Druggability Assessment of Allosteric Proteins by Dynamics Simulations in the Presence of Probe Molecules

Abstract

Druggability assessment of a target protein has emerged in recent years as an important concept in hit-to-lead optimization. A reliable and physically relevant measure of druggability would allow informed decisions on the risk of investing in a particular target. Here, we define “druggability” as a quantitative estimate of binding sites and affinities for a potential drug acting on a specific protein target. In the present study, we describe a new methodology that successfully predicts the druggability and maximal binding affinity for a series of challenging targets, including those that function through allosteric mechanisms. Two distinguishing features of the methodology are (i) simulation of the binding dynamics of a diversity of probe molecules selected on the basis of an analysis of approved drugs and (ii) identification of druggable sites and estimation of corresponding binding affinities on the basis of an evaluation of the geometry and energetics of bound probe clusters. The use of the methodology for a variety of targets such as murine double mutant-2, protein tyrosine phosphatase 1B (PTP1B), lymphocyte function-associated antigen 1, vertebrate kinesin-5 (Eg5), and p38 mitogen-activated protein kinase provides examples for which the method correctly captures the location and binding affinities of known drugs. It also provides insights into novel druggable sites and the target’s structural changes that would accommodate, if not promote and stabilize, drug binding. Notably, the ability to identify high affinity spots even in challenging cases such as PTP1B or Eg5 shows promise as a rational tool for assessing the druggability of protein targets and identifying allosteric or novel sites for drug binding.

CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites

“…Currently, the only approaches to cryptic site discovery are exhaustive site-directed small-molecule tethering by experiment (20–22), long time-scale molecular dynamics simulations by computation (6, 8, 23, 24), flexible docking (25, 26), and computational tools for identification of small-molecule binding hot spots (10, 27–30). All of these approaches are time-consuming, expensive, and/or not always successful.…”

Structure-Based Druggability Assessment of the Mammalian Structural Proteome with Inclusion of Light Protein Flexibility

“…‘Hot spots’ having all 16 probes bound are identified as druggable [19]. Bakan et al used molecular dynamics simulations for solvent mapping with a different set of small fragment probes and found that the probes bind known allosteric sites during the simulation [20]. Brown and Hajduk earlier showed that molecular dynamics simulations can capture pocket dynamics that result in a more druggable binding pocket in Bcl-xl, while preserving much of the known binding site rigidity of Akt-PH and FKBP [21].…”

“…In contrast, it is likely possible to find all known druggable sites using molecular dynamics, including some that are undetectable by our method. However, separating the signal from the noise is challenging [20], [21] since there is a tendency for many pocket openings to be observed. To be clear, our approach does not reveal any new pockets that do not already exist within the rigid structure.…”

Open source molecular modeling

“…ProDy [147] is a Python toolkit for analyzing proteins and includes facilities for trajectory analysis and druggability predictions using simulations of molecular probes [152]. …”