Steered Molecular Dynamics Simulation in Rational Drug Design

Theoretical investigation of AKT1 mutations in breast cancer: a computational approach to structural and functional insights

Breast cancer is a complex disease primarily driven by genetic mutations that disrupt crucial signaling pathways, with the AKT1 gene playing a central role in its progression. This study explores the impact of AKT1 mutations using Whole Exome Sequencing (WES), bioinformatics, and computational modeling. Using WES, we identified and prioritized significant mutations in patient samples, specifically D3N, V337M, and D3N-E169G. Comprehensive sequence and structural analyses were conducted to understand how these mutations affect specific functional domains of the AKT1 protein. To investigate the molecular consequences, molecular docking studies were performed to assess the binding affinity of AKT1 mutations with MK2206, a known allosteric inhibitor of AKT1. The docking results revealed substantial differences in interaction energies, indicating impaired inhibitor binding due to these mutations. Additionally, molecular dynamics simulations over a 500-nanosecond trajectory provided detailed insights into the structural perturbations caused by these mutations. This integrated study, combining genomic and computational approaches, offers a comprehensive understanding of how AKT1 mutations contribute to BC pathogenesis. These findings enhance our knowledge of the molecular mechanisms underlying the disease and support the development of targeted therapies to address the altered behavior of mutated AKT1, advancing personalized treatment strategies for BC.

Chemoinformatics Study of Benzodiazepine-1, 2, 3-triazole Derivatives Targeting Butyrylcholinesterase

This study aims to assess the potential bioactivity of newly designed benzodiazepine-1,2,3-triazole derivatives using in-silico methodologies, with a primary focus on elucidating their inhibitory interactions with the butyrylcholinesterase (BuChE) enzyme, which is implicated in Alzheimer's disease. We employed multiple linear regression (MLR) methods to conduct a quantitative structure-activity relationship (QSAR) analysis on a collection of 31 benzodiazepine-1,2,3-triazole derivatives, with the goal of investigating, assessing, and predicting their activities, as well as designing novel compounds. This approach yielded highly accurate results, with coefficients of determination (R²) of 0.77 and 0.81 for the training and test datasets, respectively. Additionally, the optimized compounds were subjected to an Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis, demonstrating their potential as non-hepatotoxic agents with enhanced absorption and blood-brain barrier permeability. To further validate these findings, the most favorable docking conformations were analyzed using molecular dynamics (MD) simulations with GROMACS software, predicting the stability of the formed complexes. These simulations underscored the critical role of hydrogen bonds in stabilizing the compounds at the BuChE receptor binding site. The results hold great promise for the development of innovative benzodiazepine-1,2,3-triazole derivatives as effective BuChE inhibitors, potentially leading to therapeutic interventions for Alzheimer's disease.

Identification of Novel Fourth-Generation Allosteric Inhibitors Targeting Inactive State of EGFR T790M/L858R/C797S and T790M/L858R Mutations: A Combined Machine Learning and Molecular Dynamics Approach

Targeted therapy with an allosteric inhibitor (AIs) is an important area of research in patients with epidermal growth factor receptor (EGFR) mutations. Current treatment of nonsmall cell lung cancer patients with EGFR mutations using orthosteric inhibitors faces challenges like resistance and stopping over phosphorylation. Notably AIs have been introduced to overcome this resistance and increase inhibitory potency that binds to pockets other than the ATP-binding site (orthosteric site). Recently, fourth-generation AIs, EAI045, have been discovered to potently and selectively inhibit various EGFR mutations but limited antiproliferative effects in the absence of the antibody cetuximab. The purpose of this work is to identify nontoxic, potent small AIs through various screening pipelines and explore their molecular mechanism. In the discovery of AIs, structural similarity search, high-throughput virtual screening, and machine learning-guided QSAR modeling, several candidates were identified. Machine learning was employed to guide the QSAR model based on 2D descriptors and DFT-derived quantum chemical descriptors followed by a PCA reduction technique, which enabled the prediction of the biological activity (IC50) of screened drugs against various EGFR mutations such as T790M/L858R/C797S and T790M/L858R. In addition, multinanosecond (ns) and microsecond (μs) classical molecular dynamics (MD) simulations run on protein-ligand binding complex to check the stability of binding dynamics for T790M/L858R/C797S and T790M/L858R mutations with lower IC50 and higher docking score compounds. The molecular mechanics generalized Boltzmann surface area (MM/GBSA) calculation revealed that the five hit allosteric molecules for T790M/C797S/L858R and two for T790M/L858R mutations had a high binding affinity. The results were corroborated further by MM/GBSA employing the normal-mode analysis entropy method to perform additional screening. Furthermore, the compounds' efficacy was confirmed using path-dependent ligand unbinding free energy techniques such as Jarzynski averaged free energy profiles obtained from adaptive steered MD, relative residence time, and umbrella sampling simulations, which were compared to a reference inhibitor. However, path-independent alchemical approaches like streamlined alchemical free energy perturbation and binding free energy estimator 2 (BFEE2) were employed to validate the results and identify potent compounds. These findings pave the way to identification of novel potential fourth-generation AIs, which require further experimental validation.

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

A Comprehensive In Vitro and In Silico Approach for Targeting 4-Hydroxyphenyl Pyruvate Dioxygenase: Towards New Therapeutics for Alkaptonuria

Alkaptonuria (AKU) is an ultra-rare genetic disorder caused by mutations in the homogentisate 1,2-dioxygenase (HGD) gene, leading to the accumulation of homogentisic acid (HGA). Current treatment options are limited, with Nitisinone (Orfadin or NTBC) being the only approved drug. However, its long-term use raises concerns due to significant adverse effects, highlighting the urgent need for safer alternatives. AKU manifests with progressive and often painful symptoms, severely impacting patients' quality of life. Identifying new therapeutic approaches to inhibit 4-hydroxyphenyl pyruvate dioxygenase (4-HPPD) is critical to improving outcomes for AKU patients. In this study, we present a novel integrated in vitro and in silico strategy to assess the residence time of 4-HPPD inhibitors. In particular, we evaluated several features of a set of triketone compounds including their inhibitory efficacy, residence time, and ochronotic pigment accumulation. By means of our integrated approach, we investigated the pharmacokinetic and pharmacodynamics properties of novel 4-HPPD inhibitors and provided a promising foundation for the development of safer and more effective treatments for AKU.

Evaluating the ability of in silico identified hit compounds to bind Staphylococcus aureus LcpASA using steered molecular dynamics simulations

Staphylococcus aureus is an opportunistic microorganism which can cause minor skin infections and also serious diseases, and its increasing antibiotic resistance necessitates further discovery of new targets and inhibitors for antibacterials. The transmembrane protein LcpASA that plays an essential role in the synthesis of cell wall in S. aureus has been identified as a potential drug target. In this study, we performed virtual screening of chemical compound libraries to establish their binding with target protein and molecular docking among other studies which led to identification of hit compounds with good binding affinity towards LcpASA domain and involvement of key amino acid residues in the intermolecular interactions. All molecules showed satisfactory drug-likeness properties such as ADME and non-carcinogenicity. 500 ns molecular dynamics (MD) simulations using Amber18 was performed on all molecular systems to explain the mechanism of LcpASA extracellular domain function and reveal potential hit molecules to bind the enzyme. Based on the post-MD data analysis; such as RMSD, RMSF, SASA, intermolecular hydrogen bonds, clustering analysis, anisotropic network model-based normal mode analysis and mechanical stiffness, and essential dynamics seven molecules were finally selected as hit compounds to bind LcpASA. Steered MD was employed to study the unbinding of the hit molecules.

