Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes

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.

Screening of Potential Inhibitors Targeting the Main Protease Structure of SARS-CoV-2 via Molecular Docking, and Approach with Molecular Dynamics, RMSD, RMSF, H-Bond, SASA and MMGBSA

Severe Acute Respiratory Syndrome caused by a coronavirus is a recent viral infection. There is no scientific evidence or clinical trials to indicate that possible therapies have demonstrated results in suspected or confirmed patients. This work aims to perform a virtual screening of 1430 ligands through molecular docking and to evaluate the possible inhibitory capacity of these drugs about the Mpro protease of Covid-19. The selected drugs were registered with the FDA and available in the virtual drug library, widely used by the population. The simulation was performed using the MolAiCalD algorithm, with a Lamarckian genetic model (GA) combined with energy estimation based on rigid and flexible conformation grids. In addition, molecular dynamics studies were also performed to verify the stability of the receptor-ligand complexes formed through analyses of RMSD, RMSF, H-Bond, SASA, and MMGBSA. Compared to the binding energy of the synthetic redocking coupling (-6.8 kcal/mol/RMSD of 1.34 Å), which was considerably higher, it was then decided to analyze the parameters of only three ligands: ergotamine (-9.9 kcal/mol/RMSD of 2.0 Å), dihydroergotamine (-9.8 kcal/mol/RMSD of 1.46 Å) and olysio (-9.5 kcal/mol/RMSD of 1.5 Å). It can be stated that ergotamine showed the best interactions with the Mpro protease of Covid-19 in the in silico study, showing itself as a promising candidate for treating Covid-19.

Structure based computational RNA design towards MafA transcriptional repressor implicated in multiple myeloma

Multiple myeloma is recognized as the second most common hematological cancer. MafA transcriptional repressor is an established mediator of myelomagenesis. While there are multitude of drugs available for targeting various effectors in multiple myeloma, current literature lacks a candidate RNA based MafA modulator. Thus, using the structure of MafA homodimer-consensus target DNA, a computational effort was implemented to design a novel RNA based chemical modulator against MafA. First, available MafA-consensus DNA structure was employed to generate an RNA library. This library was further subjected to global docking to select the most plausible RNA candidates, preferring to bind DNA binding region of MafA. Following global docking, MD-ready complexes that were prepared via local docking program, were subjected to 500 ns of MD simulations. First, each of these MD simulations were analyzed for relative binding free energy through MM-PBSA method, which pointed towards a strong RNA based MafA binder, RNA1. Second, through a detailed MD analysis, RNA1 was shown to prefer binding to a single monomer of the dimeric DNA binding domain of MafA using higher number of hydrophobic interactions compared with positive control MafA-DNA complex. At the final phase, a principal component analyses was conducted, which led us to identify the actual interaction region of RNA1 and MafA monomer. Overall, to our knowledge, this is the first computational study that presents an RNA molecule capable of potentially targeting MafA protein. Furthermore, limitations of our study together with possible future implications of RNA1 in multiple myeloma were also discussed.

An mRNA vaccine for pancreatic cancer designed by applying in silico immunoinformatics and reverse vaccinology approaches

Pancreatic ductal adenocarcinoma is the most prevalent pancreatic cancer, which is considered a significant global health concern. Chemotherapy and surgery are the mainstays of current pancreatic cancer treatments; however, a few cases are suitable for surgery, and most of the cases will experience recurrent episodes. Compared to DNA or peptide vaccines, mRNA vaccines for pancreatic cancer have more promise because of their delivery, enhanced immune responses, and lower proneness to mutation. We constructed an mRNA vaccine by analyzing S100 family proteins, which are all major activators of receptors for advanced glycation end products. We applied immunoinformatic approaches, including physicochemical properties analysis, structural prediction and validation, molecular docking study, in silico cloning, and immune simulations. The designed mRNA vaccine was estimated to have a molecular weight of 165023.50 Da and was highly soluble (grand average of hydropathicity of -0.440). In the structural assessment, the vaccine seemed to be a well-stable and functioning protein (Z score of -8.94). Also, the docking analysis suggested that the vaccine had a high affinity for TLR-2 and TLR-4 receptors. Additionally, the molecular mechanics with generalized Born and surface area solvation analysis of the "Vaccine-TLR-2" (-141.07 kcal/mol) and "Vaccine-TLR-4" (-271.72 kcal/mol) complexes also suggests a strong binding affinity for the receptors. Codon optimization also provided a high expression level with a GC content of 47.04% and a codon adaptation index score 1.0. The appearance of memory B-cells and T-cells was also observed over a while, with an increased level of helper T-cells and immunoglobulins (IgM and IgG). Moreover, the minimum free energy of the mRNA vaccine was predicted at -1760.00 kcal/mol, indicating the stability of the vaccine following its entry, transcription, and expression. This hypothetical vaccine offers a groundbreaking tool for future research and therapeutic development of pancreatic cancer.

Further Polycyclic Quinones of Micromonospora sp

The crude acetone extract of a marine Micromonospora sp. strain associated with Eudistoma vannnamei was fractioned with hexane and ethyl acetate. The crude extract and both soluble fractions were assayed against several bacteria strains. The new polycyclic quinones 12-hydroxy-9-propyltetracene-6,1-dione (1), 5,12-dihydroxy-4-methoxy-9-propyltetracene-5,12-dione (2), and 4,6-dihydroxy-3-methoxycarbonyl- methyl-6a-(oxobutyl)-5,12-anthraquinone (3), along with the known 4,6-dihydroxy-3-methoxycarbonyl-methyl-6a-(oxo-3-methyl-butyl)-5,12-anthraquinone (4) and 4,6-dihydroxy-3-methoxycarbonyl-methyl-6a-(oxopentyl)-5,12-anthraquinone (5) were isolated from the hexane-soluble fraction, while from the active ethyl acetate fraction were isolated the known 4,6,11-trihydroxy-9-propyltetracene-5,12-dione (6), 4-methoxy-9-propyltetracene-6,11-dione (7), 7,8,9,10-tetrahydro-9-hydroxy-4-methoxy-9-propyltetracene-6,11-dione (8), and 10β-carbomethoxy-7,8,9,10-tetrahydro-4,6,7α,9α,11-pentahydroxy-9-propyltetracene-5,12-dione (9). The structures of the new compounds were established by interpretation of HRMS and NMR techniques. A study of molecular docking was performed with the compounds from the active ethyl acetate fraction to correlate tentatively with the antimicrobial activity. Molecular docking, RMSD, RMSF, and MM-GBSA evaluations were performed to investigate the inhibitory activity of 6-8 against the protein PDB-codex 1MWT, being considered a promising target for studying drug development responsible for inhibiting replication of Staphylococcus aureus. Penicillin G was used as the standard inhibitory. Anthracyclinones 6-8 were the best hydrolase inhibitor with affinity energy -8.1 to -7.9 kcal/mol compared to penicillin G, which presented -6.9 kcal/mol. Both 8 and 7 present potent inhibitory effects against hydrolase through molecular dynamics simulation and exhibit favorable drug-like properties, promising new hydrolase blockers to fight bacterial infections from Staphylococcus aureus.

