Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb), which remains a significant global health challenge. The emergence of multidrug-resistant (MDR) Mtb strains imposes the development of new therapeutic strategies. This study focuses on the identification and evaluation of potential inhibitors against Mtb H37Ra through a comprehensive screening of an in-house chemolibrary. Subsequently, a promising pyrimidine derivative (LQM495) was identified as promising and then further investigated by experimental and in silico approaches. In this context, computational techniques were used to elucidate the potential molecular target underlying the inhibitory action of LQM495. Then, a consensus reverse docking (CRD) protocol was used to investigate the interactions between this compound and several Mtb targets. Out of 98 Mtb targets investigated, the enhanced intracellular survival (Eis) protein emerged as a target for LQM495. To gain insights into the stability of the LQM495-Eis complex, molecular dynamics (MD) simulations were conducted over a 400 ns trajectory. Further insights into its binding modes within the Eis binding site were obtained through a Quantum mechanics (QM) approach, using density functional theory (DFT), with B3LYP/D3 basis set. These calculations shed light on the electronic properties and reactivity of LQM495. Subsequently, inhibition assays and kinetic studies of the Eis activity were used to investigate the activity of LQM495. Then, an IC50 value of 11.0 ± 1.4 µM was found for LQM495 upon Eis protein. Additionally, its Vmax, Km, and Ki parameters indicated that it is a competitive inhibitor. Lastly, this study presents LQM495 as a promising inhibitor of Mtb Eis protein, which could be further explored for developing novel anti-TB drugs in the future.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome and is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development, and multiple Mpro crystals complexed with competitive inhibitors have been reported. In this study, we aimed to develop an Mpro consensus pharmacophore as a tool to expand the search for inhibitors. We generated a consensus model by aligning and summarizing pharmacophoric points from 152 bioactive conformers of SARS-CoV-2 Mpro inhibitors. Validation against a library of conformers from a subset of ligands showed that our model retrieved poses that reproduced the crystal-binding mode in 77% of the cases. Using models derived from a consensus pharmacophore, we screened >340 million compounds. Pharmacophore-matching and chemoinformatics analyses identified new potential Mpro inhibitors. The candidate compounds were chemically dissimilar to the reference set, and among them, demonstrating the relevance of our model. We evaluated the effect of 16 candidates on Mpro enzymatic activity finding that seven have inhibitory activity. Three compounds (1, 4, and 5) had IC50 values in the midmicromolar range. The Mpro consensus pharmacophore reported herein can be used to identify compounds with improved activity and novel chemical scaffolds against Mpro. The method developed for its generation is provided as an open-access code (https://github.com/AngelRuizMoreno/ConcensusPharmacophore) and can be applied to other pharmacological targets.

Drug development is a complex, costly, and time-consuming endeavor. While high-throughput screening (HTS) plays a critical role in the discovery stage, it is one of many factors contributing to these challenges. In certain contexts, virtual screening can complement the HTS, potentially offering a more streamlined approach in the initial stages of drug discovery. Molecular docking is an example of a popular virtual screening technique that is often used for this purpose; however, its effectiveness can vary greatly. This has led to the use of consensus docking approaches that combine results from different docking methods to improve the identification of active compounds and reduce the occurrence of false positives. However, many of these methods do not fully leverage the latest advancements in molecular docking. In response, we present ESSENCE-Dock (Effective Structural Screening ENrichment ConsEnsus Dock), a new consensus docking workflow aimed at decreasing false positives and increasing the discovery of active compounds. By utilizing a combination of novel docking algorithms, we improve the selection process for potential active compounds. ESSENCE-Dock has been made to be user-friendly, requiring only a few simple commands to perform a complete screening while also being designed for use in high-performance computing (HPC) environments.

Aspartic acid (D) and glutamic acid (E) play vital roles in the umami peptides. To understand their exact mechanism of action, umami peptides were collected and cut into 1/2/3/4 fragments. Connecting D/E to the N/C-termini of the fragments formed D/E consensus effect groups (DEEGs), and all fragments containing DEEG were summarized according to the ratio and ranking obtained in the above four situations. The interaction patterns between peptides in DEEG and T1R1/T1R3-VFD were compared by statistical analysis and molecular docking, and the most conservative contacts were found to be HdB_277_ARG and HdB_148_SER. The molecular docking score of the effector peptides significantly dropped compared to that of their original peptides (-1.076 ± 0.658 kcal/mol, p value < 0.05). Six types of consensus fingerprints were set according to the Top7 contacts. The exponential of relative umami was linearly correlated with ΔGbind (R2 = 0.961). Under the D/E consensus effect, the electrostatic effect of the umami peptide was improved, and the energy gap between the highest occupied molecular orbital-the least unoccupied molecular orbital (HOMO-LUMO) was decreased. The shortest path map showed that the peptides had similar T1R1-T1R3 recognition pathways. This study helps to reveal umami perception rules and provides support for the efficient screening of umami peptides based on the material richness in D/E sequences.

Around three billion people are at risk of infection by the dengue virus (DENV) and potentially other flaviviruses. Worldwide outbreaks of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack of antiviral drugs, and limitations on vaccine usage emphasize the need for novel antiviral research. Here, we propose a consensus virtual screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in silico combination of a hologram quantitative structure-activity relationship (HQSAR) model and molecular docking on characterized binding sites followed by molecular dynamics (MD) simulations, which filtered a data set of 7.6 million compounds to 2,775 hits. Lastly, docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five compounds had their antiviral activity confirmed against ZIKV, YFV, DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ± 0.8 μM), displaying aggregator characteristics for enzymatic inhibition against ZIKV NS3pro (ranging from 28 ± 7 to 70 ± 7 μM). Taken together, the compounds identified in this approach may contribute to the design of promising candidates to treat different flavivirus infections.

