Clustering Molecular Dynamics Trajectories: 1. Characterizing the Performance of Different Clustering Algorithms

CDR L3 Loop Rearrangement Switches Multispecific SPE-7 IgE Antibody From Hapten to Protein Binding

The monoclonal IgE antibody SPE-7 was originally raised against a 2,4-dinitrophenyl (DNP) target. Through its ability to adopt multiple conformations, the antibody is capable of binding to a diverse range of small haptens and large proteins. The present study examines a dataset of experimentally determined crystal structures of the SPE-7 antibody to gain insight into the mechanisms that contribute to its multispecificity. With the emergence of more and more therapeutic antibodies against a huge repertoire of different targets, our research could be of great interest for future drug development. We are able to discriminate between the different paratope-binding states in the conformational ensembles obtained by enhanced sampling molecular dynamics simulations, and to calculate their transition timescales and state probabilities. Furthermore, we describe the key residues responsible for discriminating between the different binding capacities and identify a tryptophan in a central position of the CDR L3 loop as the residue of greatest interest. The overall dynamics of the paratope appear to be mainly influenced by the CDR L3 and CDR L1 loops.

Unveiling Putative Excited State and Transmission of Binding Information in the Fluoride Riboswitch

Riboswitches regulate downstream gene expression by binding to specific small molecules or ions with multiple mechanisms to transfer the binding information. In the case of the fluoride riboswitch, the transcription termination signal is conveyed through a transient excited state (ES). In this work, we performed conventional molecular dynamics (MD) simulations, totaling 180 μs, to obtain the ES structure and investigate the mechanism underlying information transmission in Mg2+/F- binding within the fluoride riboswitch aptamer. The Mg2+/F- binding pocket exhibits various conformations in its apo form. A series of ES structures were extracted from the MD trajectories of the apo form. The dynamics of the Mg2+/F- binding pocket influenced key pair A40-U48 in ES structures. The pathway connecting the binding pocket to the pair involves interactions between the phosphate groups of U7 and G8 and the nucleobases of G8-C47-U48. Our work presents a structural ensemble of the ES and elucidates a pathway for transferring Mg2+/F- binding information, thereby facilitating the understanding of how the holo-like apo state achieves transcriptional repression.

Design and Characterization of Neutral Linker-Based Bis-Intercalator via Computer Simulations: Balancing DNA Binding and Cellular Uptake

Bis-intercalators refer to a class of chemical compounds known for their unique ability to simultaneously intercalate, or insert, into DNA at two distinct sites. These molecules typically feature two intercalating moieties connected by a linker, allowing them to engage with DNA base pairs at multiple locations. The bis-intercalation phenomenon plays a significant role in altering the DNA structure, affecting its stability, and potentially influencing various cellular processes. These compounds have gained considerable attention in medicinal chemistry and biochemistry due to their potential applications in cancer therapy, where they may interfere with DNA replication and transcription, leading to anticancer effects. Traditionally, these molecules often possess a high positive charge to enhance their affinity for the negatively charged DNA. However, due to a high positive charge, their cellular uptake is compromised, along with their enhanced potential off-target effects. In this study, we utilized bis-intercalator TOTO and replaced the charged linker segment (propane-1,3-diammonium) with a neutral peroxodisulphuric acid linker. Using molecular modeling and computer simulations (500 ns, 3 replicas), we investigated the potential of the designed molecule as a bis-intercalator and compared the properties with the control bis-intercalator bound to DNA. We observed that the designed bis-intercalator exhibited improved DNA binding (as assessed through MM-PBSA and Delphi methods) and membrane translocation permeability. With an overall reduced charge, significantly less off-target binding of the designed molecule is also anticipated. Consequently, bis-intercalators based on peroxodisulphuric linkers can potentially target DNA effectively, and their role in the future design of bis-intercalators is foreseen.

Identification of Novel Allosteric Sites of SARS-CoV-2 Papain-Like Protease (PLpro) for the Development of COVID-19 Antivirals

Coronaviruses such as SARS-CoV-2 encode a conserved papain-like protease (PLpro) that is crucial for viral replication and immune evasion, making it a prime target for antiviral drug development. In this study, three surface pockets on SARS-CoV-2 PLpro that may function as sites for allosteric inhibition were computationally identified. To evaluate the effects of these pockets on proteolytic activity, 52 residues were separately mutated to alanine. In Pocket 1, located between the Ubl and thumb domains, the introduction of alanine at T10, D12, T54, Y72, or Y83 reduced PLpro activity to <12% of that of WT. In Pocket 2, situated at the interface of the thumb, fingers, and palm domains, Q237A, S239A, H275A, and S278A inactivated PLpro. Finally, introducing alanine at five residues in Pocket 3, between the fingers and palm domains, inactivated PLpro: S212, Y213, Y251, K254, and Y305. Pocket 1 has a higher druggability score than Pockets 2 and 3. MD simulations showed that interactions within and between domains play critical roles in PLpro activity and thermal stability. The essential residues in Pockets 1 and 2 participate in a combination of intra- and inter-domain interactions. By contrast, the essential residues in Pocket 3 predominantly participate in inter-domain interactions. The most promising targets for therapeutic development are Pockets 1 and 3, which have the highest druggability score and the largest number of essential residues, respectively. Non-competitive inhibitors targeting these pockets may be antiviral agents against COVID-19 and related coronaviruses.

Assessing AF2's ability to predict structural ensembles of proteins

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction and compare the obtained structures with ensembles obtained from MD simulations and NMR. We provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. Significant minima of the free energy surfaces remain undetected.

Stereo-selectivity of enantiomeric inhibitors to ubiquitin-specific protease 7 (USP7) dissected by molecular docking, molecular dynamics simulations, and binding free energy calculations

The ubiquitin-specific protease 7 (USP7), as a member of deubiquitination enzymes, represents an attractive therapeutic target for various cancers, including prostate cancer and liver cancer. The change of the inhibitor stereocenter from the S to R stereochemistry (S-ALM → R-ALM34) markedly improved USP7 inhibitory activity. However, the molecular mechanism for the stereo-selectivity of enantiomeric inhibitors to USP7 is still unclear. In this work, molecular docking, molecular dynamics (MD) simulations, molecular mechanics/Generalized-Born surface area (MM/GBSA) calculations, and free energy landscapes were performed to address this mystery. MD simulations revealed that S-ALM34 showed a high degree of conformational flexibility compared to the R-ALM34 counterpart, and S-ALM34 binding led to the enhanced intradomain motions of USP7, especially the BL1 and BL2 loops and the two helices α4 and α5. MM/GBSA calculations showed that the binding strength of R-ALM34 to USP7 was stronger than that of S-ALM34 by - 4.99 kcal/mol, a similar trend observed by experimental data. MM/GBSA free energy decomposition was further performed to differentiate the ligand-residue spectrum. These analyses not only identified the hotspot residues interacting with R-ALM34, but also revealed that the hydrophobic interactions from F409, K420, H456, and Y514 play the major determinants in the binding of R-ALM34 to USP7. This result is anticipated to shed light on energetic basis and conformational dynamics information to aid in the design of more potent and selective inhibitors targeting USP7.

Force Fields, Quantum-Mechanical- and Molecular-Dynamics-Based Descriptors of Radiometal-Chelator Complexes

Radiopharmaceuticals are currently a key tool in cancer diagnosis and therapy. Metal-based radiopharmaceuticals are characterized by a radiometal-chelator moiety linked to a bio-vector that binds the biological target (e.g., a protein overexpressed in a particular tumor). The right match between radiometal and chelator influences the stability of the complex and the drug's efficacy. Therefore, the coupling of the radioactive element to the correct chelator requires consideration of several features of the radiometal, such as its oxidation state, ionic radius, and coordination geometry. In this work, we systematically investigated about 120 radiometal-chelator complexes taken from the Cambridge Structural Database. We considered 25 radiometals and about 30 chelators, featuring both cyclic and acyclic geometries. We used quantum mechanics methods at the density functional theoretical level to generate the general AMBER force field parameters and to perform 1 µs-long all-atom molecular dynamics simulations in explicit water solution. From these calculations, we extracted several key molecular descriptors accounting for both electronic- and dynamical-based properties. The whole workflow was carefully validated, and selected test-cases were investigated in detail. Molecular descriptors and force field parameters for the complexes considered in this study are made freely available, thus enabling their use in predictive models, molecular modelling, and molecular dynamics investigations of the interaction of compounds with macromolecular targets. Our work provides new insights in understanding the properties of radiometal-chelator complexes, with a direct impact for rational drug design of this important class of drugs.

