Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition.

We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our analysis was performed on the helical segments and loops in 18 inactive and 9 active class A G protein-coupled receptors (GPCRs). These three-dimensional (3D) structures were determined in complex with ligands. However, considering the flexible versus rigid state identified by graph-theoretic ProFlex rigidity analysis for each helix and loop segment with the ligand removed, followed by feature selection and k-nearest neighbor classification, was sufficient to identify four segments surrounding the ligand binding site whose flexibility/rigidity accurately predicts whether a GPCR is in an active or inactive state. GPCRs bound to inhibitors were similar in their pattern of flexible versus rigid regions, whereas agonist-bound GPCRs were more flexible and diverse. This new ligand-proximal flexibility signature of GPCR activity was identified without knowledge of the ligand binding mode or previously defined switch regions, while being adjacent to the known transmission switch. Following this proof of concept, the ProFlex flexibility analysis coupled with pattern recognition and activity classification may be useful for predicting whether newly designed ligands behave as activators or inhibitors in protein families in general, based on the pattern of flexibility they induce in the protein.

Protein-ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexes.

Understanding how proteins encode ligand specificity is fascinating and similar in importance to deciphering the genetic code. For protein-ligand recognition, the combination of an almost infinite variety of interfacial shapes and patterns of chemical groups makes the problem especially challenging. Here we analyze data across non-homologous proteins in complex with small biological ligands to address observations made in our inhibitor discovery projects: that proteins favor donating H-bonds to ligands and avoid using groups with both H-bond donor and acceptor capacity. The resulting clear and significant chemical group matching preferences elucidate the code for protein-native ligand binding, similar to the dominant patterns found in nucleic acid base-pairing. On average, 90% of the keto and carboxylate oxygens occurring in the biological ligands formed direct H-bonds to the protein. A two-fold preference was found for protein atoms to act as H-bond donors and ligand atoms to act as acceptors, and 76% of all intermolecular H-bonds involved an amine donor. Together, the tight chemical and geometric constraints associated with satisfying donor groups generate a hydrogen-bonding lock that can be matched only by ligands bearing the right acceptor-rich key. Measuring an index of H-bond preference based on the observed chemical trends proved sufficient to predict other protein-ligand complexes and can be used to guide molecular design. The resulting Hbind and Protein Recognition Index software packages are being made available for rigorously defining intermolecular H-bonds and measuring the extent to which H-bonding patterns in a given complex match the preference key.

Detecting the native ligand orientation by interfacial rigidity: SiteInterlock.

Understanding the physical attributes of protein-ligand interfaces, the source of most biological activity, is a fundamental problem in biophysics. Knowing the characteristic features of interfaces also enables the design of molecules with potent and selective interactions. Prediction of native protein-ligand interactions has traditionally focused on the development of physics-based potential energy functions, empirical scoring functions that are fit to binding data, and knowledge-based potentials that assess the likelihood of pairwise interactions. Here we explore a new approach, testing the hypothesis that protein-ligand binding results in computationally detectable rigidification of the protein-ligand interface. Our SiteInterlock approach uses rigidity theory to efficiently measure the relative interfacial rigidity of a series of small-molecule ligand orientations and conformations for a number of protein complexes. In the majority of cases, SiteInterlock detects a near-native binding mode as being the most rigid, with particularly robust performance relative to other methods when the ligand-free conformation of the protein is provided. The interfacial rigidification of both the protein and ligand prove to be important characteristics of the native binding mode. This measure of rigidity is also sensitive to the spatial coupling of interactions and bond-rotational degrees of freedom in the interface. While the predictive performance of SiteInterlock is competitive with the best of the five other scoring functions tested, its measure of rigidity encompasses cooperative rather than just additive binding interactions, providing novel information for detecting native-like complexes. SiteInterlock shows special strength in enhancing the prediction of native complexes by ruling out inaccurate poses. Proteins 2016; 84:1888-1901. © 2016 Wiley Periodicals, Inc.