Identification of Aurora A Kinase Allosteric Inhibitors

Identification of Aurora A kinase allosteric inhibitors: A comprehensive virtual screening through fingerprint-based similarity search, molecular docking, machine learning and molecular dynamics simulation

The Aurora A kinase (AAK) protein controls spindle assembly and promotes cell divisions in various diseases including cancer. In the present study, allosteric inhibition of AAK protein through different advanced computational screening approaches is employed to target AAK’s allosteric inhibition-modulation. Precisely, extensive computational techniques including allosteric binding sites recognition of AAK protein, fingerprint-based similarity search, multi-step molecular docking through AutoDock Vina and PLANTS, and a 100 ns molecular dynamics (MD) simulations studies were carried out followed by the calculation of binding free energy with MM-GBSA based approaches, has been employed for identification of potential allosteric inhibitors-modulators of AAK protein. The study outcome highlighted that all three identified small molecular chemical entities exhibit strong binding interaction affinity in both the docking analyses at the allosteric domain of AAK protein and also greater interaction stability in comparison to the considered standard compound. In addition, all identified three screened hits also show acceptable pharmacokinetics and medicinal chemistry properties, which certainly dictates their potentiality for becoming good drug-like compounds for inhibiting-modulating the activity of AAK protein binds with similar amino acids of the allosteric domain of AAK protein with selected three compounds. All the selected molecules were found to show an acceptable ADMET profile. Moreover, the MM-GBSA-based binding energy was found to be in the range of −8 to −35 Kcal/mol, which showed a strong association between proposed molecules and AAK protein. Comprehensive computational approach shows that the selected proposed three inhibitors of AAK protein are the best candidates as potential inhibitors.

Journal of Molecular Liquids

 

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Identification of Potent CHK2 Inhibitors-Modulators for Therapeutic Application in Cancer

Identification of Potent CHK2 Inhibitors-Modulators for Therapeutic Application in Cancer: A Machine Learning Integrated Fragment-Based Drug Design Approach

The CHK2 protein regulates the cell division cycle and responds to DNA damage. Additionally, it facilitates the repair of DNA damage and maintains the integrity of its biological processes. Dysregulation of the CHK2 protein is associated with a predisposition to harmful diseases. The current research protocol was designed to identify novel hit molecules as CHK2 inhibitors and disrupt the normal biological function of the CHK2 protein via a fragment-based drug discovery approach. The protocol involved generating fragments using the MacFrag tool, followed by a chemical similarity search utilizing RDKit to identify fragment molecules analogous to previously established CHK2 inhibitor scaffolds. The bioactive molecules were constructed using the Fragmenstein tool, followed by molecular docking simulations to investigate their binding affinity. In addition, pharmacokinetic properties were analyzed, and a molecular dynamics simulation study was conducted to assess the stability of selected compounds with CHK2 protein. Finally, five novel compounds were identified as excellent CHK2 inhibitors through the FBDD and show good binding interactions at active sites of CHK2 with beneficial ADMET properties. This research work presents novel CHK2 inhibitor molecules that have the potential to be utilized in drug discovery, serving as key leads for future advancements in healthcare industries and sectors. Check out this published article, “Identification of Potent CHK2 Inhibitors-Modulators for Therapeutic Application in Cancer: A Machine Learning Integrated Fragment-Based Drug Design Approach” in ChemistrySelect

 

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Investigating the Ribosomal-RNA: Protein Interactions and AI-Assisted Discovery of Novel Inhibitor

Investigating the Ribosomal-RNA: Protein Interactions and AI-Assisted Discovery of Novel Inhibitor

This study explores novel ligands that potentially offer superior binding and stability compared to the peptidic molecules currently associated with the ribosomal RNA–protein complex. This study aims to elucidate the molecular mechanisms underlying protein–RNA interactions and their disruptions, identify potential therapeutic targets, and explore novel compounds capable of modulating these interactions for therapeutic benefit. We conducted molecular docking and dynamics simulations using advanced computational tools such as rDock and REINVENT4 to generate novel compounds. ADMET analysis confirmed the chosen compound’s advantageous pharmacokinetic attributes and safety profiles. Among the generated compounds, C21, C23, C56, C120, and C195 were identified as the best candidate molecules for inhibiting protein–RNA interactions. These ligands demonstrated superior binding affinity and stability, outperforming the peptidic molecules bound to the reference protein–RNA–peptide complex structure.The ligand molecules were notable for their ability to settle into low–energy states, indicating a strong potential to outperform the peptide bound in the reference protein–RNA–peptide complex structure. These findings highlight the capability of these ligands to serve as more effective therapeutic agents and as superior alternatives to the current peptidic molecules, with implications for developing novel therapeutic strategies targeting protein–RNA interactions. Check out this published article, “Investigating the Ribosomal-RNA:  Protein Interactions and AI-Assisted Discovery of Novel Inhibitor” in ChemistrySelect

 

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Generaive AI Assisted Identification of novel transcriptional repressor EthR inhibitors

