Polypharmacology rescored: protein-ligand interaction profiles for remote binding site similarity assessment

Mycosporine-Like Amino Acids as a Potential Inhibitor of Tyrosinase-Related Protein 1: Computational Screening, Pharmacokinetics, and Molecular Dynamics Simulation

Melanin is the major pigment responsible for the coloring of mammalian skin, hair, and eyes to defend against ultraviolet radiation. However, excessive melanin production has resulted in numerous types of hyperpigmentation disorders. Tyrosinase-related protein 1 (TYRP1) is a transmembrane glycoprotein enzyme found in many organisms, including humans, that plays an important role in melanogenesis. Thus, controlling the enzyme activity of TYRP1 with tyrosinase inhibitors is a vital step in the treatment of hyperpigmentation problems in humans. In the present investigation, virtual screening, pharmacokinetics, drug docking, and molecular dynamics (MD) simulation were used to find the most potent drug as an inhibitor of TYRP1 to effectively treat hyperpigmentation disorder. The 3D structure of TYRP1 was retrieved from the Protein Data Bank (PDB) database (PDB ID: 5M8M) and validated by the Ramachandran plot. Pharmacokinetics and drug-likeness showed that mycosporine 2 glycine (M2G) and shinorine (SHI) were the best compounds over other ligands in the same (P-1) structural pose. However, MD simulations of the M2G showed the highest CDOCKER interaction energy (-45.182 kcal/mol) and binding affinity (-65.0529 kcal/mol) as compared to SHI and reference drugs. The molecular binding modes RMSD and RMSF plots have exhibited more relevance to the M2G ligand in comparison to other drug ligands. The bioactivity and ligand efficiency profiles revealed that M2G is the most effective compound as a TYRP1 inhibitor. Thus, M2G could be used as a most effective drug for developing valuable sunscreen products to cure hyperpigmentation-related diseases.

Identification of a binding pocket of letermovir in the terminase subunit pUL56 of human cytomegalovirus

A key step in replication of human cytomegalovirus (HCMV) is the generation and packaging of unit-length genomes into preformed capsids. The enzymes involved in this process are viral terminases. The HCMV terminase consists of two subunits, the ATPase pUL56 and the nuclease pUL89. A potential third component, pUL51, has been proposed. Letermovir is the first terminase inhibitor available for HCMV prophylaxis to allogenic hematopoietic stem cell recipients. However, mutations in the HCMV terminase subunit pUL56 and, to a lesser extent, in pUL89 or pUL51 lead to resistance. Here we focused on the drug target area in the terminase subunit pUL56. To gain further structural insights into the putative binding site of letermovir, in silico analysis of the structure was performed using Phyre2 and SwissDock. For our analysis, we used three of the most frequent mutations during letermovir treatment, C325F, C325Y and C325W. We demonstrated that all variants have a pronounced cavity reduction, leading to the letermovir binding conformations being "pushed-out" of the binding pocket. This results in a changed distribution of the Gibbs free binding energy. To circumvent the absolute resistance of C325 mutations a further modification of letermovir might solve the problem and leads to optimizing drug targeting capacity.

GRADE and X-GRADE: Unveiling Novel Protein-Ligand Interaction Fingerprints Based on GRAIL Scores

Nonbonding molecular interactions, such as hydrogen bonding, hydrophobic contacts, ionic interactions, etc., are at the heart of many biological processes, and their appropriate treatment is essential for the successful application of numerous computational drug design methods. This paper introduces GRADE, a novel interaction fingerprint (IFP) descriptor that quantifies these interactions using floating point values derived from GRAIL scores, encoding both the presence and quality of interactions. GRADE is available in two versions: a basic 35-element variant and an extended 177-element variant. Three case studies demonstrate GRADE's utility: (1) dimensionality reduction for visualizing the chemical space of protein-ligand complexes using Uniform Manifold Approximation and Projection (UMAP), showing competitive performance with complex descriptors; (2) binding affinity prediction, where GRADE achieved reasonable accuracy with minimal machine learning optimization; and (3) three-dimensional-quantitative structure-activity relationship (3D-QSAR) modeling for a specific protein target, where GRADE enhanced the performance of Morgan Fingerprints.

In vitro, in silico, and STD-NMR studies of flavonoids from Hypericum helianthemoides (Spach) Boiss. against Leishmania major pteridine reductase 1 (LmPTR1)

Apigenin (1) and 3I,8II-biapigenin (2), a dimer of apigenin, were isolated from the aerial parts of Hypericum helianthemoides (Spach) Boiss. (Hypericaceae family). This study aimed to evaluate the in vitro inhibitory effects of flavonoids 1 and 2 against Leishmania major pteridine reductase-1 (LmPTR1), an essential enzyme for the growth of Leishmania parasites and other trypanosomatid protozoa. The second objective was to understand the binding interactions and structural properties of LmPTR1 inhibition at the atomic level through extensive in silico analyses and Saturation-Transfer Difference (STD)-NMR studies. Anti-LmPTR1 results showed that the dimeric form (2) was active (IC50 of 34.65 μM), while the monomeric form (1) was inactive. Computational analyses yielded a grid score of -52.14 kcal/mol and a free energy binding score of -38.23 kcal/mol. A stable ligand-receptor complex at the LmPTR1 binding site was observed for 2. Moreover, several important binding residues in the catalytic triad (Y194 and K198) and the substrate loop (L226, S227, S229, V230, and M233) interacted with 2. The STD-NMR results corroborated the computational simulations, indicating that H-6I and H-6II of the conjugated ring system on the biapigenin structure showed the highest interaction with the LmPTR1 active site. MTT assay results for 2 against human normal fibroblast cells (BJ cells) exhibited no cytotoxicity at concentrations of 50 and 100 μM. Overall, 3I,8II-biapigenin (2) displayed promise as a candidate for in vivo studies and anti-leishmanial drug development. Further evaluation of the anti-leishmanial and anti-LmPTR1 activities of bioflavonoid 2, along with its analogues, is warranted.

Triple G-C-T Base-Coded Self-Assembling Water-Soluble Nucleobase Monomers with a Broad Scope for Biomaterial and Protein Bioconjugation Applications

We report self-assembling water-soluble nucleobase monomers with a broad scope for biomaterial and protein bioconjugation applications. These nucleobase monomers are adorned with nature-inspired triple G-C-T nucleobases featuring three recognition sites: DDA (G mimic), DAA (C mimic), and ADA (T mimic). Using readily accessible starting materials, these monomers can be synthesized in excellent yields, making them good choices for diverse applications. Their unique structural features, coupled with their water solubility, may offer exciting opportunities for the creation of new biomaterials.

Fisetin is a selective adenosine triphosphate-competitive inhibitor for mitogen-activated protein kinase kinase 4 to inhibit lipopolysaccharide-stimulated inflammation

The mitogen-activated protein kinase kinase 4 (MKK4), a member of the MAP kinase kinase family, directly phosphorylates and activates the c-Jun NH2-terminal kinases (JNK), in response to proinflammatory cytokines and cellular stresses. Regulation of the MKK4 activity is considered to be a novel approach for the prevention and treatment of inflammation. The aim of this study was to identify whether fisetin, a potential anti-inflammatory compound, targets MKK4-JNK cascade to inhibit lipopolysaccharide (LPS)-stimulated inflammatory response. RAW264 macrophage pretreated with fisetin following LPS stimulation was used as a cell model to investigate the transactivation and expression of related-inflammatory genes by transient transfection assay, electrophoretic mobility shift assay (EMSA), or enzyme-linked immunosorbent assay (ELISA), and cellular signaling as well as binding of related-signal proteins by Western blot, pull-down assay and kinase assay, and molecular modeling. The transactivation and expression of cyclooxygenase-2 (COX-2) gene as well as prostaglandin E2 (PGE2) secretion induced by LPS were inhibited by fisetin in a dose-dependent manner. Signaling transduction analysis demonstrated that fisetin selectively inhibited MKK4-JNK1/2 signaling to suppress the phosphorylation of transcription factor AP-1 without affecting the NF-κB and Jak2-Stat3 signaling as well as the phosphorylation of Src, Syk, and TAK1. Furthermore, in vitro and ex vivo pull-down assay using cell lysate or purified protein demonstrated that fisetin could bind directly to MKK4. Molecular modeling using the Molecular Operating Environment™ software indicated that fisetin docked into the ATP-binding pocket of MKK4 with a binding energy of -71.75 kcal/mol and formed a 1.70 Å hydrogen bound with Asp247 residue of MKK4. The IC50 of fisetin against MKK4 was estimated as 2.899 μM in the kinase assay, and the ATP-competitive effect was confirmed by ATP titration. Taken together, our data revealed that fisetin is a potent selective ATP-competitive MKK4 inhibitor to suppress MKK4-JNK1/2-AP-1 cascade for inhibiting LPS-induced inflammation.