A Machine Learning Approach to Identify Key Residues Involved in Protein-Protein Interactions Exemplified with SARS-CoV-2 Variants

Human infection with the coronavirus disease 2019 (COVID-19) is mediated by the binding of the spike protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the human angiotensin-converting enzyme 2 (ACE2). The frequent mutations in the receptor-binding domain (RBD) of the spike protein induced the emergence of variants with increased contagion and can hinder vaccine efficiency. Hence, it is crucial to better understand the binding mechanisms of variant RBDs to human ACE2 and develop efficient methods to characterize this interaction. In this work, we present an approach that uses machine learning to analyze the molecular dynamics simulations of RBD variant trajectories bound to ACE2. Along with the binding free energy calculation, this method was used to characterize the major differences in ACE2-binding capacity of three SARS-CoV-2 RBD variants-namely the original Wuhan strain, Omicron BA.1, and the more recent Omicron BA.5 sublineages. Our analyses assessed the differences in binding free energy and shed light on how it affects the infectious rates of different variants. Furthermore, this approach successfully characterized key binding interactions and could be deployed as an efficient tool to predict different binding inhibitors to pave the way for new preventive and therapeutic strategies.

Biosensing circulating MicroRNAs in autoinflammatory skin diseases: Focus on Hidradenitis suppurativa

MicroRNAs (miRNAs) play a crucial role in the early diagnosis of autoinflammatory diseases, with Hidradenitis Suppurativa (HS) being a notable example. HS, an autoinflammatory skin disease affecting the pilosebaceous unit, profoundly impacts patients' quality of life. Its hidden nature, with insidious initial symptoms and patient reluctance to seek medical consultation, often leads to a diagnostic delay of up to 7 years. Recognizing the urgency for early diagnostic tools, recent research identified significant differences in circulating miRNA expression, including miR-24-1-5p, miR-146a-5p, miR26a-5p, miR-206, miR338-3p, and miR-338-5p, between HS patients and healthy controls. These miRNAs serve as potential biomarkers for earlier disease detection. Traditional molecular biology techniques, like reverse transcription quantitative-polymerase chain reaction (RT-qPCR), are employed for their detection using specific primers and probes. Alternatively, short peptides offer a versatile and effective means for capturing miRNAs, providing specificity, ease of synthesis, stability, and multiplexing potential. In this context, we present a computational simulation pipeline designed for crafting peptide sequences that can capture circulating miRNAs in the blood of patients with autoinflammatory skin diseases, including HS. This innovative approach aims to expedite early diagnosis and enhance therapeutic follow-up, addressing the critical need for timely intervention in HS and similar conditions.

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

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

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

In Silico Investigations on the Synergistic Binding Mechanism of Functional Compounds with Beta-Lactoglobulin

Piceatannol (PIC) and epigallocatechin gallate (EGCG) are polyphenolic compounds with applications in the treatment of various diseases such as cancer, but their stability is poor. β-lactoglobulin (β-LG) is a natural carrier that provides a protective effect to small molecule compounds and thus improves their stability. To elucidate the mechanism of action of EGCG, PIC, and palmitate (PLM) in binding to β-LG individually and jointly, this study applied molecular docking and molecular dynamics simulations combined with in-depth analyses including noncovalent interaction (NCI) and binding free energy to investigate the binding characteristics between β-LG and compounds of PIC, EGCG, and PLM. Simulations on the binary complexes of β-LG + PIC, β-LG + EGCG, and β-LG + PLM and ternary complexes of (β-LG + PLM) + PIC, (β-LG + PLM) + EGCG, β-LG + PIC) + EGCG, and (β-LG + EGCG) + PIC were performed for comparison and characterizing the interactions between binding compounds. The results demonstrated that the co-bound PIC and EGCG showed non-beneficial effects on each other. However, the centrally located PLM was revealed to be able to adjust the binding conformation of PIC, which led to the increase in binding affinity with β-LG, thus showing a synergistic effect on the co-bound PIC. The current study of β-LG co-encapsulated PLM and PIC provides a theoretical basis and research suggestions for improving the stability of polyphenols.

Discovery of small molecule c-Maf inhibitors using molecular docking-based virtual screening, molecular dynamics simulation, and biological evaluation

Multiple myeloma (MM) is a prevalent plasma cell malignancy in the blood system that remains incurable. Given the abnormally high expression of c-Maf in most MM patients, targeting c-Maf presents an attractive therapeutic approach for treating MM malignancies. In this study, we employed a combined strategy involving molecular docking-based virtual screening, molecular dynamics (MD) simulation, and molecular mechanics/generalized Born surface area (MM/GBSA) free energy calculation on existing FDA-approved drugs. Six compounds were selected for further experimental assay: vemurafenib, sorafenib, sildenafil, fluvastatin, erlotinib, and glimepiride. Among these compounds, sorafenib and glimepiride exhibited significant inhibition of myeloma cell proliferation in the RPMI-8226 cell line. Moreover, both compounds simultaneously downregulated c-Maf protein expression to induce G1 phase arrest and apoptosis in myeloma cells. Collectively, sorafenib and glimepiride may be considered promising candidates for developing more potent c-Maf inhibitors in the future.

In silico study reveals unconventional interactions between MDC1 of DDR and Beclin-1 of autophagy

DNA damage response (DDR) and autophagy are concerned with maintaining cellular homeostasis and dysregulation of these two pathways lead to pathologic conditions including tumorigenesis. Autophagy is activated as a protective mechanism during DDR which is indicative of their functional cooperativity but the molecular mechanism leading to the convergence of these two pathways during genotoxic stress remains elusive. In this study, through in silico analysis, we have shown an interaction between the Mediator of DNA damage checkpoint 1 (MDC1), an important DDR-associated protein, and Beclin-1, an autophagy inducer. MDC1 is an adaptor or scaffold protein known to regulate DDR, apoptosis, and cell cycle progression. While, Beclin-1 is involved in autophagosome nucleation and exhibits affinity for binding to Fork-head-associated domain (FHA) containing proteins. The FHA domain is commonly conserved in DDR-related proteins including MDC1. Through molecular docking, we have predicted the modeled complex between the MDC1 FHA domain and the Beclin-1 Coiled coil domain (CCD). The docking complex was modeled using ClusPro2.0, based on the crystal structure for the dimerized MDC1 FHA domain and Beclin-1 CCD. The complex stability and binding affinities were assessed using a Ramachandran plot, MD simulation, MM/GBSA, and PRODIGY webserver. Finally, the hot-spot residues at the interface were determined using computational alanine scanning by the DrugScorePPI webserver. Our analysis unveils significant interaction between MDC1 and Beclin-1, involving hydrogen bonds, non-bonded contacts, and salt bridges and indicates MDC1 possibly recruits Beclin-1 to the DSBs, as a consequence of which Beclin-1 is able to modulate DDR.

