Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis

The artificial neural network selects saccharides from natural sources a promise for potential FimH inhibitor to prevent UTI infections

The major challenge in the development of affordable medicines from natural sources is the unavailability of logical protocols to explain their mechanism of action in biological targets. FimH (Type 1 fimbrin with D-mannose specific adhesion property), a lectin on E. coli cell surface is a promising target to combat the urinary tract infection (UTI). The present study aimed at predicting the inhibitory capacity of saccharides on FimH. As mannosides are considered FimH inhibitors, the readily accessible saccharides from the PubChem collection were utilized. The artificial neural networks (ANN)-based machine learning algorithm Self-organizing map (SOM) has been successfully employed in predicting active molecules as they could discover relationships through self-organization for the ligand-based virtual screening. Docking was used for the structure-based virtual screening and molecular dynamic simulation for validation. The result revealed that the predicted molecules malonyl hexose and mannosyl glucosyl glycerate exhibit exactly similar binding interactions and better docking scores as that of the reference bioassay active, heptyl mannose. The pharmacokinetic profile matches that of the selected bioflavonoids (quercetin malonyl hexose, kaempferol malonyl hexose) and has better values than the control drug bioflavonoid, monoxerutin. Thus, these two molecules can effectively inhibit type 1 fimbrial adhesin, as antibiotics against E. coli and can be explored as a prophylactic against UTIs. Moreover, this investigation can pave the way to the exploration of the potential benefits of plant-based treatments.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00212-5.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Evaluating Biofilm Inhibitory Potential in Fish Pathogen, Aeromonas hydrophila by Agricultural Waste Extracts and Assessment of Aerolysin Inhibitors Using In Silico Approach

Aeromonas hydrophila, an opportunistic bacteria, causes several devastating diseases in humans and animals, particularly aquatic species. Antibiotics have been constrained by the rise of antibiotic resistance caused by drug overuse. Therefore, new strategies are required to prevent appropriate antibiotic inability from antibiotic-resistant strains. Aerolysin is essential for A. hydrophila pathogenesis and has been proposed as a potential target for inventing drugs with anti-virulence properties. It is a unique method of disease prevention in fish to block the quorum-sensing mechanism of A. hydrophila. In SEM analysis, the crude solvent extracts of both groundnut shells and black gram pods exhibited a reduction of aerolysin formation and biofilm matrix formation by blocking the QS in A. hydrophila. Morphological changes were identified in the extracts treated bacterial cells. Furthermore, in previous studies, 34 ligands were identified with potential antibacterial metabolites from agricultural wastes, groundnut shells, and black gram pods using a literature survey. Twelve potent metabolites showed interactions between aerolysin and metabolites during molecular docking analysis, in that H-Pyran-4-one-2,3 dihydro-3,5 dihydroxy-6-methyl (-5.3 kcal/mol) and 2-Hexyldecanoic acid (-5.2 kcal/mol) showed promising results with potential hydrogen bond interactions with aerolysin. These metabolites showed a better binding affinity with aerolysin for 100 ns in molecular simulation dynamics. These findings point to a novel strategy for developing drugs using metabolites from agricultural wastes that may be feasible pharmacological solutions for treating A. hydrophila infections for the betterment of aquaculture.

Computational modeling of imines based anti-oxidant and anti-esterases compounds: Synthesis, single crystal and In-vitro assessment

Molecular modeling strategy was adopted to check the biological potential of the imine based molecules against free radical, acetylcholine esterase and butyrylcholine esterase. Three Schiff based compounds as (E)-2-(((4-bromophenyl)imino)methyl)-4-methylphenol (1), (E)-2-(((3-fluorophenyl)imino)methyl)-4-methylphenol (2) and (2E,2E)-2-(2-(2-hydroxy-5-methylbenzylidene)hydrazono)-1,2-diphenylethanone (3) were synthesized with high yield. The synthesized compounds were characterized with the help of modern techniques such as UV, FTIR and NMR while exact structure was depicted with Single Crystal X-Ray diffraction technique which disclosed that compound 1 is orthorhombic, while 2 and 3 are monoclinic. A hybrid functional (B3LYP) method with general basis set of 6-31 G(d,p) were applied to optimize synthesized Schiff bases. The contribution of in-between molecular contacts within a crystalline assembly of compounds were studied using Hirshfeld surface analysis (HS). In order to check the ability of the synthesized compounds toward free radical and enzyme inhibition, in vitro models were used to assess the radical scavenging and enzyme inhibition potential which depicted that compound 3 showed highest potential (57.43 ± 1.0%; DPPH, 75.09 ± 1.0%; AChE and 64.47 ± 1.0%; BChE). The ADMET assessments suggested the drug like properties of the synthesized compounds. It was concluded from results (in vitro and in silico) that synthesized compound have ability to cure the disorder related to free radical and enzyme inhibition. Compound 3 was shown to be the most active compared to other compounds.