Flavonoid-Quinoxaline Hybrid Compounds as Cathepsin Inhibitors Against Fascioliasis

Fasciola hepatica is a parasitic trematode that infects livestock animals and humans, causing significant health and economic burdens worldwide. The extensive use of anthelmintic drugs has led to the emergence of resistant parasite strains, posing a threat to treatment success. The complex life cycle of the liver fluke, coupled with limited funding and research interest, have hindered progress in drug discovery. Our group has been working in drug development against this parasite using cathepsin proteases as molecular targets, finding promising compound candidates with in vitro and in vivo efficacy. Here, we evaluated hybrid molecules that combine two chemotypes, chalcones and quinoxaline 1,4-di- N-oxides, previously found to inhibit F. hepatica cathepsin Ls and tested their in vitro activity with the isolated targets and the parasites in culture. These molecules proved to be good cathepsin inhibitors and to kill the juvenile parasites at micromolar concentrations. Also, we performed molecular docking studies to analyze the compounds-cathepsins interface, finding that the best inhibitors interact at the active site cleft and contact the catalytic dyad and residues belonging to the substrate binding pockets. We conclude that the hybrid compounds constitute promising scaffolds for the further development of new fasciolicidal compounds.

PerturbDB for unraveling gene functions and regulatory networks

Perturb-Seq combines CRISPR (clustered regularly interspaced short palindromic repeats)-based genetic screens with single-cell RNA sequencing readouts for high-content phenotypic screens. Despite the rapid accumulation of Perturb-Seq datasets, there remains a lack of a user-friendly platform for their efficient reuse. Here, we developed PerturbDB (http://research.gzsys.org.cn/perturbdb), a platform to help users unveil gene functions using Perturb-Seq datasets. PerturbDB hosts 66 Perturb-Seq datasets, which encompass 4 518 521 single-cell transcriptomes derived from the knockdown of 10 194 genes across 19 different cell lines. All datasets were uniformly processed using the Mixscape algorithm. Genes were clustered by their perturbed transcriptomic phenotypes derived from Perturb-Seq data, resulting in 421 gene clusters, 157 of which were stable across different cellular contexts. Through integrating chemically perturbed transcriptomes with Perturb-Seq data, we identified 552 potential inhibitors targeting 1409 genes, including an mammalian target of rapamycin (mTOR) signaling inhibitor, retinol, which was experimentally verified. Moreover, we developed a 'Cancer' module to facilitate the understanding of the regulatory role of genes in cancer using Perturb-Seq data. An interactive web interface has also been developed, enabling users to visualize, analyze and download all the comprehensive datasets available in PerturbDB. PerturbDB will greatly drive gene functional studies and enhance our understanding of the regulatory roles of genes in diseases such as cancer.

Similarity Metrics for Subcellular Analysis of FRET Microscopy Videos

Understanding the heterogeneity of molecular environments within cells is an outstanding challenge of great fundamental and technological interest. Cells are organized into specialized compartments, each with distinct functions. These compartments exhibit dynamic heterogeneity under high-resolution microscopy, which reflects fluctuations in molecular populations, concentrations, and spatial distributions. To enhance our comprehension of the spatial relationships among molecules within cells, it is crucial to analyze images of high-resolution microscopy by clustering individual pixels according to their visible spatial properties and their temporal evolution. Here, we evaluate the effectiveness of similarity metrics based on their ability to facilitate fast and accurate data analysis in time and space. We discuss the capability of these metrics to differentiate subcellular localization, kinetics, and structures of protein-RNA interactions in Forster resonance energy transfer (FRET) microscopy videos, illustrated by a practical example from recent literature. Our results suggest that using the correlation similarity metric to cluster pixels of high-resolution microscopy data should improve the analysis of high-dimensional microscopy data in a wide range of applications.

Flexible RNA aptamers as inhibitors of Bacillus anthracis ribosomal protein S8: Insights from molecular dynamics simulations

Bacillus anthracis, the causative agent of anthrax, poses a substantial threat to public health and national security, and is recognized as a potential bioweapon due to its capacity to form resilient spores with enduring viability. Inhalation or ingestion of even minute quantities of aerosolized spores can lead to widespread illness and fatalities, underscoring the formidable lethality of the bacterium. With an untreated mortality rate of 100%, Bacillus anthracis is a disconcerting candidate for bioterrorism. In response to this critical scenario, we employed state-of-the-art computational tools to conceive and characterize flexible RNA aptamer therapeutics tailored for anthrax. The foundational structure of the flexible RNA aptamers was designed by removing the C2'-C3' in each nucleotide unit. Leveraging the crystal structure of Bacillus anthracis ribosomal protein S8 complexed with an RNA aptamer, we explored the structural, dynamic, and energetic aspects of the modified RNA aptamer - S8 protein complexes through extensive all-atom explicit-solvent molecular dynamics simulations (400 ns, 3 replicas each), followed by drawing comparisons to the control system. Our findings demonstrate the enhanced binding competencies of the flexible RNA aptamers to the S8 protein via better shape complementarity and improved H-bond network compared to the control RNA aptamer. This research offers valuable insights into the development of RNA aptamer therapeutics targeting Bacillus anthracis, paving the way for innovative strategies to mitigate the impact of this formidable pathogen.

Impact of sulfur substitution on biotin binding affinity to streptavidin

In this study, we investigated how the replacement of the tetrahydrothiophene ring of biotin with either an oxolane or (methyl)pyrrolidine moiety may affect its molecular interactions, in an effort to identify alternative affinity ligands suitable for in vitro and in vivo applications in synthetic biology. Initial molecular dynamics (MD) simulations suggested the potential formation of a hydrogen bond between either the oxygen or nitrogen atom of the envisaged tetrahydroheteryl analogues and the Thr90 residue of streptavidin, mirroring the sulfur-centered hydrogen bond detected by the crystallographic analysis of the biotin-streptavidin interaction. Therefore, oxy-, aza-, and N-methylazabiotin were readily synthesized starting from chiral five- or six-carbon sugar precursors. Based on fluorescence-based titration experiments using the corresponding fluorescein conjugates, oxybiotin showed a binding behavior similar to biotin with streptavidin, while both amino analogues displayed lower binding capacities. Notably, azabiotin exhibited a pH-dependent interaction profile, demonstrating enhanced binding under acidic conditions but weaker binding under basic pH, which could be exploited for various purposes.

Modulation of aryl hydrocarbon receptor activity by tyrosine kinase inhibitors (ponatinib and tofacitinib)

Ponatinib and tofacitinib, established kinase inhibitors and FDA-approved for chronic myeloid leukemia and rheumatoid arthritis, are recently undergoing investigation in diverse clinical trials for potential repurposing. The aryl hydrocarbon receptor (AhR), a transcription factor influencing a spectrum of physiological and pathophysiological activities, stands as a therapeutic target for numerous diseases. This study employs molecular modelling tools and in vitro assays to identify ponatinib and tofacitinib as AhR ligands, elucidating their binding and molecular interactions in the AhR PAS-B domain. Molecular docking analyses revealed that ponatinib and tofacitinib occupy the central pocket within the primary cavity, similar to AhR agonists 2,3,7,8-tetrachlorodibenzodioxin (TCDD) and (benzo[a]pyrene) B[a]P. Our simulations also showed that these compounds exhibit good stability, stabilizing many hot spots within the PAS-B domain, including the Dα-Eα loop, which serves as a regulatory element for the binding pocket. Binding energy calculations highlighted ponatinib's superior predicted affinity, revealing F295 as a crucial residue in maintaining strong interaction with the two compounds. Our in vitro data suggest that ponatinib functions as an AhR antagonist, blocking the downstream signaling of AhR pathway induced by TCDD and B[a]P. Additionally, both tofacitinib and ponatinib cause impairment in AhR-regulated CYP1A1 enzyme activity induced by potent AhR agonists. This study unveils ponatinib and tofacitinib as potential modulators of AhR, providing valuable insights into their therapeutic roles in AhR-associated diseases and enhancing our understanding of the intricate relationship between kinase inhibitors and AhR.