The search for new efficient inhibitors of SARS-COV-2 through the De novo drug design developed by artificial intelligence

The pandemic caused by Sars-CoV-2 is a viral infection that has generated one of the most significant health problems worldwide. Previous studies report the main protease (Mpro) as a potential target for this virus, as it is considered a crucial enzyme in mediating replication and viral transcription. This work presented the construction of new bioactive compounds for possible inhibition. The De novo molecular design of drugs method in the incremental construction of a ligant model within a receptor model was used, producing new structures with the help of artificial intelligence. The research algorithm and the scoring function responsible for predicting orientation and affinity in the molecular target at the time of coupling showed, as a result of the simulation, the compound with the highest bioaffinity value, Hit 998, with the energy of -17.62 kcal/mol, and synthetic viability close to 50%. While hit 1103 presented better synthetic viability (80%), its affinity energy of -10.28 kcal/mol. Both were compared with the reference linker N3, with a binding affinity of -7.5 kcal/mol. ADMET tests demonstrated that simulated compounds have a low risk of metabolic activation and do not exert effective distribution in the CNS, suggesting a pharmacokinetic mechanism based on local action, even with high topological polarity, which resulted in low oral bioavailability. In conclusion, MMGBSA, H-bonds, RMSD, SASA, and RMSF values were also obtained through molecular dynamics to verify the stability of the receptor-ligant complex within the active protein site to seek new therapeutic propositions in the fight against the pandemic.Communicated by Ramaswamy H. Sarma.

Phytochemical study of Lantana camara flowers, ecotoxicity, antioxidant, in vitro and in silico acetylcholinesterase: molecular docking, MD, and MM/GBSA calculations

Lantana camara L. (Verbenaceae), commonly called lead cambará, has often been used in folk medicine as antiseptic, antispasmodic, against hemorrhages, flu, colds, and diarrheic. This plant is considered a weed and an ornamental and medicinal plant and is an essential source of natural organic compounds, mainly flavonoids. This work aims to investigate the chemical composition and evaluate the biological properties such as antioxidant and acetylcholinesterase of the constituents from L. camara flowers. In addition, the computational simulation was carried out with the constituents identified. The results showed that methanolic extract of the flowers of L. camara presents toxicity, antioxidant activity with 97.8% inhibition percentage in the concentration of 0.25 mg mL-1 against the DPPH radical, and acetylcholinesterase activity. The phytochemical study of extract from L. camara flowers resulted in LC-MS identification of 18 polyphenolic compounds, such as phenolic acid derivatives, phenylethanoid glycosides, and flavonoids. In the in silico study, flavonoid isoverbascoside showed affinity energy of -9.9 kcal.mol-1 with the AChE enzyme. Their phytochemical content, mainly the presence of flavonoids and phenolic compounds in L. camara extracts, may be related to the antioxidant and anticholinesterase potential observed.

Probing the Nucleic Acid Flexibility to Disarm the Viral Counter-Defense Machinery: Design and Characterization of Potent p19 Inhibitors

Plant viruses are highly destructive and significant contributors to several global pandemics and epidemics in plants. A viral disease outbreak in plants can cause a scarcity of food supply and is a severe concern to humanity. The siRNA (small interfering RNA)-mediated RNA-induced silencing complex (RISC) formation is a primary defense mechanism in plants against viruses, where the RISC binds and degrades viral mRNAs. As a counter-defense, many viruses encode RNA-silencing suppressor proteins (e.g., the p19 protein from the Tombusviridae family) for viral proliferation in plants. The functional form of p19 (homodimer) binds to plant siRNA with high affinities, thereby interrupting the RISC formation and thus preventing the viral mRNA silencing in plants. By altering the RISC formation, the p19 protein helps the virus invasion in the plant and ultimately stunts host growth. In this study, we designed several modified siRNA-based molecules for p19 inhibition. The viral p19 protein is known to interact predominantly through H-bonds with 2'-OH and phosphates of the plant siRNA. We utilized this information and in silico-designed flexible substituents of siRNA, where we removed the C2'-C3' bond in each nucleotide unit. We performed all-atom explicit-solvent molecular dynamics simulations (400 ns, 3 replicates each) for control/modified siRNA─p19 complexes (8 in total) followed by energetic estimations. Strikingly, in a few modified complexes, the siRNA not only retained the double-helical structural integrity but also displayed remarkably enhanced p19 binding compared to the control siRNA; hence, we consider it important to perform biological and chemical in vitro and in vivo studies on proposed flexible nucleic acids as p19 inhibitors for crop protection.

Synthesis and in silico study of chenodeoxycholic acid and its analogues as an alternative inhibitor of spike glycoprotein of SARS-CoV-2

COVID-19, caused by SARS-CoV-2, is a viral infection that has generated one of the most significant health problems in the world. Spike glycoprotein is a crucial enzyme in viral replication and transcription mediation. There are reports in the literature on using bile acid in the fight against this virus through in vitro tests. This work presents the synthesis of nine chenodeoxycholic acid derivatives (1-9), which were prepared by oxidation, acetylation, formylation, and esterification reactions, and the analogs 6-9 have not yet been reported in the literature and the possibility of conducting an in silico study of bile acid derivatives as a therapeutic alternative to combat the virus using glycoprotein as a macromolecular target. As a result, five compounds (1, 6-9) possessed favorable competitive interactions with the lowest energies compared to the native ligand (BLA), and the highlighted compound 9 got the best scores. At the same time, analog 1 presented the best ADME filter result. Molecular dynamics also simulated these compounds to verify their stability within the active protein site to seek new therapeutic propositions to fight against the pandemic. Physical and spectroscopic data have fully characterized all the compounds.Communicated by Ramaswamy H. Sarma.