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.

PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations

Understanding the process of ligand–protein recognition is important to unveil biological mechanisms and to guide drug discovery and design. Enhanced-sampling molecular dynamics is now routinely used to simulate the ligand binding process, resulting in the need for suitable tools for the analysis of large data sets of binding events. Here, we designed, implemented, and tested PathDetect-SOM, a tool based on self-organizing maps to build concise visual models of the ligand binding pathways sampled along single simulations or replicas. The tool performs a geometric clustering of the trajectories and traces the pathways over an easily interpretable 2D map and, using an approximate transition matrix, it can build a graph model of concurrent pathways. The tool was tested on three study cases representing different types of problems and simulation techniques. A clear reconstruction of the sampled pathways was derived in all cases, and useful information on the energetic features of the processes was recovered. The tool is available at https://github.com/MottaStefano/PathDetect-SOM.

Repurposing FDA-approved Drugs Targeting SARS-CoV2 3CLpro: a study by applying Virtual Screening, Molecular Dynamics, MM-PBSA Calculations and Covalent Docking

Background:

Since the end of 2019, the etiologic agent SAR-CoV-2 responsible for one of the most significant epidemics in history has caused severe global economic, social, and health damages. The drug repurposing approach and application of Structure-based Drug Discovery (SBDD) using in silico techniques are increasingly frequent, leading to the identification of several molecules that may represent promising potential.

Method:

In this context, here we use in silico methods of virtual screening (VS), pharmacophore modeling (PM), and fragment-based drug design (FBDD), in addition to molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM -PBSA) calculations, and covalent docking (CD) for the identification of potential treatments against SARS-CoV-2. We initially validated the docking protocol followed by VS in 1,613 FDA-approved drugs obtained from the ZINC database. Thus, we identified 15 top hits, of which three of them were selected for further simulations. In parallel, for the compounds with a fit score value ≤ of 30, we performed the FBDD protocol, where we designed 12 compounds

Result:

By applying a PM protocol in the ZINC database, we identified three promising drug candidates. Then, the 9 top hits were evaluated in simulations of MD, MM-PBSA, and CD. Subsequently, MD showed that all identified hits showed stability at the active site without significant changes in the protein's structural integrity, as evidenced by the RMSD, RMSF, Rg, SASA graphics. They also showed interactions with the catalytic dyad (His41 and Cys145) and other essential residues for activity (Glu166 and Gln189) and high affinity for MM-PBSA, with possible covalent inhibition mechanism.

Conclution:

Finally, our protocol helped identify potential compounds wherein ZINC896717 (Zafirlukast), ZINC1546066 (Erlotinib), and ZINC1554274 (Rilpivirine) were more promising and could be explored in vitro, in vivo, and clinical trials to prove their potential as antiviral agents.

Peptibody Based on FGFR1-Binding Peptides From the FGF4 Sequence as a Cancer-Targeting Agent

Targeted therapies are a promising alternative to conventional chemotherapy, with an increasing number of therapeutics targeting specific molecular aberrancies in cancer cells. One of the emerging targets for directed cancer treatments is fibroblast growth factor receptors (FGFRs), which are known to be involved in the pathogenesis and progression of multiple cancer types, specially in lung, bladder, and breast cancers. Here, we are demonstrating the development of the FGFR1-targeting agent based on the interactome screening approach, based on the isolation of binding regions from ligands interacting with the receptor. The parallel analysis by FGFR1 pull-down of chymotryptic peptides coupled with MS analysis, and PepSpot analysis yielded equivalent peptide sequences from FGF4, one of the FGFR1 ligands. Three sequences served as a basis for peptibody (Fc-fusion) generation, to overcome clinical limitations of peptidic agents, and two of them showed favorable FGFR1-binding in vitro and FGFR1-dependent internalization into cells. To validate if developed FGFR1-targeting peptibodies can be used for drug delivery, similar to the well-established concept of antibody-drug conjugates (ADCs), peptibodyF4_1 was successfully conjugated with monomethylauristatin E (MMAE), and has shown significant and specific toxicity toward FGFR1-expressing lung cancer cell lines, with nanomolar EC50 values. Essentially, the development of new effective FGFR1 binders that comprise the naturally occurring FGFR-recognition peptides and Fc region ensuring high plasma stability, and long bloodstream circulation is an interesting strategy expanding targeted anticancer agents' portfolio. Furthermore, identifying peptides effectively binding the receptor from sequences of its ligands is not limited to FGFRs and is an approach versatile enough to be a basis for a new peptide/peptibodies development strategy.