Bridging the Gap in the Structure-Function Paradigm of Enzymatic PET Degradation-Aromatic Residue Driven Balanced Interactions with Catalytic and Anchoring Subsite

Understanding all parameters contributing to enzyme activity is crucial in enzyme catalysis. For enzymatic PET degradation, this involves examining the formation of the enzyme-PET complex. In IsPETase (WT), a PET-degrading enzyme from Ideonella sakaiensis, mutating two non-catalytic residues (DM) significantly enhances activity. Such mutations, depending on their position in the tertiary structure, fine-tune enzyme function. However, detailed molecular insights into these mutations' structure-function relationship for PET degradation are lacking. This study characterizes IsPETase's catalytic ability compared to WT TfCut2 using molecular dynamics simulations and quantum mechanical methods. We explore the conformational landscape of the enzyme-PET complex and quantify residue-wise interaction energy. Notably, aromatic and hydrophobic residues Tyr, Trp, and Ile in the catalytic subsite S1, and aromatic Phe and polar Asn in the anchoring subsite S3, crucially optimize PET binding. These residues enhance PET specificity over non-aromatic plastics. Our findings suggest that the balance between binding at subsite S1 and subsite S3, which is influenced by cooperative mutations, underlies catalytic activity. This balance shows a positive correlation with experimentally obtained kcat/Km values: WT TfCut2 Collapse

Key Words

• Cooperative effect
• Enzyme catalysis
• IsPETase
• Molecular dynamics
• Mutation

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MESH Headings Collapse

Grants

• CRG/2019/002891 DST
• SPG/2022/001350 DST
• SIR/2022/000924 DST
• University of Calicut

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Affiliation(s)

Anjima James Department of Applied Chemistry, Cochin University of Science and Technology, Thrikakkara, Kochi, Kerala, 682 022, India
Anjitha Bhasi Department of Applied Chemistry, Cochin University of Science and Technology, Thrikakkara, Kochi, Kerala, 682 022, India
Susmita De Department of Chemistry, University of Calicut, Calicut University P.O., Malappuram, Kerala, 673 635, India

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Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals

The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screening systems where large amounts of chromatography data are collected from fast and routine operations are particularly well suited for both leveraging large datasets and benefiting from predictive models. Therefore, the generation of predictive models for retention time is an active area of development. However, for these predictive models to gain acceptance, researchers first must have confidence in model performance and the computational cost of building them should be minimal. In this study, a simple and cost-effective workflow for the development of machine learning models to predict retention time using only Molecular Operating Environment 2D descriptors as input for support vector regression is developed. Furthermore, we investigated the relative performance of models based on molecular descriptor space by utilizing uniform manifold approximation and projection and clustering with Gaussian mixture models to identify chemically distinct clusters. Results outlined herein demonstrate that local models trained on clusters in chemical space perform equivalently when compared to models trained on all data. Through 10-fold cross-validation on a comprehensive set containing 67,950 of our company's proprietary analytes, these models achieved coefficients of determination of 0.84 and 3 % error in terms of retention time. This promising statistical significance is found to translate from cross-validation to prospective prediction on an external test set of pharmaceutically relevant analytes. The observed equivalency of global and local modeling of large datasets is retained with METLIN's SMRT dataset, thereby confirming the wider applicability of the developed machine learning workflows for global models.

Computational insights into the mechanisms underlying structural destabilization and recovery in trafficking-deficient hERG mutants

Cardiovascular diseases are a major global health concern, responsible for a significant number of deaths each year, often linked to cardiac arrhythmias resulting from dysfunction in ion channels. Hereditary Long QT Syndrome (LQTS) is a condition characterized by a prolonged QT interval on ECG, increasing the risk of sudden cardiac death. The most common type of LQTS, LQT2, is caused by mutations in the hERG gene, affecting a potassium ion channel. The majority of these mutations disrupt the channel's trafficking to the cell membrane, leading to intracellular retention. Specific high-affinity hERG blockers (e.g., E-4031) can rescue this mutant phenotype, but the exact mechanism is unknown. This study used accelerated molecular dynamics simulations to investigate how these mutations affect the hERG channel's structure, folding, endoplasmic reticulum (ER) retention, and trafficking. We reveal that these mutations induce structural changes in the channel, narrowing its central pore and altering the conformation of the intracellular domains. These changes expose internalization signals that contribute to ER retention and degradation of the mutant hERG channels. Moreover, the study found that the trafficking rescue drug E-4031 can inhibit these structural changes, potentially rescuing the mutant channels. This research offers valuable insights into the structural issues responsible for the degradation of rescuable transmembrane trafficking mutants. Understanding the defective trafficking structure of the hERG channel could help identify binding sites for small molecules capable of restoring proper folding and facilitating channel trafficking. This knowledge has the potential to lead to mechanism-based therapies that address the condition at the cellular level, which may prove more effective than treating clinical symptoms, ultimately offering hope for individuals with hereditary Long QT Syndrome.

Identification of aryl hydrocarbon receptor allosteric antagonists from clinically approved drugs

The human aryl hydrocarbon receptor (AhR), a ligand-dependent transcription factor, plays a pivotal role in a diverse array of pathways in biological and pathophysiological events. This position AhR as a promising target for both carcinogenesis and antitumor strategies. In this study we utilized computational modeling to screen and identify FDA-approved drugs binding to the allosteric site between α2 of bHLH and PAS-A domains of AhR, with the aim of inhibiting its canonical pathway activity. Our findings indicated that nilotinib effectively fits into the allosteric pocket and forms interactions with crucial residues F82, Y76, and Y137. Binding free energy value of nilotinib is the lowest among top hits and maintains stable within its pocket throughout entire (MD) simulations time. Nilotinib has also substantial interactions with F295 and Q383 when it binds to orthosteric site and activate AhR. Surprisingly, it does not influence AhR nuclear translocation in the presence of AhR agonists; instead, it hinders the formation of the functional AhR-ARNT-DNA heterodimer assembly, preventing the upregulation of regulated enzymes like CYP1A1. Importantly, nilotinib exhibits a dual impact on AhR, modulating AhR activity via the PAS-B domain and working as a noncompetitive allosteric antagonist capable of blocking the canonical AhR signaling pathway in the presence of potent AhR agonists. These findings open a new avenue for the repositioning of nilotinib beyond its current application in diverse diseases mediated via AhR.

Thermodynamic consequences of stapling side-chains on a peptide ligand using a lactam-bridge: A theoretical study on anti-angiogenic peptides targeting VEGF

Peptides are promising therapeutic agents for various biological targets due to their high efficacy and low toxicity, and the design of peptide ligands with high binding affinity to the target of interest is of utmost importance in peptide-based drug design. Introducing a conformational constraint to a flexible peptide ligand using a side-chain lactam-bridge is a convenient and efficient method to improve its binding affinity to the target. However, in general, such a small structural modification to a flexible ligand made with the intent of lowering the configurational entropic penalty for binding may have unintended consequences in different components of the binding enthalpy and entropy, including the configurational entropy component, which are still not clearly understood. Toward probing this, we examine different components of the binding enthalpy and entropy as well as the underlying structure and dynamics, for a side-chain lactam-bridged peptide inhibitor and its flexible analog forming complexes with vascular endothelial growth factor (VEGF), using all-atom molecular dynamics simulations. It is found that introducing a side-chain lactam-bridge constraint into the flexible peptide analog led to a gain in configurational entropy change but losses in solvation entropy, solute internal energy, and solvation energy changes upon binding, pinpointing the opportunities and challenges in drug design. The present study features an interplay between configurational and solvation entropy changes, as well as the one between binding enthalpy and entropy, in ligand-target binding upon imposing a conformational constraint into a flexible ligand.

Conformational Dynamics, Energetics, and the Divergent Evolution of Allosteric Regulation: The Case of the Yeast MAPK Family

Allosteric mechanisms provide finely-tuned control over signalling proteins. Proteins of the same family may share high sequence identity and structural similarity but show distinct traits of allosteric control and evolutionary divergent regulation. Revealing the determinants of such properties may be important to understand the molecular bases of different regulatory pathways. Herein, we investigate whether and how evolutionarily-divergent traits of allosteric regulation in homologous proteins can be decoded in terms of internal dynamics and interaction networks that support functionally oriented conformations. In this framework, we start from the comparative analysis of the dynamics and energetics of the yeast MAP Kinases (MAPKs) Fus3 and Kss1 in their native basins. Importantly, distinctive dynamic and energetic stabilization features emerge, which can be related to the two proteins' differential ability to be phosphorylated and engage with the allosteric activator Ste5. We then expanded our study to other evolutionarily-related MAPKs. We show that the dynamical and energetical traits defining the distinct regulatory profiles of Fus3 and Kss1 can be traced along their evolutionary tree. Overall, our approach is able to reconnect (latent) allostery with the principal elements of protein structural stabilization and dynamics, showing how allosteric regulation was encrypted in MAPKs structure well before Ste5 appearance.