Exploring MERTK inhibitor binding mechanisms: insights from adaptive steered molecular dynamics and free energy calculation

MERTK, a promising drug target for the treatment of human leukemia and solid tumors, and the development of its small molecule inhibitors holds significant clinical potential. However, the underlying reasons for the varying activities among these inhibitors and the specifics of their binding mechanism have not been systematically investigated. By combining conventional molecular dynamics simulations, adaptive steered molecular dynamics simulations and binding free energy calculations based on molecular mechanics Poisson-Boltzmann surface area, the interaction modes of four MERTK inhibitors and dissociation behavior are discussed in detail. The results reveal additional critical amino acids, beyond the well-known hot spot residues in the kinase hinge region, that play a pivotal role in inhibitor binding. Our findings further indicate that the binding of MERTK to its inhibitors relies not only on crucial hydrogen bonding interactions but also benefits from non-polar interactions. In addition, the analysis of hydrogen bonding within kinetic trajectories and potential of mean force explained the differences in activity between different inhibitors, providing insights for the design and optimization of subsequent MERTK-targeted small molecule inhibitors.

CADD-based discovery of novel oligomeric modulators of PKM2 with antitumor activity in aggressive human glioblastoma models

Pyruvate kinase isoform M2 (PKM2) is a multifunctional enzyme capable of transitioning between monomeric, dimeric, and tetrameric states, with its oligomeric equilibrium playing a pivotal role in tumour progression and survival. The unique exon ten at the dimer-dimer interface represents an attractive target for isoform-specific modulation, offering opportunities for disrupting this equilibrium and altering tumour cell dynamics. This study identifies a novel druggable pocket at the PKM2 dimer interface through conformational analysis. This pocket was exploited in a virtual screening of a large small-molecule library, identifying two promising candidates, C599 and C998. Both compounds exhibited dose-dependent antiproliferative effects in glioblastoma cell lines and induced apoptosis, as evidenced by caspase 3/7 activation. These effects were directly linked to their inhibition of PKM2 enzymatic activity, validating the proposed mechanism of action in their rational design. ADMET studies further highlighted their strong potential as lead PKM2 inhibitors for GBM treatment. Molecular dynamics (MD) simulations and post-MD analyses, including Dynamic Cross-Correlation Maps (DCCM), Probability Density Function (PDF), and Free Energy Landscape (FEL), confirmed the stability of the protein-ligand interactions and highlighted critical residues at the dimer-dimer interface. The Steered MD simulations demonstrated the high affinity of the compounds for PKM2, as evidenced by the requirement of high rupture forces to induce an unbinding event. These results highlight the potential of the compounds as oligomeric modulators of PKM2. These findings position C599 and C998 as promising lead compounds for antitumor applications. Future studies will focus on optimising these candidates and assessing their efficacy in vivo glioblastoma models, reassuring the thoroughness of our research and the potential for further advancements.

Bright NIR-II emissive cyanine dye-loaded lipoprotein-mimicking nanoparticles for fluorescence imaging-guided and targeted NIR-II photothermal therapy of subcutaneous glioblastoma

Cyanine dye-containing nanoparticles have widely been used in "all-in-one" NIR fluorescence imaging (FI)-guided photothermal therapy (PTT) because of their intrinsically large extinction coefficient and available physical and chemical modulation methods to tune absorption and emission wavelengths. The combination of good brightness and excellent tumor-targeting capacity is the key to realize efficient NIR-II FI-guided PTT. In this study, by covalently decorating NIR-II absorptive cyanine dyes with bulky AIE motify, we demonstrate how steric hindrance suppresses π-π stacking-induced fluorescence quenching and contributes to the good brightness of NIR-II FI of subcutaneous glioblastoma. The resulting cyanine dye (C12-TPAE) is 5 times brighter than the original cyanine dye in the formulated liposomal nanoparticles and C12-TPAE-AL has a high photothermal conversion efficiency of 62.4%, with good colloidal and light stability. Importantly, the ApoE peptide is absorbed on the liposomal surface, yielding lipoprotein-mimicking nanoparticles, which achieve active targeting of glioblastoma and efficient FI-guided PTT without tumor recurrence without any side effects on normal organs (heart, kidneys, liver, spleen, or lung). This research highlights a facile design route for bright NIR-II emissive and NIR-II photothermal cyanine dyes and indicates that cyanine dye-containing biomimetic theranostic nanoplatforms are promising candidates for future precision therapy.

Molecular Dynamics Study of Chitosan Adsorption at a Silica Surface

Chitosan is a nontoxic biopolymer with many potential biomedical and material applications due to its biodegradability, biocompatibility, and antimicrobial properties. Here, fully atomistic molecular dynamics simulations and enhanced sampling methods have been used to study the adsorption mechanism of chitosan oligomers on a silica surface from an aqueous solution. The free energy of adsorption of chitosan on a silica surface was calculated to be 0.6 kcal mol-1 per monomer in 0.15 mol L-1 aqueous solution, which is comparable to k B T at room temperature. The loading capacity of chitosan on the silica surface was found to be 0.094 mg m-2, and it is dominated by charge compensation. Furthermore, the hydrogen bonding between chitosan and silica was analyzed. The nitrogen and hydroxyl group oxygen chitosan atoms were found to be the main contributors to the hydrogen bonding between chitosan and silica. These findings have the potential to guide the experimental design of chitosan-coated silica nanoparticles for applications such as drug delivery or additives for biopolymer food packaging.

Searching for novel MDM2/MDMX dual inhibitors through a drug repurposing approach

Disruption of p53-MDM2/MDMX interaction by smaller inhibitors is a promising therapeutic intervention gaining tremendous interest. However, no MDM2/MDMX inhibitors have been marketed so far. Drug repurposing is a validated, practical approach to drug discovery. In this regard, we employed structure-based virtual screening in a reservoir of marketed drugs and identified nintedanib as a new MDM2/MDMX dual inhibitor. The computational structure analysis and biochemical experiments uncover that nintedanib binds MDM2/MDMX similarly to RO2443, a dual MDM2/MDMX inhibitor. Furthermore, the mechanistic study reveals that nintedanib disrupts the physical interaction of p53-MDM2/MDMX, enabling the transcriptional activation of p53 and the subsequent cell cycle arrest and growth inhibition in p53+/+ cancer cells. Lastly, structural minimisation of nintedanib yields H3 with the equivalent potency. In summary, this work provides a solid foundation for reshaping nintedanib as a valuable lead compound for the further design of MDM2/MDMX dual inhibitors.

Steered molecular dynamics simulation as a post-process to optimize the iBRAB-designed Fab model

Therapeutic monoclonal antibodies are an effective method of treating acute infectious diseases. However, knowing which of the produced antibodies in the vast number of human antibodies can cure the disease requires a long time and advanced technology. The previously introduced iBRAB method relies on studied antibodies to design a broad-spectrum antibody capable of neutralizing antigens of many different Influenza A viral strains. To evaluate the antigen-binding fragment as an applicable drug, the therapeutic antibody profiles providing guidelines collected from clinically staged therapeutic antibodies were used to access different measurements. Although the evaluated values were within an accepted range, the modification in the amino acid sequence is required for better properties. Thus, using the steered molecular dynamics (SMD) simulation to determine the binding capacity of amino acids in the functional region, the profile of interacted amino acids of Fab with the antigen was established for modified reference. As a result, the model was modified with amino acids elimination at positions 96-97 in the heavy chain and 26-27, 91, 96-97, and 102-103 in the light chain, which has better Therapeutic Antibody Profiler evaluations than the original designation. Thus again, SMD simulation is a promising computational approach for post-modification in rational drug design.

