Modeling enzyme-ligand binding in drug discovery

Zebrafish modeling of atypical PML-RARA isoform from acute promyelocytic leukemia patient and its implications for clinical treatment

Acute promyelocytic leukemia (APL) is driven by the specific fusion gene PML-RARA produced by chromosomal translocation. Three classic isoforms, L, V, and S, are found in more than 95% of APL patients. However, atypical PML-RARA isoforms are usually associated with uncertain disease progression and treatment prognosis. Recently, we found a novel PML-RARA isoform (named PA) in a patient with atypical clinical characteristics of APL. In order to provide valuable insights for clinical treatment, we constructed the novel PML-RARA isoform zebrafish model for all-trans retinoic acid (ATRA) treatment experiments and comparison with classical isoforms. We found that the effect of PA PML-RARA on the expression of neutrophil-related genes was comparable with classical isoforms and ATRA treatment worked successfully in the zebrafish model. Sequence and structure analysis of the PA protein confirmed its similarity to classical isoforms and the fusion site of PA PML-RARA did not affect the ATRA binding site. As expected, the patient achieved complete remission within two months of treatment with ATRA in combination with arsenic trioxide (ATO) and had a favorable prognosis during the three-year follow-up. Our study highlights the accuracy and efficacy of the PML-RARA zebrafish model in combination with protein structure prediction in support of clinical treatment strategies.

Computational evaluation and benchmark study of 342 crystallographic holo-structures of SARS-CoV-2 Mpro enzyme

The coronavirus disease 19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a global health crisis with millions of confirmed cases and related deaths. The main protease (Mpro) of SARS-CoV-2 is crucial for viral replication and presents an attractive target for drug development. Despite the approval of some drugs, the search for effective treatments continues. In this study, we systematically evaluated 342 holo-crystal structures of Mpro to identify optimal conformations for structure-based virtual screening (SBVS). Our analysis revealed limited structural flexibility among the structures. Three docking programs, AutoDock Vina, rDock, and Glide were employed to assess the efficiency of virtual screening, revealing diverse performances across selected Mpro structures. We found that the structures 5RHE, 7DDC, and 7DPU (PDB Ids) consistently displayed the lowest EF, AUC, and BEDROCK scores. Furthermore, these structures demonstrated the worst pose prediction results in all docking programs. Two structural differences contribute to variations in docking performance: the absence of the S1 subsite in 7DDC and 7DPU, and the presence of a subpocket in the S2 subsite of 7DDC, 7DPU, and 5RHE. These findings underscore the importance of selecting appropriate Mpro conformations for SBVS, providing valuable insights for advancing drug discovery efforts.

Microbial Metabolites of 3-n-butylphthalide as Monoamine Oxidase A Inhibitors

Novel compounds with antidepressant activity via monoamine oxidase inhibition are being sought. Among these, derivatives of 3-n-butylphthalide, a neuroprotective lactone from Apiaceae plants, may be prominent candidates. This study aimed to obtain the oxidation products of 3-n-butylphthalide and screen them regarding their activity against the monoamine oxidase A (MAO-A) isoform. Such activity of these compounds has not been previously tested. To obtain the metabolites, we used fungi as biocatalysts because of their high oxidative capacity. Overall, 37 strains were used, among which Penicillium and Botrytis spp. were the most efficient, leading to the obtaining of three main products: 3-n-butyl-10-hydroxyphthalide, 3-n-butylphthalide-11-oic acid, and 3-n-butyl-11-hydroxyphthalide, with a total yield of 0.38-0.82 g per g of the substrate, depending on the biocatalyst used. The precursor-3-n-butylphthalide and abovementioned metabolites inhibited the MAO-A enzyme; the most active was the carboxylic acid derivative of the lactone with inhibitory constant (K_i) < 0.001 µmol/L. The in silico prediction of the drug-likeness of the metabolites matches the assumptions of Lipinski, Ghose, Veber, Egan, and Muegge. All the compounds are within the optimal range for the lipophilicity value, which is connected to adequate permeability and solubility.