Exploring the Structural Insights of Thermostable Geobacillus esterases by Computational Characterization

This study conducted an in silico analysis of two biochemically characterized thermostable esterases, Est2 and Est3, from Geobacillus strains. To achieve this, the amino acid sequences of Est2 and Est3 were examined to assess their biophysicochemical properties, evolutionary connections, and sequence similarities. Three-dimensional models were constructed and validated through diverse bioinformatics tools. Molecular dynamics (MD) simulation was employed on a pNP-C2 ligand to explore interactions between enzymes and ligand. Biophysicochemical property analysis indicated that aliphatic indices and theoretical T m values of enzymes were between 82-83 and 55-65 °C, respectively. Molecular phylogeny placed Est2 and Est3 within Family XIII, alongside other Geobacillus esterases. DeepMSA2 revealed that Est2, Est3, and homologous sequences shared 12 conserved residues in their core domain (L39, D50, G53, G55, S57, G92, S94, G96, P108, P184, D193, and H223). BANΔIT analysis indicated that Est2 and Est3 had a significantly more rigid cap domain compared to Est30. Salt bridge analysis revealed that E150-R136, E124-K165, E137-R141, and E154-K157 salt bridges made Est2 and Est3 more stable compared to Est30. MD simulation indicated that Est3 exhibited greater fluctuations in the N-terminal region including conserved F25, cap domain, and C-terminal region, notably including H223, suggesting that these regions might influence esterase catalysis. The common residues in the ligand-binding sites of Est2-Est3 were determined as F25 and L167. The analysis of root mean square fluctuation (RMSF) revealed that region 1, encompassing F25 within the β2-α1 loop of Est3, exhibited higher fluctuations compared to those of Est2. Overall, this study might provide valuable insights for future investigations aimed at improving esterase thermostability and catalytic efficiency, critical industrial traits, through targeted amino acid modifications within the N-terminal region, cap domain, and C-terminal region using rational protein engineering techniques.

Protein Retrieval via Integrative Molecular Ensembles (PRIME) through Extended Similarity Indices

Molecular dynamics (MD) simulations are ideally suited to describe conformational ensembles of biomolecules such as proteins and nucleic acids. Microsecond-long simulations are now routine, facilitated by the emergence of graphical processing units. Clustering, which groups objects based on structural similarity, is typically used to process ensembles, leading to different states, their populations, and the identification of representative structures. A popular pipeline combines hierarchical clustering for clustering and selecting the cluster centroid as representative of the cluster. Here, we propose to improve on this approach, by developing a module-Protein Retrieval via Integrative Molecular Ensembles (PRIME), that consists of tools to improve the prediction of the representative in the most populated cluster using extended continuous similarity. PRIME is integrated with our Molecular Dynamics Analysis with N-ary Clustering Ensembles (MDANCE) package and can be used as a postprocessing tool for arbitrary clustering algorithms, compatible with several MD suites. PRIME predictions produced structures that when aligned to the experimental structure were better superposed (lower RMSD). A further benefit of PRIME is its linear scaling─rather than the traditional O(N2) traditionally associated with comparisons of elements in a set.

Deciphering the shape selective conformational equilibrium of E- and Z-locked azobenzene-tetraethylammonium ion in regulating photo-switchable K+-ion channel blocking

The search for photo-switchable optopharmacological agents that can block ion channels has been a prevalent area owing to its prime advantages of reversibility and specificity over the traditional blockers. However, the quest for a higher blocking ability shown by a less stable photo-isomer to perfectly suit the requirement of the optopharmacological agents is still ongoing. To date, only a marginal improvement in terms of blocking ability is observed by the less stable E-isomer of para-substituted locked azobenzene with TEA (LAB-TEA) for the K+-ion channel. Thus, rationalization of the limitation for achieving high activity by the E-isomer is rather essential to aid the improvement of the efficiency of photoswitchable blocker drugs. Herein, we report a molecular-level analysis on the mechanism of blocking by E- and Z-LAB-TEA with the bacterial KcsA K+-ion channel using Molecular Dynamics (MD) simulation and Quantum Mechanical (QM) calculations. The positively charged TEA fragment engages in stronger electrostatic interactions, while the neutral LAB fragment engages in weaker dispersive interactions. The binding free energy calculated by Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) for E-LAB-TEA (-22.3 kcal mol-1) shows less thermodynamic preference for binding with K+-ion channels than Z-LAB-TEA (-21.6 kcal mol-1) corroborating the experimental observation. The correlation between the structure and the binding ability of E- and Z-isomers of LAB-TEA indicates that the channel gate is narrow and acts as a bottleneck for the entry of the binder molecule inside the large cavity. Upon irradiation, the Z-isomer converts into a less stable but long and planar E-isomer (ΔE of photoisomerism = 7.0 kcal mol-1, at SA2-CASPT2(6,4)/6-31+G(d)//CASSCF(6,4)/6-31+G(d)), which is structurally more suitable to fit into the narrow channel gate rather than the curved and non-planar Z-LAB-TEA. Thus, a reduction in the ionic current is observed owing to the preferential entry and subsequent blocking by E-LAB-TEA. Discontinuing the irradiation leads to conversion to the Z-isomer, the curved nature of which hinders its spontaneous release outside the cavity, thereby contributing only a small increase in the ionic current.

Molecular Modeling of Single- and Double-Hydrocarbon-Stapled Coiled-Coil Inhibitors against Bcr-Abl: Toward a Treatment Strategy for CML

The chimeric oncoprotein Bcr-Abl is the causative agent of virtually all chronic myeloid leukemias and a subset of acute lymphoblastic leukemias. As a result of the so-called Philadelphia chromosome translocation t(9;22), Bcr-Abl manifests as a constitutively active tyrosine kinase, which promotes leukemogenesis by activation of cell cycle signaling pathways. Constitutive and oncogenic activation is mediated by an N-terminal coiled-coil oligomerization domain in Bcr (Bcr-CC), presenting a therapeutic target for inhibition of Bcr-Abl activity toward the treatment of Bcr-Abl+ leukemias. Previously, we demonstrated that a rationally designed Bcr-CC mutant, CCmut3, exerts a dominant negative effect upon Bcr-Abl activity by preferential oligomerization with Bcr-CC. Moreover, we have shown that conjugation to a leukemia-specific cell-penetrating peptide (CPP-CCmut3) improves intracellular delivery and activity. However, our full-length CPP-CCmut3 construct (81 aa) is encumbered by an intrinsically high degree of conformational variability and susceptibility to proteolytic degradation relative to traditional small-molecule therapeutics. Here, we iterate a new generation of CCmut3 inhibitors against Bcr-CC-mediated Bcr-Abl assembly designed to address these constraints through incorporation of all-hydrocarbon staples spanning i and i + 7 positions in α-helix 2 (CPP-CCmut3-st). We utilize computational modeling and biomolecular simulation to evaluate single- and double-stapled CCmut3 candidates in silico for dynamics and binding energetics. We further model a truncated system characterized by the deletion of α-helix 1 and the flexible loop linker, which are known to impart high conformational variability. To study the impact of the N-terminal cyclic CPP toward model stability and inhibitor activity, we also model the full-length and truncated systems devoid of the CPP, with a cyclized CPP, and with an open-configuration CPP, for a total of six systems that comprise our library. From this library, we present lead-stapled peptide candidates to be synthesized and evaluated experimentally as our next iteration of inhibitors against Bcr-Abl.

k-Means NANI: An Improved Clustering Algorithm for Molecular Dynamics Simulations

One of the key challenges of k-means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k-means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex data sets such as those obtained from molecular simulation, k-means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k-means++ will lead to a lack of reproducibility. K-means N-Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n-ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k-means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse data sets and be used as a standalone tool or as part of our MDANCE clustering package.

PBP-A, a cyanobacterial DD-peptidase with high specificity for amidated muropeptides, exhibits pH-dependent promiscuous activity harmful to Escherichia coli

Penicillin binding proteins (PBPs) are involved in biosynthesis, remodeling and recycling of peptidoglycan (PG) in bacteria. PBP-A from Thermosynechococcus elongatus belongs to a cyanobacterial family of enzymes sharing close structural and phylogenetic proximity to class A β-lactamases. With the long-term aim of converting PBP-A into a β-lactamase by directed evolution, we simulated what may happen when an organism like Escherichia coli acquires such a new PBP and observed growth defect associated with the enzyme activity. To further explore the molecular origins of this harmful effect, we decided to characterize deeper the activity of PBP-A both in vitro and in vivo. We found that PBP-A is an enzyme endowed with DD-carboxypeptidase and DD-endopeptidase activities, featuring high specificity towards muropeptides amidated on the D-iso-glutamyl residue. We also show that a low promiscuous activity on non-amidated peptidoglycan deteriorates E. coli's envelope, which is much higher under acidic conditions where substrate discrimination is mitigated. Besides expanding our knowledge of the biochemical activity of PBP-A, this work also highlights that promiscuity may depend on environmental conditions and how it may hinder rather than promote enzyme evolution in nature or in the laboratory.

