally arrive at a potential gas phase configuration. The results demonstrated close alignment (within ∼4%) between simulated and experimental CCS values despite using different strengths and/or methods, validating their efficacy. This work lays the groundwork for future studies aimed at optimizing biasing methods and expanding the collective variables used for more accurate gas-phase structural predictions.

Protein molecules organize into an intricate alphabet of twenty amino acids and five architecture levels. The jargon "one structure, one functionality" has been challenged, considering the amount of intrinsically disordered proteins in the human genome and the requirements of hierarchical hetero- and homo-protein complexes in cell signaling. The assembly of large protein structures in health and disease is now viewed through the lens of phase separation and transition phenomena. What drives protein misfolding and aggregation? Or, more fundamentally, what hinders proteins from maintaining their native conformations, pushing them toward aggregation? Here, we explore the principles of protein folding, phase separation, and aggregation, which hinge on crucial events such as the reorganization of solvents, the chemical properties of amino acids, and their interactions with the environment. We focus on the dynamic shifts between functional and dysfunctional states of proteins and the conditions that promote protein misfolding, often leading to disease. By exploring these processes, we highlight potential therapeutic avenues to manage protein aggregation and reduce its harmful impacts on health.

Dynamics are often critical for biomolecular function. Herein we explore the role of motion in driving the maturation process of pro-IL-18, a potent pro-inflammatory cytokine that is cleaved by caspases-1 and -4 to generate the mature form of the protein. An NMR dynamics study of pro-IL-18, probing time scales over 12 orders of magnitude and focusing on 1H, 13C, and 15N spin probes along the protein backbone and amino-acid side chains, reveals a plastic structure, with millisecond time scale dynamics occurring in a pair of β-strands, β1 and β*, that show large structural variations in a comparison of caspase-free and bound pro-IL-18 states. Fits of the relaxation data to a three-site model of exchange showed that the ground state secondary structure is maintained in the excited conformers, with the side chain of I48 that undergoes a buried-to-exposed conformational change in the caspase-free to -bound transition of pro-IL-18, sampling a more extensive range of torsion angles in one of the excited states characterized, suggesting partial unpacking in this region. Hydrogen exchange measurements establish the occurrence of an additional process, whereby strands β1 and β* locally unfold. Our data are consistent with a hierarchy of dynamic events that likely prime pro-IL-18 for facile caspase binding.

Biologic drugs have emerged as a rapidly expanding and important modality, offering promising therapeutic solutions by interacting with previously "undruggable" targets, thus significantly expanding the range of modern pharmaceutical applications. However, the inherent complexity of these drugs also introduces liabilities and poses challenges in their development, necessitating efficient screening methods to evaluate the structural stability and behavior. Although nuclear magnetic resonance (NMR) spectroscopy is well-suited for detecting weak interactions, changes in dynamics, high-order structure, and association states of macromolecules in fully formulated samples, the inherent low sensitivity limits its utility as a fast screening and characterization tool. In this study, we present two fast pulsing NMR experiments, namely the band-Selective Optimized Flip-Angle Internally encoded Relaxation (SOFAIR) and the band-Selective Optimized Flip-angle Internally encoded Translational diffusion (SOFIT)), which enable rapid and reliable measurements of transverse relaxation rates and diffusion coefficients with more than 10-fold higher sensitivity compared to commonly used methods, like Carr-Purcell-Meiboom-Gill and diffusion-ordered spectroscopy, allowing the rapid assessment of biologics even at low concentrations. We demonstrated the effectiveness and versatility of these experiments by evaluating several examples, including thermally stressed proteins, proteins at different concentrations, and a therapeutic protein in various formulations. We anticipate that these novel approaches will greatly facilitate the analysis and characterization of biologics during drug discovery.