Computational assessment of lipid facilitated membrane permeation of vancomycin using force-probe molecular dynamic simulation

In this study the efficacy of different edible lipids for drug permeation enhancement of vancomycin through biological membrane was investigated using molecular dynamic simulation. In this regard, at first the ability of the lipids for complex formation with the drug was evaluated for number of most common edible lipids including tripalmitin (TPA), trimyristin (TMY), labrafil (LAB), glycerol monostearate (GMS), glycerol monooleate (GMO), Distearoylphosphorylethanolamine (DSPE), dipalmitoylphosphatidylethanolamine (DPPE), Dipalmitoylphosphatidylcholine (DPPC), cholesterol (CL), stearic acid (SA), palmitic acid (PA) and oleic acid (OA). Then the complexes were pulled thorough a bilayer membrane while the changes in force were probed. The results showed that besides the SA, PA and OA the other examined lipids were able to perform a perfect molecular complex with the drug. Also the results of pulling simulation revealed that the least of force was needed for drug transmittance through the membrane when it was covered by LAB, TMY and DSPE. These results indicated that these lipids can be the excellent materials of choice as permeation enhancer for preparing a proper oral formulation of vancomycin.Communicated by Ramaswamy H. Sarma.

Mining unique cysteine synthetases and computational study on thoroughly eliminating feedback inhibition through tunnel engineering

L-cysteine is an essential component in pharmaceutical and agricultural industries, and synthetic biology has made strides in developing new metabolic pathways for its production, particularly in archaea with unique O-phosphoserine sulfhydrylases (OPSS) as key enzymes. In this study, we employed database mining to identify a highly catalytic activity OPSS from Acetobacterium sp. (AsOPSS). However, it was observed that the enzymatic activity of AsOPSS suffered significant feedback inhibition from the product L-cysteine, exhibiting an IC50 value of merely 1.2 mM. A semi-rational design combined with tunnel analysis strategy was conducted to engineer AsOPSS. The best variant, AsOPSSA218R was achieved, totally eliminating product inhibition without sacrificing catalytic efficiency. Molecular docking and molecular dynamic simulations indicated that the binding conformation of AsOPSSA218R with L-cys was altered, leading to a reduced affinity between L-cysteine and the active pocket. Tunnel analysis revealed that the AsOPSSA218R variant reshaped the landscape of the tunnel, resulting in the construction of a new tunnel. Furthermore, random acceleration molecular dynamics simulation and umbrella sampling simulation demonstrated that the novel tunnel improved the suitability for product release and effectively separated the interference between the product release and substrate binding processes. Finally, more than 45 mM of L-cysteine was produced in vitro within 2 h using the AsOPSSA218R variant. Our findings emphasize the potential for relieving feedback inhibition by artificially generating new product release channels, while also laying an enzymatic foundation for efficient L-cysteine production.

Unlocking the Reverse Targeting Mechanisms of Cannabidiol: Unveiling New Therapeutic Avenues

Cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), the main components of Cannabis sativa plants, have attracted a significant amount of attention due to their biological activities. This study identified GPR18 as the target of partial agonist CBD activating the p42/p44 MAPK pathway leading to migration of endometrial epithelial cells. Induced fit docking (IFD) showed that the affinity of THC for GPR18 is higher than that of CBD, and molecular dynamics (MD) simulations showed that CBD-GPR18 complexes at 130/200 ns might have stable conformations, potentially activating GPR18 by changing the distances of key residues in its active pocket. In contrast, THC maintains "metastable" conformations, generating a "shrinking space" leading to full agonism of THC by adding mechanical constraints in GPR18's active pocket. Steered molecular dynamics (SMD) revealed GPR18's active pocket was influenced more by CBD's partial agonism compared with THC. This combined IFD-MD-SMD method may be used to explain the mechanism of activation of partial or full agonists of GPR18.

Exploration of small molecule compounds targeting abdominal aortic aneurysm based on CMap database and molecular dynamics simulation

OBJECTIVE

The mitigation of abdominal aortic aneurysm (AAA) growth through pharmaceutical intervention offers the potential to avert the perils associated with AAA rupture and the subsequent need for surgical intervention. Nevertheless, the existing effective drugs for AAA treatment are limited, necessitating a pressing exploration for novel therapeutic medications.

METHODS

AAA-related transcriptome data were downloaded from GEO, and differentially expressed genes (DEGs) in AAA tissue were screened for GO and KEGG enrichment analyses. Small molecule compounds and their target proteins with negative connectivity to the AAA expression profile were predicted in the Connectivity Map (CMap) database. Molecular docking and molecular dynamics simulation were performed to predict the binding of the target protein to the small molecule compound, and the MM/GBSA method was used to calculate the binding free energy. Cluster analysis was performed using the cluster tool in the GROMACS package. An AAA cell-free model was built, and CETSA experiments were used to demonstrate the binding ability of small molecules to the target protein in cells.

RESULTS

A total of 2244 DEGs in AAA were obtained through differential analysis, and the DEGs were mainly enriched in the tubulin binding biological function and cell cycle pathway. The CMap results showed that Apicidin had a potential therapeutic effect on AAA with a connectivity score of -97.74, and HDAC4 was the target protein of Apicidin. Based on literature, HDAC4-Apicidin was selected as the subsequent research object. The lowest affinity of Apicidin-HDAC4 molecular docking was -8.218 kcal/mol. Molecular dynamics simulation results indicated that Apicidin-HDAC4 could form a stable complex. MM/GBSA analysis showed a total binding free energy of -55.40 ± 0.79 kcal/mol, and cluster analysis showed that there were two main conformational clusters during the binding process, accounting for 22.4% and 57.8%, respectively. Apicidin could form hydrogen bonds with surrounding residues for stable binding. CETSA experiment proved the stable binding ability of Apicidin and HDAC4.

CONCLUSION

Apicidin inhibited HDAC4 in AAA and exhibited favorable protein-ligand interactions and stability, making it a potential candidate drug for treating AAA.

Analysis of AlphaFold and molecular dynamics structure predictions of mutations in serpins

Serine protease inhibitors (serpins) include thousands of structurally conserved proteins playing key roles in many organisms. Mutations affecting serpins may disturb their conformation, leading to inactive forms. Unfortunately, conformational consequences of serpin mutations are difficult to predict. In this study, we integrate experimental data of patients with mutations affecting one serpin with the predictions obtained by AlphaFold and molecular dynamics. Five SERPINC1 mutations causing antithrombin deficiency, the strongest congenital thrombophilia were selected from a cohort of 350 unrelated patients based on functional, biochemical, and crystallographic evidence supporting a folding defect. AlphaFold gave an accurate prediction for the wild-type structure. However, it also produced native structures for all variants, regardless of complexity or conformational consequences in vivo. Similarly, molecular dynamics of up to 1000 ns at temperatures causing conformational transitions did not show significant changes in the native structure of wild-type and variants. In conclusion, AlphaFold and molecular dynamics force predictions into the native conformation at conditions with experimental evidence supporting a conformational change to other structures. It is necessary to improve predictive strategies for serpins that consider the conformational sensitivity of these molecules.

