In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

In silico identification of putative druggable pockets in PRL3, a significant oncology target

Protein tyrosine phosphatases (PTP) have emerged as targets in diseases characterized by aberrant phosphorylations such as cancers. The activity of the phosphatase of regenerating liver 3, PRL3, has been linked to several oncogenic and metastatic pathways, particularly in breast, ovarian, colorectal, and blood cancers. Development of small molecules that directly target PRL3, however, has been challenging. This is partly due to the lack of structural information on how PRL3 interacts with its inhibitors. Here, computational methods are used to bridge this gap by evaluating the druggability of PRL3. In particular, web-based pocket prediction tools, DoGSite3 and FTMap, were used to identify binding pockets using structures of PRL3 currently available in the Protein Data Bank. Druggability assessment by molecular dynamics simulations with probes was also performed to validate these results and to predict the strength of binding in the identified pockets. While several druggable pockets were identified, those in the closed conformation show more promise given their volume and depth. These two pockets flank the active site loops and roughly correspond to pockets predicted by molecular docking in previous papers. Notably, druggability simulations predict the possibility of low nanomolar affinity inhibitors in these sites implying the potential to identify highly potent small molecule inhibitors for PRL3. Putative pockets identified here can be leveraged for high-throughput virtual screening to further accelerate the drug discovery against PRL3 and development of PRL3-directed therapeutics.

Computational identification of potential bioactive compounds from Triphala against alcoholic liver injury by targeting alcohol dehydrogenase

Alcoholic liver injury resulting from excessive alcohol consumption is a significant social concern. Alcohol dehydrogenase (ADH) plays a critical role in the conversion of alcohol to acetaldehyde, leading to tissue damage. The management of alcoholic liver injury encompasses nutritional support and, in severe cases liver transplantation, but potential adverse effects exist, and effective medications are currently unavailable. Natural products with their potential benefits and historical use in traditional medicine emerge as promising alternatives. Triphala, a traditional polyherbal formula demonstrates beneficial effects in addressing diverse health concerns, with a notable impact on treating alcoholic liver damage through enhanced liver metabolism. The present study aims to identify potential active phytocompounds in Triphala targeting ADH to prevent alcoholic liver injury. Screening 119 phytocompounds from the Triphala formulation revealed 62 of them showing binding affinity to the active site of the ADH1B protein. Promising lipid-like molecule from Terminalia bellirica, (4aS, 6aR, 6aR, 6bR, 7R, 8aR, 9R, 10R, 11R, 12aR, 14bS)-7, 10, 11-trihydroxy-9-(hydroxymethyl)-2, 2, 6a, 6b, 9, 12a-hexamethyl-1, 3, 4, 5, 6, 6a, 7, 8, 8a, 10, 11, 12, 13, 14b-tetradecahydropicene-4a-carboxylic acid showed high binding efficiency to a competitive ADH inhibitor, 4-Methylpyrazole. Pharmacokinetic analysis further confirmed the drug-likeness and non-hepatotoxicity of the top-ranked compound. Molecular dynamics simulation and MM-PBSA studies revealed the stability of the docked complexes with minimal fluctuation and consistency of the hydrogen bonds throughout the simulation. Together, computational investigations suggest that (4aS, 6aR, 6aR, 6bR, 7R, 8aR, 9R, 10R, 11R, 12aR, 14bS)-7, 10, 11-trihydroxy-9-(hydroxymethyl)-2, 2, 6a, 6b, 9, 12a-hexamethyl-1, 3, 4, 5, 6, 6a, 7, 8, 8a, 10, 11, 12, 13, 14b-tetradecahydropicene-4a-carboxylic acid from the Triphala formulation holds promise as an ADH inhibitor, suggesting an alternative therapy for alcoholic liver injury.

SiteMine: Large-scale binding site similarity searching in protein structure databases

Drug discovery and design challenges, such as drug repurposing, analyzing protein-ligand and protein-protein complexes, ligand promiscuity studies, or function prediction, can be addressed by protein binding site similarity analysis. Although numerous tools exist, they all have individual strengths and drawbacks with regard to run time, provision of structure superpositions, and applicability to diverse application domains. Here, we introduce SiteMine, an all-in-one database-driven, alignment-providing binding site similarity search tool to tackle the most pressing challenges of binding site comparison. The performance of SiteMine is evaluated on the ProSPECCTs benchmark, showing a promising performance on most of the data sets. The method performs convincingly regarding all quality criteria for reliable binding site comparison, offering a novel state-of-the-art approach for structure-based molecular design based on binding site comparisons. In a SiteMine showcase, we discuss the high structural similarity between cathepsin L and calpain 1 binding sites and give an outlook on the impact of this finding on structure-based drug design. SiteMine is available at https://uhh.de/naomi.