The ion atmosphere surrounding highly charged biomolecules, such as nucleic acids, is crucial for their dynamics, structure, and interactions. Here, we develop an approach for the explicit treatment of ions within an implicit solvent framework suitable for atomistic simulations of biomolecules. The proposed implicit solvent/explicit ions model, GBION, is based on a modified generalized Born (GB) model; it includes separate, modified GB terms for solute-ion and ion-ion interactions. The model is implemented in the AMBER package (version 24), and its performance is thoroughly investigated in atomistic molecular dynamics (MD) simulations of double-stranded DNA on a microsecond time scale. The aggregate characteristics of monovalent (Na+ and K+) and trivalent (Cobalt Hexammine, CoHex3+) counterion distributions around double-stranded DNA predicted by the model are in reasonable agreement with the experiment (where available), all-atom explicit water MD simulations, and the expectation from the Manning condensation theory. The radial distributions of monovalent cations around DNA are reasonably close to the ones obtained using the explicit water model: expressed in units of energy, the maximum deviations of local ion concentrations from the explicit solvent reference are within 1 kBT, comparable to the corresponding deviations expected between different established explicit water models. The proposed GBION model is able to simulate DNA fragments in a large volume of solvent with explicit ions with little additional computational overhead compared with the fully implicit GB treatment of ions. Ions simulated using the developed model explore conformational space at least 2 orders of magnitude faster than in the explicit solvent. These advantages allowed us to observe and explore an unexpected "stacking" mode of DNA condensation in the presence of trivalent counterions (CoHex3+) that was revealed by recent experiments.

Targeted delivery of therapeutic anticancer chimeric molecules enhances drug efficacy. Numerous studies have focused on developing novel treatments by employing cytokines, particularly interleukins, to inhibit the growth of cancer cells. In the present study, we fused interleukin 24 with the tumor-targeting peptide P20 through a rigid linker to selectively target cancer cells. The secondary structure, tertiary structure, and physicochemical characteristics of the constructed chimeric IL-24-P20 protein were predicted by using bioinformatics tools. In-silico analysis revealed that the fusion construct has a basic nature with 175 amino acids and a molecular weight of 20 kDa. By using the Rampage and ERRAT2 servers, the validity and quality of the fusion protein were evaluated. The results indicated that 93% of the chimeric proteins contained 90.1% of the residues in the favoured region, resulting in a reliable structure. Finally, docking and simulation studies were conducted via ClusPro and Desmond Schrödinger, respectively. Our results indicate that the constructed fusion protein exhibits excellent quality, interaction capabilities, validity, and stability. These findings suggest that the fusion protein is a promising candidate for targeted cancer therapy.

Neuroinflammation plays a pivotal role in the development and progression of neurodegenerative diseases, with a complex interplay between immune responses and brain activity. Understanding this interaction is crucial for identifying therapeutic targets and developing effective treatments. This study aimed to explore the neuroprotective properties of flavonoid compounds from Spondias mombin via the modulation of neuroinflammatory pathway using a comprehensive in-silico approach, including network pharmacology, molecular docking, and dynamic simulations. Active flavonoid ingredients from S. mombin were identified, and their potential protein targets were predicted through Network Pharmacology. Molecular docking was conducted to determine the binding affinities of these compounds against targets obtained from network pharmacology, prioritizing docking scores ≥ - 8.0 kcal/mol. Molecular dynamic simulations (MDS) assessed the stability and interaction profiles of these ligand-protein complexes. The docking study highlighted ≥ - 8.0 kcal/mol for the ligands (catechin and epicatechin) against FYN kinase as a significant target. However, these compounds failed the blood-brain barrier (BBB) permeability test. MDS confirmed the stability of catechin and the reference ligand at the FYN kinase active site, with notable interactions involving hydrogen bonds, hydrophobic contacts, and water bridges. GLU54 emerged as a key residue in the catechin-FYN complex stability due to its prolonged hydrogen bond interaction. The findings underscore the potential of S. mombin flavonoids as therapeutic agents against neuroinflammation, though optimization and nanotechnology-based delivery methods are suggested to enhance drug efficacy and overcome BBB limitations.