Linking triphenylphosphonium cation to a bicyclic hydroquinone improves their antiplatelet effect via the regulation of mitochondrial function

Platelets are the critical target for preventing and treating pathological thrombus formation. However, despite current antiplatelet therapy, cardiovascular mortality remains high, and cardiovascular events continue in prescribed patients. In this study, first results were obtained with ortho-carbonyl hydroquinones as antiplatelet agents; we found that linking triphenylphosphonium cation to a bicyclic ortho-carbonyl hydroquinone moiety by a short alkyl chain significantly improved their antiplatelet effect by affecting the mitochondrial functioning. The mechanism of action involves uncoupling OXPHOS, which leads to an increase in mitochondrial ROS production and a decrease in the mitochondrial membrane potential and OCR. This alteration disrupts the energy production by mitochondrial function necessary for the platelet activation process. These effects are responsive to the complete structure of the compounds and not to isolated parts of the compounds tested. The results obtained in this research can be used as the basis for developing new antiplatelet agents that target mitochondria.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Design and Synthesis of Novel Macrocyclic Derivatives as Potent and Selective Cyclin-Dependent Kinase 7 Inhibitors

The duality of function (cell cycle regulation and gene transcription) of cyclin-dependent kinase 7 (CDK7) makes it an attractive oncology target and the discovery of CDK7 inhibitors has been a long-term pursuit by academia and pharmaceutical companies. However, achieving selective leading compounds is still difficult owing to the similarities among the ATP binding pocket. Herein, we detail the design and synthesis of a series of macrocyclic derivatives with pyrazolo[1,5-a]-1,3,5-triazine core structure as potent and selective CDK7 inhibitors. The diverse manners of macrocyclization led to distinguished selectivity profiles of the CDK family. Molecular dynamics (MD) simulation explained the binding difference between 15- and 16-membered macrocyclic compounds. Further optimization generated compound 37 exhibiting good CDK7 inhibitory activity and high selectivity over other CDKs. This work clearly demonstrated macrocyclization is a versatile method to finely tune the selectivity profile of small molecules and MD simulation can be a valuable tool in prioritizing designs of the macrocycle.

Computational screening of natural MtbDXR inhibitors for novel anti-tuberculosis compound discovery

DXR (1-deoxy-d-xylulose-5-phosphate reductoisomerase) is an essential enzyme in the Methylerythritol 4-phosphate (MEP) pathway, which is used by M. tuberculosis and a few other pathogens. This essential enzyme in the isoprenoid synthesis pathway has been previously reported as an important target for antibiotic drug design. However, till now, there is no record of any drug-like safe molecule to inhibit MtbDXR. Numerous plant species have been traditionally used for tuberculosis therapies. In this study, we selected six plant species with anti-tubercular properties. The chemoinformatic screening was performed on 352 phytochemicals from those plants against the MtbDXR protein. After molecular docking analysis, we filtered the top five compounds, CID: 5280443 (Apigenin), CID: 3220 (Emodin), CID: 5280863 (Kaempferol), CID: 5280445 (Luteolin), and CID: 6101979 (beta-Hydroxychalcone), based on binding affinity. Molecular dynamics simulations disclosed the stability of the compounds at the active site of the proteins. Finally, in silico ADME and toxicity evaluations confirmed the compounds to be effective and safe for oral administration. Thus, our findings identified three drug-like safe molecules- Apigenin, Kaempferol, and beta-Hydroxychalcone, that showed good stability in the protein's active site. The results of this computational approach may act as an initial instruction for future in vitro and in vivo testing to identify natural drug-like compounds to treat tuberculosis.Communicated by Ramaswamy H. Sarma.

Exploring the potential of phytochemicals as inhibitors of 3'-phosphoadenosine 5'-phosphosulfate synthase 1 targeting cancer therapy

3'-phosphoadenosine 5'-phosphosulfate synthase 1 (PAPSS1) is an enzyme that critically synthesises the biologically active form of sulfate (PAPS) for all sulfation reactions. The discovery of PAPSS1 as a possible drug target for cancer therapy, specifically in non-small cell lung cancer, has prompted us to investigate potential small-molecule inhibitors of PAPSS1. Here, a structure-based virtual screening method was used to search for phytochemicals in the IMPPAT database to find potential inhibitors of PAPSS1. The primary hits were selected based on their physicochemical, ADMET, and drug-like properties. Then, the binding affinities were calculated and analyzed the interactions to identify safer and more effective hits. The research identified two phytochemicals, Guggulsterone and Corylin, that exhibited significant affinity and specific interaction with the ATP-binding pocket of PAPSS1. Structural observations made by molecular docking were further accompanied by molecular dynamics (MD) simulations and principal component analysis (PCA) to examine the conformational changes and stability of PAPSS1 with the elucidated compounds Guggulsterone and Corylin. MD simulation results suggested that the binding of Guggulsterone and Corylin stabilizes the PAPSS1 structure, leading to fewer conformational changes. This implies that these compounds may be useful in developing PAPSS1 inhibitors for the therapeutic development against non-small cell lung cancer (NSCLC). This study highlights the potential of phytochemicals as PAPSS1 inhibitors and the utility of computational approaches in drug discovery.Communicated by Ramaswamy H. Sarma.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Tyrosine phosphorylation of recombinant hirudin increases affinity to thrombin and antithrombotic activity

Thrombin is one of the key enzymes of the blood coagulation system and a promising target for the development of anticoagulants. One of the most specific natural thrombin inhibitors is hirudin, contained in the salivary glands of medicinal leeches. The medicinal use of recombinant hirudin is limited because of the lack of sulfation on Tyr63, resulting in a 10-fold decrease in activity compared to native (sulfated) hirudin. In the present work, a set of hirudin derivatives was tested for affinity to thrombin: phospho-Tyr63, Tyr63(carboxymethyl)Phe, and Tyr63Glu mutants, which mimic Tyr63 sulfation and Gln65Glu mutant and lysine-succinylated hirudin, which enhance the overall negative charge of hirudin, as well as sulfo-hirudin and desulfo-hirudin as references. Using steered molecular dynamics simulations with subsequent umbrella sampling, phospho-hirudin was shown to exhibit the highest affinity to thrombin among all hirudin analogs, including native sulfo-hirudin; succinylated hirudin was also prospective. Phospho-hirudin exhibited the highest antithrombotic activity in in vitro assay in human plasma. Taking into account the modern methods for obtaining phospho-hirudin and succinylated hirudin, they are prospective as anticoagulants in clinical practice.

Mechanistic insights into ligand dissociation from the SARS-CoV-2 spike glycoprotein

The COVID-19 pandemic, driven by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spurred an urgent need for effective therapeutic interventions. The spike glycoprotein of the SARS-CoV-2 is crucial for infiltrating host cells, rendering it a key candidate for drug development. By interacting with the human angiotensin-converting enzyme 2 (ACE2) receptor, the spike initiates the infection of SARS-CoV-2. Linoleate is known to bind the spike glycoprotein, subsequently reducing its interaction with ACE2. However, the detailed mechanisms underlying the protein-ligand interaction remain unclear. In this study, we characterized the pathways of ligand dissociation and the conformational changes associated with the spike glycoprotein by using ligand Gaussian accelerated molecular dynamics (LiGaMD). Our simulations resulted in eight complete ligand dissociation trajectories, unveiling two distinct ligand unbinding pathways. The preference between these two pathways depends on the gate distance between two α-helices in the receptor binding domain (RBD) and the position of the N-linked glycan at N343. Our study also highlights the essential contributions of K417, N121 glycan, and N165 glycan in ligand unbinding, which are equally crucial in enhancing spike-ACE2 binding. We suggest that the presence of the ligand influences the motions of these residues and glycans, consequently reducing accessibility for spike-ACE2 binding. These findings enhance our understanding of ligand dissociation from the spike glycoprotein and offer significant implications for drug design strategies in the battle against COVID-19.