Generative AI, molecular docking and molecular dynamics simulations assisted identification of novel transcriptional repressor EthR inhibitors to target Mycobacterium tuberculosis

Tuberculosis (TB) remains a persistent global health threat, with Mycobacterium tuberculosis (Mtb) continuing to be a leading cause of mortality worldwide. Despite efforts to control the disease, the emergence of multi-drug-resistant (MDR) and extensively drug-resistant (XDR) TB strains presents a significant challenge to conventional treatment approaches. Addressing this challenge requires the development of novel anti-TB drug molecules. This study employed de novo drug design approaches to explore new EthR ligands and ethionamide boosters targeting the crucial enzyme InhA involved in mycolic acid synthesis in Mtb. Leveraging REINVENT4, a modern open-source generative AI framework, the study utilized various optimization algorithms such as transfer learning, reinforcement learning, and curriculum learning to design small molecules with desired properties. Specifically, focus was placed on molecule optimization using the Mol2Mol option, which offers multinomial sampling with beam search. The study’s findings highlight the identification of six promising compounds exhibiting enhanced activity and improved physicochemical properties through structure-based drug design and optimization efforts. These compounds offer potential candidates for further preclinical and clinical development as novel therapeutics for TB treatment, providing new avenues for combating drug-resistant TB strains and improving patient outcomes. Check out this published article, “Generative AI, molecular docking and molecular dynamics simulations assisted identification of novel transcriptional repressor EthR inhibitors to target Mycobacterium tuberculosis” in Heliyon

 

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Gen-AI Methods for Identification of Novel Inhibitors of MmPL3 Transporter of Mycobacterium tuberculosis

Gen-AI Methods, Molecular Docking and Molecular Dynamics Simulations for Identification of Novel Inhibitors of MmPL3 Transporter of Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), the bacterium responsible for tuberculosis (TB), employs mycolic acids to build its cell wall. This robust structure plays a vital role in protecting the bacterium from external threats and contributes to its resistance against antibiotics. Mycobacterial membrane protein Large 3 (MmpL3), a secondary resistance nodulation division transporter, is essential in mycolic acid biosynthesis, transporting mycolic acid precursors into the periplasm using the proton motive force. Due to its role in bacterial cell wall formation, it is a promising target for new tuberculosis treatments. In this study, starting with 85 known MmPL3 compounds, the artificial intelligence (AI)-assisted tool “Design of Druglike Analogues (DeLA-Drug)” was employed to generate about 15,000 novel molecules. These compounds were then subjected to structure-based high-throughput virtual screening and molecular dynamics (MD) simulations to identify potential novel inhibitors of MmpL3. The binding affinity was obtained by docking the above molecules at the SQ109 binding site in MmPL3, followed by pharmacokinetics and toxicity, which were used to reduce the chemical space. Finally, five ligands were subjected to 100 ns MD simulations to investigate the binding energetics of inhibitors to MmpL3. These compounds demonstrated stable binding and favorable drug-like properties, indicating that they could serve as potential novel inhibitors of MmpL3 for Mtb. Check out this published article, “Gen-AI Methods, Molecular Docking and Molecular Dynamics Simulations for Identification of Novel Inhibitors of MmPL3 Transporter of Mycobacterium tuberculosis” in Journal of Computational Biophysics and Chemistry

 

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ML and Fingerprint Based Similarity Search for novel ERK2 inhibitors

Machine learning-integrated and fingerprint-based similarity search against the immuno-oncology library for identification of novel ERK2 inhibitors

The extracellular signal-regulated kinase 2 (ERK2) protein plays a pivotal role in regulating cell division cycles and signal ing pathways essential for various biological processes. ERK2 inhibition is a promising therapeutic approach for diseases like cardiovascular deformities, neurodegenerative disorders, and other forms of cancers. The current study presents novel compounds potentially inhibiting ERK2 activity, thus disrupting its cellular functions. A thorough structural assessment of the available crystallographic information was undertaken. The protein’s active site was deciphered, and the experimental grid space of inhibitors interaction was allocated. The study proceeded further with a precise inhibitor search employing a “similarity search” algorithm based on the previously reported kinase inhibitors. Schematic virtual screening method combined with molecular docking steps were executed to enlist the probable hits. AI/ML-based pharmacokinetics proper ties helped streamline hits’ initial chemical space and select the most potent leads. Complexes formed by these compounds were analyzed for their stability by molecular dynamics (MD) simulations. Post dynamics statistical calculations, viz., pro tein backbone and ligand RMSD, the radius of gyration, and the constitutive amino acids fluctuations (RMSF), confirmed the protein–ligand association over a period of 300 ns. The magnitude of co-ordinations was estimated by intermolecular H-bond count and the MMGBSA calculations. The free energy landscape (FEL) and principal component analysis (PCA) demonstrated the thermodynamical feasibility of the complex formation with an affinity greater than the previously reported inhibitors. This study, thus, presents a promising avenue for advancing the drug discovery process by identifying novel ERK2 protein inhibitors with potential benefits for healthcare. Check out this published article, “Machine learning-integrated and fingerprint-based similarity search against the immuno-oncology library for identification of novel ERK2 inhibitors” in Structural Chemistry