The potential of Mitragyna speciosa leaves as a natural source of antioxidants for disease prevention

Mitragyna speciosa is famous for its addictive effect. On the other hand, this plant has good potential as an antioxidant agent, and so far, it was not explicitly explained what the most contributing compound in the leaves to that activity is. This study has been conducted using several computational methods to determine which compounds are the most active in interacting with cytochrome P450, myeloperoxidase, and NADPH oxidase proteins. First, virtual screening was carried out based on molecular docking, followed by profiling the properties of adsorption, distribution, metabolism, excretion, and toxicity (ADMET); the second one is the molecular dynamics (MD) simulations for 100 ns. The virtual screening results showed that three compounds acted as inhibitors for each protein: (-)-epicatechin, sitogluside, and corynoxeine. The ADMET profiles of the three compounds exhibit good drug ability and toxicity. The trajectories study from MD simulations predicts that the complexes of these three compounds with their respective target proteins are stable. Furthermore, these compounds identified in this computational study can be a potential guide for future experiments aimed at assessing the antioxidant properties through in vitro testing.

Exploring the Potential of Biomimetic Peptides in Targeting Fibrillar and Filamentous Alpha-Synuclein-An In Silico and Experimental Approach to Parkinson's Disease

Alpha-synuclein (ASyn) is a protein that is known to play a critical role in Parkinson's disease (PD) due to its propensity for misfolding and aggregation. Furthermore, this process leads to oxidative stress and the formation of free radicals that cause neuronal damage. In this study, we have utilized a biomimetic approach to design new peptides derived from marine natural resources. The peptides were designed using a peptide scrambling approach where antioxidant moieties were combined with fibrillary inhibition motifs in order to design peptides that would have a dual targeting effect on ASyn misfolding. Of the 20 designed peptides, 12 were selected for examining binding interactions through molecular docking and molecular dynamics approaches, which revealed that the peptides were binding to the pre-NAC and NAC (non-amyloid component) domain residues such as Tyr39, Asn65, Gly86, and Ala85, among others. Because ASyn filaments derived from Lewy body dementia (LBD) have a different secondary structure compared to pathogenic ASyn fibrils, both forms were tested computationally. Five of those peptides were utilized for laboratory validation based on those results. The binding interactions with fibrils were confirmed using surface plasmon resonance studies, where EQALMPWIWYWKDPNGS, PYYYWKDPNGS, and PYYYWKELAQM showed higher binding. Secondary structural analyses revealed their ability to induce conformational changes in ASyn fibrils. Additionally, PYYYWKDPNGS and PYYYWKELAQM also demonstrated antioxidant properties. This study provides insight into the binding interactions of varying forms of ASyn implicated in PD. The peptides may be further investigated for mitigating fibrillation at the cellular level and may have the potential to target ASyn.

Exploring novel natural compound-based therapies for Duchenne muscular dystrophy management: insights from network pharmacology, QSAR modeling, molecular dynamics, and free energy calculations

Muscular dystrophies encompass a heterogeneous group of rare neuromuscular diseases characterized by progressive muscle degeneration and weakness. Among these, Duchenne muscular dystrophy (DMD) stands out as one of the most severe forms. The present study employs an integrative approach combining network pharmacology, quantitative structure-activity relationship (QSAR) modeling, molecular dynamics (MD) simulations, and free energy calculations to identify potential therapeutic targets and natural compounds for DMD. Upon analyzing the GSE38417 dataset, it was found that individuals with DMD exhibited 290 upregulated differentially expressed genes (DEGs) compared to healthy controls. By utilizing gene ontology (GO) and protein-protein interaction (PPI) network analysis, this study provides insights into the functional roles of the identified DEGs, identifying ten hub genes that play a critical role in the pathology of DMD. These key genes include DMD, TTN, PLEC, DTNA, PKP2, SLC24A, FBXO32, SNTA1, SMAD3, and NOS1. Furthermore, through the use of ligand-based pharmacophore modeling and virtual screening, three natural compounds were identified as potential inhibitors. Among these, compounds 3874518 and 12314417 have demonstrated significant promise as an inhibitor of the SMAD3 protein, a crucial factor in the fibrotic and inflammatory mechanisms associated with DMD. The therapeutic potential of the compounds was further supported by molecular dynamics simulation and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis. These findings suggest that the compounds are viable candidates for experimental validation against DMD.

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Isoliquiritigenin: a potential drug candidate for the management of erectile dysfunction

OBJECTIVE

This study aimed to assess the erectogenic properties of isoliquiritigenin taking sildenafil (SDF) as the standard.

METHODS

The binding affinity of isoliquiritigenin (ISL) with the erectile marker proteins (endothelial nitric oxide synthase [eNOS] and enzyme phosphodiesterase type 5 [PDE5]) was investigated using Autodock Vina, which was validated using molecular dynamics simulation. Furthermore, the effect of ISL on the eNOS and PDE5 messenger ribonucleic acid (mRNA) expression and the sexual behavior of mice was investigated, along with the assessment of the pharmacokinetics of ISL.

KEY FINDINGS

The results revealed that the binding affinity of ISL-eNOS/PDE5 and SDF-eNOS/PDE5 was in the range of -7.5 to -8.6 kcal/mol. The ISL-eNOS/PDE5 complexes remained stable throughout the 100 ns simulation period. Root mean square deviation, Rg, SASA, hydrogen, and hydrophobic interactions were similar between ISL-eNOS/PDE5 and SDF-eNOS/PDE5. Analysis of mRNA expressions in paroxetine (PRX)-induced ED mice showed that the co-administration of PRX with ISL reduced PDE5 and increased eNOS mRNA expression, similar to the co-administered group (PRX+SDF). The sexual behavior study revealed that the results of PRX+ISL were better than those of the PRX+SDF group. Pharmacokinetic evaluation further demonstrated that ISL possesses drug-like properties.

CONCLUSIONS

The results showed that ISL is equally potent as SDF in terms of binding affinity, specific pharmacological properties, and modulating sexual behavior.

Examining sialic acid derivatives as potential inhibitors of SARS-CoV-2 spike protein receptor binding domain

Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has been the primary reason behind the COVID-19 global pandemic which has affected millions of lives worldwide. The fundamental cause of the infection is the molecular binding of the viral spike protein receptor binding domain (SP-RBD) with the human cell angiotensin-converting enzyme 2 (ACE2) receptor. The infection can be prevented if the binding of RBD-ACE2 is resisted by utilizing certain inhibitors or drugs that demonstrate strong binding affinity towards the SP RBD. Sialic acid based glycans found widely in human cells and tissues have notable propensity of binding to viral proteins of the coronaviridae family. Recent experimental literature have used N-acetyl neuraminic acid (Sialic acid) to create diagnostic sensors for SARS-CoV-2, but a detailed interrogation of the underlying molecular mechanisms is warranted. Here, we perform all atom molecular dynamics (MD) simulations for the complexes of certain Sialic acid-based molecules with that of SP RBD of SARS CoV-2. Our results indicate that Sialic acid not only reproduces a binding affinity comparable to the RBD-ACE2 interactions, it also assumes the longest time to dissociate completely from the protein binding pocket of SP RBD. Our predictions corroborate that a combination of electrostatic and van der Waals energies as well the polar hydrogen bond interactions between the RBD residues and the inhibitors influence free energy of binding.Communicated by Ramaswamy H. Sarma.