Septoria nodorum blotch (SNB), caused by Parastagonospora nodorum, is a disease of durum and common wheat initiated by the recognition of pathogen-produced necrotrophic effectors (NEs) by specific wheat genes. The wheat gene Snn1 was previously cloned, and it encodes a wall-associated kinase that directly interacts with the NE SnTox1 leading to programmed cell death and ultimately the development of SNB. Here, sequence analysis of Snn1 from 114 accessions including diploid, tetraploid, and hexaploid wheat species revealed that some wheat lines possess two copies of Snn1 (designated Snn1-B1 and Snn1-B2) approximately 120 kb apart. Snn1-B2 evolved relatively recently as a paralog of Snn1-B1, and both genes have undergone diversifying selection. Three point mutations associated with the formation of the first SnTox1-sensitive Snn1-B1 allele from a primitive wild wheat were identified. Four subsequent and independent SNPs, three in Snn1-B1 and one in Snn1-B2, converted the sensitive alleles to insensitive forms. Protein modeling indicated these four mutations could abolish Snn1-SnTox1 compatibility either through destabilization of the Snn1 protein or direct disruption of the protein-protein interaction. A high-throughput marker was developed for the absent allele of Snn1, and it was 100% accurate at predicting SnTox1-insensitive lines in both durum and spring wheat. Results of this study increase our understanding of the evolution, diversity, and function of Snn1-B1 and Snn1-B2 genes and will be useful for marker-assisted elimination of these genes for better host resistance.

The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.

In this study, our focus was on investigating H-1,2,3-triazole derivative HP661 as a novel and highly efficient oral OXPHOS inhibitor, with its molecular-level inhibitory mechanism not yet fully understood. We selected the ND1, NDUFS2, and NDUFS7 subunits of Mitochondrial Complex I as the receptor proteins and established three systems for comparative analysis: protein-IACS-010759, protein-lead compound 10, and protein-HP661. Through extensive analysis involving 500 ns Gaussian molecular dynamics simulations, we gained insights into these systems. Additionally, we constructed a Markov State Models to examine changes in secondary structures during the motion processes. The research findings suggest that the inhibitor HP661 enhances the extensibility and hydrophilicity of the receptor protein. Furthermore, HP661 induces the unwinding of the α-helical structure in the region of residues 726-730. Notably, key roles were identified for Met37, Phe53, and Pro212 in the binding of various inhibitors. In conclusion, we delved into the potential molecular mechanisms of triazole derivative HP661 in inhibiting Complex I. These research outcomes provide crucial information for a deeper understanding of the mechanisms underlying OXPHOS inhibition, offering valuable theoretical support for drug development and disease treatment design.

Influenza A virus causes severe respiratory illnesses, especially in developing nations where most child deaths under 5 occur due to lower respiratory tract infections. The RIG-I protein acts as a sensor for viral dsRNA, triggering interferon production through K63-linked poly-ubiquitin chains synthesized by TRIM25. However, the influenza A virus's NS1 protein hinders this process by binding to TRIM25, disrupting its association with RIG-I and preventing downstream interferon signalling, contributing to the virus's evasion of the immune response.

In our study we used structural-based drug designing, molecular simulation, and binding free energy approaches to identify the potent phytocompounds from various natural product databases (>100,000 compounds) able to inhibit the binding of NS1 with the TRIM25.

The molecular screening identified EA-8411902 and EA-19951545 from East African Natural Products Database, NA-390261 and NA-71 from North African Natural Products Database, SA-65230 and SA- 4477104 from South African Natural Compounds Database, NEA- 361 and NEA- 4524784 from North-East African Natural Products Database, TCM-4444713 and TCM-6056 from Traditional Chinese Medicines Database as top hits. The molecular docking and binding free energies results revealed that these compounds have high affinity with the specific active site residues (Leu95, Ser99, and Tyr89) involved in the interaction with TRIM25. Additionally, analysis of structural dynamics, binding free energy, and dissociation constants demonstrates a notably stronger binding affinity of these compounds with the NS1 protein. Moreover, all selected compounds exhibit exceptional ADMET properties, including high water solubility, gastrointestinal absorption, and an absence of hepatotoxicity, while adhering to Lipinski's rule.