 

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Integrated ML and Physics based de novo Design

Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors

Tuberculosis is an infectious disease that has become endemic worldwide. The causative bacteria Mycobacterium tuberculosis (Mtb) is targeted via several exciting drug targets. One newly discovered target is the Fatty Acyl-CoA synthase, which plays a significant role in activating the long-chain fatty acids.This study aims to generate novel compounds using Machine Learning (ML) algorithms to inhibit this synthase. Experimentally derived bioactive compounds were chosen from ChEMBL and used as inputs for effective molecule generation by Reinvent4. The ML-based de novo drug design (DNDD) approach successfully generated a diverse library of novel molecules targeting Fatty Acyl-CoA synthase. After rigorous molecular docking and binding free energy analysis, four new compounds were identified as potential lead candidates with promising inhibitory effects on Mtb lipid metabolism. The study demonstrated the effectiveness of a machine-learning approach in generating novel drug candidates against Mtb. The identified hit compounds show potential as inhibitors of Fatty Acyl-CoA synthase, offering a new avenue for developing treatments for tuberculosis, particularly in combating drug-resistant strains. Check out this published article, “Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors” in Expert Opinion on Drug Discovery

 

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Deep Learning Guided Designing Of Pks13 Inhibitors

Pharmacophore Guided Deep Learning Approach to Identify Novel Inhibitors Targeting Mycobacterial Polyketide Synthase Pks13-TE Domain

Tuberculosis (TB), an enduring global health challenge, persists due to the rise of drug-resistant Mycobacterium tuberculosis (MTB) strains. Among potential therapeutic targets, Pks13, a protein crucial for mycolic acid biosynthesis, is key for Mtb’s virulence and survival. This study employed a pharmacophore-based drug design approach, employing the Pharmacophore-Guided Molecular Generation (PGMG) tool to target Pks13 inhibitors. Aromatic, hydrophobic, positive ion and hydrogen bond acceptors pharmacophoric features were identified from co-crystal ligands. Candidate compounds underwent evaluation of pharmacokinetic properties with the ADMET_AI tool. Further refinement involved molecular docking with PLANTS software, absolute binding free energy calculations via KDeep, and toxicity assessments using eToxPred. MM-GBSA, PCA, DCCM, and FEL were incorporated to validate and refine inhibitors accurately. From this analysis, we discovered five novel hit molecules. We conclude that the screened hit compounds may act as potential inhibitors targeting Pks13 and further preclinical and clinical studies may pave the way for developing them as effective therapeutic agents for the treatment of MTB. The article has been published in the Journal of Molecular Structure, and it can be available at Science Direct.

 

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In-Silico Studies for Kinase PknB Peptide Design

Identification And Design Of Novel Antimicrobial Peptides Targeting Mycobacterial Protein Kinase Pkn

Antimicrobial peptides (AMPs) are increasingly favored over small molecule inhibitors due to their multifunctionality, ease of synthesis, and target specificity. Our recent study aimed to identify an AMP that inhibits PknB of M. tuberculosis, a serine/threonine protein kinase, by binding at its hinge region. A library of 5626 AMPs was prepared and categorized by length, and molecular docking identified peptides with consistent high-affinity binding to PknB’s active site. Twenty-six peptides were shortlisted and further grouped by length, with five chosen for molecular dynamic simulations in Gromacs based on binding affinity. Post-simulation analysis identified a 15-mer peptide as the most effective, leading to residue substitution to enhance interactions. Elastic Network Model (ENM) analysis further evaluated emphasizing the importance of peptide length and residue composition in developing effective peptide-based inhibitors. Article “Identification and Design of Novel Antimicrobial Peptides Targeting Mycobacterial Protein Kinase Pkn” published in The Protein Journal.

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AI/ML Approaches For Pks13 Inhibitors Discovery

Identification of novel hit molecules targeting M. tuberculosis polyketide synthase 13 by combining generative AI and physics-based methods

Our latest research gives insight into the critical Pks13-TE domain of mycobacteria, employing a mix of AI and physics-based tools to identify new inhibitors. We utilized AI-ML tools such as Reinvent 4, pKCSM, KDeep, and SwissADME, alongside physics-based methods like AutoDock Vina, PLANTS, MDS, and MM-GBSA. Known Pks13-TE inhibitors were curated and used as input to generate novel inhibitors, which were then filtered through rigorous molecular docking and dynamics simulations. This meticulous process reduced the chemical space, retaining only the most promising compounds based on their interactions, stability, and binding energies. Further ADMET testing of these compounds revealed that ligands Mt1 to Mt6 possess excellent pharmacokinetic, pharmacodynamic, and toxicity profiles, making them strong candidates for drug development. The study “Identification of novel hit molecules targeting M. tuberculosis polyketide synthase 13 by combining generative AI and physics-based methods”, published in Computers in Biology and Medicine at Science Direct, showcases a robust framework for the discovery of potential Pks13-TE inhibitors.

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