Exploring N-myristoyltransferase as a promising drug target against parasitic neglected tropical diseases

Neglected tropical diseases (NTDs) constitute a group of approximately 20 infectious diseases that mainly affect the impoverished population without basic sanitation in tropical countries. These diseases are responsible for many deaths worldwide, costing billions of dollars in public health investment to treat and control these infections. Among them are the diseases caused by protozoa of the Trypanosomatid family, which constitute Trypanosoma cruzi (Chagas disease), Trypanosoma brucei (sleeping sickness), and Leishmaniasis. In addition, there is a classification of other diseases, called the big three, AIDS, tuberculosis, and malaria, which are endemic in countries with tropical conditions. Despite the high mortality rates, there is still a gap in the treatment. The drugs have a high incidence of side effects and protozoan resistance, justifying the investment in developing new alternatives. In fact, the Target-Based Drug Design (TBDD) approach is responsible for identifying several promising compounds, and among the targets explored through this approach, N-myristoyltransferase (NMT) stands out. It is an enzyme related to the co-translational myristoylation of N-terminal glycine in various peptides. The myristoylation process is a co-translation that occurs after removing the initiator methionine. This process regulates the assembly of protein complexes and stability, which justifies its potential as a drug target. In order to propose NMT as a potential target for parasitic diseases, this review will address the entire structure and function of this enzyme and the primary studies demonstrating its promising potential against Leishmaniasis, T. cruzi, T. brucei, and malaria. We hope our information can help researchers worldwide search for potential drugs against these diseases that have been threatening the health of the world's population.

Phytochemical Profiling, Biological Activities, and In Silico Molecular Docking Studies of Causonis trifolia (L.) Mabb. & J.Wen Shoot

Causonis trifolia (L.) Mabb. & J.Wen, commonly known as "fox grape", is an ethnomedicinally important twining herb of the Vitaceae family, and it is used by ethnic communities for its wide range of therapeutic properties. Our research aims to investigate the chemical composition; antioxidant, anti-inflammatory, and antidiabetic activities; and mechanisms of interaction between the identified selective chemical compounds and the target proteins associated with antioxidant, anti-inflammatory, and antidiabetic effects of the optimised phenolic extract of Causonis trifolia (L.) Mabb. & J.Wen, shoot (PECTS) to endorse the plant as a potential drug candidate for a future bioprospecting programme. Here, we employed the response surface methodology (RSM) with a Box-Behnken design to enrich the methanolic extract of C. trifolia shoot with phenolic ingredients by optimising three key parameters: solvent concentration (% v/v, methanol:water), extraction temperature (°C), and extraction duration (hours). From the quantitative phytochemical estimation, it was evident that the PECTS contained good amounts of phenolics, flavonoids, tannins, and alkaloids. During the HPLC analysis, we identified a total of eight phenolic and flavonoid compounds (gallic acid, catechin hydrate, chlorogenic acid, caffeic acid, p-coumaric acid, sinapic acid, coumarin, and kaempferol) and quantified their respective contents from the PECTS. The GC-MS analysis of the PECTS highlighted the presence of 19 phytochemicals. In addition, the bioactivity study of the PECTS showed remarkable potentiality as antioxidant, anti-inflammatory, and antidiabetic agents. In silico molecular docking and computational molecular modelling were employed to investigate the anti-inflammatory, antioxidant, and antidiabetic properties of the putative bioactive compounds derived from the PECTS using the GC-MS technique to understand the drug-receptor interactions, including their binding pattern. Out of the 19 phytocompounds identified by the GC-MS analysis, one compound, ergosta-5,22-dien-3-ol, acetate, (3β,22E), exhibited the best binding conformations with the target proteins involved in anti-inflammatory (e.g., Tnf-α and Cox-2), antioxidant (SOD), and antidiabetic (e.g., α-amylase and aldo reductase) activities. The nontoxic nature of this optimised extract was also evident during the in vitro cell toxicity assay against the Vero cell line and the in vivo acute toxicity study on BALB/c mice. We believe the results of the present study will pave the way for the invention of novel drugs efficacious for several ailments using the C. trifolia plant.

Modulation of Cytoskeleton, Protein Trafficking, and Signaling Pathways by Metabolites from Cucurbitaceae, Ericaceae, and Rosaceae Plant Families

One promising frontier within the field of Medical Botany is the study of the bioactivity of plant metabolites on human health. Although plant metabolites are metabolic byproducts that commonly regulate ecological interactions and biochemical processes in plant species, such metabolites also elicit profound effects on the cellular processes of human and other mammalian cells. In this regard, due to their potential as therapeutic agents for a variety of human diseases and induction of toxic cellular responses, further research advances are direly needed to fully understand the molecular mechanisms induced by these agents. Herein, we focus our investigation on metabolites from the Cucurbitaceae, Ericaceae, and Rosaceae plant families, for which several plant species are found within the state of Florida in Hillsborough County. Specifically, we compare the molecular mechanisms by which metabolites and/or plant extracts from these plant families modulate the cytoskeleton, protein trafficking, and cell signaling to mediate functional outcomes, as well as a discussion of current gaps in knowledge. Our efforts to lay the molecular groundwork in this broad manner hold promise in supporting future research efforts in pharmacology and drug discovery.