Probing the Activation Mechanisms of Agonist DPI-287 to Delta-Opioid Receptor and Novel Agonists Using Ensemble-Based Virtual Screening with Molecular Dynamics Simulations

Pain drugs targeting mu-opioid receptors face major addiction problems that have caused an epidemic. The delta-opioid receptor (DOR) has shown to not cause addictive effects when bound to an agonist. While the active conformation of the DOR in complex with agonist DPI-287 has been recently solved, there are still no FDA-approved agonists targeting it, providing the opportunity for structure-based virtual screening. In this study, the conformational plasticity of the DOR was probed using molecular dynamics (MD) simulations, identifying two representative conformations from clustering analysis. The two MD conformations as well as the crystal conformation of DOR were used to screen novel compounds from the ZINC database (17 million compounds), in which 69 drugs were picked as potential compounds based on their docking scores. Notably, 37 out of the 69 compounds were obtained from the simulated conformations. The binding stability of the 69 compounds was further investigated using MD simulations. Based on the MM-GBSA binding energy and the predicted drug properties, eight compounds were chosen as the most favorable, six of which were from the simulated conformations. Using a dynamic network model, the communication between the crystal agonist and the top eight molecules with the receptor was analyzed to confirm if these novel compounds share a similar activation mechanism to the crystal ligand. Encouragingly, docking of these eight compounds to the other two opioid receptors (kappa and mu) suggests their good selectivity toward DOR.

Identification of coastal pesticide pollutants as potent inhibitors of Bacillus pasteurii urease mediated calcium carbonate precipitation: a computational approach

Microbially induced calcite precipitation (MICP) through urease enzyme has attained a lot of recognition in various fields of civil engineering and geotechnology for stabilizing the strength of soil and various concrete materials. The activity of urease has been found to be affected by various factors like temperature, substrate concentrations, pH of the medium, presence of inhibitors, etc. Through this study, the outcome of the interaction of pesticides (commonly found in Indian coastal regions) on Bacillus pasteurii urease, a major organism reported for MICP studies has been investigated in silico. The results from the study revealed that the enzyme has higher interactions of -4.1, -3.2, and -3.4 kJ/mol with common pesticides like dichloro diphenyl dichloro ethane(DDD), dichloro diphenyl trichloroe thane (DDT), and methyl parathion of organochlorides and organophosphates class. From the molecular dynamics simulation analysis, complex 1 (DDD -receptor) has been found to have the highest and more compact structure followed by methyl parathion -receptor. Prime MM-GBSA analysis also revealed the highest binding energy of -27.8 kcal/mol with the protein and DDD. Thus, it can be inferred from the current study that pesticides, particularly, DDD, DDT, and methyl parathion present in the coastal areas may have an impact on urease. This interaction can result in the inhibition of the urease activity of B. pasteurii, thus preventing the biomineralization process. This study would be the first report on the computational approach to understanding the interaction of prominent pesticides on the coastal region and B. pasteurii urease.Communicated by Ramaswamy H. Sarma.

Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

De novo design of bioactive phenol and chromone derivatives for inhibitors of Spike glycoprotein of SARS-CoV-2 in silico

This work presents the synthesis of 12 phenol and chromone derivatives, prepared by the analogs, and the possibility of conducting an in silico study of its derivatives as a therapeutic alternative to combat the SARS-CoV-2, pathogen responsible for COVID-19 pandemic, using its S-glycoprotein as a macromolecular target. After the initial screening for the ranking of the products, it was chosen which structure presented the best energy bond with the target. As a result, derivative 4 was submitted to a molecular growth study using artificial intelligence, where 8436 initial structures were obtained that passed through the interaction filters and similarity to the active glycoprotein pocket through the MolAICal computational package. Thus, 557 Hits with active configuration were generated, which is very promising compared to the BLA reference link for inhibiting the biological target. Molecular dynamics also simulated these compounds to verify their stability within the active protein site to seek new therapeutic propositions to fight against the pandemic. The Hit 48 and 250 are the most active compounds against SARS-CoV-2. In summary, the results show that the Hit 250 would be more active than the natural compound, which could be further developed for further testing against SARS-CoV-2. The study employs the de novo approach to design new drugs, combining artificial intelligence and molecular dynamics simulations to create efficient molecular structures. This research aims to contribute to the development of effective therapeutic strategies against the pandemic.

An updated dataset and a structure-based prediction model for protein-RNA binding affinity

Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.

Uncovering the Kinetic Characteristics and Degradation Preference of PROTAC Systems with Advanced Theoretical Analyses

Proteolysis-targeting chimeras (PROTACs), which can selectively induce the degradation of target proteins, represent an attractive technology in drug discovery. A large number of PROTACs have been reported, but due to the complicated structural and kinetic characteristics of the target-PROTAC-E3 ligase ternary interaction process, the rational design of PROTACs is still quite challenging. Here, we characterized and analyzed the kinetic mechanism of MZ1, a PROTAC that targets the bromodomain (BD) of the bromodomain and extra terminal (BET) protein (Brd2, Brd3, or Brd4) and von Hippel-Lindau E3 ligase (VHL), from the kinetic and thermodynamic perspectives of view by using enhanced sampling simulations and free energy calculations. The simulations yielded satisfactory predictions on the relative residence time and standard binding free energy (r_p > 0.9) for MZ1 in different Brd^BD-MZ1-VHL ternary complexes. Interestingly, the simulation of the PROTAC ternary complex disintegration illustrates that MZ1 tends to remain on the surface of VHL with the BD proteins dissociating alone without a specific dissociation direction, indicating that the PROTAC prefers more to bind with E3 ligase at the first step in the formation of the target-PROTAC-E3 ligase ternary complex. Further exploration of the degradation difference of MZ1 in different Brd systems shows that the PROTAC with higher degradation efficiency tends to leave more lysine exposed on the target protein, which is guaranteed by the stability (binding affinity) and durability (residence time) of the target-PROTAC-E3 ligase ternary complex. It is quite possible that the underlying binding characteristics of the Brd^BD-MZ1-VHL systems revealed by this study may be shared by different PROTAC systems as a general rule, which may accelerate rational PROTAC design with higher degradation efficiency.

Revisiting MMPBSA by Adoption of MC-Based Surface Area/Volume, ANI-ML Potentials, and Two-Valued Interior Dielectric Constant

Here, we report the accuracy improvements of molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations by adoption of ANI-ML potentials in replacement of MM terms, the use of solvent-accessible surface area (SASA) and volume (SAV) values from the Monte Carlo sampling of the probe, and introducing two different interior dielectric constants for electrostatic interactions of protein-ligand (P-L) and polar solvation term in the MMPBSA calculations. Our results show that the Pearson correlation coefficients of MMPBSA-calculated values with respect to experimental binding free energies can be drastically improved from 0.48 to 0.90 by adoption of ANI-ML potentials in replacement of MM energy terms in the equation, referred to as ANI-PBSA. Moreover, we show that the SASA/SAV-combined equation in the scaled particle theory (SPT) can be a better choice to model nonpolar solvation term, reaching nearly the same accuracy by ANI-PBSA calculations. Finally, we introduce two different values of interior dielectric constants, which could be an alternative strategy between the single and variable constant definitions.