Glycogen synthase kinase-3 beta (GSK3β) is a serine/threonine kinase that plays key roles in glycogen metabolism, Wnt/β-catenin signaling cascade, synaptic modulation, and multiple autophagy-related signaling pathways. GSK3β is an attractive target for drug discovery since its aberrant activity is involved in the development of neurodegenerative diseases such as Alzheimer\'s and Parkinson\'s disease. In the present study, multiple machine learning models aimed at identifying novel GSK3β inhibitors were developed and evaluated for their predictive reliability. The most powerful models were combined in a consensus approach, which was used to screen about 2 million commercial compounds. Our consensus machine learning-based virtual screening led to the identification of compounds G1 and G4, which showed inhibitory activity against GSK3β in the low-micromolar and sub-micromolar range, respectively. These results demonstrated the reliability of our virtual screening approach. Moreover, docking and molecular dynamics simulation studies were employed for predicting reliable binding modes for G1 and G4, which represent two valuable starting points for future hit-to-lead and lead optimization studies.

BACKGROUND: Despite the promising clinical potential of bone morphogenetic protein (BMP)-related therapies for bone formation, their side effects warrant the need for alternative therapeutic peptides. BMP family members can aid in bone repair; however, peptides derived from BMP2/ 4 have not yet been investigated.
METHODS: In this study, three candidates BMP2/4 consensus peptide (BCP) 1, 2, and 3 were identified and their ability to induce osteogenesis in C2C12 cells was analyzed. First, an alkaline phosphatase (ALP) staining assay was performed to evaluate the osteogenic effects of BCPs. Next, the effects of BCPs on RNA expression levels and protein abundances of osteogenic markers were explored. Furthermore, the transcriptional activity of ALP by BCP1 and in silico molecular docking model on BMP type IA receptor (BRIA) were performed.
RESULTS: BCP1-3 induced higher RUNX2 expression than BMP2. Interestingly, among them, BCP1 significantly promoted osteoblast differentiation more than BMP2 in ALP staining with no cytotoxicity. BCP1 significantly induced the osteoblast markers, and the highest RUNX2 expression was observed at 100 ng/mL compared to other concentrations. In transfection experiments, BCP1 stimulated osteoblast differentiation via RUNX2 activation and the Smad signaling pathway. Finally, in silico molecular docking suggested the possible binding sites of BCP1 on BRIA.
CONCLUSIONS: These results show that BCP1 promotes osteogenicity in C2C12 cells. This study suggests that BCP1 is the most promising candidate peptide to replace BMP2 for osteoblast differentiation.

For the development of multi-peptide vaccine, identification of antigenic epitopes is crucial. If it is done using wet lab techniques, the identification process can be time-consuming, laborious, and cost-intensive. In silico tools, on the other hand, enable researchers to predict potential epitopes with little to no cost for further in vivo and in vitro testing. The rapid identification process using in silico tools helps in responding to health emergencies faster. Developing an efficient and high coverage vaccine is one of the ways to reduce morbidity and mortality rates of the diseases and protect the affected populations. In this chapter, we introduce the necessary tools and methodology for the identification and characterization of antigenic epitopes to design a multi-epitope vaccine using varicella-zoster virus as an example vector model.

Cyclooxygenase-2 (COX-2) is the key enzyme responsible for the conversion of arachidonic acid to prostaglandins that display pro-inflammatory properties and thus, it is a potential target protein to develop anti-inflammatory drugs. In this study, chemical and bio-informatics approaches have been employed to find a novel potent andrographolide (AGP) analog as a COX-2 inhibitor having better pharmacological properties than aspirin and rofecoxib (controls). The full amino acid sequenced human Alpha fold (AF) COX-2 protein (604AA) was selected and validated for its accuracy against the reported COX-2 protein structures (PDB ID: 5F19, 5KIR, 5F1A, 5IKQ and 1V0X) followed by multiple sequence alignment analysis to establish the sequence conservation. The systematic virtual screening of 237 AGP analogs against AF-COX-2 protein yielded 22 lead compounds based on the binding energy score (< - 8.0 kcal/mol). These were further screened out to 7 analogs by molecular docking analysis and investigated further for ADMET prediction, ligand efficiency metrics calculations, quantum mechanical analysis, MD simulation, electrostatic potential energy (EPE) docking simulation, and MM/GBSA. In-depth analysis revealed that AGP analog A3 (3-[2-[(1R,4aR,5R,6R,8aR)-6-hydroxy-5,6,8a-trimethyl-2-methylidene-3,4,4a,5,7,8-hexahydro-1H-naphthalen-1-yl]ethylidene]-4-hydroxyoxolan-2-one) forms the most stable complex with the AF-COX-2 showing the least RMSD value (0.37 ± 0.03 nm), a good number of hydrogen bonds (protein-ligand H-bond = 11, and protein H-bond = 525), minimum EPE score (- 53.81 kcal/mol), and lowest MM-GBSA before and after simulation (- 55.37 and - 56.25 kcal/mol, respectively) value compared to other analogs and controls. Thus, we suggest that the identified A3 AGP analog could be developed as a promising plant-based anti-inflammatory drug by inhibiting COX-2.

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.