Our molecular simulation findings highlight that the identified compounds demonstrate high affinity for specific active site residues involved in the NS1-TRIM25 interaction, exhibit exceptional ADMET properties, and adhere to drug-likeness criteria, thus presenting promising candidates for further development as antiviral agents against influenza A virus infections.

The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.

Nonheme Fe(II) and 2-oxoglutarate (2OG)-dependent histone lysine demethylases 2A (KDM2A) catalyze the demethylation of the mono- or dimethylated lysine 36 residue in the histone H3 peptide (H3K36me1/me2), which plays a crucial role in epigenetic regulation and can be involved in many cancers. Although the overall catalytic mechanism of KDMs has been studied, how KDM2 catalysis takes place in contrast to other KDMs remains unknown. Understanding such differences is vital for enzyme redesign and can help in enzyme-selective drug design. Herein, we employed molecular dynamics (MD) and combined quantum mechanics/molecular mechanics (QM/MM) to explore the complete catalytic mechanism of KDM2A, including dioxygen diffusion and binding, dioxygen activation, and substrate oxidation. Our study demonstrates that the catalysis of KDM2A is controlled by the conformational change of the second coordination sphere (SCS), specifically by a change in the orientation of Y222, which unlocks the 2OG rearrangement from off-line to in-line mode. The study demonstrates that the variant Y222A makes the 2OG rearrangement more favorable. Furthermore, the study reveals that it is the size of H3K36me3 that prevents the 2OG rearrangement, thus rendering the enzyme inactivity with trimethylated lysine. Calculations show that the SCS and long-range interacting residues that stabilize the HAT transition state in KDM2A differ from those in KDM4A, KDM7B, and KDM6A, thus providing the basics for the enzyme-selective redesign and modulation of KDM2A without influencing other KDMs.

Since the mechanism underlying real-time acquisition of mechanical strength during laser-induced skin wound fusion remains unclear, and collagen is the primary constituent of skin tissue, this study investigates the structural and mechanical alterations in collagen at temperatures ranging from 40 °C to 60 °C using various spectroscopic techniques and molecular dynamics calculations. The COMSOL Multiphysics coupling is employed to simulate the three-dimensional temperature field, stress-strain relationship, and light intensity distribution in the laser thermal affected zone of skin wounds during dual-beam laser welding process. Raman spectroscopy, synchronous fluorescence spectroscopy and circular dichroism measurement results confirm that laser energy activates biological activity in residues, leading to a transformation in the originally fractured structure of collagen protein for enhanced mechanical strength. Molecular dynamics simulations reveal that stable hydrogen bonds form at amino acid residues within the central region of collagen protein when the overall temperature peak around the wound reaches 60 °C, thereby providing stability to previously fractured skin incisions and imparting instantaneous strength. However, under a 55 °C system, Type I collagen ensures macrostructural stability while activating biological properties at amino acid bases to promote wound healing function; this finding aligns with experimental analysis results. The COMSOL simulation outcomes also correspond well with macroscopic morphology after laser welding samples, confirming that by maintaining temperatures between 55 °C-60 °C during laser welding of skin incisions not only can certain instantaneous mechanical strength be achieved but irreversible thermal damage can also be effectively controlled. It is anticipated that these findings will provide valuable insights into understanding the healing mechanism for laser-welded skin wounds.

Over the last decade chemical exchange saturation transfer (CEST) NMR methods have emerged as powerful tools to characterize biomolecular conformational dynamics occurring between a visible major state and 'invisible' minor states. The ability of the CEST experiment to detect these minor states, and provide precise exchange parameters, hinges on using appropriate B1 field strengths during the saturation period. Typically, a pair of B1 fields with ω1 (=2πB1) values around the exchange rate kex are chosen. Here we show that the transverse relaxation rate of the minor state resonance (R2,B) also plays a crucial role in determining the B1 fields that lead to the most informative datasets. Using [Formula: see text]  ≥ kex, to guide the choice of B1, instead of kex, leads to data wherefrom substantially more accurate exchange parameters can be derived. The need for higher B1 fields, guided by K, is demonstrated by studying the conformational exchange in two mutants of the 71 residue FF domain with kex ∼ 11 s-1 and ∼ 72 s-1, respectively. In both cases analysis of CEST datasets recorded using B1 field values guided by kex lead to imprecise exchange parameters, whereas using B1 values guided by K resulted in precise site-specific exchange parameters. The conclusions presented here will be valuable while using CEST to study slow processes at sites with large intrinsic relaxation rates, including carbonyl sites in small to medium sized proteins, amide 15N sites in large proteins and when the minor state dips are broadened due to exchange among the minor states.