Isolation, characterization and in silico study of propenamide alkaloids from Hymenoepmecis bicolor poison against active μ-opioid receptor

Some insects produce venoms to defend against predators and directly interact with opioid receptors. In the present study, it was identified two alkaloids in the wasp venom species Hymenoepimecis bicolor. It was demonstrated that these could act as potential inhibitors of opioid receptors through their robust affinity to the receptors. The interaction profile was given to opioid receptors (μOR), with 60% of targets similar to alkaloid 1, with 0.25 probability, and 46.7% of targets similar to alkaloid 2, with a probability 0.17 of affinity as a target, which is considered signaling macromolecules and can mediate the most potent analgesic and addictive properties of opiate alkaloids. Notably, both alkaloids showed -7.6 kcal/mol affinity to the morphine agonies through six residues, Gly124, Asp147, Trp293, Ile296, Ile322, and Tyr326. These observations suggest further research on opioid receptors using in vitro studies of possible therapeutic applications.Communicated by Ramaswamy H. Sarma.

Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson-Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein-ligand binding free energies using the MM-PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM-PBSA method.

Naphthoquinones biflorin and bis-biflorin (Capraria biflora) as possible inhibitors of the fungus Candida auris polymerase: molecular docking, molecular dynamics, MM/GBSA calculations and in silico drug-likeness study

A new worldwide concern has emerged with the recent emergence of infections caused by Candida auris. This reflects its comparative ease of transmission, substantial mortality, and the increasing level of resistance seen in the three major classes of antifungal drugs. Efforts to create a better design for structure-based drugs that described numerous modifications and the search for secondary metabolic structures derived from plant species are likely to reduce the virulence of several fungal pathogens. In this context, the present work aimed to evaluate in silico two naphthoquinones isolated from the roots of Capraria biflora, biflorin, and its dimmer, bis-biflorin, as potential inhibitors of Candida auris polymerase. Based on the simulation performed with the two naphthoquinones, biflorin and bis-biflorin, it can be stated that bis-biflorin showed the best interactions with Candida auris polymerase. Still, biflorin also demonstrated favorable coupling energy. Predictive pharmacokinetic assays suggest that biflorin has high oral bioavailability and more excellent metabolic stability compared to the bis-biflorin analogue. constituting a promising pharmacological tool.Communicated by Ramaswamy H. Sarma.

Molecular mechanism of ATP and RNA binding to Zika virus NS3 helicase and identification of repurposed drugs using molecular dynamics simulations

Congenital Zika virus syndrome has caused a public health emergency of international concern. So far, there are no drugs available to prevent or treat the infection caused by Zika virus. The Zika virus NS3 helicase is a potential protein target for drug discovery due to its vital role in viral genome replication. NS3 helicase unwinds the viral RNA to enable the reproduction of the viral genome by the NS5 protein. NS3 helicase has two crucial binding sites; the ATP binding site and the RNA binding site. We used molecular docking and molecular dynamics (MD) simulations to study the structural behavior of Zika virus NS3 helicase in its apo form and in the presence of ATP, single-stranded RNA, and both ATP-RNA to understand their potential implications in NS3 helicase activity. Further, we have carried out virtual screening of FDA approved drugs, followed by molecular docking to identify the ATP-competitive hit molecules as probable Zika virus NS3 helicase inhibitors. The MD simulations trajectories were analyzed using normal mode analysis and principal component analysis that reveals fluctuations in the R-loop. These findings aid in understanding the molecular mechanisms of the simultaneous binding of ATP and RNA, and guide the design and discovery of new inhibitors of the Zika virus NS3 helicase as a promising drug target to treat the Zika virus infection. Communicated by Ramaswamy H. Sarma.

Investigating RNA-protein recognition mechanisms through supervised molecular dynamics (SuMD) simulations

Ribonucleic acid (RNA) plays a key regulatory role within the cell, cooperating with proteins to control the genome expression and several biological processes. Due to its characteristic structural features, this polymer can mold itself into different three-dimensional structures able to recognize target biomolecules with high affinity and specificity, thereby attracting the interest of drug developers and medicinal chemists. One successful example of the exploitation of RNA's structural and functional peculiarities is represented by aptamers, a class of therapeutic and diagnostic tools that can recognize and tightly bind several pharmaceutically relevant targets, ranging from small molecules to proteins, making use of the available structural and conformational freedom to maximize the complementarity with their interacting counterparts. In this scientific work, we present the first application of Supervised Molecular Dynamics (SuMD), an enhanced sampling Molecular Dynamics-based method for the study of receptor-ligand association processes in the nanoseconds timescale, to the study of recognition pathways between RNA aptamers and proteins, elucidating the main advantages and limitations of the technique while discussing its possible role in the rational design of RNA-based therapeutics.

HIF1 inhibition of the biflavonoids against pancreas cancer: drug-likeness, bioavailability, ADMET, PASS, molecular docking, molecular dynamics, and MM/GBSA calculations

Pancreatic cancer is an aggressive disease with a high death rate and is difficult to treat. This disease, in the most cases, is asymptomatic until it progresses to an advanced stage. Therefore, the search for bioactive molecules is urgent to combat pancreatic cancer. Then, this work analyzed the interaction potential of agathisflavone and amentoflavone molecules against the HIF1 target using the ADMET, molecular docking, and molecular dynamics simulations. More recent drug-likeness filters that combine physicochemical and physiological parameters have shown that high polar surface area (TPSA > 75 Ų) drives biflavonoids out of the toxic drug space of Pfizer dataset. Regarding the pharmacokinetic descriptors, it was possible to notice that Amentoflavone showed a better order of passive cell permeability (P_app = 8 × 10^-6cm/s) and better metabolic stability, biotransformed by aromatic hydroxylation reactions by the CYP3A4 isoenzyme on the human liver, that favor its hepatic clearance. The molecular docking and molecular dynamics simulations indicated the high interaction potential and stability between the ligands analyzed (highlighted the amentoflavone molecule), respectively. The MM/GBSA calculations showed that the amentoflavone ligand registered the highest ΔG binding value of -32.6957 kcal/mol with the HIF1 target. Then, this molecule may be used as a potential inhibitor of pancreatic cancer. In this perspective, the present work represents an initial step in the virtual bioprospecting a pharmacological tool for treating of pancreatic cancer.Communicated by Ramaswamy H. Sarma.

Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses

Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.

