Systematic Analysis of Covalent and Allosteric Protein Kinase Inhibitors.

In drug discovery, protein kinase inhibitors (PKIs) are intensely investigated as drug candidates in different therapeutic areas. While ATP site-directed, non-covalent PKIs have long been a focal point in protein kinase (PK) drug discovery, in recent years, there has been increasing interest in allosteric PKIs (APKIs), which are expected to have high kinase selectivity. In addition, as compounds acting by covalent mechanisms experience a renaissance in drug discovery, there is also increasing interest in covalent PKIs (CPKIs). There are various reasons for this increasing interest such as the anticipated high potency, prolonged residence times compared to non-competitive PKIs, and other favorable pharmacokinetic properties. Due to the popularity of PKIs for therapeutic intervention, large numbers of PKIs and large volumes of activity data have accumulated in the public domain, providing a basis for large-scale computational analysis. We have systematically searched for CPKIs containing different reactive groups (warheads) and investigated their potency and promiscuity (multi-PK activity) on the basis of carefully curated activity data. For seven different warheads, sufficiently large numbers of CPKIs were available for detailed follow-up analysis. For only three warheads, the median potency of corresponding CPKIs was significantly higher than of non-covalent PKIs. However, for CKPIs with five of seven warheads, there was a significant increase in the median potency of at least 100-fold compared to PKI analogues without warheads. However, in the analysis of multi-PK activity, there was no general increase in the promiscuity of CPKIs compared to non-covalent PKIs. In addition, we have identified 29 new APKIs in X-ray structures of PK-PKI complexes. Among structurally characterized APKIs, 13 covalent APKIs in complexes with five PKs are currently available, enabling structure-based investigation of PK inhibition by covalent-allosteric mechanisms.

Structure- and Similarity-Based Survey of Allosteric Kinase Inhibitors, Activators, and Closely Related Compounds.

Allosteric kinase inhibitors are thought to have high selectivity and are prime candidates for kinase drug discovery. In addition, the exploration of allosteric mechanisms represents an attractive topic for basic research and drug design. Although the identification and characterization of allosteric kinase inhibitors is still far from being routine, X-ray structures of kinase complexes have been determined for a significant number of such inhibitors. On the basis of structural data, allosteric inhibitors can be confirmed. We report a comprehensive survey of allosteric kinase inhibitors and activators from publicly available X-ray structures, map their binding sites, and determine their distribution over binding pockets in kinases. In addition, we discuss structural features of these compounds and identify active structural analogues and high-confidence target annotations, indicating additional activities for a subset of allosteric inhibitors. This contribution aims to provide a detailed structure-based view of allosteric kinase inhibition.

Systematic comparison of competitive and allosteric kinase inhibitors reveals common structural characteristics.

Allosteric and ATP-competitive kinase inhibitors act by distinct mechanisms and are expected to have high and low kinase selectivity, respectively. This also raises the question whether or not these different types of inhibitors might be structurally distinct. To address this question, we have assembled data sets of currently available competitive and allosteric kinase inhibitors confirmed by X-ray crystallography and systematically compared these compounds on the basis of different structural criteria. Many competitive and allosteric inhibitors were found to contain the same or similar substructures and a subset of allosteric inhibitors was found to share core structures with ATP site-directed inhibitors. In some instances, small chemical modifications of common cores were found to yield either allosteric or competitive inhibitors. Hence, these different categories of inhibitors with distinct mechanisms of action were often structurally related and represented much more of a structural continuum than discrete states. Additional target annotations were frequently identified for competitive inhibitors, but were rare for allosteric inhibitors. As a part of this study, our collection of kinase inhibitors and the associated information are made freely available to enable further assessment of chemical modifications that distinguish similar kinase inhibitors with distinct mechanisms of action.

Data set of competitive and allosteric protein kinase inhibitors confirmed by X-ray crystallography.