Cyclin-dependent kinases (CDKs), particularly CDK4 and CDK2, are crucial for cell cycle progression from the Gap 1 (G1) to the Synthesis (S) phase by phosphorylating targets such as the Retinoblastoma Protein (Rb). CDK4, paired with cyclin-D, operates in the long G1 phase, while CDK2 with cyclin-E, manages the brief G1-to-S transition, enabling DNA replication. Aberrant CDK signaling leads to uncontrolled cell proliferation, which is a hallmark of cancer. Exactly how they accomplish their catalytic phosphorylation actions with distinct efficiencies poses the fundamental, albeit overlooked question. Here we combined available experimental data and modeling of the active complexes to establish their conformational functional landscapes to explain how the two cyclin/CDK complexes differentially populate their catalytically competent states for cell cycle progression. Our premise is that CDK catalytic efficiencies could be more important for cell cycle progression than the cyclin-CDK biochemical binding specificity and that efficiency is likely the prime determinant of cell cycle progression. We observe that CDK4 is more dynamic than CDK2 in the ATP binding site, the regulatory spine, and the interaction with its cyclin partner. The N-terminus of cyclin-D acts as an allosteric regulator of the activation loop and the ATP-binding site in CDK4. Integrated with a suite of experimental data, we suggest that the CDK4 complex is less capable of remaining in the active catalytically competent conformation, and may have a lower catalytic efficiency than CDK2, befitting their cell cycle time scales, and point to critical residues and motifs that drive their differences. Our mechanistic landscape may apply broadly to kinases, and we propose two drug design strategies: (i) allosteric Inhibition by conformational stabilization for targeting allosteric CDK4 regulation by cyclin-D, and (ii) dynamic entropy-optimized targeting which leverages the dynamic, entropic aspects of CDK4 to optimize drug binding efficacy.

In November 2019, the world faced a pandemic called SARS-CoV-2, which became a major threat to humans and continues to be. To overcome this, many plants were explored to find a cure.

Therefore, this research was planned to screen out the active constituents from Artemisia annua that can work against the viral main protease Mpro as this non-structural protein is responsible for the cleavage of replicating enzymes of the virus. Twenty-five biocompounds belonging to different classes namely alpha-pinene, beta-pinene, carvone, myrtenol, quinic acid, caffeic acid, quercetin, rutin, apigenin, chrysoplenetin, arteannunin b, artemisinin, scopoletin, scoparone, artemisinic acid, deoxyartemisnin, artemetin, casticin, sitogluside, beta-sitosterol, dihydroartemisinin, scopolin, artemether, artemotil, artesunate were selected. Virtual screening of these ligands was carried out against drug target Mpro by CB dock.

Quercetin, rutin, casticin, chrysoplenetin, apigenin, artemetin, artesunate, sopolin and sito-gluside were found as hit compounds. Further, ADMET screening was conducted which represented Chrysoplenetin as a lead compound. Azithromycin was used as a standard drug. The interactions were studied by PyMol and visualized in LigPlot. Furthermore, the RMSD graph shows fluctuations at various points at the start of simulation in Top1 (Azithromycin) complex system due to structural changes in the helix-coil-helix and beta-turn-beta changes at specific points resulting in increased RMSD with a time frame of 50 ns. But this change remains stable after the extension of simulation time intervals till 100 ns. On other side, the Top2 complex system remains highly stable throughout the time scale. No such structural dynamics were observed bu the ligand attached to the active site residues binds strongly.