In silico binding affinity prediction for metabotropic glutamate receptors using both endpoint free energy methods and a machine learning-based scoring function

Metabotropic glutamate receptors (mGluRs) play an important role in regulating glutamate signal pathways, which are involved in neuropathy and periphery homeostasis. mGluR4, which belongs to Group III mGluRs, is most widely distributed in the periphery among all the mGluRs. It has been proved that the regulation of this receptor is involved in diabetes, colorectal carcinoma and many other diseases. However, the application of structure-based drug design to identify small molecules to regulate the mGluR4 receptor is limited due to the absence of a resolved mGluR4 protein structure. In this work, we first built a homology model of mGluR4 based on a crystal structure of mGluR8, and then conducted hierarchical virtual screening (HVS) to identify possible active ligands for mGluR4. The HVS protocol consists of three hierarchical filters including Glide docking, molecular dynamic (MD) simulation and binding free energy calculation. We successfully prioritized active ligands of mGluR4 from a set of screening compounds using HVS. The predicted active ligands based on binding affinities can almost cover all the experiment-determined active ligands, with only one ligand missed. The correlation between the measured and predicted binding affinities is significantly improved for the MM-PB/GBSA-WSAS methods compared to the Glide docking method. More importantly, we have identified hotspots for ligand binding, and we found that SER157 and GLY158 tend to contribute to the selectivity of mGluR4 ligands, while ALA154 and ALA155 could account for the ligand selectivity to mGluR8. We also recognized other 5 key residues that are critical for ligand potency. The difference of the binding profiles between mGluR4 and mGluR8 can guide us to develop more potent and selective modulators. Moreover, we evaluated the performance of IPSF, a novel type of scoring function trained by a machine learning algorithm on residue-ligand interaction profiles, in guiding drug lead optimization. The cross-validation root-mean-square errors (RMSEs) are much smaller than those by the endpoint methods, and the correlation coefficients are comparable to the best endpoint methods for both mGluRs. Thus, machine learning-based IPSF can be applied to guide lead optimization, albeit the total number of actives/inactives are not big, a typical scenario in drug discovery projects.

Fast Prediction of Binding Affinities of SARS-CoV-2 Spike Protein and Its Mutants with Antibodies through Intermolecular Interaction Modeling-Based Machine Learning

Since the introduction of the novel SARS-CoV-2 virus (COVID-19) in late 2019, various new variants have appeared with mutations that confer resistance to the vaccines and monoclonal antibodies that were developed in response to the wild-type virus. As we continue through the pandemic, an accurate and efficient methodology is needed to help predict the effects certain mutations will have on both our currently produced therapeutics and those that are in development. Using published cryo-electron microscopy and X-ray crystallography structures of the spike receptor binding domain region with currently known antibodies, in the present study, we created and cross-validated an intermolecular interaction modeling-based multi-layer perceptron machine learning approach that can accurately predict the mutation-caused shifts in the binding affinity between the spike protein (wild-type or mutant) and various antibodies. This validated artificial intelligence (AI) model was used to predict the binding affinity (Kd) of reported SARS-CoV-2 antibodies with various variants of concern, including the most recently identified "Deltamicron" (or "Deltacron") variant. This AI model may be employed in the future to predict the Kd of developed novel antibody therapeutics to overcome the challenging antibody resistance issue and develop structural bases for the effects of both current and new mutants of the spike protein. In addition, the similar AI strategy and approach based on modeling of the intermolecular interactions may be useful in development of machine learning models predicting binding affinities for other protein-protein binding systems, including other antibodies binding with their antigens.

Cytosolic microRNA-inducible nuclear translocation of Cas9 protein for disease-specific genome modification

MicroRNA-dependent mRNA decay plays an important role in gene silencing by facilitating posttranscriptional and translational repression. Inspired by this intrinsic nature of microRNA-mediated mRNA cleavage, here, we describe a microRNA-targeting mRNA as a switch platform called mRNA bridge mimetics to regulate the translocation of proteins. We applied the mRNA bridge mimetics platform to Cas9 protein to confer it the ability to translocate into the nucleus via cleavage of the nuclear export signal. This system performed programmed gene editing in vitro and in vivo. Combinatorial treatment with cisplatin and miR-21-EZH2 axis-targeting CRISPR Self Check-In improved sensitivity to chemotherapeutic drugs in vivo. Using the endogenous microRNA-mediated mRNA decay mechanism, our platform is able to remodel a cell's natural biology to allow the entry of precise drugs into the nucleus, devoid of non-specific translocation. The mRNA bridge mimetics strategy is promising for applications in which the reaction must be controlled via intracellular stimuli and modulates Cas9 proteins to ensure safe genome modification in diseased conditions.

Combined study of docking and molecular dynamics against DNV-3 SN1 protein by bixinoids

Dengue (DENV), Zica virus (ZIKV), and Chikungunya fever (CHIK) are tropical diseases that have caused a lot of problems in general worldwide. Transmitted by mosquitoes of the species Aedes aegypti and albopictus, they have not been completely eradicated in the country, and their proliferation has only increased in the Northeast region. Within the structure of the virus, it is possible to verify the presence of glycoprotein SN1, which is responsible for its replication. If this macromolecule is inhibited using a specific or complex linker, it can interrupt its replication activity. An alternative to this problem has been using structures derived from natural products that have pharmacological properties. A dynamic and molecular docking combined study used computational simulation in the four isomeric forms of bixin against the SN1 protein. The Z,E-bixin and E,E-bixin isomers, both with affinity energy -6.7 and -6.5 Kcal/mol, presented the best results. Thus, bixin and its isomers, found in annatto seeds, maybe an initial proposal in the search for prototype compounds to study to fight this lethal virus in the future.Communicated by Ramaswamy H. Sarma.

RNA-ligand molecular docking: advances and challenges

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

Generalized Methodology for the Quick Prediction of Variant SARS-CoV-2 Spike Protein Binding Affinities with Human Angiotensin-Converting Enzyme II

Variants of the SARS-CoV-2 virus continue to remain a threat 2 years from the beginning of the pandemic. As more variants arise, and the B.1.1.529 (Omicron) variant threatens to create another wave of infections, a method is needed to predict the binding affinity of the spike protein quickly and accurately with human angiotensin-converting enzyme II (ACE2). We present an accurate and convenient energy minimization/molecular mechanics Poisson-Boltzmann surface area methodology previously used with engineered ACE2 therapeutics to predict the binding affinity of the Omicron variant. Without any additional data from the variants discovered after the publication of our first model, the methodology can accurately predict the binding of the spike/ACE2 variant complexes. From this methodology, we predicted that the Omicron variant spike has a Kd of ∼22.69 nM (which is very close to the experimental Kd of 20.63 nM published during the review process of the current report) and that spike protein of the new "Stealth" Omicron variant (BA.2) will display a Kd of ∼12.9 nM with the wild-type ACE2 protein. This methodology can be used with as-yet discovered variants, allowing for quick determinations regarding the variant's infectivity versus either the wild-type virus or its variants.