Exploring Aromatic Cage Flexibility Using Cosolvent Molecular Dynamics Simulations─An In-Silico Case Study of Tudor Domains

Cosolvent molecular dynamics (MD) simulations have proven to be powerful in silico tools to predict hotspots for binding regions on protein surfaces. In the current study, the method was adapted and applied to two Tudor domain-containing proteins, namely Spindlin1 (SPIN1) and survival motor neuron protein (SMN). Tudor domains are characterized by so-called aromatic cages that recognize methylated lysine residues of protein targets. In the study, the conformational transitions from closed to open aromatic cage conformations were investigated by performing MD simulations with cosolvents using six different probe molecules. It is shown that a trajectory clustering approach in combination with volume and atomic distance tracking allows a reasonable discrimination between open and closed aromatic cage conformations and the docking of inhibitors yields very good reproducibility with crystal structures. Cosolvent MDs are suitable to capture the flexibility of aromatic cages and thus represent a promising tool for the optimization of inhibitors.

Molecular dynamics of structural effects of reactive carbonyl species derivate of lipid peroxidation on bovine serum albumin

BACKGROUND

Serum albumin is the most abundant protein in the Mammalia blood plasma at where plays a decisive role in the transport wide variety of hydrophobic ligands. BSA undergoes oxidative modifications like the carbonylation by the reactive carbonyl species (RCSs) 4-hydroxy-2-nonenal (HNE), 4 hydroxy-2-hexenal (HHE), malondialdehyde (MDA) and 4-oxo-2-nonenal (ONE), among others. The structural and functional changes induced by protein carbonylation have been associated with the advancement of neurodegenerative, cardiovascular, metabolic and cancer diseases.

METHODS

To elucidate structural effects of protein carbonylation with RCSs on BSA, parameters for six new non-standard amino acids were designated and molecular dynamics simulations of its mono‑carbonylated-BSA systems were conducted in the AMBER force field. Trajectories were evaluated by RMSD, RMSF, PCA, RoG and SASA analysis.

RESULTS

An increase in the conformational instability for all proteins modified with local changes were observed, without significant changes on the BSA global three-dimensional folding. A more relaxed compaction level and major solvent accessible surface area for modified systems was found. Four regions of high molecular fluctuation were identified in all modified systems, being the subdomains IA and IIIB those with the most remarkable local conformational changes. Regarding essential modes of domain movements, it was evidenced that the most representatives were those related to IA subdomain, while IIIB subdomain presented discrete changes.

CONCLUSIONS

RCSs induces local structural changes on mono‑carbonylated BSA. Also, this study extends our knowledge on how carbonylation by RCSs induce structural effects on proteins.

Evaluation of surfactant-aided polycyclic aromatic hydrocarbon biodegradation by molecular docking and molecular dynamic simulation in the marine environment

Marine oil spills directly cause polycyclic aromatic hydrocarbons (PAHs) pollution and affect marine organisms due to their toxic property. Chemical and bio-based dispersants composed of surfactants and solvents are considered effective oil spill-treating agents. Dispersants enhance oil biodegradation in the marine environment by rapidly increasing their solubility in the water column. However, the effect of dispersants, especially surfactants, on PAHs degradation by enzymes produced by microorganisms has not been studied at the molecular level. The role of the cytochrome P450 (CYP) enzyme in converting contaminants into reactive metabolites during the biodegradation process has been evidenced, but the activity in the presence of surfactants is still ambiguous. Thus, this study focused on the evaluation of the impact of chemical and bio-surfactants (i.e., Tween 80 (TWE) and Surfactin (SUC)) on the biodegradation of naphthalene (NAP), chrysene (CHR), and pyrene (PYR), the representative components of PAHs, with CYP enzyme from microalgae Parachlorella kessleri using molecular docking and molecular dynamics (MD) simulation. The molecular docking analysis revealed that PAHs bound to residues at the CYP active site through hydrophobic interactions for biodegradation. The MD simulation showed that the surfactant addition changed the enzyme conformation in the CYP-PAH complexes to provide more interactions between the enzyme and PAHs. This led to an increase in the enzyme's capability to degrade PAHs. Binding free energy (ΔG‌‌Bind) calculations confirmed that surfactant treatment could enhance PAHs degradation by the enzyme. The SUC gave a better result on NAP and PYR biodegradation based on ΔG‌‌Bind, while TWE facilitated the biodegradation of CHR. The research outputs could greatly facilitate evaluating the behaviors of oil spill-treating agents and oil spill response operations in the marine environment.

Identification of potential anti-mucor agents by targeting endothelial cell receptor glucose-regulated protein-78 using in silico approach

Mucormycosis is a fungal infection of the sinuses, brain and lungs that is the cause of approximately 50% mortality rate despite the available first-line therapy. Glucose-Regulated Protein 78 (GRP78) is already reported to be a novel host receptor that mediates invasion and damage of human endothelial cells by Rhizopus oryzae and Rhizopus delemar, the most common etiologic species of Mucorales. The expression of GRP78 is also regulated by the levels of iron and glucose in the blood. There are several antifungal drugs in the market but they pose a serious side effect to the vital organs of the body. Therefore, there is an immediate need to discover effective drug molecules having increased efficacy with no side effects. With the help of various computational tools, the current study was attempted to determine potential antimucor agents against GRP78. The receptor molecule GRP78 was screened against 8820 known drugs deposited in DrugBank library using high-throughput virtual screening method. Total top 10 compounds were selected based on the binding energies greater than the reference co-crystal molecule. Furthermore, molecular dynamic (MD) simulations using AMBER were performed to calculate the stability of the top-ranked compounds in the active site of GRP78. After extensive computational studies, we propose that two compounds (CID439153 and CID5289104) have inhibitory potency against mucormycosis and can serve as potential drugs that can form the basis of treating mucormycosis disease.Communicated by Ramaswamy H. Sarma.

Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors

The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds' intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.

Synthesis, NMR kinetics and dynamic structure of a 17-mer heptaloop RNA hairpin carrying a 3-N-methyluridine nucleotide residue in the loop region

A 17-mer RNA hairpin (5'GGGAGUXAGCGGCUCCC3') carrying 3-N-methyluridine (m3U) at position X (m3U7-RNA), designed to represent the anticodon stem-loop (ACSL) region of tRNAs to study an open loop state (O-state), was synthesized, purified by HPLC, and characterized by MALDI-ToF_MS and NMR methods. 1H-NMR data revealed primary (P-state in 56.1%), secondary (S-state in 43.9%) and tertiary (∼5-6%) ACSL conformations. Exchange rate constant (kex) for interconversion between P and S states is 112 sec-1 (<Δω ∼454 rad/sec), confirming a slow exchange regime between the two states. Forward (kPS) and backward (kSP) rate constants are 49.166 sec-1 and 62.792 sec-1, respectively, leading to a longer life-time (20.339 msec) for the P-state and a shorter life-time (15.926 msec) for the S-state. In accordance with conformational populations determined by 1H-NMR, dynamics of the P/S/tertiary states of m3U7-RNA and its wild-type counterpart (wt-RNA) were studied by three independent MD production simulations. Cluster analysis revealed that wt-RNA reflects the structural characteristics of the ACSL region of tRNAs. The P-state of m3U7-RNA was found to be structurally similar to wt-RNA but lacks an intraloop H-bond between m3U7 and C10 (U33 and nt36 in tRNAs). In the S-state of m3U7-RNA, m3U7 flips out of the loop region. O-state loop conformations of m3U7-RNA were also clustered (4.8%), wherein the loop nucleotides m3U7.A8.G9.C10.G11 stack one after another. We propose that the O-state of m3U7-RNA is the most suitable conformation that makes the loop accessible for complementary nucleotides and for non-enzymatic primordial replication of small circular RNAs.Communicated by Ramaswamy H. Sarma.

Structural and energetic insights into the selective inhibition of PKMYT1 against WEE1

Protein kinase, membrane-associated tyrosine/threonine 1 (PKMYT1), a member of the WEE family and responsible for the regulation of CDK1 phosphorylation, has been considered a promising therapeutic target for cancer therapy. However, the highly structural conservation of the ATP-binding sites of the WEE family poses a challenge to the design of selective inhibitors for PKMYT1. Here, molecular docking, multiple microsecond-length molecular dynamics (MD) simulations and end-point free energy calculations were performed to uncover the molecular mechanism of the binding selectivity of RP-6306 toward PKMYT1 over its highly homologous kinase WEE1. The binding specificity of RP-6306 reported in previous experimental bioassays was clarified by MD simulations and binding free energy calculations. Further, the binding free energy prediction indicated that the binding selectivity of RP-6306 largely derived from the difference in the protein-ligand electrostatic interactions. The per-residue free energy decomposition suggested that the non-conserved gatekeeper residue in the hinge domain of PKMYT1/WEE1, Thr187/Asn376, is the critical factor responsible for the binding selectivity of RP-6306 toward PKMYT1. In addition, a water-mediated hydrogen bond was formed between RP-6306 and Gly191 at the hinge domain in the PKMYT1/RP-6306 complex, which was absent in the WEE1/RP-6306 complex. This study is expected to offer useful information for the design of more potent and selective PKMYT1 inhibitors.Communicated by Ramaswamy H. Sarma.