This study facilitates researchers to develop and discover more effective and specific therapeutic agents against SARS-CoV-2 and other viral infections. Finally, chrysoplenetin was identified as a more potent drug candidate to act against the viral main protease, which in the future can be helpful.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to over six million deaths worldwide. In human immune system, the type 1 interferon (IFN) pathway plays a crucial role in fighting viral infections. However, the ORF8 protein of the virus evade the immune system by interacting with IRF3, hindering its nuclear translocation and consequently downregulate the type I IFN signaling pathway. To block the binding of ORF8-IRF3 and inhibit viral pathogenesis a quick discovery of an inhibitor molecule is needed. Therefore, in the present study, the interface between the ORF8 and IRF3 was targeted on a high-affinity carbon nanotube by using computational tools. After analysis of 62 carbon nanotubes by multiple docking with the induced fit model, the top five compounds with high docking scores of - 7.94 kcal/mol, - 7.92 kcal/mol, - 7.28 kcal/mol, - 7.19 kcal/mol and - 7.09 kcal/mol (top hit1-5) were found to have inhibitory activity against the ORF8-IRF3 complex. Molecular dynamics analysis of the complexes revealed the high compactness of residues, stable binding, and strong hydrogen binding network among the ORF8-nanotubes complexes. Moreover, the total binding free energy for top hit1-5 was calculated to be - 43.21 ± 0.90 kcal/mol, - 41.17 ± 0.99 kcal/mol, - 48.85 ± 0.62 kcal/mol, - 43.49 ± 0.77 kcal/mol, and - 31.18 ± 0.78 kcal/mol respectively. These results strongly suggest that the identified top five nanotubes (hit1-5) possess significant potential for advancing and exploring innovative drug therapies. This underscores their suitability for subsequent in vivo and in vitro experiments, marking them as promising candidates worthy of further investigation.

Molecular Dynamics (MD) is a computational technique widely used to evaluate a molecular system's thermodynamic properties and conformational behavior over time. In particular, the energy analysis of a protein conformation ensemble produced though MD simulations plays a crucial role in explaining the relationship between protein dynamics and its mechanism of action. In this research work, the HINT (Hydropathic INTeractions) LogP-based scoring function was first used to handle MD trajectories and investigate the molecular basis behind the intricate PPARγ mechanism of activation. The Peroxisome Proliferator-Activated Receptor γ (PPARγ) is an emblematic example of a highly flexible protein due to the extended ω-loop delimiting the active site, and it is responsible for the receptor's ability to bind chemically different compounds. In this work, we focused on the PPARγ complex with Rosiglitazone, a common anti-diabetic compound and analyzed the molecular basis of the flexible ω-loop stabilization effect produced by the Oleic Acid co-binding. The HINT-based analysis of the produced MD trajectories allowed us to account for all of the energetic contributions involved in interconverting between conformational states and describe the intramolecular interactions between the flexible ω-loop and the helix H3 triggered by the allosteric binding mechanism.

An inverse coarse-graining protocol is presented for generating and validating atomistic structures of large (bio-) molecules from conformations obtained via a coarse-grained sampling method. Specifically, the protocol is implemented and tested based on the (coarse-grained) PRIME20 protein model (P20/SAMC), and the resulting all-atom conformations are simulated using conventional biomolecular force fields. The phase space sampling at the coarse-grained level is performed with a stochastical approximation Monte Carlo approach. The method is applied to a series of polypeptides, specifically dimers of polyglutamine with varying chain length in aqueous solution. The majority (>70 %) of the conformations obtained from the coarse-grained peptide model can successfully be mapped back to atomistic structures that remain conformationally stable during 10 ns of molecular dynamics simulations. This work can be seen as the first step towards the overarching goal of improving our understanding of protein aggregation phenomena through simulation methods.

