The Interplay Between Molecular Modeling and Chemoinformatics to Characterize Protein–Ligand and Protein–Protein Interactions Landscapes for Drug Discovery

Introducing the NUATEI Consortium: A Mexican Research Program for the Identification of Natural and Synthetic Antimicrobial Compounds for Prevalent Infectious Diseases

The need for new drugs to treat human infections is a global health concern. Diseases like tuberculosis, trypanosomiasis, amoebiasis, and AIDS remain significant problems, especially in developing countries like Mexico. Despite existing treatments, issues such as resistance and adverse effects drive the search for new alternatives. Herein, we introduce the NUATEI research consortium, made up of experts from the Institute of Biomedical Research at UNAM, who identify and obtain natural and synthetic compounds and test their effects against human pathogens using in vitro and in vivo models. The consortium has evaluated hundreds of natural extracts and compounds against the pathogens causing tuberculosis, trypanosomiasis, amoebiasis, and AIDS, rendering promising results, including a patent with potential for preclinical studies. This paper presents the rationale behind the formation of this consortium, as well as its objectives and strategies, emphasizing the importance of natural and synthetic products as sources of antimicrobial compounds and the relevance of the diseases studied. Finally, we briefly describe the methods of the evaluation of the compounds in each biological model and the main achievements. The potential of the consortium to screen numerous compounds and identify new therapeutic agents is highlighted, demonstrating its significant contribution to addressing these infectious diseases.

Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors

Activity cliffs (ACs) are pairs of structurally similar molecules with significantly different affinities for a biotarget, posing a challenge in computer-assisted drug discovery. This study focuses on protein kinases, significant therapeutic targets, with some exhibiting ACs while others do not despite numerous inhibitors. The hypothesis that the presence of ACs is dependent on the target protein and its complete structural context is explored. Machine learning models were developed to link protein properties to ACs, revealing specific tripeptide sequences and overall protein properties as critical factors in ACs occurrence. The study highlights the importance of considering the entire protein matrix rather than just the binding site in understanding ACs. This research provides valuable insights for drug discovery and design, paving the way for addressing ACs-related challenges in modern computational approaches.

Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data

Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.

PDB-BRE: A ligand-protein interaction binding residue extractor based on Protein Data Bank

Proteins typically exert their biological functions by interacting with other biomolecules or ligands. The study of ligand-protein interactions is crucial in elucidating the biological mechanisms of proteins. Most existing studies have focused on analyzing ligand-protein interactions, and they ignore the additional situational of inserted and modified residues. Besides, the resources often support only a single ligand type and cannot obtain satisfied results in analyzing novel complexes. Therefore, it is important to develop a general analytical tool to extract the binding residues of ligand-protein interactions in complexes fully. In this study, we propose a ligand-protein interaction binding residue extractor (PDB-BRE), which can be used to automatically extract interacting ligand or protein-binding residues from complex three-dimensional (3D) structures based on the RCSB Protein Data Bank (RCSB PDB). PDB-BRE offers a notable advantage in its comprehensive support for analyzing six distinct types of ligands, including proteins, peptides, DNA, RNA, mixed DNA and RNA entities, and non-polymeric entities. Moreover, it takes into account the consideration of inserted and modified residues within complexes. Compared to other state-of-the-art methods, PDB-BRE is more suitable for massively parallel batch analysis, and can be directly applied for downstream tasks, such as predicting binding residues of novel complexes. PDB-BRE is freely available at http://bliulab.net/PDB-BRE.

A comprehensive review on the pancreatic lipase inhibitory peptides: A future anti-obesity strategy

Dysregulation of lipid homeostasis contributes to obesity and can directly lead to several critical public health concerns globally. This paper aimed to present a brief review of related properties and the use of pancreatic lipase inhibitors as the future weight loss drug discovery and development procured from a wide range of natural sources. A total of 176 pancreatic lipase inhibitory peptides were identified from recent publications and peptide databases. These peptides were classified into three categories according to their peptide length and further analyzed using bioinformatic approaches to identify their structural activity relationship. Molecular docking analyses were conducted for each amino acid at the terminal position of the peptides to predict the binding affinity between peptide-enzyme protein complexes based on intermolecular contact interactions. Overall, the observations revealed the features of the inhibitory peptides and their inhibitory mechanisms and interactions. These findings strived to benefit scientists whose research may be relevant to anti-obesity drug development and/or discovery thereby support effective translation of preclinical research for humans’ health being.