In Silico Drug Repurposing Against PSMB8 as a Potential Target for Acute Myeloid Leukemia Treatment

PSMB8 emerges as a prominent gene associated with cancer survival, yet its potential therapeutic role in acute myeloid leukemia (AML) remains unexplored within the existing literature. The principal aim of this study is to systematically screen an expansive library of molecular entities, curated from various databases to identify the prospective inhibitory agents with an affinity for PSMB8. A comprehensive assortment of molecular compounds obtained from the ZINC15 database was subjected to molecular docking simulations with PSMB8 by using the AutoDock tool in PyRx (version 0.9.9) to elucidate binding affinities. Following the docking simulations, a select subset of molecules underwent further investigation through comprehensive ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis employing AdmetSar and SwissADME tools. Finally, RMSD, RMSF, Rg, and H bond analyses were conducted via GROMACS to determine the best conformationally dynamic molecule that represents the candidate agent for the study. Following rigorous evaluation, Adozelesin, Fiduxosin, and Rimegepant have been singled out based on considerations encompassing bioavailability scores, compliance with filter criteria, and acute oral toxicity levels. Additionally, ligand interaction analysis indicates that Adozelesin and Fiduxosin exhibit an augmented propensity for hydrogen bond formation, a factor recognized for its facilitative role in protein-ligand interactions. After final analyses, we report that Fiduxosin may offer a treatment possibility by reversing the low survival rates caused by PSMB8 high activation in AML. This study represents a strategic attempt to repurpose readily available pharmaceutical agents, potentially obviating the need for de novo drug development, and thereby offering promising avenues for therapeutic intervention in specific diseases.

In silico screening for potential inhibitors from the phytocompounds of Carica papaya against Zika virus NS5 protein

Background

The Zika virus (ZIKV) infection has emerged as a global health threat. The causal reasoning is that Zika infection is linked to the development of microcephaly in newborns and Guillain-Barré syndrome in adults. With no clinically approved antiviral treatment for ZIKV, the need for the development of potential inhibitors against the virus is essential. In this study, we aimed to screen phytochemicals from papaya ( Carica papaya L.) against NS5 protein domains of ZIKV.

Methods

Approximately 193 phytochemicals from an online database (IMPACT) were subjected to molecular docking using AutoDock Vina against the NS5-MTase protein domain (5WXB) and -RdRp domain (5U04).

Results

Our results showed that β-sitosterol, carpaine, violaxanthin, pseudocarpaine, Δ7-avenasterols, Rutin, and cis-β-carotene had the highest binding affinity to both protein domains, with β-sitosterol having the most favorable binding energy. Furthermore, ADMET analysis revealed that selected compounds had good pharmacokinetic properties and were nontoxic.

Conclusions

Our findings suggest that papaya-derived phytochemicals could be potential candidates for developing antiviral drugs against ZIKV. However, further experimental studies using cell lines and in vivo models are needed to validate their efficacy and safety.

Antifouling activities of proteinase K and α-amylase enzymes: Laboratory bioassays and in silico analysis

The application of enzymes as antifoulants is one of the environment-friendly strategies in biofouling management. In this study, antifouling activities of commercially available proteinase K and α-amylase enzymes were evaluated using barnacle larva and biofilm-forming bacteria as test organisms. The enzymes were also tested against barnacle cement protein through in silico analysis. The results showed that both enzymes inhibited the attachment of bacteria and settlement of barnacle larvae on the test surface. The lowest minimum inhibitory concentration of 0.312 mg ml-1 was exhibited by proteinase K against biofilm-forming bacteria. The calculated LC50 values for proteinase K and α-amylase against the barnacle nauplii were 91.8 and 230.96 mg ml-1 respectively. While α-amylase showed higher antibiofilm activity, proteinase K exhibited higher anti-larval settlement activity. Similarly, in silico analysis of the enzymes revealed promising anti-settlement activity, as the enzymes showed good binding scores with barnacle cement protein. Overall, the results suggested that the enzymes proteinase K and α-amylase could be used in antifouling coatings to reduce the settlement of biofouling on artificial materials in the marine environment.

Toxicity assays and in silico approach to assess the impacts of chlorine dioxide on survival, respiration and some biochemical markers of marine zooplankton

Chlorination is the common antifouling method in desalination and power plant water intake structures to control microbial and macrofouling growth. In this study, the impacts of chlorine dioxide on toxicity, metabolic activity and biochemical markers like glutathione S-transferase and catalase enzyme activity were tested using four zooplankton species (Centropages sp., Acartia sp., Oncaea sp., and Calanus sp.) collected from the Red Sea. The zooplankton species were treated with different concentrations (0.02, 0.05, 0.1, 0.2, and 0.5 mg L-1) of chlorine dioxide. Further, chlorite, the main decomposition product of chlorine dioxide, was used for molecular docking studies against glutathione S-transferase and catalase enzymes. The results indicated the LC50 range of 0.552-1.643 mg L-1 for the studied zooplankton species. The respiration rate of the zooplankton increased due to the chlorine dioxide treatment with a maximum of 0.562 μg O2 copepod h-1 in Acartia. The glutathione S-transferase and catalase enzyme activities showed elevated values in zooplankton treated with chlorine dioxide. Molecular docking of chlorite with enzymes involved in antioxidant defense activity, such as glutathione S-transferase and catalase enzyme showed weak interactions. Overall, this study yielded significant insights for understanding the effects of chlorine dioxide on the survival, metabolism, and biochemical composition of marine zooplankton.

Design, synthesis, and evaluation of purine and pyrimidine-based KRAS G12D inhibitors: Towards potential anticancer therapy

Oncogenic RAS mutations, commonly observed in human tumors, affect approximately 30% of cancer cases and pose a significant challenge for effective cancer treatment. Current strategies to inhibit the KRAS G12D mutation have shown limited success, emphasizing the urgent need for new therapeutic approaches. In this study, we designed and synthesized several purine and pyrimidine analogs as inhibitors for the KRAS G12D mutation. Our synthesized compounds demonstrated potent anticancer activity against cell lines with the KRAS G12D mutation, effectively impeding their growth. They also exhibited low toxicity in normal cells, indicating their selective action against cancer cells harboring the KRAS G12D mutation. Notably, the lead compound, PU1-1 induced the programmed cell death of KRAS G12D-mutated cells and reduced the levels of active KRAS and its downstream signaling proteins. Moreover, PU1-1 significantly shrunk the tumor size in a pancreatic xenograft model induced by the KRAS G12D mutation, further validating its potential as a therapeutic agent. These findings highlight the potential of purine-based KRAS G12D inhibitors as candidates for targeted cancer therapy. However, further exploration and optimization of these compounds are essential to meet the unmet clinical needs of patients with KRAS-mutant cancers.