In Silico Screening of Chlorogenic Acids from Plant Sources against Human Translocase-I to Identify Competitive Inhibitors to Treat Diabetes

Chlorogenic acids (CHLs) are known to competitively bind to translocase-I (T1) of the glucose-6-phosphatase (G6 Pase) system, thereby inhibiting the transport of glucose-6-phosphate (G6P). This competitive binding results in a consequential reduction in blood sugar levels. In this study, steered molecular dynamics (SMD) simulation is employed to investigate the interaction between T1 and G6P, aiming to gain insights into the binding dynamics and diffusion process of G6P through T1. A database comprising 41 CHLs sourced from various plants was developed, subjected to minimization, and screened against T1 through conventional docking methods. The docked conformations were fed into a newly developed customized scoring method incorporating contact-based weights to assess the binding affinities that systematically rank and identify the most effective competitive inhibitors. Among the screened CHLs, 1-methoxy 3,5-dicaffeoylquinic acid, 3,4 dicaffeoyl quinic acid, and 3,4,5-tricaffeoylquinic acid stood out as the top three inhibitors, showcasing crucial atomic interactions with key residues within the binding pocket of T1, and these CHLs are sourced from readily available plants, diminishing reliance on coffee as the predominant CHL source. Along with the devised scoring function, which serves as a valuable tool for virtual screening and lead optimization in drug development, this study also marks a pioneering effort as it involves the modeling of the human translocase and unravels the mechanism of binding and diffusion of G6P within human T1, providing valuable insights into the structural prerequisites for successfully inhibiting the G6P system, laying the foundation for a rational approach to drug design. This research contributes to the progress of drug discovery strategies focused on the G6P system, presenting potential therapeutic avenues for addressing metabolic disorders linked to an impaired glucose metabolism.

Unlocking Opportunities for Mycobacterium leprae and Mycobacterium ulcerans

In the recent decade, scientific communities have toiled to tackle the emerging burden of drug-resistant tuberculosis (DR-TB) and rapidly growing opportunistic nontuberculous mycobacteria (NTM). Among these, two neglected mycobacteria species of the Acinetobacter family, Mycobacterium leprae and Mycobacterium ulcerans, are the etiological agents of leprosy and Buruli ulcer infections, respectively, and fall under the broad umbrella of neglected tropical diseases (NTDs). Unfortunately, lackluster drug discovery efforts have been made against these pathogenic bacteria in the recent decade, resulting in the discovery of only a few countable hits and majorly repurposing anti-TB drug candidates such as telacebec (Q203), P218, and TB47 for current therapeutic interventions. Major ignorance in drug candidate identification might aggravate the dramatic consequences of rapidly spreading mycobacterial NTDs in the coming days. Therefore, this Review focuses on an up-to-date account of drug discovery efforts targeting selected druggable targets from both bacilli, including the accompanying challenges that have been identified and are responsible for the slow drug discovery. Furthermore, a succinct discussion of the all-new possibilities that could be alternative solutions to mitigate the neglected mycobacterial NTD burden and subsequently accelerate the drug discovery effort is also included. We anticipate that the state-of-the-art strategies discussed here may attract major attention from the scientific community to navigate and expand the roadmap for the discovery of next-generation therapeutics against these NTDs.

Designing collagens to shed light on the multi-scale structure-function mapping of matrix disorders

Collagens are the most abundant structural proteins in the extracellular matrix of animals and play crucial roles in maintaining the structural integrity and mechanical properties of tissues and organs while mediating important biological processes. Fibrillar collagens have a unique triple helix structure with a characteristic repeating sequence of (Gly-X-Y)n. Variations within the repetitive sequence can cause misfolding of the triple helix, resulting in heritable connective tissue disorders. The most common variations are single-point missense mutations that lead to the substitution of a glycine residue with a bulkier amino acid (Gly → X). In this review, we will first discuss the importance of collagen's triple helix structure and how single Gly substitutions can impact its folding, structure, secretion, assembly into higher-order structures, and biological functions. We will review the role of "designer collagens," i.e., synthetic collagen-mimetic peptides and recombinant bacterial collagen as model systems to include Gly → X substitutions observed in collagen disorders and investigate their impact on structure and function utilizing in vitro studies. Lastly, we will explore how computational modeling of collagen peptides, especially molecular and steered molecular dynamics, has been instrumental in probing the effects of Gly substitutions on structure, receptor binding, and mechanical stability across multiple length scales.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Magnetic solid phase extraction coupled to HPLC-UV for highly sensitive analysis of mono-hydroxy polycyclic aromatic hydrocarbons in urine

BACKGROUND

As a common pollutant, the carcinogenic properties of polycyclic aromatic hydrocarbons have garnered considerable attention. Trace metabolites of polycyclic aromatic hydrocarbons can be detected in urine as a non-invasively approach to monitor the exposure level. Nonetheless, the urine samples have the disadvantages of being large in volume and containing numerous impurities. Given the growing demand to study metabolites with low abundance and potential biomarkers, there is a pressing need for a preconcentration and high-throughput technique for effectively handling complex liquid samples.

RESULTS

Polystyrene-coated magnetic nanoparticles were used to establish a novel magnetic extraction method for monohydroxy polycyclic aromatic hydrocarbons in urine samples. Polystyrene magnetic nanoparticles are an ideal absorbent for solid-phase extraction. After the material was mixed with the sample and adsorbed the target analyte, the analytes on the material were eluted and quantified using high-performance liquid chromatography. Influencing factors were optimized, and the proposed method achieved desirable sensitivity in analyzing low-abundance metabolites in large volumes of complex urine samples. The recoveries of intra-day and inter-day were 78.0-118.0 % and 81.0 %-115.0 %, respectively. The intra-day and inter-day reproducibility were less than 4.5 % and 8.6 %, respectively. The limits of detection were in the range of 0.009-0.041 ng mL-1, and the limits of quantification were in the range of 0.030-0.135 ng mL-1.

SIGNIFICANCE AND NOVELTY

The application of reusable polystyrene-coated magnetic solid-phase nanoparticles as adsorbents makes the extraction of monohydroxy polycyclic aromatic hydrocarbons from urine samples economical and environmentally benign. The proposed method is simple, sensitive, and efficient compared to existing techniques. The nanoparticles are easy to prepare, showing potential for rapid screening of complex bulk bio-samples in batches with high efficiency and low budget.

Network Pharmacology, Molecular Docking Analysis and Molecular Dynamics Simulation of Scutellaria baicalensis in the Treatment of Liver Fibrosis

BACKGROUND

Traditional Chinese medicine Scutellaria Baicalensis (SB), one of the clinical firstline heat-clearing drugs, has obvious symptomatic advantages for hepatic fibrosis with dampness-heat stasis as its syndrome. We aim to predict and validate the potential mechanism of Scutellaria baicalensis active ingredients against liver fibrosis more scientifically and effectively.

METHODS

The underlying mechanism of Scutellaria baicalensis in inhibiting hepatic fibrosis was studied by applying network pharmacology, molecular docking and molecular dynamics simulation. Expression levels of markers in activated Hepatic Stellate Cells (HSC) after administration of three Scutellaria baicalensis extracts were determined by Western blot and Real-time PCR, respectively, in order to verify the anti-fibrosis effect of the active ingredients Results: There are 164 common targets of drugs and diseases screened and 115 signaling pathways obtained, which were mainly associated with protein phosphorylation, senescence and negative regulation of the apoptotic process. Western blot and Real-time PCR showed that Scutellaria baicalensis extracts could reduce the expression of HSC activation markers, and Oroxylin A had the strongest inhibitory effect on it. Molecular docking results showed that Oroxylin A had high binding activity to target proteins. Molecular dynamics simulation demonstrates promising stability of the Oroxylin A-AKT1 complex over the simulated MD time of 200 ns.