Identifying novel aryl hydrocarbon receptor (AhR) modulators from clinically approved drugs: In silico screening and In vitro validation

The aryl hydrocarbon receptor (AhR) functions as a vital ligand-activated transcription factor, governing both physiological and pathophysiological processes. Notably, it responds to xenobiotics, leading to a diverse array of outcomes. In the context of drug repurposing, we present here a combined approach of utilizing structure-based virtual screening and molecular dynamics simulations. This approach aims to identify potential AhR modulators from Drugbank repository of clinically approved drugs. By focusing on the AhR PAS-B binding pocket, our screening protocol included binding affinities calculations, complex stability, and interactions within the binding site as a filtering method. Comprehensive evaluations of all DrugBank small molecule database revealed ten promising hits. This included flibanserin, butoconazole, luliconazole, naftifine, triclabendazole, rosiglitazone, empagliflozin, benperidol, nebivolol, and zucapsaicin. Each exhibiting diverse binding behaviors and remarkably very low binding free energy. Experimental studies further illuminated their modulation of AhR signaling, and showing that they are consistently reducing AhR activity, except for luliconazole, which intriguingly enhances the AhR activity. This work demonstrates the possibility of using computational modelling as a quick screening tool to predict new AhR modulators from extensive drug libraries. Importantly, these findings hold immense therapeutic potential for addressing AhR-associated disorders. Consequently, it offers compelling prospects for innovative interventions through drug repurposing.

Role of UDP-N-acetylmuramic acid in the regulation of MurA activity revealed by molecular dynamics simulations

The peptidoglycan biosynthesis pathway plays a vital role in bacterial cells, and facilitates peptidoglycan layer formation, a fundamental structural component of the bacterial cell wall. The enzymes in this pathway are candidates for antibiotic development, as most do not have mammalian homologues. The UDP-N-acetylglucosamine (UNAG) enolpyruvyl transferase enzyme (MurA) in the peptidoglycan pathway cytoplasmic step is responsible for the phosphoenolpyruvate (PEP)-UNAG catalytic reaction, forming UNAG enolpyruvate and inorganic phosphate. Reportedly, UDP-N-acetylmuramic acid (UNAM) binds tightly to MurA forming a dormant UNAM-PEP-MurA complex and acting as a MurA feedback inhibitor. MurA inhibitors are complex, owing to competitive binding interactions with PEP, UNAM, and UNAG at the MurA active site. We used computational methods to explore UNAM and UNAG binding. UNAM showed stronger hydrogen-bond interactions with the Arg120 and Arg91 residues, which help to stabilize the closed conformation of MurA, than UNAG. Binding free energy calculations using end-point computational methods showed that UNAM has a higher binding affinity than UNAG, when PEP is attached to Cys115. The unbinding process, simulated using τ-random acceleration molecular dynamics, showed that UNAM has a longer relative residence time than UNAG, which is related to several complex dissociation pathways, each with multiple intermediate metastable states. This prevents the loop from opening and exposing the Arg120 residue to accommodate UNAG and potential new ligands. Moreover, we demonstrate the importance of Cys115-linked PEP in closed-state loop stabilization. We provide a basis for evaluating novel UNAM analogues as potential MurA inhibitors. PUBLIC SIGNIFICANCE: MurA is a critical enzyme involved in bacterial cell wall biosynthesis and is involved in antibiotic resistance development. UNAM can remain in the target protein's active site for an extended time compared to its natural substrate, UNAG. The prolonged interaction of this highly stable complex known as the 'dormant complex' comprises UNAM-PEP-MurA and offers insights into antibiotic development, providing potential options against drug-resistant bacteria and advancing our understanding of microbial biology.

Flanking Domains Modulate α-Synuclein Monomer Structure: A Molecular Dynamics Domain Deletion Study

Aggregates of misfolded α-synuclein proteins (asyn) are key markers of Parkinson's disease. Asyn proteins have three domains: an N-terminal domain, a hydrophobic NAC core implicated in aggregation, and a proline-rich C-terminal domain. Proteins with truncated C-terminal domains are known to be prone to aggregation and suggest that studying domain-domain interactions in asyn monomers could help elucidate the role of the flanking domains in modulating protein structure. To this end, we used Gaussian accelerated molecular dynamics (GAMD) to simulate wild-type (WT), N-terminal truncated (DN), C-terminal truncated (ΔC), and isolated NAC domain variants (isoNAC). Using clustering and contact analysis, we found that N- and C-terminal domains interact via electrostatic interactions, while the NAC and N-terminal domains interact through hydrophobic contacts. Our work also suggests that the C-terminal domain does not interact directly with the NAC domain but instead interacts with the N-terminal domain. Removal of the N-terminal domain led to increased contacts between NAC and C-terminal domains and the formation of interdomain β-sheets. Removal of either flanking domain also resulted in increased compactness of every domain. We also found that the contacts between flanking domains results in an electrostatic potential (ESP) that could possibly lead to favorable interactions with anionic lipid membranes. Removal of the C-terminal domain disrupts the ESP in a way that is likely to over-stabilize protein-membrane interactions. All of this suggests that one of the roles of the flanking domains may be to modulate the protein structure in a way that helps maintain elongation, hide hydrophobic residue from the solvent, and maintain an ESP that aids favorable interactions with the membrane.

Investigating the Aryl Hydrocarbon Receptor Agonist/Antagonist Conformational Switch Using Well-Tempered Metadynamics Simulations

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that mediates biological signals to control various complicated cellular functions. It plays a crucial role in environmental sensing and xenobiotic metabolism. Dysregulation of AhR is associated with health concerns, including cancer and immune system disorders. Upon binding to AhR ligands, AhR, along with heat shock protein 90 and other partner proteins undergoes a transformation in the nucleus, heterodimerizes with the aryl hydrocarbon receptor nuclear translocator (ARNT), and mediates numerous biological functions by inducing the transcription of various AhR-responsive genes. In this manuscript, the 3-dimensional structure of the entire human AhR is obtained using an artificial intelligence tool, and molecular dynamics (MD) simulations are performed to study different structural conformations. These conformations provide insights into the protein's function and movement in response to ligand binding. Understanding the dynamic behavior of AhR will contribute to the development of targeted therapies for associated health conditions. Therefore, we employ well-tempered metadynamics (WTE-metaD) simulations to explore the conformational landscape of AhR and obtain a better understanding of its functional behavior. Our computational results are in excellent agreement with previous experimental findings, revealing the closed and open states of helix α1 in the basic helix-loop-helix (bHLH domain) in the cytoplasm at the atomic level. We also predict the inactive form of AhR and identify Arginine 42 as a key residue that regulates switching between closed and open conformations in existing AhR modulators.

CREST-A program for the exploration of low-energy molecular chemical space

Conformer-rotamer sampling tool (CREST) is an open-source program for the efficient and automated exploration of molecular chemical space. Originally developed in Pracht et al. [Phys. Chem. Chem. Phys. 22, 7169 (2020)] as an automated driver for calculations at the extended tight-binding level (xTB), it offers a variety of molecular- and metadynamics simulations, geometry optimization, and molecular structure analysis capabilities. Implemented algorithms include automated procedures for conformational sampling, explicit solvation studies, the calculation of absolute molecular entropy, and the identification of molecular protonation and deprotonation sites. Calculations are set up to run concurrently, providing efficient single-node parallelization. CREST is designed to require minimal user input and comes with an implementation of the GFNn-xTB Hamiltonians and the GFN-FF force-field. Furthermore, interfaces to any quantum chemistry and force-field software can easily be created. In this article, we present recent developments in the CREST code and show a selection of applications for the most important features of the program. An important novelty is the refactored calculation backend, which provides significant speed-up for sampling of small or medium-sized drug molecules and allows for more sophisticated setups, for example, quantum mechanics/molecular mechanics and minimum energy crossing point calculations.

