Exploring Protein-Peptide Recognition Pathways Using a Supervised Molecular Dynamics Approach.
Peptides have gained increased interest as therapeutic agents during recent years. The high specificity and relatively low toxicity of peptide drugs derive from their extremely tight binding to their targets. Indeed, understanding the molecular mechanism of protein-peptide recognition has important implications in the fields of biology, medicine, and pharmaceutical sciences. Even if crystallography and nuclear magnetic resonance are offering valuable atomic insights into the assembling of the protein-peptide complexes, the mechanism of their recognition and binding events remains largely unclear. In this work we report, for the first time, the use of a supervised molecular dynamics approach to explore the possible protein-peptide binding pathways within a timescale reduced up to three orders of magnitude compared with classical molecular dynamics. The better and faster understating of the protein-peptide recognition pathways could be very beneficial in enlarging the applicability of peptide-based drug design approaches in several biotechnological and pharmaceutical fields.
Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
Veronica
V
Salmaso
Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
Mattia
M
Sturlese
Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
Alberto
A
Cuzzolin
Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy. Electronic address: stefano.moro@unipd.it.
Stefano
S
Moro
D015195:Drug Design; D008958:Models, Molecular; D056004:Molecular Dynamics Simulation; D010455:Peptides / Q000737:chemistry / Q000378:metabolism*; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D000069553:Supervised Machine Learning
D010455:Peptides; D011506:Proteins
BAD; Bcl-X(L); MDM2; molecular dynamics (MD); p53; peptidomimetics; protein-peptide docking; protein-peptide recognition; supervised molecular dynamics (SuMD)