CONCLUSION

Scutellaria baicalensis active ingredients may inhibit HSC proliferation, reduce the generation of pro-inflammatory factors and block the anti-inflammatory effect of inflammatory signal transduction by inducing HSC apoptosis and senescence, thus achieving the effect of anti-fibrosis.

Loop Evolutionary Patterns Shape Catalytic Efficiency of TRI101/201 for Trichothecenes: Insights into Protein-Substrate Interactions

Trichothecenes are highly toxic mycotoxins produced by Fusarium fungi, while TRI101/201 family enzymes play a crucial role in detoxification through acetylation. Studies on the substrate specificity and catalytic kinetics of TRI101/201 have revealed distinct kinetic characteristics, with significant differences observed in catalytic efficiency toward deoxynivalenol, while the catalytic efficiency for T-2 toxin remains relatively consistent. In this study, we used structural bioinformatics analysis and a molecular dynamics simulation workflow to investigate the mechanism underlying the differential catalytic activity of TRI101/201. The findings revealed that the binding stability between trichothecenes and TRI101/201 hinges primarily on a hydrophobic cage structure within the binding site. An intrinsic disordered loop, termed loop cover, defined the evolutionary patterns of the TRI101/201 protein family that are categorized into four subfamilies (V1/V2/V3/M). Furthermore, the unique loop displayed different conformations among these subfamilies' structures, which served to disrupt (V1/V2/V3) or reinforce (M) the hydrophobic cages. The disrupted cages enhanced the water exposure of the hydrophilic moieties of substrates like deoxynivalenol and thereby hindered their binding to the catalytic sites of V-type enzymes. In contrast, this water exposure does not affect substrates like T-2 toxin, which have more hydrophobic substituents, resulting in a comparable catalytic efficiency of both V- and M-type enzymes. Overall, our studies provide theoretical support for understanding the catalytic mechanism of TRI101/201, which shows how an intrinsic disordered loop could impact the protein-ligand binding and suggests a direction for rational protein design in the future.

Terahertz waves regulate the mechanical unfolding of tau pre-mRNA hairpins

Intermolecular interactions, including hydrogen bonds, dominate the pairing and unpairing of nucleic acid chains in the transfer process of genetic information. The energy of THz waves just matches with the weak interactions, so THz waves may interact with biomolecules. Here, the dynamic effects of THz electromagnetic (EM) waves on the mechanical unfolding process of RNA hairpins (WT-30nt and its mutants, rHP, SARS-CoV-2, and SRV-1 SF206) are investigated using steered molecular dynamics (SMD) simulations. The results show that THz waves can either promote the unfolding of the double helix of the RNA hairpin during the initial unfolding phase (4-21.8 THz) or significantly enhance (23.8 and 25.5 THz) or weaken (37.4 and 41.2 THz) its structural stability during unfolding. Our findings have important implications for applying THz waves to regulate dynamic deconvolution processes, such as gene replication, transcription, and translation.

The Energetic Landscape of Catch Bonds in TCR Interfaces

Recognition of peptide/MHC complexes by αβ TCRs has traditionally been viewed through the lens of conventional receptor-ligand theory. Recent work, however, has shown that TCR recognition and T cell signaling can be profoundly influenced and tuned by mechanical forces. One outcome of applied force is the catch bond, where TCR dissociation rates decrease (half-lives increase) when limited force is applied. Although catch bond behavior is believed to be widespread in biology, its counterintuitive nature coupled with the difficulties of describing mechanisms at the structural level have resulted in considerable mystique. In this review, we demonstrate that viewing catch bonds through the lens of energy landscapes, barriers, and the ensuing reaction rates can help demystify catch bonding and provide a foundation on which atomic-level TCR catch bond mechanisms can be built.

Machine Learning Methods for Small Data Challenges in Molecular Science

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

Structural Manipulations of Marine Natural Products Inspire a New Library of 3-Amino-1,2,4-Triazine PDK Inhibitors Endowed with Antitumor Activity in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the main aggressive types of cancer, characterized by late prognosis and drug resistance. Among the main factors sustaining PDAC progression, the alteration of cell metabolism has emerged to have a key role in PDAC cell proliferation, invasion, and resistance to standard chemotherapeutic agents. Taking into account all these factors and the urgency in evaluating novel options to treat PDAC, in the present work we reported the synthesis of a new series of indolyl-7-azaindolyl triazine compounds inspired by marine bis-indolyl alkaloids. We first assessed the ability of the new triazine compounds to inhibit the enzymatic activity of pyruvate dehydrogenase kinases (PDKs). The results showed that most of derivatives totally inhibit PDK1 and PDK4. Molecular docking analysis was executed to predict the possible binding mode of these derivatives using ligand-based homology modeling technique. Evaluation of the capability of new triazines to inhibit the cell growth in 2D and 3D KRAS-wild-type (BxPC-3) and KRAS-mutant (PSN-1) PDAC cell line, was carried out. The results showed the capacity of the new derivatives to reduce cell growth with a major selectivity against KRAS-mutant PDAC PSN-1 on both cell models. These data demonstrated that the new triazine derivatives target PDK1 enzymatic activity and exhibit cytotoxic effects on 2D and 3D PDAC cell models, thus encouraging further structure manipulation for analogs development against PDAC.

Computational analysis of protein-ligand interaction by targeting a cell cycle restrainer

BACKGROUND AND OBJECTIVE

The cyclin-dependent kinases 4/6 (CDK4/6) are among the most crucial controllers of the cell cycle, and their abnormal activity may induce uncontrolled cell multiplication, leading to cancers. The FDA currently approved three CDK4/6 inhibitors, however, they are associated with a variety of side effects. Thus it is required to design/develop novel potent and safe CDK4/6 inhibitors.

METHODS

In the present work, we furnished an integrated in-silico approach followed by steered molecular dynamics (SMD) simulations to identify molecules that can be developed into novel CDK4/6 inhibitors.

RESULTS

Out of thirty-two 3-methyleneisoindolin-1-one molecules we selected top three M18, M24, and M32 molecules as potential drug candidates based on their respective interaction energies. According to the robust 250 ns MD simulations and thermodynamic free energy, M24 was the best molecule in comparison to palbociclib. In SMD, M24 required ∼205.587 kJ/mol/nm external pulling force, while palbociclib needed ∼160.97 kJ/mol/nm to dissociate from the binding pocket of the CDK4.

CONCLUSIONS

The high pulling force required for M24 dissociation from the binding site denotes stronger binding with CDK4. Therefore, M24 offers the possibility of a critical starting structure in developing effective CDK4 inhibitors.

Evolution, Stability, and Applicability of Surfactant Aggregates in Targeted Delivery

Self-assembly/self-aggregation of surfactant molecules in bulk and the vicinity of a surface has been a topic of interest for decades because of its utilization in numerous modern technical applications. In this article, the results of molecular dynamics simulations are reported to investigate the self-aggregation of sodium dodecyl sulfate (SDS) at an interface of mica and water. SDS molecules starting from lower to higher surface concentrations tend to create distinct aggregated structures in the vicinity of a mica surface. The structural properties, such as density profiles, radial distribution functions, and thermodynamic properties like excess entropy and second virial coefficient, are calculated to address the bits and pieces of the self-aggregation. The change in the free energy for aggregates of varied sizes approaching the surface from the bulk aqueous solution, along with the change in their shapes during the process in terms of change in the radius of gyration and its components, is reported respectively to model a generic pathway for a surfactant-based targeted delivery system.

