Ligand-based pharmacophore modeling and molecular dynamic simulation approaches to identify putative MMP-9 inhibitors

Sanapalli, Bharat Kumar Reddy; Yele, Vidyasrilekha; Jupudi, Srikanth; Karri, Veera Venkata Satyanarayana Reddy

doi:10.1039/d1ra03891e

Cited by 7 publications

(2 citation statements)

References 52 publications

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“…In the atom-based QSAR models study of the whole molecular structure of the ligands is carried out hence it is more useful in explaining the structure activity relationships. During the generation of atom-based 3D-QSAR models, the structural features of each atom is treated as van der Waals spheres [35,40]. The atoms are treated as hydrogen bond donor-D (hydrogen bonded to elements like N, O, P, and S), hydrophobic or nonpolar-H (C, Cl, Br, F, I), negative ionic group-N (atoms of negative charge), positive ionic group-P (atoms of positive charge), electronwithdrawing including hydrogen bond acceptor -W (non-ionic atoms like N, O) and miscellaneous-X (other types of atoms) as per simple internal rules [41,42].…”

Section: Pharmacophore Modellingmentioning

confidence: 99%

Computational approaches: Atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted Anti-tubercular agents

Panigrahi,

Sahu

2024

Preprint

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Tuberculosis (TB) has become the biggest threat towards human society due to the rapid rise in resistance of the causative bacteria Mycobacterium tuberculosis (MTB) against the available anti-tubercular drugs. There is an urgent need to design new multi-targeted anti-tubercular agents to overcome the resistance species of MTB through computational design tools. With this aim in the present work, a combination of atom-based three-dimensional quantitative structure-activity relationship (3D-QSAR), six-point pharmacophore (AHHRRR), and molecular docking analysis was performed on a series of fifty-eight anti-tubercular agents. The generated QSAR model showed statistically significant correlation co-efficient R2, Q2, and Pearson r-factor of 0.9521, 0.8589, and 0.8988 respectively indicating good predictive ability. Molecular docking study was performed for the data set of compounds with the two important anti-tubercular target proteins, Enoyl acyl carrier protein reductase (InhA) (PDBID: 2NSD) and Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) (PDBID: 4FDO). Using the similarity search principle virtual screening was performed on 237 compounds retrieved from the Pubchem database to identify potent multitargeted anti-tubercular agents. The screened compound, MK3 showed the highest docking score of -9.2 and − 8.3 Kj/mol towards both the target proteins InhA and DprE1 were picked for 100ns molecular dynamic simulation study using GROMACS. From the data generated, the compound MK3 showed thermodynamic stability and effective binding within the active binding pocket of both target proteins without much deviation. The result of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and energy gap analysis predicts the molecular reactivity and stability of the identified molecule. Based on the result of the above studies the proposed compound MK3 can be successfully used for the development of a novel multi-targeted anti-tubercular agent with high binding affinity and favourable ADME-T properties.

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Section: Pharmacophore Modellingmentioning

confidence: 99%

Computational approaches: Atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted Anti-tubercular agents

Panigrahi,

Sahu

2024

Preprint

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“…Sanapalli et al designed and validated a robust five-point 3D-QSAR model that contains two hydrogen-bond donors (HBD), one hydrophobic region (HY), and two aromatic rings (RA). Through virtual screening and molecular docking approaches, two compounds were identified as selective MMP-9 inhibitors, including compounds 22 and 23, due to their significant interactions with the active residues [89]. Furthermore, three ligand-based pharmacophore models have been developed based on known MMP-9 inhibitors.…”

Section: Inhibitormentioning

confidence: 99%

Novel Matrix Metalloproteinase-9 (MMP-9) Inhibitors in Cancer Treatment

Rashid,

Bardaweel

2023

IJMS

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Matrix metalloproteinases (MMPs) belong to a family of zinc-dependent proteolytic metalloenzymes. MMP-9, a member of the gelatinase B family, is characterized as one of the most intricate MMPs. The crucial involvement of MMP-9 in extracellular matrix (ECM) remodeling underscores its significant correlation with each stage of cancer pathogenesis and progression. The design and synthesis of MMP-9 inhibitors is a potentially attractive research area. Unfortunately, to date, there is no effective MMP-9 inhibitor that passes the clinical trials and is approved by the FDA. This review primarily focuses on exploring the diverse strategies employed in the design and advancement of MMP-9 inhibitors, along with their anticancer effects and selectivity. To illuminate the essential structural characteristics necessary for the future design of novel MMP-9 inhibitors, the current narrative review highlights several recently discovered MMP-9 inhibitors exhibiting notable selectivity and potency.

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Identifying Potent Breast Cancer Inhibitors Against ERα Target Using Pharmacophore Model, 3D‐QSAR and MD Studies

Rajagopal,

Arumugasamy,

Raman

et al. 2024

ChemistrySelect

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Abstractthe current investigation, 109 known ERα inhibitors have been developed in the current research; pharmacophore modeling, Molecular docking, MM‐GBSA, and MD study have been performed to investigate the binding affinity of 9‐anilinoacridines with heterocyclic substitutes as selective ERα inhibitors for breast carcinoma. Pharmacophore model have been developed by Schrodinger suite 2019–2 phasemodule. To predict binding free energy of the ligands in complex with PDB and post docked energy minimization was performed by Prime, MM‐GB/SA module. The Induced fit docking studies were performed on the ligand modulated dynamic behaviour of the protein molecular dynamics study. A statistical substantial 3D ‐ QSAR design was created using the pharmacophore hypothesis. 109 known ERα inhibitors have been developed with pIC50 values between 4.0 and 6.0 and were used in ligand‐based pharmacophore modelling and 3D‐QSAR analysis. R2 (0.8294), Q2 (0.7~0.8), and F value (83.5) were used to statistically validate the developed five‐point hypothesis DHRRR1 employing a minimum square of four. Molecular dynamics simulations were run to comprehend the conformational changes and ligand stability at the protein active pocket. The predicted 3D‐QSAR model significantly correlated with experimentally reported in‐vitro antitumor activity. These in‐silico discoveries will help in the future search for potent ERα inhibitors with desirable pharmacophoric properties.

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Ligand-based pharmacophore modeling and molecular dynamic simulation approaches to identify putative MMP-9 inhibitors

Abstract: MMP-9 is a calcium-dependent zinc endopeptidase that plays a crucial role in various diseases and is a ubiquitous target for many classes of drugs.

Cited by 7 publications

References 52 publications

Computational approaches: Atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted Anti-tubercular agents

Computational approaches: Atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted Anti-tubercular agents

Novel Matrix Metalloproteinase-9 (MMP-9) Inhibitors in Cancer Treatment

Identifying Potent Breast Cancer Inhibitors Against ERα Target Using Pharmacophore Model, 3D‐QSAR and MD Studies

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