Loading of DOX into a tetrahedral DNA nanostructure: the corner does matter

With the rapid development of nanotechnology, various DNA nanostructures have been synthesized and widely used in drug delivery. However, the underlying mechanisms of drug molecule loading into the DNA nanostructure are still elusive. In this work, we systematically investigate the interactions of a tetrahedral DNA nanostructure (TDN) with the anti-cancer drug doxorubicin (DOX) by combining molecular docking and all-atom molecular dynamics simulations. It is found that there are five possible binding modes in the single TDN-DOX interactions, namely the outside-corner mode, the inside-corner mode, the major-groove mode, the minor-groove mode, and the intercalation mode, where the van der Waals (VDW) interaction and the electrostatic (ELE) interaction dominate in the case of unionized DOX and ionized DOX, respectively. Moreover, with the increase of the DOX number, some of the interaction modes may disappear and the inside-corner mode is the most energy-favorable mode. The present study enhances the molecular understanding of the role of TDN as the drug carrier, which may provide a useful guideline for the future design of DNA nanostructures.

Identification of Hotspot Residues in Binding of SARS-CoV-2 Spike and Human ACE2 Proteins

Coronaviruses are a large family of viruses that can cause respiratory infections with varying severity from common cold to severe diseases such as novel coronavirus disease (COVID-19). COVID-19 has been declared as a global pandemic by the World Health Organization on March 11, 2020 and with the development of vaccines it slowed down as of this date. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses its spike glycoprotein (Sgp) to bind human angiotensin-converting enzyme 2 (hACE2) receptor, and mediates membrane fusion and virus entry. The recognition of Sgp to human ACE2 and its high affinity for it has been of great importance since this provides the first step in viral entry to human cells. Therefore, it is crucial to identify key residues (hotspots) in this process. In this study, computational alanine scanning has been performed for Sgp and hACE2. The residues identified with significance in binding and other residues in close proximity were studied further through molecular mechanics-based protein binding free energy change prediction methods. Moreover, the interfacial residues in both proteins were investigated for their cooperative binding. Additionally, folding free energy changes upon mutation to Ala were calculated to assess their effect on stability of Sgp and hACE2. Our results taken together with findings from previous studies revealed the residues that are most significant and are relatively significant in binding of Sgp to human ACE2 protein.

Docking and scoring for nucleic acid-ligand interactions: Principles and current status

Nucleic acid (NA)-ligand interactions have crucial roles in many cellular processes and, thus, are increasingly attracting therapeutic interest in drug discovery. Molecular docking is a valuable tool for studying molecular interactions. However, because NAs differ significantly from proteins in both their physical and chemical properties, traditional docking algorithms and scoring functions for protein-ligand interactions might not be applicable to NA-ligand docking. Therefore, various sampling strategies and scoring functions for NA-ligand interactions have been developed. Here, we review the basic principles and current status of docking algorithms and scoring functions for DNA/RNA-ligand interactions. We also discuss challenges and limitations of current docking and scoring approaches.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Fast Prediction of Binding Affinities of the SARS-CoV-2 Spike Protein Mutant N501Y (UK Variant) with ACE2 and Miniprotein Drug Candidates

A recently identified variant of SARS-CoV-2 virus, known as the United Kingdom (UK) variant (lineage B.1.1.7), has an N501Y mutation on its spike protein. SARS-CoV-2 spike protein binds with angiotensin-converting enzyme 2 (ACE2), a key protein for the viral entry into the host cells. Here, we report an efficient computational approach, including the simple energy minimizations and binding free energy calculations, starting from an experimental structure of the binding complex along with experimental calibration of the calculated binding free energies, to rapidly and reliably predict the binding affinities of the N501Y mutant with human ACE2 (hACE2) and recently reported miniprotein and hACE2 decoy (CTC-445.2) drug candidates. It has been demonstrated that the N501Y mutation markedly increases the ACE2-spike protein binding affinity (Kd) from 22 to 0.44 nM, which could partially explain why the UK variant is more infectious. The miniproteins are predicted to have ∼10,000- to 100,000-fold diminished binding affinities with the N501Y mutant, creating a need for design of novel therapeutic candidates to overcome the N501Y mutation-induced drug resistance. The N501Y mutation is also predicted to decrease the binding affinity of a hACE2 decoy (CTC-445.2) binding with the spike protein by ∼200-fold. This convenient computational approach along with experimental calibration may be similarly used in the future to predict the binding affinities of potential new variants of the spike protein.

Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions

The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then developed regression algorithms separately for MPDs and MPRs by introducing novel geometric partition-based energy features and interface-based structural features. Through feature selection and ensemble learning, similar computational frameworks that integrated energy- and nonenergy-based models were established to estimate the binding affinity changes resulting from MPDs and MPRs, but the selected features for the final models were different and therefore reflected the specificity of these two mutation classes. Furthermore, the proposed methodology was extended to the identification of mutations that significantly decreased the binding affinities. Extensive validations indicated that our algorithm generally performed better than the state-of-the-art methods on both the regression and classification tasks. The webserver and software are freely available at http://liulab.hzau.edu.cn/PEMPNI and https://github.com/hzau-liulab/PEMPNI.

Protein-nucleic acid interactions play important roles in various cellular processes. Missense mutations occurring in DNA- or RNA-binding proteins (termed MPDs and MPRs, respectively) could change the binding affinities of these interactions. Previous studies have compared protein-DNA and protein-RNA interactions from multifaceted viewpoints, but less attention has been given to the similarities and specific differences between the effects of MPDs and MPRs and between the methodologies for predicting the affinity changes induced by the two mutation classes. Therefore, we systematically compared their impacts and demonstrated that MPDs and MPRs could have specific preferences for binding affinity changes. These observations motivated us to construct regression models separately for MPDs and MPRs by introducing novel energy and nonenergy descriptors. Although similar frameworks were developed to estimate these two categories of mutation effects, different descriptors were selected in the regression models and further revealed the specificity of mutation classes. The interplay between the energy and nonenergy modules effectively improved prediction performance. Our algorithm can also be adopted to disentangle mutations significantly decreasing binding affinities from other mutations.

Combination of multiple computational methods revealing specific sub-sectional recognition and hydrogen-bond dependent transportation of CKII peptide fragment in O-GlcNAc transferase

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Mechanism of CKII peptide recognition, transportation and binding in OGT is obtained.

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Peptide delivery is strong exothermic, highly dependent on hydrogen bond network.

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Typical ‘spread’ & ‘V’ conformation change noticed for peptide accompanies stable OGT.