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

Multiprotein Inhibitory Effect of Dietary Polyphenol Rutin from Whole Green Jackfruit Flour Targeting Different Stages of Diabetes Mellitus: Defining a Bio-Computational Stratagem

The anti-diabetic potential of whole unripe jackfruit (peel with pulp, flake, and seed) was investigated using inhibitory assays for α-glucosidase, α-amylase, aldose reductase, and glycation at multiple stages. Using activity-guided repeated fractionation on a silica gel column chromatography, dietary flavonoid rutin with potent antihyperglycemic activity was extracted from the methanol extract of whole jackfruit flour (MJ). Rutin was found to inhibit both α-glucosidase (IC50: 7.86 µg/mL) and α-amylase (IC50: 22.00 µg/mL) in a competitive manner of inhibition with low Ki values. In addition, in vitro glycation experiments revealed that rutin prevented each stage of protein glycation as well as the production of intermediate molecules. Furthermore, rutin significantly inhibited aldose reductase (IC50: 2.75 µg/mL) in a non-competitive manner. During in silico studies, molecular docking and molecular dynamics simulation studies have suggested that rutin has a high binding affinity for the enzymes studied, which could explain its inhibitory effects. Rutin interacted with the key residues of the target enzymes’ inhibitor binding sites. Compared to the controls used, rutin had a higher binding efficiency as well as stability in the inhibitor binding pocket of the target enzymes. According to our findings, the presence of rutin is more likely to be associated with the potential of MJ in antihyperglycemic activity via inhibition of α-glucosidase and in anti-diabetic action via inhibition of the polyol pathway and protein glycation. The bio-computational study indicates rutin as a potential lead inhibitor of all the target enzymes used and could be used as an effective anti-diabetic drug in the near future.

Inhibitory Effect of Polyphenols from the Whole Green Jackfruit Flour against α-Glucosidase, α-Amylase, Aldose Reductase and Glycation at Multiple Stages and Their Interaction: Inhibition Kinetics and Molecular Simulations

For the first time, α-glucosidase, α-amylase, aldose reductase, and glycation at multiple stages inhibitory assays were used to explore the antidiabetic potential of whole unripe jackfruit (peel with pulp, flake, and seed). Two polyphenols (phenolic acids) with strong antihyperglycaemic activity were isolated from the methanol extract of whole jackfruit flour (MJ) using activity-guided repeated fractionation on a silica gel column chromatography. The bioactive compounds isolated were identified as 3-(3,4-Dihydroxyphenyl)-2-propenoic acid (caffeic acid: CA) and 4-Hydroxy-3,5-dimethoxybenzoic acid (syringic acid: SA) after various physicochemical and spectroscopic investigations. CA (IC50: 8.0 and 26.90 µg/mL) and SA (IC50: 7.5 and 25.25 µg/mL) were identified to inhibit α-glucosidase and α-amylase in a competitive manner with low Ki values. In vitro glycation experiments further revealed that MJ and its components inhibited each stage of protein glycation as well as the generation of intermediate chemicals. Furthermore, CA (IC50: 3.10) and SA (IC50: 3.0 µg/mL) inhibited aldose reductase effectively in a non-competitive manner, respectively. The binding affinity of these substances towards the enzymes examined has been proposed by molecular docking and molecular dynamics simulation studies, which may explain their inhibitory activities. The found potential of MJ in antihyperglycaemic activity via inhibition of α-glucosidase and in antidiabetic action via inhibition of the polyol pathway and protein glycation is more likely to be related to the presence of the phenolic compounds, according to our findings.

Electrospun nanofibers promote wound healing: theories, techniques, and perspectives

At present, the clinical strategies for treating chronic wounds are limited, especially when it comes to pain relief and rapid wound healing. Therefore, there is an urgent need to develop alternative treatment methods. This paper provides a systematic review on recent researches on how electrospun nanofiber scaffolds promote wound healing and how the electrospinning technology has been used for fabricating multi-dimensional, multi-pore and multi-functional nanofiber scaffolds that have greatly promoted the development of wound healing dressings. First, we provide a review on the four stages of wound healing, which is followed by a discussion on the evolvement of the electrospinning technology, what is involved in electrospinning devices, and factors affecting the electrospinning process. Finally, we present the possible mechanisms of electrospun nanofibers to promote wound healing, the classification of electrospun polymers, cell infiltration favoring fiber scaffolds, antibacterial fiber scaffolds, and future multi-functional scaffolds. Although nanofiber scaffolds have made great progress as a type of multi-functional biomaterial, major challenges still remain for commercializing them in a way that fully meets the needs of patients.

Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery

Current advances in systems biology suggest a new change of paradigm reinforcing the holistic nature of the drug discovery process. According to the principles of systems biology, a simple drug perturbing a network of targets can trigger complex reactions. Therefore, it is possible to connect initial events with final outcomes and consequently prioritize those events, leading to a desired effect. Here, we introduce a new concept, 'Systemic Chemogenomics/Quantitative Structure-Activity Relationship (QSAR)'. To elaborate on the concept, relevant information surrounding it is addressed. The concept is challenged by implementing a systemic QSAR approach for phenotypic virtual screening (VS) of candidate ligands acting as neuroprotective agents in Parkinson's disease (PD). The results support the suitability of the approach for the phenotypic prioritization of drug candidates.

Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

In light of the high relevance of polypharmacology, multi-target screening is a major trend in drug discovery.

Using the fragment molecular orbital method to investigate agonist-orexin-2 receptor interactions

The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity and is essential for an efficient structure-based drug discovery (SBDD) process. Clearly, to begin SBDD, a structure is needed, and although there has been fantastic progress in solving G-protein-coupled receptor (GPCR) crystal structures, the process remains quite slow and is not currently feasible for every GPCR or GPCR-ligand complex. This situation significantly limits the ability of X-ray crystallography to impact the drug discovery process for GPCR targets in 'real-time' and hence there is still a need for other practical and cost-efficient alternatives. We present here an approach that integrates our previously described hierarchical GPCR modelling protocol (HGMP) and the fragment molecular orbital (FMO) quantum mechanics (QM) method to explore the interactions and selectivity of the human orexin-2 receptor (OX2R) and its recently discovered nonpeptidic agonists. HGMP generates a 3D model of GPCR structures and its complexes with small molecules by applying a set of computational methods. FMO allowsab initioapproaches to be applied to systems that conventional QM methods would find challenging. The key advantage of FMO is that it can reveal information on the individual contribution and chemical nature of each residue and water molecule to the ligand binding that normally would be difficult to detect without QM. We illustrate how the combination of both techniques provides a practical and efficient approach that can be used to analyse the existing structure-function relationships (SAR) and to drive forward SBDD in a real-world example for which there is no crystal structure of the complex available.

Molecular Modeling and Chemoinformatics to Advance the Development of Modulators of Epigenetic Targets: A Focus on DNA Methyltransferases

In light of the emerging field of Epi-informatics, ie, computational methods applied to epigenetic research, molecular docking, and dynamics, pharmacophore and activity landscape modeling and QSAR play a key role in the development of modulators of DNA methyltransferases (DNMTs), one of the major epigenetic target families. The increased chemical information available for modulators of DNMTs has opened up the avenue to explore the epigenetic relevant chemical space (ERCS). Herein, we discuss recent progress on the identification and development of inhibitors of DNMTs as potential epi-drugs and epi-probes that have been driven by molecular modeling and chemoinformatics methods. We also survey advances on the elucidation of their structure-activity relationships and exploration of ERCS. Finally, it is illustrated how computational approaches can be applied to identify modulators of DNMTs in food chemicals.

Activity and property landscape modeling is at the interface of chemoinformatics and medicinal chemistry

Property landscape modeling (PLM) methods are at the interface of experimental sciences and computational chemistry. PLM are becoming a common strategy to describe systematically structure-property relationships of datasets. Thus far, PLM have been used mainly in medicinal chemistry and drug discovery. Herein, we survey advances on key topics on PLM with emphasis on questions often raised regarding the outcomes of the property landscape studies. We also emphasize on concepts of PLM that are being extended to other experimental areas beyond drug discovery. Topics discussed in this paper include applications of PLM to further characterize protein-ligand interactions, the utility of PLM as a quantitative and descriptive approach, and the statistical validation of property cliffs.

Interaction Fingerprints and Their Applications to Identify Hot Spots

Binding recognition is in the core of how nature controls processes in living cells, how enzyme-substrate binding leads to catalysis and how drugs modulate enzymes and receptors to convey a desirable physiological response. Thus, understanding binding recognition in a systematic manner is paramount, not only to understand biological processes but also to be able to design and discover new bioactive compounds. One such way to analyze binding interactions is through the development of binding interaction fingerprints. Here, we present the methodology to develop interaction fingerprints with three different software platforms along with two representative examples.