Steered Molecular Dynamics Simulations Study on FABP4 Inhibitors

Ordinary small molecule de novo drug design is time-consuming and expensive. Recently, computational tools were employed and proved their efficacy in accelerating the overall drug design process. Molecular dynamics (MD) simulations and a derivative of MD, steered molecular dynamics (SMD), turned out to be promising rational drug design tools. In this paper, we report the first application of SMD to evaluate the binding properties of small molecules toward FABP4, considering our recent interest in inhibiting fatty acid binding protein 4 (FABP4). FABP4 inhibitors (FABP4is) are small molecules of therapeutic interest, and ongoing clinical studies indicate that they are promising for treating cancer and other diseases such as metabolic syndrome and diabetes.

Microscopic Understanding of the Conformational Stability of the Aggregated Nonamyloid β Components of α-Synuclein

Self-association of α-synuclein peptides into oligomeric species and ordered amyloid fibrils is associated with Parkinson's disease, a progressive neurodegenerative disorder. In particular, the peptide domain formed between the residues Glu-61 (or E61) and Val-95 (or V95) of α-synuclein, typically termed the "nonamyloid β component" (NAC), is known to play critical roles in forming aggregated structures. In this work, we have employed molecular dynamics simulations to explore the conformational properties and relative stabilities of aggregated protofilaments of different orders, namely, tetramer (P(4)), hexamer (P(6)), octamer (P(8)), decamer (P(10)), dodecamer (P(12)), and tetradecamer (P(14)), formed by the NAC domains of α-synuclein. Besides, center-of-mass pulling and umbrella sampling simulation methods have also been employed to characterize the mechanistic pathway of peptide association/dissociation and the corresponding free energy profiles. Structural analysis showed that the disordered C-terminal loop and the central core regions of the peptide units lead to more flexible and distorted structures of the lower order protofilaments (P(4) and P(6)) as compared to the higher order ones. Interestingly, our calculation shows the presence of multiple distinctly populated conformational states for the lower order protofilament P(4), which may drive the oligomerization process along multiple pathways to form different polymorphic α-synuclein fibrillar structures. It is further observed that the nonpolar interaction between the peptides and the corresponding nonpolar solvation free energy play a dominant role in stabilizing the aggregated protofilaments. Importantly, our result showed that reduced cooperativity during the binding of a peptide unit beyond a critical size of the protofilament (P(12)) leads to less favorable binding free energy of a peptide.

Phosphorylation-Competent Metastable State of Escherichia coli Toxin HipA

Phosphorylation is a key post-translational modification that alters the functional state of many proteins. The Escherichia coli toxin HipA, which phosphorylates glutamyl-tRNA synthetase and triggers bacterial persistence under stress, becomes inactivated upon autophosphorylation of Ser150. Interestingly, Ser150 is phosphorylation-incompetent in the crystal structure of HipA since it is deeply buried ("in-state"), although in the phosphorylated state it is solvent exposed ("out-state"). To be phosphorylated, a minor population of HipA must exist in the phosphorylation-competent "out-state" (solvent-exposed Ser150), not detected in the crystal structure of unphosphorylated HipA. Here we report a molten-globule-like intermediate of HipA at low urea (∼4 kcal/mol unstable than natively folded HipA). The intermediate is aggregation-prone, consistent with a solvent exposed Ser150 and its two flanking hydrophobic neighbors (Val/Ile) in the "out-state". Molecular dynamics simulations showed the HipA "in-out" pathway to contain multiple free energy minima with an increasing degree of Ser150 solvent exposure with the free energy difference between the "in-state" and the metastable exposed state(s) to be ∼2-2.5 kcal/mol, with unique sets of hydrogen bonds and salt bridges associated with the metastable loop conformations. Together, the data clearly identify the existence of a phosphorylation-competent metastable state of HipA. Our results not only suggest a mechanism of HipA autophosphorylation but also add to a number of recent reports on unrelated protein systems where the common proposed mechanism for phosphorylation of buried residues is their transient exposure even without phosphorylation.

Structure-based discovery of novel α-aminoketone derivatives as dual p53-MDM2/MDMX inhibitors for the treatment of cancer

The function of the p53 protein is impaired by the overexpression of its negative regulator murine double minute 2 protein (MDM2) and homologous protein MDMX. Disruption of the p53-MDM2/MDMX interaction to restore the transcriptional function of p53 is considered a promising strategy for cancer therapy. To design dual MDM2/MDMX inhibitors, the binding modes of MDM2 or MDMX with their inhibitors are elucidated. Several hot-spot residues of MDM2 or MDMX are identified by molecular dynamics simulations, alanine scanning and MM-GBSA calculations. Then, focusing on the interaction with hot-spot residues, two series of derivatives bearing 1,3-diketone and α-aminoketone scaffolds are designed and synthesized. Among these compounds, C16 is identified as the most potent compound with low micromolar binding affinities with MDM2 and MDMX. C16 also displays moderate antiproliferative activities against MDM2-overexpressing and MDMX-overexpressing cells, with IC₅₀ values of 0.68 μM in HCT116 cells and 0.54 μM in SH-SY5Y cells. Furthermore, C16 inhibits cell migration and invasion, reactivates the function of p53, arrests the cell cycle and induces cellular apoptosis in HCT116 and SH-SY5Y cells. Collectively, C16 can be developed as a dual MDM2 and MDMX inhibitor for cancer therapy.

Insights into the Effects and Mechanism of Andrographolide-Mediated Recovery of Susceptibility of Methicillin-Resistant Staphylococcus aureus to β-Lactam Antibiotics

The frequent resistance associated with β-lactam antibiotics and the high frequency of mutations in β-lactamases constitute a major clinical challenge that can no longer be ignored. Andrographolide (AP), a natural active compound, has been shown to restore susceptibility to β-lactam antibiotics. Fluorescence quenching and molecular simulation showed that AP quenched the intrinsic fluorescence of β-lactamase BlaZ and stably bound to the residues in the catalytic cavity of BlaZ. Of note, AP was found to reduce the stability of the cell wall (CW) in methicillin-resistant Staphylococcus aureus (MRSA), and in combination with penicillin G (PEN), it significantly induced CW roughness and dispersion and even caused its disintegration, while the same concentration of PEN did not. In addition, transcriptome sequencing revealed that AP induced a significant stress response and increased peptidoglycan (PG) synthesis but disrupted its cross-linking, and it repressed the expression of critical genes such as mecA, blaZ, and sarA. We also validated these findings by quantitative reverse transcription-PCR (qRT-PCR). Association analysis using the GEO database showed that the alterations caused by AP were similar to those caused by mutations in the sarA gene. In summary, AP was able to restore the susceptibility of MRSA to β-lactam antibiotics, mainly by inhibiting the β-lactamase BlaZ, by downregulating the expression of critical resistance genes such as mecA and blaZ, and by disrupting CW homeostasis. In addition, restoration of susceptibility to antibiotics could be achieved by inhibiting the global regulator SarA, providing an effective solution to alleviate the problem of bacterial resistance. IMPORTANCE Increasingly, alternatives to antibiotics are being used to mitigate the rapid onset and development of bacterial resistance, and the combination of natural compounds with traditional antibiotics has become an effective therapeutic strategy. Therefore, we attempted to discover more mechanisms to restore susceptibility and effective dosing strategies. Andrographolide (AP), as a natural active ingredient, can mediate recovery of susceptibility of MRSA to β-lactam antibiotics. AP bound stably to the β-lactamase BlaZ and impaired its hydrolytic activity. Notably, AP was able to downregulate the expression of critical resistance genes such as mecA, blaZ, and sarA. Meanwhile, it disrupted the CW cross-linking and homeostasis, while the same concentration of penicillin could not. The multiple inhibitory effect of AP resensitizes intrinsically resistant bacteria to β-lactam antibiotics, effectively prolonging the use cycle of these antibiotics and providing an effective solution to reduce the dosage of antibiotics and providing a theoretical reference for the prevention and control of MRSA.