Substitution Models of Protein Evolution with Selection on Enzymatic Activity

Substitution models of evolution are necessary for diverse evolutionary analyses including phylogenetic tree and ancestral sequence reconstructions. At the protein level, empirical substitution models are traditionally used due to their simplicity, but they ignore the variability of substitution patterns among protein sites. Next, in order to improve the realism of the modeling of protein evolution, a series of structurally constrained substitution models were presented, but still they usually ignore constraints on the protein activity. Here, we present a substitution model of protein evolution with selection on both protein structure and enzymatic activity, and that can be applied to phylogenetics. In particular, the model considers the binding affinity of the enzyme-substrate complex as well as structural constraints that include the flexibility of structural flaps, hydrogen bonds, amino acids backbone radius of gyration, and solvent-accessible surface area that are quantified through molecular dynamics simulations. We applied the model to the HIV-1 protease and evaluated it by phylogenetic likelihood in comparison with the best-fitting empirical substitution model and a structurally constrained substitution model that ignores the enzymatic activity. We found that accounting for selection on the protein activity improves the fitting of the modeled functional regions with the real observations, especially in data with high molecular identity, which recommends considering constraints on the protein activity in the development of substitution models of evolution.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

In silico modeling revealed phytomolecules derived from Cymbopogon citratus (DC.) leaf extract as promising candidates for malaria therapy

The emergence of varying levels of resistance to currently available antimalarial drugs significantly threatens global health. This factor heightens the urgency to explore bioactive compounds from natural products with a view to discovering and developing newer antimalarial drugs with novel mode of actions. Therefore, we evaluated the inhibitory effects of sixteen phytocompounds from Cymbopogon citratus leaf extract against Plasmodium falciparum drug targets such as P. falciparum circumsporozoite protein (PfCSP), P. falciparum merozoite surface protein 1 (PfMSP1) and P. falciparum erythrocyte membrane protein 1 (PfEMP1). In silico approaches including molecular docking, pharmacophore modeling and 3D-QSAR were adopted to analyze the inhibitory activity of the compounds under consideration. The molecular docking results indicated that a compound swertiajaponin from C. citratus exhibited a higher binding affinity (-7.8 kcal/mol) to PfMSP1 as against the standard artesunate-amodiaquine (-6.6 kcal/mol). Swertiajaponin also formed strong hydrogen bond interactions with LYS29, CYS30, TYR34, ASN52, GLY55 and CYS28 amino acid residues. In addition, quercetin another compound from C. citratus exhibited significant binding energies -6.8 and -8.3 kcal/mol with PfCSP and PfEMP1, respectively but slightly lower than the standard artemether-lumefantrine with binding energies of -7.4 kcal/mol against PfCSP and -8.7 kcal/mol against PfEMP1. Overall, the present study provides evidence that swertiajaponin and other phytomolecules from C. citratus have modulatory properties toward P. falciparum drug targets and thus may warrant further exploration in early drug discovery efforts against malaria. Furthermore, these findings lend credence to the folkloric use of C. citratus for malaria treatment.Communicated by Ramaswamy H. Sarma.

Molecular Docking Study of Pramipexole Derivatives as Radiopharmaceutical Candidates for Brain Imaging

Neurodegenerative diseases are a global priority disease. According to the World Health Organization (WHO), it is estimated that by 2050, there will be 152 million people with neurodegenerative diseases. Currently, the method used for brain imaging is PET/CT, with the most widely used radiopharmaceutical being 18F-FDG [7,8]. 18F-FDG is used to determine brain glucose metabolism, however the 18F-FDG does not have a specific receptor. Pramipexole compounds can cross the blood-brain barrier and have high specificity for dopamine receptors. By modifying and labelling the structure of pramipexole derivatives, it is expected to obtain pramipexole derivatives with a good affinity to the dopamine receptor. In order to reduce the risk of failure, radiation hazard, and research funds, in this research, a molecular docking study was carried out using the targeted docking method between dopamine receptor proteins and 22 pramipexole derivatives using AutoDock 4, GaussView software for structure preparation, Gaussian software for energy calculation, and Biovia Discovery Studio for structure visualization. The experimental results showed that the free binding energy of pramipexole derivatives for the dopamine receptor was obtained in the range of -2.81 to -5.84 kcal/mol. The best free binding energy value was obtained for compound PD-7, with a free binding energy value of -5.84 kcal/mol, while the RMSD value obtained was 0.6 A°, and amino acid residues that interacted with compound PD-7 among others: Leu347, Leu343, Tyr198, Leu199, Glu202, Phe201, and Val203 with hydrophobic and hydrogen bond interactions. The PD-7 compound was labeled using the radioisotope iodine-131 based on the free binding energy value. From the experimental results, compound 131I-PD-7 showed a free binding energy value of -4.66 kcal/mol and interacts with the similar amino acid residues as compound PD-7. These results indicate that the compound131I-PD-7 is a potential candidate to be studied further as a radiopharmaceutical candidate for brain imaging.

AI-Powered Virtual Screening of Large Compound Libraries Leads to the Discovery of Novel Inhibitors of Sirtuin-1

The discovery of new scaffolds and chemotypes via high-throughput screening is tedious and resource intensive. Yet, there are millions of small molecules commercially available, rendering comprehensive in vitro tests intractable. We show how smart algorithms reduce large screening collections to target-specific sets of just a few hundred small molecules, allowing for a much faster and more cost-effective hit discovery process. We showcase the application of this virtual screening strategy by preselecting 434 compounds for Sirtuin-1 inhibition from a library of 2.6 million compounds, corresponding to 0.02% of the original library. Multistage in vitro validation ultimately confirmed nine chemically novel inhibitors. When compared to a competitive benchmark study for Sirtuin-1, our method shows a 12-fold higher hit rate. The results demonstrate how AI-driven preselection from large screening libraries allows for a massive reduction in the number of small molecules to be tested in vitro while still retaining a large number of hits.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

Molecular docking and dynamic simulations of quinoxaline 1,4-di-N-oxide as inhibitors for targets from Trypanosoma cruzi, Trichomonas vaginalis, and Fasciola hepatica

CONTEXT

Quinoxaline 1,4-di-N-oxide is a scaffold with a wide array of biological activities, particularly its use to develop new antiparasitic agents. Recently, these compounds have been described as trypanothione reductase (TR), triosephosphate isomerase (TIM), and cathepsin-L (CatL) inhibitors from Trypanosoma cruzi, Trichomonas vaginalis, and Fasciola hepatica, respectively.

METHODS

Therefore, the main objective of this work was to analyze quinoxaline 1,4-di-N-oxide derivatives of two databases (ZINC15 and PubChem) and literature by molecular docking, dynamic simulation and complemented by MMPBSA, and contact analysis of molecular dynamics' trajectory on the active site of the enzymes to know their potential effect inhibitory. Interestingly, compounds Lit_C777 and Zn_C38 show preference as potential TcTR inhibitors over HsGR, with favorable energy contributions from residues including Pro398 and Leu399 from Z-site, Glu467 from γ-Glu site, and His461, part of the catalytic triad. Compound Lit_C208 shows potential selective inhibition against TvTIM over HsTIM, with favorable energy contributions toward TvTIM catalytic dyad, but away from HsTIM catalytic dyad. Compound Lit_C388 was most stable in FhCatL with a higher calculated binding energy by MMPBSA analysis than HsCatL, though not interacting with catalytic dyad, holding favorable energy contribution from residues oriented at FhCatL catalytic dyad. Therefore, these kinds of compounds are good candidates to continue researching and confirming their activity through in vitro studies as new selective antiparasitic agents.

Time to Change: A Systems Pharmacology Approach to Disentangle Mechanisms of Drug-Induced Mitochondrial Toxicity

An increasing number of commonly prescribed drugs are known to interfere with mitochondrial function, which is associated with almost half of all Food and Drug Administration black box warnings, a variety of drug withdrawals, and attrition of drug candidates. This can mainly be attributed to a historic lack of sensitive and specific assays to identify the mechanisms underlying mitochondrial toxicity during drug development. In the last decade, a better understanding of drug-induced mitochondrial dysfunction has been achieved by network-based and structure-based systems pharmacological approaches. Here, we propose the implementation of a tiered systems pharmacology approach to detect adverse mitochondrial drug effects during preclinical drug development, which is based on a toolset developed to study inherited mitochondrial disease. This includes phenotypic characterization, profiling of key metabolic alterations, mechanistic studies, and functional in vitro and in vivo studies. Combined with binding pocket similarity comparisons and bottom-up as well as top-down metabolic network modeling, this tiered approach enables identification of mechanisms underlying drug-induced mitochondrial dysfunction. After validation of these off-target mechanisms, drug candidates can be adjusted to minimize mitochondrial activity. Implementing such a tiered systems pharmacology approach could lead to a more efficient drug development trajectory due to lower drug attrition rates and ultimately contribute to the development of safer drugs. SIGNIFICANCE STATEMENT: Many commonly prescribed drugs adversely affect mitochondrial function, which can be detected using phenotypic assays. However, these methods provide only limited insight into the underlying mechanisms. In recent years, a better understanding of drug-induced mitochondrial dysfunction has been achieved by network-based and structure-based system pharmacological approaches. Their implementation in preclinical drug development could reduce the number of drug failures, contributing to safer drug design.

Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance

Identification of novel chemotypes with biological activity similar to a known active molecule is an important challenge in drug discovery called 'scaffold hopping'. Small-, medium-, and large-step scaffold hopping efforts may lead to increasing degrees of chemical structure novelty with respect to the parent compound. In the present paper, we focus on the problem of large-step scaffold hopping. We assembled a high quality and well characterized dataset of scaffold hopping examples comprising pairs of active molecules and including a variety of protein targets. This dataset was used to build a benchmark corresponding to the setting of real-life applications: one active molecule is known, and the second active is searched among a set of decoys chosen in a way to avoid statistical bias. This allowed us to evaluate the performance of computational methods for solving large-step scaffold hopping problems. In particular, we assessed how difficult these problems are, particularly for classical 2D and 3D ligand-based methods. We also showed that a machine-learning chemogenomic algorithm outperforms classical methods and we provided some useful hints for future improvements.

Number of Hydrogen Bonds per Unit Solvent Accessible Surface Area: A Descriptor of Functional States of Proteins

Proteins function close to native and near-native conformations. These states are evolutionarily selected to ensure the effect of mutations is minimized. The structural organization of a protein is hierarchical and modular, which reduces the dimensionality of the configurational space of the native states. Thus, finding appropriate descriptors that define the native state among all possible states of a protein is a problem of immense interest. The present study explores the correlation between solvent accessible surface areas (SASAs) and different intraprotein as well as protein-water hydrogen bonds of 55 single-chain globular proteins from four different structural classes (all α, all β, α+β, and α/β), 16 multichain proteins, and 4 macromolecular complexes. A systematic analysis of the solvent accessible surface area and intraprotein and protein-water hydrogen bonds suggests a linear relationship between SASAs and hydrogen bonds. The number of protein-water hydrogen bonds per unit SASA ranges from 3 to 4 for all the different structural protein classes. In contrast, the number of intramolecular hydrogen bonds per unit SASA, including the mainchain-mainchain, mainchain-sidechain, and sidechain-sidechain, varies between 0.75 to 2. The solvation free energy of a protein linearly decreases with SASA. Our study also shows that the solvation free energy/SASA varies from -75 to -105 kJ mol^-1 nm^-2 across all the native states studied here. The number conservancy of intraprotein hydrogen bonds per unit SASA possibly imparts structural stability to the native structure. On the other hand, 3-4 protein-water hydrogen bonds per unit SASA are possibly required to maintain a balance between the solubility and functionality of the native states. This study provides a basis for synthetic biologists to design new folds with improved functionality.

Anti-Alopecia Activity of Alkaloids Group from Noni Fruit against Dihydrotestosterone-Induced Male Rabbits and Its Molecular Mechanism: In Vivo and In Silico Studies

Androgenic alopecia (AA) is a condition that most commonly affects adult men and is caused by an increase in the hormone dihydrotestosterone (DHT) in the hair follicles. Anti-alopecia drugs should be discovered for hair follicles to enter the anagen growth phase. Therefore, this study evaluated the hair growth-promoting activity of Noni fruit’s water, ethyl acetate, n-hexane fractions, and sub-fractions from the active fraction in the alopecia male white rabbit model. The Matias method was modified by inducing rabbits using DHT for 17 days, followed by topical application of Noni fruit solution for 21 days. Meanwhile, hair growth was evaluated by histological observation of the follicular density and the anagen/telogen (A/T) ratio in skin tissue. In the first stage, five groups of male white rabbits were studied to obtain the active fraction; DHT+Minoxidil as standard, DHT+vehicle (NaCMC 1%), DHT+FW, DHT+FEA, and DHT+FH. The FEA as the active fraction was followed by open-column chromatography separation (DCM:Methanol) with a gradient of 10% to produce sub-fractions. In the second stage, the six main sub-fraction groups of male rabbits studied were DHT+FEA-1 to DHT+FEA-6. The follicular density of groups FEA-3 was 78.00 ± 1.52 compared with 31.55 ± 1.64 and 80.12 ± 1.02 in the Vehicle and Minoxidil groups. Additionally, group FEA-3 showed large numbers of anagen follicles with an A/T ratio of 1.64/1 compared to the vehicle group of 1/1.50 and 1.39/1 for Minoxidil control. Group FEA-3 was identified by LC-MS/MS-QTOF, followed by molecular docking to the androgen receptor (PDB: 4K7A), causing alopecia. The results showed that three alkaloid compounds with skeleton piperazine and piperidine, namely (compounds 2 (−4.99 Kcal/mol), 3 (−4.60 Kcal/mol), and 4 (−4.57 Kcal/mol)) had a binding affinity similar to Minoxidil, with also has alkaloid skeleton piperidine−pyrimidine (−4.83 Kcal/mol). The dynamic behavior showed the stability of all androgen receptor compounds with good RMSD, SMSF, and SASA values after being studied with 100 ns molecular dynamics (MD) simulations. This study produced a common thread in discovering a class of alkaloid compounds as inhibitors of androgen receptors that cause alopecia.

Bioinsecticidal activity of actinomycete secondary metabolites against the acetylcholinesterase of the legume’s insect pest Acyrthosiphon pisum: a computational study

Background

Acyrthosiphon pisum or pea aphid is an insect of the Aphididae family, which attacks various species of legumes such as beans and peas. This pest causes economically heavy crop losses around the world. The use of conventional chemical insecticides is the only way to control its development. However, the harmful consequences of these chemicals are well known. They pollute various compartments of the environment, thus constituting a major risk for human and environmental health. The search for a more ecological alternative, respectful of the environment is, therefore, a necessity. Actinomycetes represent a source of biologically active secondary metabolites, such as antibiotics and biopesticidal agents. In this study, 150 secondary metabolites of actinomycetes have made the objective of an in silico research by molecular docking, by screening their potential inhibitors against the enzyme acetylcholinesterase (AChE) of A. pisum.

Results

The 3D structure of AChE, unavailable in the PDB database, was first modeled using the Modeller program, then the stereochemical quality of the model was validated. The molecular docking performed by the Autodock Vina algorithm allowed the selection of two metabolites giving binding energy equal to or lower than that of the co-crystallized inhibitor tetrahydro-acridine (−10.3Kcal/mol). The top-two metabolites are diazepinomicine (−10.9 Kcal/mol), and hygromycin (−10.3 Kcal/mol). These components have shown numerous interactions with the key residues of the catalytic site of the AChE enzyme, indicating their potential to inhibit its biological activity. The environmental and health safety of these components, as well as their bioavailability, were also studied by the verification of several pharmacokinetic and ADMET criteria. Diazepinomicine has shown excellent results verifying most of the criteria studied. A 50-ns MD simulation was also performed in order to test the stability of the complexes formed.

Conclusions

In addition to its favorable pharmacokinetic properties, the special chemical structure of diazepinomicin allows this molecule to interact intensely with AChE notably through the involvement of its two groups farnesyl diphosphate and dibenzodiazepinone which ensure several hydrogen and hydrophobic interactions, that offers very high stability to the complex AChE diazepinomicin. In conclusion, diazepinomicin can be suggested as a potential bioinsecticidal agent against the pest A. pisum.