Situating the phosphonated calixarene-cytochrome C association by molecular dynamics simulations

Protein-calixarenes binding plays an increasingly central role in many applications, spanning from molecular recognition to drug delivery strategies and protein inhibition. These ligands obey a specific bio-supramolecular chemistry, which can be revealed by computational approaches, such as molecular dynamics simulations. In this paper, we rely on all-atom, explicit-solvent molecular dynamics simulations to capture the electrostatically driven association of a phosphonated calix-[4]-arene with cytochome-C, which critically relies on surface-exposed paired lysines. Beyond two binding sites identified in direct agreement with the x-ray structure, the association has a larger structural impact on the protein dynamics. Then, our simulations allow a direct comparison to analogous calixarenes, namely, sulfonato, similarly reported as "molecular glue." Our work can contribute to a robust in silico predictive tool to assess binding sites for any given protein of interest for crystallization, with the specificity of a macromolecular cage whose endo/exo orientation plays a role in the binding.

k-Means NANI: an improved clustering algorithm for Molecular Dynamics simulations

One of the key challenges of k-means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k-means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex datasets such as those obtained from molecular simulation, k-means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k-means++ will lead to a lack of reproducibility. K-means N-Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n-ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k-means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse datasets and be used as a standalone tool or as part of our MDANCE clustering package.

High-efficiency removal of pyrethroids using a redesigned odorant binding protein

Pyrethroids are ubiquitously present in environmental media and threaten both the ecosystem and human health. To explore effective ways to remove pyrethroids from the environment, an odorant binding protein (OBP) with affinity for various pyrethroids was investigated. Initially, the target OBP, Spodoptera littoralis pheromone binding protein 1 (SlitPBP1), underwent redesign to enhance its affinity for pyrethroids. The modified SlitPBP1E97ND106E demonstrated a substantially increased affinity for deltamethrin (DeltaM), with a dissociation constant of 0.77 ± 0.17 μM. The affinity of SlitPBP1E97ND106E for other pyrethroids also increased to varying extents. Consequently, SlitPBP1E97ND106E displayed a markedly enhanced capability to adsorb and remove pyrethroids. When exposed to free SlitPBP1E97ND106E in solution, the reduction in DeltaM surged from 16.78 ± 0.32% to 97.51 ± 0.56%. SlitPBP1E97ND106E was immobilized by coupling the protein to Ni2+-NTA agarose resin. Liquid chromatography results attested to the superior efficacy of immobilized SlitPBP1E97ND106E in removing pyrethroids, especially DeltaM. No significant differences in pyrethroid removal were detected across various water samples. Our findings introduce a potent tool for pyrethroid removal. A wider range of OBPs can similarly be optimized and applied to remove organic pollutants, including but not limited to pesticides.

Molecular modeling of ARF6 dysregulation caused by mutations in IQSEC2

IQSEC2 gene mutations are associated with epilepsy, autism, and intellectual disability. The primary function IQSEC2, mediated via its Sec 7 domain, is to act as a guanine nucleotide exchange factor for ARF6. We sought to develop a molecular model, which may explain the aberrant Sec 7 activity on ARF6 of different human IQSEC2 mutations. We integrated experimental data of IQSEC2 mutants with protein structure prediction by the RaptorX server combined with molecular modeling and molecular dynamics simulations. Normally, apocalmodulin (apoCM) binds to IQSEC2 resulting in its N-terminal fragment inhibiting access of its Sec 7 domain to ARF6. An increase in Ca2+ concentration destabilizes the interaction of IQSEC2 with apoCM and removes steric hindrance of Sec 7 binding with ARF6. Mutations at amino acid residue 350 of IQSEC2 result in loss of steric hindrance of Sec 7 binding with ARF6 leading to constitutive activation of ARF6 by Sec 7. On the other hand, a mutation at amino acid residue 359 of IQSEC2 results in constitutive hindrance of Sec 7 binding to ARF6 leading to the loss of the ability of IQSEC2 to activate ARF6. These studies provide a model for dysregulation of IQSEC2 Sec 7 activity by mutant IQSEC2 proteins.Communicated by Ramaswamy H. Sarma.

Iron uptake pathway of Escherichia coli as an entry route for peptide nucleic acids conjugated with a siderophore mimic

Bacteria secrete various iron-chelators (siderophores), which scavenge Fe3+ from the environment, bind it with high affinity, and retrieve it inside the cell. After the Fe3+ uptake, bacteria extract the soluble iron(II) from the siderophore. Ferric siderophores are transported inside the cell via the TonB-dependent receptor system. Importantly, siderophore uptake paths have been also used by sideromycins, natural antibiotics. Our goal is to hijack the transport system for hydroxamate-type siderophores to deliver peptide nucleic acid oligomers into Escherichia coli cells. As siderophore mimics we designed and synthesized linear and cyclic Nδ-acetyl-Nδ-hydroxy-l-ornithine based peptides. Using circular dichroism spectroscopy, we found that iron(III) is coordinated by the linear trimer with hydroxamate groups but not by the cyclic peptide. The internal flexibility of the linear siderophore oxygen atoms and their interactions with Fe3+ were confirmed by all-atom molecular dynamics simulations. Using flow cytometry we found that the designed hydroxamate trimer transports PNA oligomers inside the E. coli cells. Growth recovery assays on various E. coli mutants suggest the pathway of this transport through the FhuE outer-membrane receptor, which is responsible for the uptake of the natural iron chelator, ferric-coprogen. This pathway also involves the FhuD periplasmic binding protein. Docking of the siderophores to the FhuE and FhuD receptor structures showed that binding of the hydroxamate trimer is energetically favorable corroborating the experimentally suggested uptake path. Therefore, this siderophore mimic, as well as its conjugate with PNA, is most probably internalized through the hydroxamate pathway.

The Potential of Cyclodextrins as Inhibitors for the BM2 Protein: An In Silico Investigation

The influenza BM2 transmembrane domain (BM2TM), an acid-activated proton channel, is an attractive antiviral target due to its essential roles during influenza virus replication, whereas no effective inhibitors have been reported for BM2. In this study, we draw inspiration from the properties of cyclodextrins (CDs) and hypothesize that CDs of appropriate sizes may possess the potential to act as inhibitors of the BM2TM proton channel. To explore this possibility, molecular dynamics simulations were employed to assess their inhibitory capabilities. Our findings reveal that CD4, CD5, and CD6 are capable of binding to the BM2TM proton channel, resulting in disrupted water networks and reduced hydrogen bond occupancy between H19 and the solvent within the BM2TM channel necessary for proton conduction. Notably, CD4 completely obstructs the BM2TM water channel. Based on these observations, we propose that CD4, CD5, and CD6 individually contribute to diminishing the proton transfer efficiency of the BM2 protein, and CD4 demonstrates promising potential as an inhibitor for the BM2 proton channel.

Deciphering the Molecular Association of Human CRIP1a with an Agonist-Bound Cannabinoid Receptor 1

Cannabinoid receptor 1 (CB1) is a class A G-protein-coupled receptor that plays important roles in several physiological and pathophysiological processes. Therefore, targeted regulation of CB1 activity is a potential therapeutic strategy for several diseases, including neurological disorders. Apart from cannabinoid ligands, CB1 signaling can also be regulated by different CB1-associated proteins. In particular, the cannabinoid receptor interacting protein 1a (CRIP1a) associates with an activated CB1 receptor and alters the G-protein selectivity, thereby reducing the agonist-mediated signal transduction of the CB1 receptor. Experimental evidence suggests that two peptides corresponding to the distal and central C-terminal segments of CB1 could interact with CRIP1a. However, our knowledge of the molecular basis of CB1-CRIP1a recognition is still limited. In this work, we use an extensive combination of computational methods to build the first comprehensive atomistic model human CB1-CRIP1a complex. Our model provides novel structural insights into the interactions of CRIP1a with a membrane-embedded, complete, agonist-bound CB1 receptor in humans. Our results highlight the key residues that stabilize the CB1-CRIP1a complex, which will be useful to guide in vitro mutagenesis experiments. Furthermore, our human CB1-CRIP1a complex presents a model system for structure-based drug design to target this physiologically important complex for modulating CB1 activity.

Sources, variability, long-term trends, and radiative forcing of aerosols in the Arctic: implications for Arctic amplification

Atmospheric pollution in the Arctic has been an important driver for the ongoing climate change there. Increase in the Arctic aerosols causes the phenomena of Arctic haze and Arctic amplification. Our analysis of aerosol optical depth (AOD), black carbon (BC), and dust using ground-based, satellite, and reanalysis data in the Arctic for the period 2003-2019 shows that the lowest amount of all these is found in Greenland and Central Arctic. There is high AOD, BC, and dust in the northern Eurasia and parts of North America. All aerosols show their highest values in spring. Significant positive trends in AOD (> 0.003 year-1) and BC (0.0002-0.0003 year-1) are found in the northwestern America and northern Asia. Significant negative trends are observed for dust (- 0.0001 year-1) around Central Arctic. Seasonal analysis of AOD, BC, and dust reveals an increasing trend in summer and decreasing trend in spring in the Arctic. The major sources of aerosols are the nearby Europe, Russia, and North America regions, as assessed using the potential source contribution function (PSCF). Anthropogenic emissions from the transport, energy, and household sectors along with natural sources such as wildfires contribute to the positive trends of aerosols in the Arctic. These increasing aerosols in the Arctic influence Arctic amplification through radiative effects. Here, we find that the net aerosol radiative forcing is high in Central Arctic, Greenland, Siberia, and Canadian Arctic, about 2-4 W/m2, which can influence the regional temperature. Therefore, our study can assist policy decisions for the mitigation of Arctic haze and Arctic amplification in this environmental fragile region of the Earth.

