A Proteometric Analysis of Human Kinome: Insight into Discriminant Conformation-dependent Residues

Evolutionary divergence in the conformational landscapes of tyrosine vs serine/threonine kinases

Inactive conformations of protein kinase catalytic domains where the DFG motif has a "DFG-out" orientation and the activation loop is folded present a druggable binding pocket that is targeted by FDA-approved 'type-II inhibitors' in the treatment of cancers. Tyrosine kinases (TKs) typically show strong binding affinity with a wide spectrum of type-II inhibitors while serine/threonine kinases (STKs) usually bind more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. To investigate this, we use sequence covariation analysis with a Potts Hamiltonian statistical energy model to guide absolute binding free-energy molecular dynamics simulations of 74 protein-ligand complexes. Using the calculated binding free energies together with experimental values, we estimated free-energy costs for the large-scale (~17-20 Å) conformational change of the activation loop by an indirect approach, circumventing the very challenging problem of simulating the conformational change directly. We also used the Potts statistical potential to thread large sequence ensembles over active and inactive kinase states. The structure-based and sequence-based analyses are consistent; together they suggest TKs evolved to have free-energy penalties for the classical 'folded activation loop' DFG-out conformation relative to the active conformation, that is, on average, 4-6 kcal/mol smaller than the corresponding values for STKs. Potts statistical energy analysis suggests a molecular basis for this observation, wherein the activation loops of TKs are more weakly 'anchored' against the catalytic loop motif in the active conformation and form more stable substrate-mimicking interactions in the inactive conformation. These results provide insights into the molecular basis for the divergent functional properties of TKs and STKs, and have pharmacological implications for the target selectivity of type-II inhibitors.

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

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.

Modeling conformational flexibility of kinases in inactive states

Kinase structures in the inactive "DFG-out" state provide a wealth of druggable binding site variants. The conformational plasticity of this state can be mainly described by different conformations of binding site-forming elements such as DFG motif, A-loop, P-loop, and αC-helix. Compared to DFG-in structures, DFG-out structures are largely underrepresented in the Protein Data Bank (PDB). Thus, structure-based drug design efforts for DFG-out inhibitors may benefit from an efficient approach to generate an ensemble of DFG-out structures. Accordingly, the presented modeling pipeline systematically generates homology models of kinases in several DFG-out conformations based on a sophisticated creation of template structures that represent the major states of the flexible structural elements. Eighteen template classes were initially selected from all available kinase structures in the PDB and subsequently employed for modeling the entire kinome in different DFG-out variants by fusing individual structural elements to multiple chimeric template structures. Molecular dynamics simulations revealed that conformational transitions between the different DFG-out states generally do not occur within trajectories of a few hundred nanoseconds length. This underlines the benefits of the presented homology modeling pipeline to generate relevant conformations of "DFG-out" kinase structures for subsequent in silico screening or binding site analysis studies.

PKIDB: A Curated, Annotated and Updated Database of Protein Kinase Inhibitors in Clinical Trials

The number of protein kinase inhibitors (PKIs) approved worldwide continues to grow steadily, with 39 drugs approved in the period between 2001 and January 2018. PKIs on the market have been the subject of many reviews, and structure-property relationships specific to this class of drugs have been inferred. However, the large number of PKIs under development is often overlooked. In this paper, we present PKIDB (Protein Kinase Inhibitor Database), a monthly-updated database gathering approved PKIs as well as PKIs currently in clinical trials. The database compiles currently 180 inhibitors ranging from phase 0 to 4 clinical trials along with annotations extracted from seven public resources. The distribution and property ranges of standard physicochemical properties are presented. They can be used as filters to better prioritize compound selection for future screening campaigns. Interestingly, more than one-third of the kinase inhibitors violate at least one Lipinski’s rule. A Principal Component Analysis (PCA) reveals that Type-II inhibitors are mapped to a distinct chemical space as compared to orally administrated drugs as well as to other types of kinase inhibitors. Using a Principal Moment of Inertia (PMI) analysis, we show that PKIs under development tend to explore new shape territories as compared to approved PKIs. In order to facilitate the analysis of the protein space, the kinome tree has been annotated with all protein kinases being targeted by PKIs. Finally, we analyzed the pipeline of the pharmaceutical companies having PKIs on the market or still under development. We hope that this work will assist researchers in the kinase field in identifying and designing the next generation of kinase inhibitors for still untargeted kinases. The PKIDB database is freely accessible from a website at http://www.icoa.fr/pkidb and can be easily browsed through a user-friendly spreadsheet-like interface.

The Future of Computational Chemogenomics

Following the elucidation of the human genome, chemogenomics emerged in the beginning of the twenty-first century as an interdisciplinary research field with the aim to accelerate target and drug discovery by making best usage of the genomic data and the data linkable to it. What started as a systematization approach within protein target families now encompasses all types of chemical compounds and gene products. A key objective of chemogenomics is the establishment, extension, analysis, and prediction of a comprehensive SAR matrix which by application will enable further systematization in drug discovery. Herein we outline future perspectives of chemogenomics including the extension to new molecular modalities, or the potential extension beyond the pharma to the agro and nutrition sectors, and the importance for environmental protection. The focus is on computational sciences with potential applications for compound library design, virtual screening, hit assessment, analysis of phenotypic screens, lead finding and optimization, and systems biology-based prediction of toxicology and translational research.