Spectroscopic details on the molecular structure of pyrimidine‑2‑thiones heterocyclic compounds: computational and antiviral activity against the main protease enzyme of SARS-CoV-2

Computational tools in investigating of spectral heterocyclic compounds ranges based on pyrimidine‑2‑thiones, take some importance in identifying their molecular and electronic behavior. Some charcoal heterocyclic compounds were previously synthesized in our laboratory and their experimental results were compared with the computational evaluation. Computational spectroscopic analytical items (IR, NMR and UV–Vis) were calculated using the more popular DFT methods and the predicted results were compared with the reported experimental ones. Quantum and chemical parameters were calculated and molecular electrostatic surface potential (MEP) was studied which predicted the highly electronic sites around the compounds. Some molecular properties (ionization energy, electron affinity, energy gap, hardness, electronegativity, electrophilicity index, static dipole moment and average linear polarizability) of these Schiff bases which were computed at B3LYP/6-31G(d,p) level in aqueous phase. Benchmark analysis was performed for three ab initio functionals such B3LYP, BPV86 and B3PW91 methods to explain the data resulted from NMR spectra. The docking study of some selected previously synthesized compounds was performed using the viral Mpro enzyme protein in compared to a k36 reference ligand inhibitor. The study indicated the ability of the synthesized compounds to form H-bond and hydrophobic (VDW, π-alkyl and π-sulfur) interactions with Mpro enzyme receptor with high inhibition effect of compound L2.

PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures

Knowledge of protein-ligand binding sites (LBSs) enables research ranging from protein function annotation to structure-based drug design. To this end, we have previously developed a stand-alone tool, P2Rank, and the web server PrankWeb (https://prankweb.cz/) for fast and accurate LBS prediction. Here, we present significant enhancements to PrankWeb. First, a new, more accurate evolutionary conservation estimation pipeline based on the UniRef50 sequence database and the HMMER3 package is introduced. Second, PrankWeb now allows users to enter UniProt ID to carry out LBS predictions in situations where no experimental structure is available by utilizing the AlphaFold model database. Additionally, a range of minor improvements has been implemented. These include the ability to deploy PrankWeb and P2Rank as Docker containers, support for the mmCIF file format, improved public REST API access, or the ability to batch download the LBS predictions for the whole PDB archive and parts of the AlphaFold database.

Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model

Clostridium thermocellum is a promising candidate for consolidated bioprocessing because it can directly ferment cellulose to ethanol. Despite significant efforts, achieved yields and titers fall below industrially relevant targets. This implies that there still exist unknown enzymatic, regulatory, and/or possibly thermodynamic bottlenecks that can throttle back metabolic flow. By (i) elucidating internal metabolic fluxes in wild-type C. thermocellum grown on cellobiose via ¹³C-metabolic flux analysis (¹³C-MFA), (ii) parameterizing a core kinetic model, and (iii) subsequently deploying an ensemble-docking workflow for discovering substrate-level regulations, this paper aims to reveal some of these factors and expand our knowledgebase governing C. thermocellum metabolism. Generated ¹³C labeling data were used with ¹³C-MFA to generate a wild-type flux distribution for the metabolic network. Notably, flux elucidation through MFA alluded to serine generation via the mercaptopyruvate pathway. Using the elucidated flux distributions in conjunction with batch fermentation process yield data for various mutant strains, we constructed a kinetic model of C. thermocellum core metabolism (i.e. k-ctherm138). Subsequently, we used the parameterized kinetic model to explore the effect of removing substrate-level regulations on ethanol yield and titer. Upon exploring all possible simultaneous (up to four) regulation removals we identified combinations that lead to many-fold model predicted improvement in ethanol titer. In addition, by coupling a systematic method for identifying putative competitive inhibitory mechanisms using K-FIT kinetic parameterization with the ensemble-docking workflow, we flagged 67 putative substrate-level inhibition mechanisms across central carbon metabolism supported by both kinetic formalism and docking analysis.

