Predicting FFAR4 agonists using structure-based machine learning approach based on molecular fingerprints.

Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols were further validated by Molecular Docking and via ADME and Toxicity predictions. The shortlisted compounds were then subjected to MD Simulations of the membrane-bound FFAR4-ligand complexes for 100 ns each. Molecular analyses, encompassing binding interactions, RMSD, RMSF, RoG, PCA, and FEL, were conducted to scrutinize the protein-ligand complexes at the inter-atomic level. The analyses revealed significant interactions of the shortlisted compounds with the crucial residues of FFAR4 previously documented. FFAR4 as part of the complexes demonstrated consistent RMSDs, ranging from 3.57 to 3.64, with minimal residue fluctuations 5.27 to 6.03 nm, suggesting stable complexes. The gyration values fluctuated between 22.8 to 23.5 nm, indicating structural compactness and orderliness across the studied systems. Additionally, distinct conformational motions were observed in each complex, with energy contours shifting to broader energy basins throughout the simulation, suggesting thermodynamically stable protein-ligand complexes. The two compounds CHEMBL2012662 and CHEMBL64616 are presented as potential FFAR4 agonists, based on these insights and in-depth analyses. Collectively, these findings advance our comprehension of FFAR4's functions and mechanisms, highlighting these compounds as potential FFAR4 agonists worthy of further exploration as innovative treatments for metabolic and immune-related conditions.

Uncovering PPAR-γ agonists: An integrated computational approach driven by machine learning.

Peroxisome proliferator-activated receptor gamma (PPAR-γ) serves as a nuclear receptor with a pivotal function in governing diverse facets of metabolic processes. In diabetes, the prime physiological role of PPAR-γ is to enhance insulin sensitivity and regulate glucose metabolism. Although PPAR-γ agonists such as Thiazolidinediones are effective in addressing diabetes complications, it is vital to be mindful that they are associated with substantial side effects that could potentially give rise to health challenges. The recent surge in the discovery of selective modulators of PPAR-γ inspired us to formulate an integrated computational strategy by leveraging the promising capabilities of both machine learning and in silico drug design approaches. In pursuit of our objectives, the initial stage of our work involved constructing an advanced machine learning classification model, which was trained utilizing chemical information and physicochemical descriptors obtained from known PPAR-γ modulators. The subsequent application of machine learning-based virtual screening, using a library of 31,750 compounds, allowed us to identify 68 compounds having suitable characteristics for further investigation. A total of four compounds were identified and the most favorable configurations were complemented with docking scores ranging from -8.0 to -9.1 kcal/mol. Additionally, the compounds engaged in hydrogen bond interactions with essential conserved residues including His323, Leu330, Phe363, His449 and Tyr473 that describe the ligand binding site. The stability indices investigated herein for instance root-mean-square fluctuations in the backbone atoms indicated higher mobility in the region of orthosteric site in the presence of agonist with the deviation peaks in the range of 0.07-0.69 nm, signifying moderate conformational changes. The deviations at global level revealed that the average values lie in the range of 0.25-0.32 nm. In conclusion, our identified hits particularly, CHEMBL-3185642 and CHEMBL-3554847 presented outstanding results and highlighted the stable conformation within the orthosteric site of PPAR-γ to positively modulate the activity.

A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors.

Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for further research, the significant structural conservation among PI3Ks makes it extremely difficult to develop new isoform-selective PI3Kα inhibitors. Embracing this challenge, we herein designed a hybrid protocol by integrating machine learning (ML) with in silico drug-designing strategies. A deep learning classification model was developed and trained on the physicochemical descriptors data of known PI3Kα inhibitors and used as a screening filter for a database of small molecules. This approach led us to the prediction of 662 compounds showcasing appropriate features to be considered as PI3Kα inhibitors. Subsequently, a multiphase molecular docking was applied to further characterize the predicted hits in terms of their binding affinities and binding modes in the targeted cavity of the PI3Kα. As a result, a total of 12 compounds were identified whereas the best poses highlighted the efficiency of these ligands in maintaining interactions with the crucial residues of the protein to be targeted for the inhibition of associated activity. Notably, potential activity of compound 12 in counteracting PI3Kα function was found in a previous in vitro study. Following the drug-likeness and pharmacokinetic characterizations, six compounds (compounds 1, 2, 3, 6, 7, and 11) with suitable ADME-T profiles and promising bioavailability were selected. The mechanistic studies in dynamic mode further endorsed the potential of identified hits in blocking the ATP-binding site of the receptor with higher binding affinities than the native inhibitor, alpelisib (BYL-719), particularly the compounds 1, 2, and 11. These outcomes support the reliability of the developed classification model and the devised computational strategy for identifying new isoform-selective drug candidates for PI3Kα inhibition.

