Algorithms for Macro-Molecular Pocket Detection (A-MOP)

Type: National Project

Duration: from 2015 Jul 01 to 2016 Jun 30

Financed by: UTAustin/Portugal FCT

Prime Contractor: INESC-ID (Other)

Project Web Site: https://sites.google.com/site/algmop

This research proposal addresses a classical, yet still open, challenge in structurebased drug design (SBDD): to correctly predict which small molecule (i.e., ligand) would bind to a specific protein and, consequently, which are the implications on its function. Understanding how a ligand interacts with the protein, more specifically, how a ligand fits in its binding site on the protein surface, requires advanced algorithms for macro-molecular pocket detection. Such algorithms are also mandatory for several other SBDD tasks, namely, to identify protein binding sites or pockets where ligands can bind, to predict which ligands might bind, how strong their bindings will be, and to assess their impact this might have on the protein function. In general, pocket prediction algorithms split into two main categories: energy- and geometry-based algorithms. This proposal focuses on geometry-based algorithms since, for large-scale prediction of potential binding pockets, these algorithms are faster than energy-based algorithms.

Partnerships

  • INESC-ID (Other)
  • Universidade da Beira Interior (University) - Covilhã, Portugal

Principal Investigators

Members