Design of novel disturbing peptides against ACE2 SARS-CoV-2 spike-binding region by computational approaches

The SARS-CoV-2, the virus which is responsible for COVID-19 disease, employs its spike protein to recognize its receptor, angiotensin-converting enzyme 2 (ACE2), and subsequently enters the host cell. In this process, the receptor-binding domain (RBD) of the spike has an interface with the α1-helix of the peptidase domain (PD) of ACE2. This study focuses on the disruption of the protein-protein interaction (PPI) of RBD-ACE2. Among the residues in the template (which was extracted from the ACE2), those with unfavorable energies were selected for substitution by mutagenesis. As a result, a library of 140 peptide candidates was constructed and the binding affinity of each candidate was evaluated by molecular docking and molecular dynamics simulations against the α1-helix of ACE2. Finally, the most potent peptides P23 (GFNNYFPHQSYGFMPTNGVGY), P28 (GFNQYFPHQSYGFPPTNGVGY), and P31 (GFNRYFPHQSYGFCPTNGVGY) were selected and their dynamic behaviors were studied. The results showed peptide inhibitors increased the radius, surface accessible area, and overall mobility of residues of the protein. However, no significant alteration was seen in the key residues in the active site. Meanwhile, they can be proposed as promising agents against COVID-19 by suppressing the viral attachment and curbing the infection at its early stage. The designed peptides showed potency against beta, gamma, delta, and omicron variants of SARS-CoV-2.

Concepts and applications of chemical fingerprint for hit and lead screening

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.

Metabolomics analysis of milk thistle lipids to identify drought-tolerant genes

Milk thistle is an oil and medicinal crop known as an alternative oil crop with a high level of unsaturated fatty acids, which makes it a favorable edible oil for use in food production. To evaluate the importance of Milk thistle lipids in drought tolerance, an experiment was performed in field conditions under three different water deficit levels (Field capacity (FC), 70% FC and 40% FC). After harvesting seeds of the plant, their oily and methanolic extracts were isolated, and subsequently, types and amounts of lipids were measured using GC-MS. Genes and enzymes engaged in biosynthesizing of these lipids were identified and their expression in Arabidopsis was investigated under similar conditions. The results showed that content of almost all measured lipids of milk thistle decreased under severe drought stress, but genes (belonged to Arabidopsis), which were involved in their biosynthetic pathway showed different expression patterns. Genes biosynthesizing lipids, which had significant amounts were selected and their gene and metabolic network were established. Two networks were correlated, and for each pathway, their lipids and respective biosynthesizing genes were grouped together. Four up-regulated genes including PXG3, LOX2, CYP710A1, PAL and 4 down-regulated genes including FATA2, CYP86A1, LACS3, PLA2-ALPHA were selected. The expression of these eight genes in milk thistle was similar to Arabidopsis under drought stress. Thus, PXG3, PAL, LOX2 and CYP86A1 genes that increased expression were selected for protein analysis. Due to the lack of protein structure of these genes in the milk thistle, modeling homology was performed for them. The results of molecular docking showed that the four proteins CYP86A1, LOX2, PAL and PXG3 bind to ligands HEM, 11O, ACT and LIG, respectively. HEM ligand was involved in production of secondary metabolites and dehydration tolerance, and HEM binding site remained conserved in various plants. CA ligands were involved in synthesis of cuticles and waxes. Overall, this study confirmed the importance of lipids in drought stress tolerance in milk thistle.

Biocidal action, characterization, and molecular docking of Mentha piperita (Lamiaceae) leaves extract against Culex quinquefasciatus (Diptera: Culicidae) larvae

Mosquitoes are found in tropical and subtropical areas and are the carriers of a variety of diseases that are harmful to people’s health. e.g., malaria, filariasis, chikungunya, dengue fever, etc. Although several insecticides are available, however, due to insect resistance and environmental hazards, more eco-friendly chemicals are needed for insect control. So, the current research was planned to explore the prospective of Mentha piperita to be used for the formulation of larvicides against mosquito Culex quinquefasciatus. The ethanolic and water extracts of M. piperita leaves were prepared using the soxhlet apparatus. The extracts were dried and subjected to prepare five concentrations multiple of 80 ppm. Each concentration was applied for its larvicidal efficacy setting an experiment (in triplicate) in plastic containers of 1000 ml with extracts, 30 larvae of all four instars separately, and fed with dog biscuits along with controls. Observations were taken after each 12 hrs. till 72 hrs. The antioxidant perspective of M. piperita was determined by DPPH radical scavenging, total antioxidant capacity, and ferric reducing power assays. Using brine shrimp lethality bioactivity, the cytotoxic study was perceived. Standard techniques were used to classify the M. piperita extract using preliminary qualitative and quantitative phytochemicals, UV-Vis spectroscopy, FT-IR, and GC-MS analysis. M. piperita ethanolic leaves extract after 24 hrs. of exposure in 400 ppm showed 93% (LC₅₀ = 208.976 ppm) mortality in ethanolic extract and 80% (LC₉₀ = 246.900 ppm) in the water extract. In treated larvae, biochemical examination revealed a substantial (P<0.05) decrease in proteins, carbohydrates, and fat contents. The ethanol extract of M. piperita was the most efficient, killing brine shrimp nauplii in 50% to 90% of cases. TAC (125.4 3.5gAAE/mg DW) and FRP (378.1 1.0gAAE/mg DW) were highest in the ethanolic extract of M. piperita. The presence of medicinally active components such as alkaloids, carbohydrates, flavonoids, and others in M. piperita leaves extract in ethanol was discovered. The UV-Vis spectrum showed two peaks at 209.509 and 282.814 nm with the absorption of 2.338 and 0.796 respectively. The FT-IR consequences exhibited the occurrence of alcohols, alkanes, aldehyde, aromatic rings, ether linkage, ester, and halo- compounds. The GC-MS analysis according to peak (%) area and retention time showed ten phytochemicals consisting of six major and four minor compounds. Among all the compounds, 1, 2-benzene dicarboxylic acid, and 3-ethyl-5, 5-dimethyl -6-phenyl bound well to the NS3 protease domain with PDB ID: 2FOM. Hence, for the prevention of health hazards and mosquito control, M. Piperita is a potential source of chemicals for insecticide formulation.

Anticancer potential of β-sitosterol and oleanolic acid as through inhibition of human estrogenic 17beta-hydroxysteroid dehydrogenase type-1 based on an in silico approach

The human estrogenic enzyme 17beta-hydroxysteroid dehydrogenase type-1 (HSD17B1) provides biosynthesis regulation of active estrogen in stimulating the development of breast cancer through cell proliferation. The β-sitosterol is classified as a steroid compound and is actually a type of triterpenoid compound that has a similar structure to a steroid. This similarity provides a great opportunity for the inhibitor candidate to bind to the HDS17B1 enzyme because of the template similarity on the active site. Several in silico approaches have been applied in this study to examine the potential of these two inhibitor candidates. Pharmacokinetic studies showed positive results by meeting several drug candidate criteria, such as drug-likeness, bioavailability, and ADMET properties. A combination of molecular docking and MD simulation showed good conformational interaction of the inhibitors and HSD17B1. Prediction of binding free energy (ΔG bind) using the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) approach shows ΔG bind (kcal mol-1) of C1-HSD17B1: -49.31 ± 0.23 and C2-HSD17B1: -33.54 ± 0.34. Meanwhile, decomposition energy analysis (ΔG residue bind) suggested several key residues that were also responsible for the interaction with inhibitors, such as C1-HSD17B1 (six residues: Leu96, Leu149, Pro187, Met193, Val225, and Phe226) and C2-HSD17B1 (four residues: Ile14, Gly94, Pro187, and Val188). Hopefully, the obtained results from this research could be considered for the mechanistic inhibition of the HSDS17B1 enzyme at molecular and atomistic levels.