Characterization of Posttranslationally Modified PHF-1 Tau Peptides Using Gaussian Accelerated Molecular Dynamics Simulation

The microtubule-associated protein, Tau, is an intrinsically disordered protein that plays a crucial role in neurodegenerative diseases like Alzheimer's disease. The posttranslational modifications across the Tau protein domains are involved in regulating Tau protein's function and disease onset. Of the various posttranslational modifications at Ser, Thr, and Tyr sites, O-GlcNAcylation and phosphorylation are the most critical ones, playing a vital role in Tau aggregation and tauopathies. To understand the function, it is essential to characterize the structural changes associated with Tau modification. Previous experimental studies have focused on high-resolution nuclear magnetic resonance techniques to structurally characterize the effect of phosphorylation, O-GlcNAcylation, and combination of both PTMs on Tau conformation in small peptides centered on the PHF-1 epitope from amino acid 392 to 411. The structural characterization using atomistic molecular dynamics simulation of such disordered peptides requires long simulation time, proper sampling method, and utilization of appropriate force fields for accurate determination of conformational ensembles, resembling the experimental data. This chapter details the protocol for the structural characterization of modified Tau peptides using the CHARMM36m force field and enhanced sampling methods like Gaussian accelerated molecular dynamics (GaMD) simulation. We have focused on a detailed explanation of the GaMD method and analyses of molecular dynamics trajectories to explain the relationship between two modifications, phospho- and glyco-, at C-terminus of Tau protein and its stable conformation over the longer simulation timeframes. The analyses involve energetics reweighting, clustering of simulation trajectories, and characterization of secondary structure using circular dichroism data from the simulation. The reader can utilize this protocol to investigate the structures of complex proteins, especially the disordered ones.

Exploring the mechanism of the PTP1B inhibitors by molecular dynamics and experimental study

Protein tyrosine phosphatase 1B (PTP1B) has proven to be an attractive target for the treatment of cancer, diabetes and other diseases. Although many PTP1B inhibitors with various scaffolds have been developed, there is still a lack of PTP1B inhibitor with high specificity and acceptable pharmacological properties. Therefore, it is urgent to develop more methods to explore complex action mode of PTP1B and ligands for designing ideal PTP1B modulators. In this work, we developed a potential molecular dynamics (MD) analytic mode to analyze the mechanism of active compounds 6a and 6e against PTP1B from different perspectives, including the stable ability, interactions and binding site of ligand and protein, the binding energy, relative movement between residues and changes in protein internal interactions. The simulated results demonstrated that compound 6a bound more stably to the active pocket of PTP1B than 6e due to its smaller molecular volume (326 Å3), matched electronegativity, and enhanced the positive correlation motion of residues, especially for WPD loop and P loop. Lastly, compound 6a as a competitive inhibitor for PTP1B was verified by enzyme kinetic assay. This work successfully studied the mechanism of compound 6a against PTP1B from various aspects, enriched the analysis of interaction mode between PTP1B and inhibitors. In summary, we hope that this work could provide more theoretical information for designing and developing more novel and ideal PTP1B inhibitors in the future.

Computational Modeling of Stapled Coiled-Coil Inhibitors Against Bcr-Abl: Toward a Treatment Strategy for CML

The chimeric oncoprotein Bcr-Abl is the causative agent of virtually all chronic myeloid leukemias (CML) and a subset of acute lymphoblastic leukemias (ALL). As a result of the so-called Philadelphia Chromosome translocation t(9;22), Bcr-Abl manifests as a constitutively active tyrosine kinase which promotes leukemogenesis by activation of cell cycle signaling pathways. Constitutive and oncogenic activation is mediated by an N-terminal coiled-coil oligomerization domain in Bcr (Bcr-CC), presenting a therapeutic target for inhibition of Bcr-Abl activity toward the treatment of Bcr-Abl+ leukemias. Previously, we demonstrated that a rationally designed Bcr-CC mutant, CCmut3, exerts a dominant negative effect upon Bcr-Abl activity by preferential oligomerization with Bcr-CC. Moreover, we have shown conjugation to a leukemia-specific cell-penetrating peptide (CPP-CCmut3) improves intracellular delivery and activity. However, our full-length CPP-CCmut3 construct (81 aa) is encumbered by an intrinsically high degree of conformational variability and susceptibility to proteolytic degradation, relative to traditional small molecule therapeutics. Here, we iterate a new generation of our inhibitor against Bcr-CC mediated Bcr-Abl assembly that is designed to address these constraints through incorporation of all-hydrocarbon staples spanning i, i + 7 positions in helix α2 (CPP-CCmut3-st). We utilize computational modeling and biomolecular simulation to design and characterize single and double staple candidates in silico, evaluating binding energetics and building upon our seminal work modeling single hydrocarbon staples when applied to a truncated Bcr-CC sequence. This strategy enables us to efficiently build, characterize, and screen lead single/double stapled CPP-CCmut3-st candidates for experimental studies and validation in vitro and in vivo. In addition to full-length CPP-CCmut, we model a truncated system characterized by deletion of helix α1 and the flexible-loop linker, which are known to impart high conformational variability. To study the impact of the N-terminal cyclic CPP toward model stability and inhibitor activity, we also model the full-length and truncated systems without CPP, with cyclized CPP, and with linear CPP, for a total of six systems which comprise our library. From this library, we present lead stapled peptide candidates to be synthesized and evaluated experimentally as our next-generation inhibitors against Bcr-Abl.

The Discovery of Novel Small Oxindole-Based Inhibitors Targeting the SARS-CoV-2 Main Protease (Mpro )

With the potential for coronaviruses to re-emerge and trigger future pandemics, the urgent development of antiviral inhibitors against SARS-CoV-2 is essential. The Mpro enzyme is crucial for disease progression and the virus's life cycle. It possesses allosteric sites that can hinder its catalytic activity, with some of these sites located at or near the dimerization interface. Among them, sites #2 and #5 possess druggable pockets and are predicted to bind drug-like molecules. Consequently, a commercially available ligand library containing ~7 million ligands was used to target site #2 via structure-based virtual screening. After extensive filtering, docking, and post-docking analyses, 53 compounds were chosen for biological testing. An oxindole derivative was identified as a Mpro non-competitive reversible inhibitor with a Ki of 115 μM and an IC50 of 101.9 μM. Throughout the 200 ns-long MD trajectories, our top hit has shown a very stable binding mode, forming several interactions with residues in sites #2 and #5. Moreover, derivatives of our top hit were acquired for biological testing to gain deeper insights into their structure-activity relationship. To sum up, drug-like allosteric inhibitors seem promising and can provide us with an additional weapon in our war against the recent pandemic, and possibly other coronaviruses-caused diseases.

Prion-like Aggregation of the Heptapeptide GNNQQNY into Amyloid Nanofiber Is Governed by Configuration Entropy

A major cause of prion infectivity is the early formation of small, fibril-like aggregates consisting of the heptapeptide GNNQQNY. The prion aggregates exhibit a unique stacking mode in which the hydrophobic tyrosine (Y) is exposed outward, forming a bilayer β-sheet-stacking zipper structure. This stacking mode of the prion peptides, termed "Y-outward" structure for convenience, goes against the common understanding that, for other amyloid-forming peptides, the hydrophobic residues should be hidden within the peptide fibril, referred to as "Y-inward" structure. To explore the extraordinary stacking behaviors of the prion GNNQQNY peptides, two fibril models are constructed in a fashion of "Y-outward" and "Y-inward" stackings and then studied in silico to examine their thermodynamic stabilities and disaggregation pathways. The "Y-inward" structure indeed exhibits stronger thermodynamic stability than the "Y-outward" structure, according to potential energy and stacking energy calculations. To show how the peptide fibrils dissociate, we illustrated two disaggregation pathways. A dihedral-based free energy landscape was then calculated to examine the conformational degrees of freedom of the GNNQQNY chains in the "Y-outward" and "Y-inward" structures. Peptide chains lose more configurational entropy in the "Y-inward" structure than in the "Y-outward" structure, indicating that the prion peptides are prone to aggregate in a fashion of "Y-outward" stacking pattern due to its low conformational constraints. The prion-like aggregation of the GNNQQNY peptides into amyloid fibrils is primarily governed by the configuration entropy.