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Specific subsection of peptide has diverse performance in its recognition and delivery.

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Multiple methods combination may be used in other bio-system with flexible substrate.

O-linked β-N-acetyl-D-glucosamine (O-GlcNAc) transferase (OGT) is an essential enzyme in many cellular physiological catalytic reactions that regulates protein O-GlcNAcylation. Aberrant O-GlcNAcylation is related to insulin resistance, diabetic complications, cancer and neurodegenerative diseases. Understanding the peptide delivery in OGT is significant in comprehending enzymatic catalytic process, target-protein recognition and pathogenic mechanism. Herein extensive molecular dynamics (MD) simulations combined with various techniques are utilized to study the recognizing and binding mechanism of peptide fragment extracted from casein kinase II by OGT from atomic level. The residues of His496, His558, Thr633, Lys634, and Pro897 are demonstrated to play a dominant role in the peptide stabilization via hydrogen bonds and σ-π interaction, whose van der Waals and non-polar solvent effects provide the main driving force. In addition, two channels are identified. The delivery mode, mechanism together with thermodynamic and dynamic characterizations for the most favorable channel are determined. The peptide is more inclined to be recognized by OGT through the cavity comprised of residues 799–812, 893–899, and 865–871, and Tyr13-terminal is prior recognized to Met26-terminal. The transportation process is accompanied with conformation changes between the “spread” and “V” shapes. The whole process is strong exothermic that is highly dependent on the variation of hydrogen bond interactions between peptide and OGT as well as the performance of different subsections of peptide. Besides that, multiple computational methods combinations may contribute meaningfully to calculation of similar bio-systems with long and flexible substrate.

Schizandrin A exhibits potent anticancer activity in colorectal cancer cells by inhibiting heat shock factor 1

Heat shock factor 1 (HSF1) is a powerful multifaceted oncogenic modifier that plays a role in maintaining the protein balance of cancer cells under various stresses. In recent studies, there have been reports of increased expression of HSF1 in colorectal cancer (CRC) cells, and the depletion of the HSF1 gene knockdown has inhibited colon cancer growth both in vivo and in vitro. Therefore, HSF1 is a promising target for colon cancer treatment and chemoprevention. In the present study, we found that Schizandrin A (Sch A) significantly inhibited the growth of CRC cell lines by inducing cell cycle arrest, apoptosis and death. Through HSE luciferase reporter assay and quantitative PCR (qPCR), we identified Sch A as a novel HSF1 inhibitor. In addition, Sch A could effectively inhibit the induction of HSF1 target proteins such as heat-shock protein (HSP) 70 (HSP70) and HSP27, whether in heat shock or normal temperature culture. In the Surface Plasmon Resonance (SPR) experiment, Sch A showed moderate affinity with HSF1, further confirming that Sch A might be a direct HSF1 inhibitor. The molecular docking and molecular dynamic simulation results of HSF1/Sch A suggested that Sch A formed key hydrogen bond and hydrophobic interactions with HSF1, which may contribute to its potent HSF1 inhibition. These findings provide clues for the design of novel HSF1 inhibitors and drug candidates for colon cancer treatment.

Targeting the N-terminal domain of the RNA-binding protein of the SARS-CoV-2 with high affinity natural compounds to abrogate the protein-RNA interaction: a molecular dynamics study

The emergence of COVID-19 took the world by shock in December 2019, starting from Wuhan, China and swiftly spreading across the globe. The number of COVID-19 cases continues to rise which is a global burden on the health care system worldwide. Efforts are continuing to come up with a solution either to develop a small molecular inhibitor or vaccine, but still no success. In the fight against SARS-CoV-2, targeting a different protein of the SARS-CoV-2 is the need of the hour to impede and relinquish the current pandemic. Therefore, in this study, computational modelling and simulation approaches are used to target the N-terminal domain of the phosphor-nucleoprotein (RNA binding protein), which is primarily responsible for binding and packing the viral genome to get ribonucleoprotein complex (RNP). Our multi-step drug screening approach shortlisted potential drugs. These top hits were confirmed by re-docking which revealed that the interacting molecules block the key residues i.e. Thr57, His59, Ser105, Arg107, and Arg177 and thus ultimately block the NTD from RNA recognition. Furthermore, the activity of the top four hits was also confirmed by using molecular dynamics simulation and free energy calculation. Our analysis suggests that these top hits possess strong inhibitory properties and should be tested experimentally. In conclusion, we hope these top hits would abrogate the binding of RNA and the NTD of the SARS-CoV-2, which might be helpful to combat COVID-19.Communicated by Ramaswamy H. Sarma.

Promising inhibitors of nsp2 of CHIKV using molecular docking and temperature-dependent molecular dynamics simulations

Infection due to the Chikungunya virus (CHIKV) has taken the life of lots of people; and researchers are working to find the vaccine or promisng drug candidates against this viral infection. In this work, the authors have designed one component reaction based on the thia-/oxa-azolidineone and created a library of 2000 molecules based on the product obtained. Further, the compounds were screened through the docking using iGemdock against the non-structural protein 2 (nsp2) of CHIKV. Molecular docking gives the binding energy (BE) or energy for the formation of the complex between the designed compound and nsp2 of CHIKV; and CMPD222 gave the lowest energy. This is based on the energy obtained from van der Waal's interaction, hydrogen bonding and electrostatic instructions. Further, molecular dynamics simulations (MDS) of nsp2 of CHIKV with and without screened compound (222) were performed to validate the docking results and the change in free energy for the formation of the complex is -10.8327 kcal/mol. To explore the potential of CMPD222, the MDS of the CMPD222-nsp2 of CHIKV were performed at different temperatures (325, 350, 375 and 400 K) to understand the inhibition of the protease. MM-GBSA calculations were performed to determined change in entropy, change in enthalpy and change in free energy to understand the inhibition. Maximum inhibition of nsp2 of CHIKV with CMPD222 is observed at 375 K with a change in free energy of -19.3754 kcal/mol.Communicated by Ramaswamy H. Sarma.

Progress of tubulin polymerization activity detection methods

Tubulin, an important target in tumor therapy, is one of the hotspots in the field of antineoplastic drugs in recent years, and it is of great significance to design and screen new inhibitors for this target. Natural products and chemical synthetic drugs are the main sources of tubulin inhibitors. However, due to the variety of compound structure types, it has always been difficult for researchers to screen out polymerization inhibitors with simple operation, high efficiency and low cost. A large number of articles have reported the screening methods of tubulin inhibitors and their biological activity. In this article, the biological activity detection methods of tubulin polymerization inhibitors are reviewed. Thus, it provides a theoretical basis for the further study of tubulin polymerization inhibitors and the selection of methods for tubulin inhibitors.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.