A New Strategy for Multitarget Drug Discovery/Repositioning Through the Identification of Similar 3D Amino Acid Patterns Among Proteins Structures: The Case of Tafluprost and its Effects on Cardiac Ion Channels

The identification of similar three-dimensional (3D) amino acid patterns among different proteins might be helpful to explain the polypharmacological profile of many currently used drugs. Also, it would be a reasonable first step for the design of novel multitarget compounds. Most of the current computational tools employed for this aim are limited to the comparisons among known binding sites, and do not consider several additional important 3D patterns such as allosteric sites or other conserved motifs. In the present work, we introduce Geomfinder2.0, which is a new and improved version of our previously described algorithm for the deep exploration and discovery of similar and druggable 3D patterns. As compared with the original version, substantial improvements that have been incorporated to our software allow: (i) to compare quaternary structures, (ii) to deal with a list of pairs of structures, (iii) to know how druggable is the zone where similar 3D patterns are detected and (iv) to significantly reduce the execution time. Thus, the new algorithm achieves up to 353x speedup as compared to the previous sequential version, allowing the exploration of a significant number of quaternary structures in a reasonable time. In order to illustrate the potential of the updated Geomfinder version, we show a case of use in which similar 3D patterns were detected in the cardiac ions channels NaV1.5 and TASK-1. These channels are quite different in terms of structure, sequence and function and both have been regarded as important targets for drugs aimed at treating atrial fibrillation. Finally, we describe the in vitro effects of tafluprost (a drug currently used to treat glaucoma, which was identified as a novel putative ligand of NaV1.5 and TASK-1) upon both ion channels’ activity and discuss its possible repositioning as a novel antiarrhythmic drug.

Computational Simulations Highlight the IL2Rα Binding Potential of Polyphenol Stilbenes from Fenugreek

Widely used in global households, fenugreek is well known for its culinary and medicinal uses. The various reported medicinal properties of fenugreek are by virtue of the different natural phytochemicals present in it. Regarded as a promising target, interleukin 2 receptor subunit alpha (IL2Rα) has been shown to influence immune responses. In the present research, using in silico techniques, we have demonstrated the potential IL2Rα binding properties of three polyphenol stilbenes (desoxyrhaponticin, rhaponticin, rhapontigenin) from fenugreek. As the first step, molecular docking was performed to assess the binding potential of the fenugreek phytochemicals with IL2Rα. All three phytochemicals demonstrated interactions with active site residues. To confirm the reliability of our molecular docking results, 100 ns molecular dynamics simulations studies were undertaken. As discerned by the RMSD and RMSF analyses, IL2Rα in complex with the desoxyrhaponticin, rhaponticin, and rhapontigenin indicated stability. The RMSD analysis of the phytochemicals alone also demonstrated no significant structural changes. Based on the stable molecular interactions and comparatively slightly better MM/PBSA binding free energy, rhaponticin seems promising. Additionally, ADMET analysis performed for the stilbenes indicated that all of them obey the ADMET rules. Our computational study thus supports further in vitro IL2Rα binding studies on these stilbenes, especially rhaponticin.

Structure-based protein-ligand interaction fingerprints for binding affinity prediction

Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.

Differences in Gluco and Galacto Substrate-Binding Interactions in a Dual 6Pβ-Glucosidase/6Pβ-Galactosidase Glycoside Hydrolase 1 Enzyme from Bacillus licheniformis

Bacterial glycoside hydrolase 1 (GH1) enzymes with 6-phospho-β-galactosidase and 6-phospho-β-glucosidase activities have the important task of releasing phosphorylated and nonphosphorylated monosaccharides into the cytoplasm. Curiously, dual 6-phospho-β-galactosidase/6-phospho-β-glucosidase (dual-phospho) enzymes have broad specificity and are able to hydrolyze galacto- and gluco-derived substrates. This study investigates the structure and substrate specificity of a GH family 1 enzyme from Bacillus licheniformis, hereafter known as BlBglC. The enzyme structure has been solved, and sequence analysis, molecular dynamics simulations, and binding free energy calculations offered evidence of dual-phospho activity. Both test ligands p-nitrophenyl-β-d-galactoside-6-phosphate (PNP6Pgal) and p-nitrophenyl-β-d-glucoside-6-phosphate (PNP6Pglc) demonstrated strong binding to BlBglC although the pose and interactions of the PNP6Pglc triplicates were slightly more consistent. Interestingly, known specificity-inducing residues, Gln23 and Trp433, bind strongly to the ligand O3 hydroxyl group in the PNP6Pgal-BlBglC complex and to the ligand O4 hydroxyl group in the PNP6Pglc-BlBglC complex. Additionally, the BlBglC-His124 residue is a major contributor of hydrogen bonds to the PNP6Pgal O3 hydroxyl group but does not form any hydrogen bonds with PNP6Pglc. On the other hand, BlBglC residues Tyr173, Tyr301, Gln302, and Thr321 form hydrogen bonds with PNP6Pglc but not PNP6Pgal. These findings provide important details of the broad specificity of dual-phospho activity GH1 enzymes.

2D SIFt: a matrix of ligand-receptor interactions

Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d.

Identification of potential plant bioactive as SARS-CoV-2 Spike protein and human ACE2 fusion inhibitors

The Spike receptor binding domain (S-RBD) from SARS-CoV-2, a crucial protein for the entrance of the virus into target cells is known to cause infection by binding to a cell surface protein. Hence, reckoning therapeutics for the S-RBD of SARS-CoV-2 may address a significant way to target viral entry into the host cells. Herein, through in-silico approaches (Molecular docking, molecular dynamics (MD) simulations, and end-state thermodynamics), we aimed to screen natural molecules from different plants for their ability to inhibit S-RBD of SARS-CoV-2. We prioritized the best interacting molecules (Diacetylcurcumin and Dicaffeoylquinic acid) by analysis of protein-ligand interactions and subjected them for long-term MD simulations. We found that Dicaffeoylquinic acid interacted prominently with essential residues (Lys417, Gln493, Tyr489, Phe456, Tyr473, and Glu484) of S-RBD. These residues are involved in interactions between S-RBD and ACE2 and could inhibit the viral entry into the host cells. The in-silico analyses indicated that Dicaffeoylquinic acid and Diacetylcurcumin might have the potential to act as inhibitors of SARS-CoV-2 S-RBD. The present study warrants further in-vitro and in-vivo studies of Dicaffeoylquinic acid and Diacetylcurcumin for validation and acceptance of their inhibitory potential against S-RBD of SARS-CoV-2.

Molecular dynamics, quantum mechanics and docking studies of some Keap1 inhibitors - An insight into the atomistic mechanisms of their antioxidant potential

Inhibitors of Keap1 would disrupt the covalent interaction between Keap1 and Nrf2 to unleash Nrf2 transcriptional machinery that orchestrates its cellular antioxidant, cytoprotective and detoxification processes thereby, protecting the cells against oxidative stress mediated diseases. In this in silico research, we investigated the Keap1 inhibiting potential of fifty (50) antioxidants using pharmacokinetic ADMET profiling, bioactivity assessment, physicochemical studies, molecular docking investigation, molecular dynamics and Quantum mechanical-based Density Functional Theory (DFT) studies using Keap1 as the apoprotein control. Out of these 50 antioxidants, Maslinic acid (MASA), 18-alpha-glycyrrhetinic acid (18-AGA) and resveratrol stand out by passing the RO5 (Lipinski rule of 5) for the physicochemical properties and ADMET studies. These three compounds also show high binding affinity of -10.6 kJ/mol, -10.4 kJ/mol and -7.8 kJ/mol at the kelch pocket of Keap1 respectively. Analysis of the 20ns trajectories using RMSD, RMSF, ROG and h-bond parameters revealed the stability of these compounds after comparing them with Keap1 apoprotein. Furthermore, the electron donating and accepting potentials of these compounds was used to investigate their reactivity using Density Functional Theory (HOMO and LUMO) and it was revealed that resveratrol had the highest stability based on its low energy gap. Our results predict that the three compounds are potential drug candidates with domiciled therapeutic functions against oxidative stress-mediated diseases. However, resveratrol stands out as the compound with the best stability and therefore, could be the best candidate with the best therapeutic efficacy.

