Computational Analysis of Crystallization Additives for the Identification of New Allosteric Sites

Abstract

Allosteric effect can modulate the biological activity of a protein. Thus, the discovery of new allosteric sites is very attractive for designing new modulators or inhibitors. Here, we propose an innovative way to identify allosteric sites, based on crystallization additives (CA), used to stabilize proteins during the crystallization process. Density and clustering analyses of CA, applied on protein kinase and nuclear receptor families, revealed that CA are not randomly distributed around protein structures, but they tend to aggregate near common sites. All orthosteric and allosteric cavities described in the literature are retrieved from the analysis of CA distribution. In addition, new sites were identified, which could be associated to putative allosteric sites. We proposed an efficient and easy way to use the structural information of CA to identify allosteric sites. This method could assist medicinal chemists for the design of new allosteric compounds targeting cavities of new drug targets.

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

“…Based on additives' locations, distribution, and propensities towards protein surface structures, allosteric sites can be featured and identified. 174 Classic models typically deploy empirical parameters based on known structural information; however, computer vision theory is a novel allosteric site-featurization method that considers a 3D protein structure to be a 3D image, which can be represented as a voxel grid. Within it, each voxel corresponds to multiple channels and reflects various atomic densities within the complex.…”

“…In addition to the fingerprints reported by AlloSite, other characteristic parameters that can be extracted from allosteric sites include geometrical and physio‐chemical properties, such as the ligandability concept exploited by the P2Rank and PrankWeb methods, 171,172 and the cooperative changes of residual solvent exposure within secondary structures reported by Porter et al 173 In addition to intrinsic structural features, Fogha and colleagues found that crystallization additives within complex systems can provide hints that aid in the detection of allosteric sites. Based on additives' locations, distribution, and propensities towards protein surface structures, allosteric sites can be featured and identified 174 . Classic models typically deploy empirical parameters based on known structural information; however, computer vision theory is a novel allosteric site‐featurization method that considers a 3D protein structure to be a 3D image, which can be represented as a voxel grid.…”

Engineering an Allosteric Control of Protein Function

“…The allosteric binding site could be visualized by the crystallographic studies of the compound and protein complex. 47 Recently, Fogha et al 48 proposed a new method to identify allosteric sites by analyzing the density and clustering of crystallization additives. They found that these additives used to stabilize proteins during crystallization tend to distribute near allosteric sites, which is an efficient experimental mean for allosteric site prediction.…”

Prediction of allosteric sites and signaling: Insights from benchmarking datasets

“…38 Similarly, not concentrating on cognate ligands, Fogha et al performed computational analysis of the density and clustering of crystallization additives that are used to stabilize proteins during the process of crystallization. 39 These methods, although achieving some promising predictability for putative allosteric sites, focus merely on the potential binding pockets on the protein and do not consider the effects of binding at these sites on the protein, which is the key concept of allostery. Therefore, these approaches alone are not sufficient to identify potential allosteric sites.…”