Identification of novel small molecule inhibitors for solute carrier SGLT1; a computational exploration.

Diabetes results in substantial disabilities, diminished quality of life, and mortality that imposes a huge economic burden on societies and governments worldwide. Despite the absence of specific oral therapies at present, there exists an urgent requirement to develop a novel drug for the treatment of diabetes mellitus. The membrane protein sodium glucose co-transporters (SGLT1) present a captivating therapeutic target for diabetes, given its pivotal role in facilitating glucose absorption in the small intestine, offering immense promise for potential therapeutic intervention. In this connection, the present study is aimed at identifying potential inhibitors of SGLT1 from a small molecule database, including compounds from both natural as well as synthetic origins. A comprehensive approach was employed, by integrating homology modeling, ligand-based pharmacophore modeling, virtual screening, and molecular docking simulation. The process resulted in the identification of 16 new compounds, featuring similar attributes as observed for the documented actives. In a systematic screening procedure, five potential virtual hits were selected for simulation studies followed by subsequent binding free energy calculations, providing deeper insight into the time-dependent behavior of protein-ligand complexes in a dynamic state. In conclusion, our findings demonstrated that the identified compounds, particularly compounds 81 and 91, exhibit enhanced stability and favorable binding affinities with the target protein, marking them promising candidates for further investigations.Communicated by Ramaswamy H. Sarma.

Identification of potent anti-immunogenic agents through virtual screening, 3D-QSAR studies, and in vitro experiments.

A wealth of literature has highlighted the discovery of various immune modulators, frequently used in clinical practice, yet associated with numerous drawbacks. In light of this pharmacological deficiency, medical scientists are motivated to develop new immune modulators with minimized adverse effects yet retaining the improved therapeutic potential. T-cell differentiation and growth are central to human defense and are regulated by interleukin-2 (IL-2), an immune-modulatory cytokine. However, scientific investigation is hindered due to its flat binding site and widespread hotspot residues. In this regard, a prompt and logical investigation guided by integrated computational techniques was undertaken to unravel new and potential leads against IL-2. In particular, the combination of score-based and pharmacophore-based virtual screening approaches were employed, reducing the data from millions of small molecules to a manageable number. Subsequent docking and 3D-QSAR prediction via CoMFA further helped remove false positives from the data. The reliability of the model was assessed via standard metrics, which explain the model's fitness and the robustness of the model in predicting the activity of new compounds. The extensive virtual screening herein led to the identification of a total of 24 leads with potential anti-IL-2 activity. Furthermore, the theoretical findings were corroborated with in vitro testing, further endorsing the anti-inflammatory potential of the identified leads.

Structural basis for the mutation-induced dysfunction of the human IL-15/IL-15α receptor complex.

In silico strategies offer a reliable, fast, and inexpensive, way compared to the clumsy in vitro approaches to boost understanding of the effect of amino acid substitution on the structure and consequently the associated function of proteins. In the present work, we report an atomistic-based, reliable in silico structural and energetic framework of the interactions between the receptor-binding domain of the Interleukin-15 (IL-15) protein and its receptor Interleukin-15α (IL-15α), consequently, providing qualitative and quantitative details of the key molecular determinants in ligand/receptor recognition. Molecular dynamics simulations were used to investigate the dynamic behavior of the specific binding between IL-15 and IL-15α followed by estimation of the free energies via molecular mechanics/generalized Born surface area (MM/GBSA). In particular, residues Y26, E46, E53, and E89 of the IL-15 protein receptor-binding domain are identified as main hot spots, shaping and governing the stability of the assembly. These results can be used for the development of neutralizing antibodies and the effective structure-based design of protein-protein interaction inhibitors against the so-called orphan disease, vitiligo.