A data set was generated comprising currently available competitive and allosteric human protein kinase inhibitors confirmed by X-ray crystallography. This data set has been used to systematically explore structural relationships between these types of inhibitors with different mechanisms of action. A major finding of this study has been that these different inhibitor types frequently displayed structural relationships and essentially represented a structural continuum [1]. Use of the data set is not limited to the inhibitor-centric exploration of structural relationships. The collection of kinase inhibitors with structurally confirmed distinct mechanisms of action can also be used, for example, to aid in structure-based drug design or the search for new allosteric kinase inhibitors.

Identifying representative kinases for inhibitor evaluation via systematic analysis of compound-based target relationships.

We have investigated the question if kinases could be identified whose compound binding characteristics represent those of many other kinases. Therefore, compound-based relationships between kinases were systematically explored on the basis of a large curated collection of inhibitors with multi-kinase activity. With the aid of network analysis, individual kinases were identified that shared at least 50 inhibitors with more than 100 other kinases. Combinations of three to four kinases with many compound-based relationships were found to represent ∼150 kinases distributed across the human kinome. These findings have implications for the practice of medicinal chemistry because small numbers of kinases can be prioritized for compound testing that are expected to account for binding characteristics of many others. Hence, choosing three or four representative kinases may be sufficient for a first-path control evaluation of new candidate compounds to assess their potential for promiscuity in kinase inhibition.

Kinase inhibitor data set for systematic analysis of representative kinases across the human kinome.

A large set of multi-kinase inhibitors with high-confidence activity data was assembled and used to generate network representations revealing kinase relationships based upon shared inhibitors [1]. Compounds and activity annotations were originally selected from public repositories and organized in an in-house database from which the data set was extracted and curated. The new data set comprises more than 36,000 inhibitors with multiple activity annotations for a total of 420 human kinases (providing 81% coverage of the human kinome), representing a total of ∼127,000 kinase-inhibitor interactions. Use of the data is not limited to the network application reported in [1]. It can also be used, for example, for different types of compound promiscuity analysis or machine learning (such a multi-task modeling). In addition, the data set provides a large resource for complementing kinase drug discovery projects with external compound information.

Large-Scale Comparison of Alternative Similarity Search Strategies with Varying Chemical Information Contents.

Similarity searching (SS) is a core approach in computational compound screening and has a long tradition in pharmaceutical research. Over the years, different approaches have been introduced to increase the information content of search calculations and optimize the ability to detect compounds having similar activity. We present a large-scale comparison of distinct search strategies on more than 600 qualifying compound activity classes. Challenging test cases for SS were identified and used to evaluate different ways to further improve search performance, which provided a differentiated view of alternative search strategies and their relative performance. It was found that search results could not only be improved by increasing compound input information but also by focusing similarity calculations on database compounds. In the presence of multiple active reference compounds, asymmetric SS with high weights on chemical features of target compounds emerged as an overall preferred approach across many different activity classes. These findings have implications for practical virtual screening applications.

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability.

This study aims at improving upon existing activity predictions methods by augmenting chemical structure fingerprints with bio-activity based fingerprints derived from high-throughput screening (HTS) data (HTSFPs) and thereby showcasing the benefits of combining different descriptor types. This type of descriptor would be applied in an iterative screening scenario for more targeted compound set selection. The HTSFPs were generated from HTS data obtained from PubChem and combined with an ECFP4 structural fingerprint. The bioactivity-structure hybrid (BaSH) fingerprint was benchmarked against the individual ECFP4 and HTSFP fingerprints. Their performance was evaluated via retrospective analysis of a subset of the PubChem HTS data. Results showed that the BaSH fingerprint has improved predictive performance as well as scaffold hopping capability. The BaSH fingerprint identified unique compounds compared to both the ECFP4 and the HTSFP fingerprint indicating synergistic effects between the two fingerprints. A feature importance analysis showed that a small subset of the HTSFP features contribute most to the overall performance of the BaSH fingerprint. This hybrid approach allows for activity prediction of compounds with only sparse HTSFPs due to the supporting effect from the structural fingerprint.