Machine learning approaches in predicting allosteric sites

Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked the development of numerous computational approaches, such as the identification of allosteric binding sites, to facilitate allosteric drug discovery. Building on the success of machine learning (ML) models for solving complex problems in biology and chemistry, several ML models for predicting allosteric sites have been developed. In this review, we provide an overview of these models and discuss future perspectives powered by the field of artificial intelligence such as protein language models.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

In Silico Exploration of the Trypanothione Reductase (TryR) of L. mexicana

Human leishmaniasis is a neglected tropical disease which affects nearly 1.5 million people every year, with Mexico being an important endemic region. One of the major defense mechanisms of these parasites is based in the polyamine metabolic pathway, as it provides the necessary compounds for its survival. Among the enzymes in this route, trypanothione reductase (TryR), an oxidoreductase enzyme, is crucial for the Leishmania genus' survival against oxidative stress. Thus, it poses as an attractive drug target, yet due to the size and features of its catalytic pocket, modeling techniques such as molecular docking focusing on that region is not convenient. Herein, we present a computational study using several structure-based approaches to assess the druggability of TryR from L. mexicana, the predominant Leishmania species in Mexico, beyond its catalytic site. Using this consensus methodology, three relevant pockets were found, of which the one we call σ-site promises to be the most favorable one. These findings may help the design of new drugs of trypanothione-related diseases.

In Silico Evaluation, Phylogenetic Analysis, and Structural Modeling of the Class II Hydrophobin Family from Different Fungal Phytopathogens

The class II hydrophobin group (HFBII) is an extracellular group of proteins that contain the HFBII domain and eight conserved cysteine residues. These proteins are exclusively secreted by fungi and have multiple functions with a probable role as effectors. In the present study, a total of 45 amino acid sequences of hydrophobin class II proteins from different phytopathogenic fungi were retrieved from the NCBI database. We used the integration of well-designed bioinformatic tools to characterize and predict their physicochemical parameters, novel motifs, 3D structures, multiple sequence alignment (MSA), evolution, and functions as effector proteins through molecular docking. The results revealed new features for these protein members. The ProtParam tool detected the hydrophobicity properties of all proteins except for one hydrophilic protein (KAI3335996.1). Out of 45 proteins, six of them were detected as GPI-anchored proteins by the PredGPI server. Different 3D structure templates with high pTM scores were designed by Multifold v1, AlphaFold2, and trRosetta. Most of the studied proteins were anticipated as apoplastic effectors and matched with the ghyd5 gene of Fusarium graminearum as virulence factors. A protein-protein interaction (PPI) analysis unraveled the molecular function of this group as GTP-binding proteins, while a molecular docking analysis detected a chitin-binding effector role. From the MSA analysis, it was observed that the HFBII sequences shared conserved 2 Pro (P) and 2 Gly (G) amino acids besides the known eight conserved cysteine residues. The evolutionary analysis and phylogenetic tree provided evidence of episodic diversifying selection at the branch level using the aBSREL tool. A detailed in silico analysis of this family and the present findings will provide a better understanding of the HFBII characters and evolutionary relationships, which could be very useful in future studies.

Role of Computer-Aided Drug Design in Drug Development

The introduction of computational techniques to pharmaceutical chemistry and molecular biology in the 20th century has changed the way people develop drugs [...].

Computational Guided Drug Targets Identification against Extended-Spectrum Beta-Lactamase-Producing Multi-Drug Resistant Uropathogenic Escherichia coli

Urinary tract infections (UTIs) are one of the most frequent bacterial infections in the world, both in the hospital and community settings. Uropathogenic Escherichia coli (UPEC) are the predominant etiological agents causing UTIs. Extended-spectrum beta-lactamase (ESBL) production is a prominent mechanism of resistance that hinders the antimicrobial treatment of UTIs caused by UPEC and poses a substantial danger to the arsenal of antibiotics now in use. As bacteria have several methods to counteract the effects of antibiotics, identifying new potential drug targets may help in the design of new antimicrobial agents, and in the control of the rising trend of antimicrobial resistance (AMR). The public availability of the entire genome sequences of humans and many disease-causing organisms has accelerated the hunt for viable therapeutic targets. Using a unique, hierarchical, in silico technique using computational tools, we discovered and described potential therapeutic drug targets against the ESBL-producing UPEC strain NA114. Three different sets of proteins (chokepoint, virulence, and resistance genes) were explored in phase 1. In phase 2, proteins shortlisted from phase 1 were analyzed for their essentiality, non-homology to the human genome, and gut flora. In phase 3, the further shortlisted putative drug targets were qualitatively characterized, including their subcellular location, broad-spectrum potential, and druggability evaluations. We found seven distinct targets for the pathogen that showed no similarity to the human proteome. Thus, possibilities for cross-reactivity between a target-specific antibacterial and human proteins were minimized. The subcellular locations of two targets, ECNA114_0085 and ECNA114_1060, were predicted as cytoplasmic and periplasmic, respectively. These proteins play an important role in bacterial peptidoglycan biosynthesis and inositol phosphate metabolism, and can be used in the design of drugs against these bacteria. Inhibition of these proteins will be helpful to combat infections caused by MDR UPEC.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Dynamic regulation and key roles of ribonucleic acid methylation

Ribonucleic acid (RNA) methylation is the most abundant modification in biological systems, accounting for 60% of all RNA modifications, and affects multiple aspects of RNA (including mRNAs, tRNAs, rRNAs, microRNAs, and long non-coding RNAs). Dysregulation of RNA methylation causes many developmental diseases through various mechanisms mediated by N ⁶-methyladenosine (m⁶A), 5-methylcytosine (m⁵C), N ¹-methyladenosine (m¹A), 5-hydroxymethylcytosine (hm⁵C), and pseudouridine (Ψ). The emerging tools of RNA methylation can be used as diagnostic, preventive, and therapeutic markers. Here, we review the accumulated discoveries to date regarding the biological function and dynamic regulation of RNA methylation/modification, as well as the most popularly used techniques applied for profiling RNA epitranscriptome, to provide new ideas for growth and development.