The PEG-responding desiccome of the alder microsymbiont Frankia alni

Actinorhizal plants are ecologically and economically important. Symbiosis with nitrogen-fixing bacteria allows these woody dicotyledonous plants to colonise soils under nitrogen deficiency, water-stress or other extreme conditions. However, proteins involved in xerotolerance of symbiotic microorganisms have yet to be identified. Here we characterise the polyethylene glycol (PEG)-responding desiccome from the most geographically widespread Gram-positive nitrogen-fixing plant symbiont, Frankia alni, by next-generation proteomics, taking advantage of a Q-Exactive HF tandem mass spectrometer equipped with an ultra-high-field Orbitrap analyser. A total of 2,052 proteins were detected and quantified. Under osmotic stress, PEG-grown F. alni cells increased the abundance of envelope-associated proteins like ABC transporters, mechano-sensitive ion channels and Clustered Regularly Interspaced Short Palindromic Repeats CRISPR-associated (cas) components. Conjointly, dispensable pathways, like nitrogen fixation, aerobic respiration and homologous recombination, were markedly down-regulated. Molecular modelling and docking simulations suggested that the PEG is acting on Frankia partly by filling the inner part of an up-regulated osmotic-stress large conductance mechanosensitive channel.

Leishmania infantum 5'-Methylthioadenosine Phosphorylase presents relevant structural divergence to constitute a potential drug target

BACKGROUND

The 5'-methylthioadenosine phosphorylase (MTAP), an enzyme involved in purine and polyamine metabolism and in the methionine salvage pathway, is considered as a potential drug target against cancer and trypanosomiasis. In fact, Trypanosoma and Leishmania parasites lack de novo purine pathways and rely on purine salvage pathways to meet their requirements. Herein, we propose the first comprehensive bioinformatic and structural characterization of the putative Leishmania infantum MTAP (LiMTAP), using a comparative computational approach.

RESULTS

Sequence analysis showed that LiMTAP shared higher identity rates with the Trypanosoma brucei (TbMTAP) and the human (huMTAP) homologs as compared to the human purine nucleoside phosphorylase (huPNP). Motifs search using MEME identified more common patterns and higher relatedness of the parasite proteins to the huMTAP than to the huPNP. The 3D structures of LiMTAP and TbMTAP were predicted by homology modeling and compared to the crystal structure of the huMTAP. These models presented conserved secondary structures compared to the huMTAP, with a similar topology corresponding to the Rossmann fold. This confirmed that both LiMTAP and TbMTAP are members of the NP-I family. In comparison to the huMTAP, the 3D model of LiMTAP showed an additional α-helix, at the C terminal extremity. One peptide located in this specific region was used to generate a specific antibody to LiMTAP. In comparison with the active site (AS) of huMTAP, the parasite ASs presented significant differences in the shape and the electrostatic potentials (EPs). Molecular docking of 5'-methylthioadenosine (MTA) and 5'-hydroxyethylthio-adenosine (HETA) on the ASs on the three proteins predicted differential binding modes and interactions when comparing the parasite proteins to the human orthologue.

CONCLUSIONS

This study highlighted significant structural peculiarities, corresponding to functionally relevant sequence divergence in LiMTAP, making of it a potential drug target against Leishmania.

ADME-Space: a new tool for medicinal chemists to explore ADME properties

We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.

Global vision of druggability issues: applications and perspectives

During the preliminary stage of a drug discovery project, the lack of druggability information and poor target selection are the main causes of frequent failures. Elaborating on accurate computational druggability prediction methods is a requirement for prioritizing target selection, designing new drugs and avoiding side effects. In this review, we describe a survey of recently reported druggability prediction methods mainly based on networks, statistical pocket druggability predictions and virtual screening. An application for a frequent mutation of p53 tumor suppressor is presented, illustrating the complementarity of druggability prediction approaches, the remaining challenges and potential new drug development perspectives.