Prediction of Protein Kinase-Ligand Interactions through 2.5D Kinochemometrics

So far, 518 protein kinases have been identified in the human genome. They share a common mechanism of protein phosphorylation and are involved in many critical biological processes of eukaryotic cells. Deregulation of the kinase phosphorylation function induces severe illnesses such as cancer, diabetes, or inflammatory diseases. Many actors in the pharmaceutical domain have made significant efforts to design potent and selective protein kinase inhibitors as new potential drugs. Because the ATP binding site is highly conserved in the protein kinase family, the design of selective inhibitors remains a challenge and has negatively impacted the progression of drug candidates to late-stage clinical development. The work presented here adopts a 2.5D kinochemometrics (KCM) approach, derived from proteochemometrics (PCM), in which protein kinases are depicted by a novel 3D descriptor and the ligands by 2D fingerprints. We demonstrate in two examples that the protein descriptor successfully classified protein kinases based on their group membership and their Asp-Phe-Gly (DFG) conformation. We also compared the performance of our models with those obtained from a full 2D KCM model and QSAR models. In both cases, the internal validation of the models demonstrated good capabilities to distinguish "active" from "inactive" protein kinase-ligand pairs. However, the external validation performed on two independent data sets showed that the two statistical models tended to overestimate the number of "inactive" pairs.

Lead Discovery of Type II BRAF V600E Inhibitors Targeting the Structurally Validated DFG-Out Conformation Based upon Selected Fragments

The success of the first approved kinase inhibitor imatinib has spurred great interest in the development of type II inhibitors targeting the inactive DFG-out conformation, wherein the Phe of the DFG motif at the start of the activation loop points into the ATP binding site. Nevertheless, kinase inhibitors launched so far are heavily biased toward type I inhibitors targeting the active DFG-in conformation, wherein the Phe of the DFG motif flips by approximately 180° relative to the inactive conformation, resulting in Phe and Asp swapping their positions. Data recently obtained with structurally validated type II inhibitors supported the conclusion that type II inhibitors are more selective than type I inhibitors. In our type II BRAF V600E inhibitor lead discovery effort, we identified phenylaminopyrimidine (PAP) and unsymmetrically disubstituted urea as two fragments that are frequently presented in FDA-approved protein kinase inhibitors. We therefore defined PAP and unsymmetrically disubstituted urea as privileged fragments for kinase drug discovery. A pharmacophore for type II inhibitors, 4-phenylaminopyrimidine urea (4-PAPU), was assembled based upon these privileged fragments. Lead compound SI-046 with BRAF V600E inhibitory activity comparable to the template compound sorafenib was in turn obtained through preliminary structure-activity relationship (SAR) study. Molecular docking suggested that SI-046 is a bona fide type II kinase inhibitor binding to the structurally validated "classical DFG-out" conformation of BRAF V600E. Our privileged fragments-based approach was shown to efficiently deliver a bona fide type II kinase inhibitor lead. In essence, the theme of this article is to showcase the strategy and rationale of our approach.

Conformation-Selective Analogues of Dasatinib Reveal Insight into Kinase Inhibitor Binding and Selectivity

In the kinase field, there are many widely held tenets about conformation-selective inhibitors that have yet to be validated using controlled experiments. We have designed, synthesized, and characterized a series of kinase inhibitor analogues of dasatinib, an FDA-approved kinase inhibitor that binds the active conformation. This inhibitor series includes two Type II inhibitors that bind the DFG-out inactive conformation and two inhibitors that bind the αC-helix-out inactive conformation. Using this series of compounds, we analyze the impact that conformation-selective inhibitors have on target binding and kinome-wide selectivity.

Hydrophobic Core Variations Provide a Structural Framework for Tyrosine Kinase Evolution and Functional Specialization

Protein tyrosine kinases (PTKs) are a group of closely related enzymes that have evolutionarily diverged from serine/threonine kinases (STKs) to regulate pathways associated with multi-cellularity. Evolutionary divergence of PTKs from STKs has occurred through accumulation of mutations in the active site as well as in the commonly conserved hydrophobic core. While the functional significance of active site variations is well understood, relatively little is known about how hydrophobic core variations contribute to PTK evolutionary divergence. Here, using a combination of statistical sequence comparisons, molecular dynamics simulations, mutational analysis and in vitro thermostability and kinase assays, we investigate the structural and functional significance of key PTK-specific variations in the kinase core. We find that the nature of residues and interactions in the hydrophobic core of PTKs is strikingly different from other protein kinases, and PTK-specific variations in the core contribute to functional divergence by altering the stability and dynamics of the kinase domain. In particular, a functionally critical STK-conserved histidine that stabilizes the regulatory spine in STKs is selectively mutated to an alanine, serine or glutamate in PTKs, and this loss-of-function mutation is accommodated, in part, through compensatory PTK-specific interactions in the core. In particular, a PTK-conserved phenylalanine in the I-helix appears to structurally and functionally compensate for the loss of STK-histidine by interacting with the regulatory spine, which has far-reaching effects on enzyme activity, inhibitor sensing, and stability. We propose that hydrophobic core variations provide a selective advantage during PTK evolution by increasing the conformational flexibility, and therefore the allosteric potential of the kinase domain. Our studies also suggest that Tyrosine Kinase Like kinases such as RAF are intermediates in PTK evolutionary divergence inasmuch as they share features of both PTKs and STKs in the core. Finally, our studies provide an evolutionary framework for identifying and characterizing disease and drug resistance mutations in the kinase core.

Proteins evolve new functions through accumulation of mutations in the primary sequence. Understanding how naturally occurring mutations shape protein function can provide insights into how non-natural mutations contribute to disease. Here, we identify sequence variants associated with the functional specialization of tyrosine kinases, a large and medically important class of proteins associated with the evolution of complex multicellular functions and diseases such as cancer. We find that mutations distal from the active site contribute to functional specialization by altering the stability, activity and dynamics of the kinase core. Our findings have implications for understanding the evolution of allosteric regulation in tyrosine kinases, and in predicting the structural and functional impact of disease and drug resistance mutations in the kinase core.