Illustrating the Effect of Small Molecules Derived from Natural Resources on Amyloid Peptides

The onset of amyloidogenic diseases is associated with the misfolding and aggregation of proteins. Despite extensive research, no effective therapeutics are yet available to treat these chronic degenerative diseases. Targeting the aggregation of disease-specific proteins is regarded as a promising new approach to treat these diseases. In the past few years, rapid progress in this field has been made in vitro, in vivo, and in silico to generate potential drug candidates, ranging from small molecules to polymers to nanoparticles. Small molecular probes, mostly those derived from natural sources, have been of particular interest among amyloid inhibitors. Here, we summarize some of the most important natural small molecular probes which can inhibit the aggregation of Aβ, hIAPP, and α-syn peptides and discuss how their binding efficacy and preference for the peptides vary with their structure and conformation. This provides a comprehensive idea of the crucial factors which should be incorporated into the future design of novel drug candidates useful for the treatment of amyloid diseases.

Current Trends and Changes in Use of Membrane Molecular Dynamics Simulations within Academia and the Pharmaceutical Industry

There has been an almost exponential increase in the use of molecular dynamics simulations in basic research and industry over the last 5 years, with almost a doubling in the number of publications each year. Many of these are focused on neurological membranes, and biological membranes in general, applied to the medical industry. A smaller portion have utilized membrane simulations to answer more basic questions related to the function of specific proteins, chemicals or biological processes. This review covers some newer studies, alongside studies from the last two decades, to determine changes in the field. Some of these are basic, while others are more profound, such as multi-component embedded membrane machinery. It is clear that many facets of the discipline remain the same, while the focus on and uses of the technology are broadening in scope and utilization as a general research tool. Analysis of recent literature provides an overview of the current methodologies, covers some of the recent trends or advances and tries to make predictions of the overall path membrane molecular dynamics will follow in the coming years. In general, the overview presented is geared towards the general scientific community, who may wish to introduce the use of these methodologies in light of these changes, making molecular dynamic simulations more feasible for general scientific or medical research.

Interactions between curcumin and human salt-induced kinase 3 elucidated from computational tools and experimental methods

Natural products are widely used for treating mitochondrial dysfunction-related diseases and cancers. Curcumin, a well-known natural product, can be potentially used to treat cancer. Human salt-induced kinase 3 (SIK3) is one of the target proteins for curcumin. However, the interactions between curcumin and human SIK3 have not yet been investigated in detail. In this study, we studied the binding models for the interactions between curcumin and human SIK3 using computational tools such as homology modeling, molecular docking, molecular dynamics simulations, and binding free energy calculations. The open activity loop conformation of SIK3 with the ketoenol form of curcumin was the optimal binding model. The I72, V80, A93, Y144, A145, and L195 residues played a key role for curcumin binding with human SIK3. The interactions between curcumin and human SIK3 were also investigated using the kinase assay. Moreover, curcumin exhibited an IC₅₀ (half-maximal inhibitory concentration) value of 131 nM, and it showed significant antiproliferative activities of 9.62 ± 0.33 µM and 72.37 ± 0.37 µM against the MCF-7 and MDA-MB-23 cell lines, respectively. This study provides detailed information on the binding of curcumin with human SIK3 and may facilitate the design of novel salt-inducible kinases inhibitors.

Advanced simulations and screening to repurposing a 3C protease inhibitor against the rupintrivir-resistant human norovirus-induced gastroenteritis

Human norovirus (HuNoV) causes acute viral gastroenteritis in all age groups, and dehydration and severe diarrhea in the elderly. The World Health Organization reports ∼1.45 million deaths from acute gastroenteritis annually in the world. Rupintrivir, an inhibitory medicine against the human rhinovirus C3 protease, has been reported to inhibit HuNoV 3C protease. However, several HuNoV 3C protease mutations have been revealed to reduce the susceptibility of HuNoV to rupintrivir. The structural details behind rupintrivir-resistance of these single-point mutations (A105V and I109V) are not still clear. Hence, in this study, a combination of computational techniques were used to determine the rupintrivir-resistance mechanism and to propose an inhibitor against wild-type and mutant HuNoV 3C protease through structure-based virtual screening. Dynamic structural results indicated the unstable binding of rupintrivir at the cleft binding site of the wild-type and mutant 3C proteases, leading to its detachment. Our findings presented that the domain II of the HuNoV 3C protease had a critical role in binding of inhibitory molecules. Binding energy computations, steered molecular dynamics and umbrella sampling simulations confirmed that amentoflavone, the novel suggested inhibitor, strongly binds to the cleft site of all protease models and has a good structural stability in the complex system along the molecular dynamic simulations. Our in silico study proposed the selected compound as a potential inhibitor against the HuNoV 3C protease. However, additional experimental and clinical studies are required to corroborate the therapeutic efficacy of the compound.

Exploring the Pivotal Components Influencing the Side Effects Induced by an Analgesic-Antitumor Peptide from Scorpion Venom on Human Voltage-Gated Sodium Channels 1.4 and 1.5 through Computational Simulation

Voltage-gated sodium channels (VGSCs, or Nav) are important determinants of action potential generation and propagation. Efforts are underway to develop medicines targeting different channel subtypes for the treatment of related channelopathies. However, a high degree of conservation across its nine subtypes could lead to the off-target adverse effects on skeletal and cardiac muscles due to acting on primary skeletal muscle sodium channel Nav1.4 and cardiac muscle sodium channel Nav1.5, respectively. For a long evolutionary process, some peptide toxins from venoms have been found to be highly potent yet selective on ion channel subtypes and, therefore, hold the promising potential to be developed into therapeutic agents. In this research, all-atom molecular dynamic methods were used to elucidate the selective mechanisms of an analgesic-antitumor β-scorpion toxin (AGAP) with human Nav1.4 and Nav1.5 in order to unravel the primary reason for the production of its adverse reactions on the skeletal and cardiac muscles. Our results suggest that the rational distribution of residues with ring structures near position 38 and positive residues in the C-terminal on AGAP are critical factors to ensure its analgesic efficacy. Moreover, the substitution for residues with benzene is beneficial to reduce its side effects.

Molecular Dynamics Simulations of the Cardiac Ryanodine Receptor Type 2 (RyR2) Gating Mechanism

Mutations in the cardiac ryanodine receptor type 2 (RyR2) have been linked to fatal cardiac arrhythmias such as catecholaminergic polymorphic ventricular tachycardia (CPVT). While many CPVT mutations are associated with an increase in Ca²⁺ leak from the sarcoplasmic reticulum, the mechanistic details of RyR2 channel gating are not well understood, and this poses a barrier in the development of new pharmacological treatments. To address this, we explore the gating mechanism of the RyR2 using molecular dynamics (MD) simulations. We test the effect of changing the conformation of certain structural elements by constructing chimera RyR2 structures that are derived from the currently available closed and open cryo-electron microscopy (cryo-EM) structures, and we then use MD simulations to relax the system. Our key finding is that the position of the S4-S5 linker (S4S5L) on a single subunit can determine whether the channel as a whole is open or closed. Our analysis reveals that the position of the S4S5L is regulated by interactions with the U-motif on the same subunit and with the S6 helix on an adjacent subunit. We find that, in general, channel gating is crucially dependent on high percent occupancy interactions between adjacent subunits. We compare our interaction analysis to 49 CPVT1 mutations in the literature and find that 73% appear near a high percent occupancy interaction between adjacent subunits. This suggests that disruption of cooperative, high percent occupancy interactions between adjacent subunits is a primary cause of channel leak and CPVT in mutant RyR2 channels.