Molecular Recognition and Drug-Lead Identification: What Can Molecular Simulations Tell Us?

Molecular recognition and ligand binding involving proteins underlie the
most important life processes within the cell, such as substrate
transport, catalysis, signal transmission, receptor trafficking, gene
regulation, switching on and off of biochemical pathways.
Despite recent successes in predicting the structures of many
protein-substrate complexes, the dynamic aspects of binding have been
largely neglected by computational/theoretical investigations. Recently,
several groups have started tackling these problems with the use of
experimental and simulation methods and developed models describing the
variation of protein dynamics upon complex formation, shedding light on
how substrate or inhibitor binding can alter protein flexibility and
function. The study of ligand-induced dynamic variations has also been
exploited to review the concept of allosteric changes, in the absence of
major conformational changes.
In this context, the study of the influence of protein motions on signal
transduction and on catalytic activities has been used to develop
pharmacophore models based on ensembles of protein conformations. These
models, taking flexibility explicitly into account, are able to
distinguish active inhibitors versus nonactive drug-like compounds, to
define new molecular motifs and to preferentially identify specific
ligands for a certain protein target.
The application of these methods holds great promise in advancing
structure-based drug discovery and medicinal chemistry in general,
opening up the possibility to explore broader chemical spaces than is
normally done in an efficient way.
In this review, examples illustrating the extent to which simulations
can be used to understand these phenomena will be presented along with
examples of methodological developments to increase physical
understanding of the processes and improve the possibility to rationally
design new molecules.