2018 edition

José Antonio Amengual Rigo

Computational approaches to drug discovery and enzyme engineering have seen an increase in popularity, guided by the surge in computing power and accessibility. Atomistic simulations are in the foreground of such techniques since they can provide a high-resolution dynamical model of many biophysical problems.

One example of an atomistic technique is the Protein Energy Landscape Exploration (PELE)[1], a method that combines Monte Carlo sampling with protein structure prediction techniques. PELE outstands in modeling protein/ligand interactions, being recently highlighted in the latest CSAR challenge (a blind benchmark for docking and scoring methods) as a “remarkable achievement in drug design”[2]. PELE keeps expanding horizons with the implementation of an adaptive reinforcement learning procedure[3], opening up the way for fast, reliable simulations of protein dynamics employing modern multi-core computational resources. Currently, the ongoing introduction of a ligand growing technique in combination with an accurate scoring function and a virtual screening platform intends to revolutionize PELE as an excellent tool for drug design, being capable of performing hit finding and lead optimization.

This communication is intended to give a general survey of previous results, current and future projects in relation with the different applications PELE has in the fields of ligand migration/binding, enzyme engineering and drug discovery studies. This versatility of PELE is demonstrated by seeing how it can deal with small ligands, large polysaccharides or even antibodies, in small datasets (tens of structures) or medium size datasets (thousands of complexes) with equal efficiency.


  1. Borrelli, K, W, Vitalis, A, Alcantara, R, Guallar, V, 2005. PELE: Protein Energy Landscape Exploration. A Novel Monte Carlo Based Technique. J. Chem. Theory Comput. 1, 1304–1311.
  2. Carlson, H. A. et al., 2016. CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma. J. Chem. Inf. Model. 56, 1063–1077.
  3. Lecina, D, Gilabert, JF, Guallar, V, 2017. Adaptive simulations, towards interactive protein-ligand modeling. Sci. Rep. 7.