This paper sheds light on the meaning of hydrogen/deuterium exchange-mass spectrometry (HDX-MS) data. HDX-MS data provide not structural information but dynamic information on an analyte protein. First, the reaction mechanism of backbone amide HDX reaction is considered and the correlation between the parameters from an X-ray crystal structure and the protection factors of HDX reactions of cytochrome c is evaluated. The presence of H-bonds in a protein structure has a strong influence on HDX rates which represent protein dynamics, while the solvent accessibility only weakly affects the HDX rates. Second, the energy diagrams of the HDX reaction at each residue in the presence and absence of perturbation are described. Whereas the free energy change upon mutation can be directly measured by the HDX rates, the free energy change upon ligand binding may be complicated due to the presence of unbound analyte protein in the protein-ligand mixture. Third, the meanings of HDX and other biophysical techniques are explained using a hypothetical protein folding well. The shape of the protein folding well describes the protein dynamics and provides Boltzmann distribution of open and closed states which yield HDX protection factors, while a protein's crystal structure represents a snapshot near the bottom of the well. All biophysical data should be consistent yet provide different information because they monitor different parts of the same protein folding well.

Vitamin B12 is a group of biologically active cobalamin compounds. In this study, we investigated the inhibitory effects of methylcobalamin (MeCbl) and hydroxocobalamin acetate (OHCbl Acetate) on protein tyrosine phosphatase 1B (PTP1B). MeCbl and OHCbl Acetate exhibited an IC50 of approximately 58.390 ± 2.811 μM and 8.998 ± 0.587 μM, respectively. The Ki values of MeCbl and OHCbl Acetate were 25.01 μM and 4.04 μM respectively. To elucidate the inhibition mechanism, we conducted a 500 ns Gaussian accelerated molecular dynamics (GaMD) simulation. Utilizing PCA and tICA, we constructed Markov state models (MSM) to examine secondary structure changes during motion. Our findings revealed that the α-helix at residues 37-42 remained the most stable in the PTP1B-OHCbl Acetate system. Furthermore, upon binding of OHCbl Acetate or MeCbl, the WPD loop of PTP1B moved inward to the active pocket, forming a closed conformation and potentially obstructs substrate entry. Protein-ligand interaction analysis and MM-PBSA showed that OHCbl Acetate exhibited lower binding free energy and engaged in more residue interactions with PTP1B. In summary, our study confirmed the substantial inhibitory activity of OHCbl Acetate against PTP1B, with its inhibitory potency notably surpassing that of MeCbl. We demonstrated potential molecular mechanisms of OHCbl Acetate inhibiting PTP1B.

The ethylene-forming enzyme (EFE) is an Fe(II), 2-oxoglutarate (2OG), and l-arginine (l-Arg)-dependent oxygenase that either forms ethylene and three CO2/bicarbonate from 2OG or couples the decarboxylation of 2OG to C5 hydroxylation of l-Arg. l-Arg binds with C5 toward the metal center, causing 2OG to change from monodentate to chelate metal interaction and OD1 to OD2 switch of D191 metal coordination. We applied anaerobic UV-visible spectroscopy, X-ray crystallography, and computational approaches to three EFE systems with high-resolution structures. The ineffective l-Arg analogue l-canavanine binds to the EFE with O5 pointing away from the metal center while promoting chelate formation by 2OG but fails to switch the D191 metal coordination from OD1 to OD2. Substituting alanine for R171 that interacts with 2OG and l-Arg inactivates the protein, prevents metal chelation by 2OG, and weakens l-Arg binding. The R171A EFE had electron density at the 2OG binding site that was identified by mass spectrometry as benzoic acid. The substitution by alanine of Y306 in the EFE, a residue 12 Å away from the catalytic metal center, generates an interior cavity that leads to multiple local and distal structural changes that reduce l-Arg binding and significantly reduce the enzyme activity. Flexibility analyses revealed correlated and anticorrelated motions in each system, with important distinctions from the wild-type enzyme. In combination, the results are congruent with the currently proposed enzyme mechanism, reinforce the importance of metal coordination by OD2 of D191, and highlight the importance of the second coordination sphere and longer range interactions in promoting EFE activity.