Interaction of Selected Terpenoids From Dalbergia sissoo With Catalytic Domain of Matrix Metalloproteinase-1: An In Silico Assessment of Their Anti-wrinkling Potential

Matrix metalloproteinase-1 (MMP-1) is a predominant collagenase enzyme that cleaves collagen fibers, contributing to skin wrinkling. Matrix metalloproteinase-1 inhibitors of herbal origin may provide an earnest probability to offer a novel curative approach against MMP-1-mediated collagenolysis, prompted by ultraviolet (UV)-induced overexpression of MMP-1. In this in silico study, we have explored the MMP-1 inhibitory potential of selected terpenoids from Dalbergia sissoo extracts. Two triterpenoids (lupeol and betulin), 1 diterpenoid (phytol), and 1 ester derivative of lupeol (lupeol acetate) were studied along with a reference inhibitor (doxycycline) using molecular docking approach. Non covalent interaction between the target ligands was found. Lupeol was found interacting with amino acid (AA) residues in the catalytic domain of MMP-1 with 3 hydrogen bonds (H-bond) formation, phytol with 1 and doxycycline with 2 H-bonds, whereas betulin and lupeol acetate were not able to form any H-bond with the AA residues in the catalytic site of the target protein. However, hydrophobic interaction between these ligands and protein was evident with select residues. The binding affinity of lupeol was highest (binding free energy, ΔG = −8.24 kcal/mol), which was greater than reference drug, doxycycline (ΔG = −8.05 kcal/mol). Lupeol acetate and phytol displayed a ΔG value of −7.12 and −7.06 kcal/mol, respectively, whereas betulin holds less binding affinity for the target receptor (ΔG = −4.66 kcal/mol). In silico pharmacokinetic studies demonstrated drug-like properties of the ligand compounds. This study shows that hydroxyl groups present in the ligands play a substantial role in establishing protein ligand interaction via hydrogen bonding.

In Silico Laboratory: Tools for Similarity-Based Drug Discovery

Computational methods that predict and evaluate binding of ligands to receptors implicated in different pathologies have become crucial in modern drug design and discovery. Here, we describe protocols for using the recently developed package of computational tools for similarity-based drug discovery. The ProBiS stand-alone program and web server allow superimposition of protein structures against large protein databases and predict ligands based on detected binding site similarities. GenProBiS allows mapping of human somatic missense mutations related to cancer and non-synonymous single nucleotide polymorphisms and subsequent visual exploration of specific interactions in connection to these mutations. We describe protocols for using LiSiCA, a fast ligand-based virtual screening software that enables easy screening of large databases containing billions of small molecules. Finally, we show the use of BoBER, a web interface that enables user-friendly access to a large database of bioisosteric and scaffold hopping replacements.

Recognizing five molecular ligand-binding sites with similar chemical structure

Accurate identification of ligand-binding sites and discovering the protein-ligand interaction mechanism are important for understanding proteins' functions and designing new drugs. Meanwhile, accurate computational prediction and mechanism research are two grand challenges in proteomics. In this article, ligand-binding residues of five ligands (ATP, ADP, GTP, GDP, and NAD) are predicted as a group, due to their similar chemical structures and close biological function relations. The data set of binding sites by five ligands (ATP, ADP, GTP, GDP, and NAD) are collated from Biolip database. Then, five features, containing increment of diversity value, matrix scoring value, auto-covariance, secondary structure information, and surface accessibility information are used in binding site predictions. The support vector machine (SVM) model is used with the five features to predict ligand-binding sites. Finally, prediction results are tested by fivefold cross validation. Accuracy (Acc) of five ligands (ATP, ADP, GTP, GDP, and NAD) achieves 77.4%, 71.2%, 82.1%, 82.9%, and 85.3%, respectively; and Matthew correlation coefficient (MCC) of the above five ligands achieves 0.549, 0.424, 0.643, 0.659, and 0.702, respectively. The research result shows that for ligands with similar chemical structures, microenvironment of their binding sites and their sensitivities to features are similar, while, differences of their ligand-binding properties exist at the same time. © 2019 Wiley Periodicals, Inc.

Evaluation of Chemotherapeutic Activity of the Selected Bases' Analogues of Nucleic Acids Supported by ab initio Various Quantum Chemical Calculations

BACKGROUND

Pharmacological and physicochemical classification of bases' selected analogues of nucleic acids is proposed in the study.

OBJECTIVE

Structural parameters received by the PCM (Polarizable Continuum Model) with several types of calculation methods for the structures in vacuo and in the aquatic environment together with the huge set of extra molecular descriptors obtained by the professional software and literature values of biological activity were used to search the relationships.

METHODS

Principal Component Analysis (PCA) together with Factor Analysis (FA) and Multiple Linear Regressions (MLR) as the types of the chemometric approach based on semi-empirical ab initio molecular modeling studies were performed.

RESULTS

The equations with statistically significant descriptors were proposed to demonstrate both the common and differentiating characteristics of the bases' analogues of nucleic acids based on the quantum chemical calculations and biological activity data.