Highlights in TMPRSS2 inhibition mechanism with guanidine derivatives approved drugs for COVID-19 treatment.

Transmembrane protease serine 2 (TMPRSS2) has been identified as a critical key for the entry of coronaviruses into human cells by cleaving and activating the spike protein of SARS-CoV-2. To block the TMPRSS2 function, 18 approved drugs, containing the guanidine group were tested against TMPRSS2's ectodomain (7MEQ). Among these drugs, Famotidine, Argatroban, Guanadrel and Guanethidine strongly binds with TMPRSS2 S1 pocket with estimated Fullfitness energies of -1847.12, -1630.87, -1605.81 and -1600.52 kcal/mol, respectively. A significant number of non-covalent interactions such as hydrogen bonding, hydrophobic and electrostatic interactions were detected in protein-ligand complexes. In addition, the ADMET analysis revealed a perfect concurrence with the aptitude of these drugs to be developed as an anti-SARS-CoV-2 therapeutics. Further, MD simulation and binding free energy calculations were performed to evaluate the dynamic behavior and stability of protein-ligand complexes. The results obtained herein highlight the enhanced stability and good binding affinities of the Argatroban and Famotidine towards the target protein, hence might act as new scaffolds for TMPRSS2 inhibition.Communicated by Ramaswamy H. Sarma.

Exploiting Dengue Virus Protease as a Therapeutic Target: Current Status, Challenges and Future Avenues.

Dengue, the oldest and the most prevalent mosquito-borne illness, is caused by the dengue virus (DENV), from the family of Flaviviridae. It infects approximately 400 million individuals per annum, with approximately half of the global population residing in high-risk areas. The factors attributed to the geographic expansion of dengue, include urbanization, population density, modern means of transportation, international travels, habit modification, climate change, virus genetics, vector capacity, and poor vector control. Despite the significant progress made in the past against dengue, no effective antiviral therapy is currently available. Among the structural and non-structural proteins encoded by DENV genome, the NS2B-NS3 protease complex is amongst the extensively studied targets for the development of antiviral therapeutics owing to its multiple roles in virus life cycle. Furthermore, protease inhibitors were found to be successful in Hepatitis C Virus (HCV) and Human Immunodeficiency Virus (HIV). Likewise, several peptidic, peptide derived/peptidomimetic, and small molecules inhibitors have been identified as DENV protease inhibitors. Unfortunately, none of them have resulted in a clinically approved drug. Considering all the abovementioned facts, this review descriptively explains the molecular mechanism and therapeutic potential of DENV protease along with an up to date information on various competitive inhibitors reported against DENV protease. This review might be helpful for the researchers working in this area to understand the critical aspects of DENV protease that will help them develop effective and novel inhibitors against DENV to protect lives of millions of people worldwide.

An in-silico evaluation of COVID-19 main protease with clinically approved drugs.

A novel strain of coronavirus, namely, SARS-CoV-2 identified in Wuhan city of China in December 2019, continues to spread at a rapid rate worldwide. There are no specific therapies available and investigations regarding the treatment of this disease are still lacking. In order to identify a novel potent inhibitor, we performed blind docking studies on the main virus protease Mpro with eight approved drugs belonging to four pharmacological classes such as: anti-malarial, anti-bacterial, anti-infective and anti-histamine. Among the eight studied compounds, Lymecycline and Mizolastine appear as potential inhibitors of this protease. When docked against Mpro crystal structure, these two compounds revealed a minimum binding energy of -8.87 and -8.71 kcal/mol with 168 and 256 binding modes detected in the binding substrate pocket, respectively. Further, to study the interaction mechanism and conformational dynamics of protein-ligand complexes, Molecular dynamic simulation and MM/PBSA binding free calculations were performed. Our results showed that both Lymecycline and Mizolastine bind in the active site. And exhibited good binding affinities towards target protein. Moreover, the ADMET analysis also indicated drug-likeness properties. Thus it is suggested that the identified compounds can inhibit Chymotrypsin-like protease (3CLpro) of SARS-CoV-2.