Title of article :
Micro-Environmental Signature of The Interactions between Druggable Target Protein, Dipeptidyl Peptidase-IV, and Anti-Diabetic Drugs
Author/Authors :
Chakraborty، Chiranjib نويسنده Department of Bio-Informatics, School of Computer and Information Sciences, Galgotias University, Greater Noida, , , Mallick، Bidyut نويسنده Departments of Physics, Galgotias College of Engineering and Technology, Greater Noida, India , , Ranjan Sharma، Ashish نويسنده Institute for Skeletal Aging and Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Korea , , Sharma، Garima نويسنده Institute for Skeletal Aging and Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Korea , , Jagga، Supriya نويسنده Institute for Skeletal Aging and Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Korea , , Priya Doss، C George نويسنده Department of Integrative Biology, VIT University, Vellore Tamil Nadu, India , , Nam، Ju-Suk نويسنده Institute for Skeletal Aging and Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Korea , , Lee، Sang-Soo نويسنده ,
Abstract :
Objective: Druggability of a target protein depends on the interacting micro-environment
between the target protein and drugs. Therefore, a precise knowledge of the interacting
micro-environment between the target protein and drugs is requisite for drug discovery
process. To understand such micro-environment, we performed in silico interaction analysis
between a human target protein, Dipeptidyl Peptidase-IV (DPP-4), and three anti-diabetic
drugs (saxagliptin, linagliptin and vildagliptin).
Materials and Methods: During the theoretical and bioinformatics analysis of micro-environmental
properties, we performed drug-likeness study, protein active site predictions,
docking analysis and residual interactions with the protein-drug interface. Micro-environmental
landscape properties were evaluated through various parameters such as binding
energy, intermolecular energy, electrostatic energy, van der Waals’+H-bond+desolvo
energy (EVHD) and ligand efficiency (LE) using different in silico methods. For this study, we
have used several servers and software, such as Molsoft prediction server, CASTp server,
AutoDock software and LIGPLOT server.
Results: Through micro-environmental study, highest log P value was observed for linagliptin
(1.07). Lowest binding energy was also observed for linagliptin with DPP-4 in the
binding plot. We also identified the number of H-bonds and residues involved in the hydrophobic
interactions between the DPP-4 and the anti-diabetic drugs. During interaction, two
H-bonds and nine residues, two H-bonds and eleven residues as well as four H-bonds and
nine residues were found between the saxagliptin, linagliptin as well as vildagliptin cases
and DPP-4, respectively.
Conclusion: Our in silico data obtained for drug-target interactions and micro-environmental
signature demonstrates linagliptin as the most stable interacting drug among the
tested anti-diabetic medicines.