CONCLUSION

The obtained QSAR models can be used for predicting and explaining the activity of studied molecules.

Computational Outlook of Marine Compounds as Anti-Cancer Representatives Targeting BCL-2 and Survivin

INTRODUCTION

The regulation of apoptosis via compounds originated from marine organisms signifies a new wave in the field of drug discovery. Marine organisms produce potent compounds as they hold the phenomenal diversity in chemical structures. The main focus of drug development is anticancer therapy.

METHODS

Expertise on manifold activities of compounds helps in the discovery of their derivatives for preclinical and clinical experiment that promotes improved activity of compounds for cancer patients.

RESULTS

These marine derived compounds stimulate apoptosis in cancer cells by targeting Bcl-2 and Survivin, highlighting the fact that instantaneous targeting of these proteins by novel derivatives results in efficacious and selective killing of cancer cells.

CONCLUSION

Our study reports the identification of Aplysin and Haterumaimide J as Bcl-2 inhibitors and Cortistatin A as an inhibitor of survivin protein, from a sequential virtual screening approach.

A Computational Approach for Prioritizing Selection of Therapies Targeting Drug Resistant Variation in Anaplastic Lymphoma Kinase

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase implicated as a driver of a number of cancer types, and activates cellular pathways involved in cell proliferation and differentiation. Tyrosine kinase inhibitors (TKIs) are a small molecule therapeutic that blocks ALK function, but tumor evolution leads to the rapid emergence of drug resistant somatic variation and necessitates selection of a new treatment strategy. Computational simulations of protein:drug interactions were used to investigate the impact of seven drug resistant mutations on binding to eleven TKIs approved, or under investigation, for treatment of ALK positive cancers. The results show variant specific disruptions to TKI molecular interactions, and demonstrate the potential to aid prioritization of therapeutic interventions. Validation remains a challenge due to the complex dependence of biomolecular interactions on the local biophysical environment, but improvements to the underlying structural model and continued curation efforts will improve the clinical utility of computational predictions.

Enzymatic reactions involving the heteroatoms from organic substrates

Several enzymatic reactions of heteroatom-containing compounds have been explored as unnatural substrates. Considerable advances related to the search for efficient enzymatic systems able to support a broader substrate scope with high catalytic performance are described in the literature. These reports include mainly native and mutated enzymes and whole cells biocatalysis. Herein, we describe the historical background along with the progress of biocatalyzed reactions involving the heteroatom(S, Se, B, P and Si) from hetero-organic substrates.

Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates

There are numerous applications that use the structures of protein-ligand complexes from the PDB, such as 3D pharmacophore identification, virtual screening, and fragment-based drug design. The structures underlying these applications are potentially much more informative if they contain biologically relevant bound ligands, with high similarity to the cognate ligands. We present a study of ligand-enzyme complexes that compares the similarity of bound and cognate ligands, enabling the best matches to be identified. We calculate the molecular similarity scores using a method called PARITY (proportion of atoms residing in identical topology), which can conveniently be combined to give a similarity score for all cognate reactants or products in the reaction. Thus, we generate a rank-ordered list of related PDB structures, according to the biological similarity of the ligands bound in the structures.

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We present PARITY, matching atoms in identical topology to gauge ligand similarity

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Bound-cognate ligand similarity is a useful metric for ranking PDB structures

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Only 26% of enzyme structures in the PDB have bound-cognate ligand similarity ≥0.7

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We provide rank-ordered lists of PDBs with the most biologically relevant ligands

Discovery of Mycobacterium tuberculosis InhA Inhibitors by Binding Sites Comparison and Ligands Prediction

Drug discovery is usually focused on a single protein target; in this process, existing compounds that bind to related proteins are often ignored. We describe ProBiS plugin, extension of our earlier ProBiS-ligands approach, which for a given protein structure allows prediction of its binding sites and, for each binding site, the ligands from similar binding sites in the Protein Data Bank. We developed a new database of precalculated binding site comparisons of about 290000 proteins to allow fast prediction of binding sites in existing proteins. The plugin enables advanced viewing of predicted binding sites, ligands' poses, and their interactions in three-dimensional graphics. Using the InhA query protein, an enoyl reductase enzyme in the Mycobacterium tuberculosis fatty acid biosynthesis pathway, we predicted its possible ligands and assessed their inhibitory activity experimentally. This resulted in three previously unrecognized inhibitors with novel scaffolds, demonstrating the plugin's utility in the early drug discovery process.