Debio-0932, a second generation oral Hsp90 inhibitor, induces apoptosis in MCF-7 and MDA-MB-231 cell lines

Heat shock protein 90 (Hsp90) is a key chaperone that is abnormally expressed in cancer cells, and therefore, designing novel compounds to inhibit chaperone activities of the Hsp90 is a promising therapeutic approach for cancer drug discovery. Debio-0932 is a second-generation Hsp90 inhibitor that exhibited promising anticancer activity against a wide variety of cancer types with a strong binding affinity for Hsp90 and high oral bioavailability. Anticancer activities of the Debio-0932 were tested in MCF-7 and MDA-MB-231 cell lines. Molecular docking results indicated that Debio-0932 was selectively bound to the ATP binding pocket of the Hsp90 with an estimated free energy of binding - 7.24 kcal/mol. Antiproliferative activity of Debio-0932 was determined by XTT assay and Debio-0932 exhibited a cytotoxic effect on MCF-7 and MDA-MB-231 cells in a time and dose-depended manner. Apoptosis inducer role of Debio-0932 was evaluated in MCF-7 and MDA-MB-231 cells with fluorometric apoptosis/necrosis detection kit. Treatment with Debio-0932 stimulated apoptosis in both breast cancer cell lines. mRNA and protein expression levels of Bax, Bcl-2 and Casp-9 were determined in MCF-7 and MDA-MB-231 cells by RT-PCR and Western blotting respectively. Debio-0932 stimulated the down-regulation of anti-apoptotic protein Bcl-2 and the up-regulation of apoptotic protein Bax and cleavage of Casp-9 in cancer cells. Moreover, the anti-invasive potential of Debio-0932 was evaluated in endothelial cells (HUVEC) by wound-healing assay. Debio-0932 decreased the migration of HUVEC cells as compared to the control group. These results indicate that Debio-0932 is a promising compound to treat triple-negative breast cancer and hormone receptor-positive breast cancer, and their metastases.

ML-DTI: Mutual Learning Mechanism for Interpretable Drug-Target Interaction Prediction

Deep learning (DL) provides opportunities for the identification of drug-target interactions (DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most of the existing DL-based methods formulate the drug and target encoder as two independent modules without considering the relationship between them. In this study, we propose a mutual learning mechanism to bridge the gap between the two encoders. We formulated the DTI problem from a global perspective by inserting mutual learning layers between the two encoders. The mutual learning layer was achieved by multihead attention and position-aware attention. The neural attention mechanism also provides effective visualization, which makes it easier to analyze a model. We evaluated our approach using three benchmark kinase data sets under different experimental settings and compared the proposed method to three baseline models. We found that the four methods yielded similar results in the random split setting (training and test sets share common drugs and targets), while the proposed method increases the predictive performance significantly in the orphan-target and orphan-drug split setting (training and test sets share only targets or drugs). The experimental results demonstrated that the proposed method improved the generalization and interpretation capability of DTI modeling.

Toward an Understanding of Pan-Assay Interference Compounds and Promiscuity: A Structural Perspective on Binding Modes

Pan-assay interference compounds (PAINS) are promiscuous compound classes that produce false positive hits in high-throughput screenings. Yet, the mechanisms of PAINS activity are poorly understood. Although PAINS are often associated with protein reactivity, several recent studies have shown that they also mediate noncovalent interactions. Aiming at a deep understanding of PAINS promiscuity, we performed an analysis of the Protein Data Bank to characterize the binding modes of PAINS. We explored the binding mode conservation of 34 PAINS classes present in 871 ligands and among 517 protein targets. The two major findings of this work are the following: First, different PAINS classes exhibit different levels of binding mode conservation. Our novel classification of PAINS based on binding mode similarity enables a rational assessment of PAINS from a structural perspective. Second, PAINS classes with variable binding modes can bind with high affinity. The evaluation of noncovalent binding modes of PAINS-like compounds sheds light on the mechanisms of promiscuous binding. Our findings could facilitate the decisions on how to deal with PAINS and help scientists to understand why PAINS produce hits in their screenings.

Biochemical and Computational Studies of the Interaction between a Glucosamine Derivative, NAPA, and the IKKα Kinase

The glucosamine derivative 2-(N-Acetyl)-L-phenylalanylamido-2-deoxy-β-D-glucose (NAPA), was shown to inhibit the kinase activity of IKKα, one of the two catalytic subunits of IKK complex, decreasing the inflammatory status in osteoarthritis chondrocytes. In the present work we have investigated the inhibition mechanism of IKKα by NAPA by combining computational simulations, in vitro assays and Mass Spectrometry (MS) technique. The kinase in vitro assay was conducted using a recombinant IKKα and IKKtide, a 20 amino acid peptide substrate derived from IkBα kinase protein and containing the serine residues Ser32 and Ser36. Phosphorylated peptide production was measured by Ultra Performance Liquid Chromatography coupled with Mass Spectrometry (UPLC-MS), and the atomic interaction between IKKα and NAPA has been studied by molecular docking and Molecular Dynamics (MD) approaches. Here we report that NAPA was able to inhibit the IKKα kinase activity with an IC₅₀ of 0.5 mM, to decrease the K_m value from 0.337 mM to 0.402 mM and the V_max from 0.0257 mM·min−1 to 0.0076 mM·min−1. The computational analyses indicate the region between the KD, ULD and SDD domains of IKKα as the optimal binding site explored by NAPA. Biochemical data indicate that there is a non-significant difference between K_m and K_i whereas there is a statistically significant difference between the two V_max values. This evidence, combined with computational results, consistently indicates that the inhibition is non-competitive, and that the NAPA binding site is different than that of ATP or IKKtide.

Experimental approach, theoretical investigation and molecular docking of 2- chloro-5-fluoro phenol antibacterial compound

The molecular structural dimerization of biologically potent 2-chloro-5-fluoro phenol (2C5FP) is optimized. A combined experimental and theoretical characteristics of vibrational spectral determinations (NMR, FT-IR and Raman) on 2-chloro-5-fluoro phenol (2C5FP) were used at DFT-B3LYP/6–31++G (d,p) level of computation. A close coherence is achieved when experimentally observed wave numbers are compared with calculated wave numbers by refinement of the scale factors. Calculated values of global chemical descriptors of the present molecule reveal significant molecular stability and chemical reactivity. Non-Linear optical (NLO) property of the present molecule is investigated by determining the second order non linear parameter of first hyperpolarizability β. Moreover, hydrogen bond and thermodynamic parameters at various temperatures are determined and discussed. Investigated compound 2C5FP possesses a better antibacterial activity against Echerichia coli, Streptococcus aureus, Pseudomonas aureus,and Staphylococcus aureus, respectively. The title molecule is subjected to molecular docking studies with two different proteins, namely Staphylococcus aureus Tyrosyl-tRNA synthetase (PDB ID: 1JIL) and human dihydroorotate dehydrogenase (hDHODH) (PDB ID: 6CJF). The results of molecular docking analysis support the antibacterial activity and demonstrate a strong interaction with the DHODH inhibitor.

Structure based virtual screening identifies small molecule effectors for the sialoglycan binding protein Hsa

Infective endocarditis (IE) is a cardiovascular disease often caused by bacteria of the viridans group of streptococci, which includes Streptococcus gordonii and Streptococcus sanguinis. Previous research has found that serine-rich repeat (SRR) proteins on the S. gordonii bacterial surface play a critical role in pathogenesis by facilitating bacterial attachment to sialylated glycans displayed on human platelets. Despite their important role in disease progression, there are currently no anti-adhesive drugs available on the market. Here, we performed structure-based virtual screening using an ensemble docking approach followed by consensus scoring to identify novel small molecule effectors against the sialoglycan binding domain of the SRR adhesin protein Hsa from the S. gordonii strain DL1. The screening successfully predicted nine compounds which were able to displace the native ligand (sialyl-T antigen) in an in vitro assay and bind competitively to Hsa. Furthermore, hierarchical clustering based on the MACCS fingerprints showed that eight of these small molecules do not share a common scaffold with the native ligand. This study indicates that SRR family of adhesin proteins can be inhibited by diverse small molecules and thus prevent the interaction of the protein with the sialoglycans. This opens new avenues for discovering potential drugs against IE.