Computational Modeling of Kinase Inhibitor Selectivity.
Ontology highlight
ABSTRACT:
SUBMITTER: Subramanian G
PROVIDER: S-EPMC4669537 | biostudies-literature | 2010 Nov
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:MotivationKinases play a significant role in diverse disease signaling pathways and understanding kinase inhibitor selectivity, the tendency of drugs to bind to off-targets, remains a top priority for kinase inhibitor design and clinical safety assessment. Traditional approaches for kinase selectivity analysis using biochemical activity and binding assays are useful but can be costly and are often limited by the kinases that are available. On the other hand, current computational kinase selectivity prediction methods are computational intensive and can rarely achieve sufficient accuracy for large-scale kinome wide inhibitor selectivity profiling.ResultsHere, we present a KinomeFEATURE database for kinase binding site similarity search by comparing protein microenvironments characterized using diverse physiochemical descriptors. Initial selectivity prediction of 15 known kinase inhibitors achieved an >90% accuracy and demonstrated improved performance in comparison to commonly used kinase inhibitor selectivity prediction methods. Additional kinase ATP binding site similarity assessment (120 binding sites) identified 55 kinases with significant promiscuity and revealed unexpected inhibitor cross-activities between PKR and FGFR2 kinases. Kinome-wide selectivity profiling of 11 kinase drug candidates predicted novel as well as experimentally validated off-targets and suggested structural mechanisms of kinase cross-activities. Our study demonstrated potential utilities of our approach for large-scale kinase inhibitor selectivity profiling that could contribute to kinase drug development and safety assessment.Availability and implementationThe KinomeFEATURE database and the associated scripts for performing kinase pocket similarity search can be downloaded from the Stanford SimTK website (https://simtk.org/projects/kdb).Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6330000 | biostudies-literature
Project description:Gleevec is a potent inhibitor of Abl tyrosine kinase but not of the highly homologous c-Src kinase. Because the ligand binds to an inactive form of the protein in which an Asp-Phe-Gly structural motif along the activation loop adopts a so-called DFG-out conformation, it was suggested that binding specificity was controlled by a "conformational selection" mechanism. In this context, the binding affinity displayed by the kinase inhibitor G6G poses an intriguing challenge. Although it possesses a chemical core very similar to that of Gleevec, G6G is a potent inhibitor of both Abl and c-Src kinases. Both inhibitors bind to the DFG-out conformation of the kinases, which seems to be in contradiction with the conformational selection mechanism. To address this issue and display the hidden thermodynamic contributions affecting the binding selectivity, molecular dynamics free energy simulations with explicit solvent molecules were carried out. Relative to Gleevec, G6G forms highly favorable van der Waals dispersive interactions upon binding to the kinases via its triazine functional group, which is considerably larger than the corresponding pyridine moiety in Gleevec. Upon binding of G6G to c-Src, these interactions offset the unfavorable free energy cost of the DFG-out conformation. When binding to Abl, however, G6G experiences an unfavorable free energy penalty due to steric clashes with the phosphate-binding loop, yielding an overall binding affinity that is similar to that of Gleevec. Such steric clashes are absent when G6G binds to c-Src, due to the extended conformation of the phosphate-binding loop.
| S-EPMC4210138 | biostudies-literature
Project description:Poly(aspartic acid) (PAA) is a biodegradable water-soluble anionic polymer that can potentially replace poly(acrylic acid) for industrial applications and has shown promise for regenerative medicine and drug delivery. This paper describes an efficient and sustainable route that uses protease catalysis to convert l-aspartate diethyl ester (Et2-Asp) to oligo(β-ethyl-α-aspartate), oligo(β-Et-α-Asp). Comparative studies of protease activity for oligo(β-Et-α-Asp) synthesis revealed α-chymotrypsin to be the most efficient. Papain, which is highly active for l-glutamic acid diethyl ester (Et2-Glu) oligomerization, is inactive for Et2-Asp oligomerization. The assignment of α-linkages between aspartate repeat units formed by α-chymotrypsin catalysis is based on nuclear magnetic resonance (NMR) trifluoacetic acid titration, circular dichroism, and NMR structural analysis. The influence of reaction conditions (pH, temperature, reaction time, and buffer/monomer/α-chymotrypsin concentrations) on oligopeptide yield and average degree of polymerization (DPavg) was determined. Under preferred reaction conditions (pH 8.5, 40 °C, 0.5 M Et2-Asp, 3 mg/mL α-chymotrypsin), Et2-Asp oligomerizations reached maximum oligo(β-Et-α-Asp) yields of ∼60% with a DPavg of ∼12 (M n 1762) in just 5 min. Computational modeling using Rosetta software gave relative energies of substrate docking to papain and α-chymotrypsin active sites. The substrate preference calculated by Rosetta modeling of α-chymotrypsin and papain for Et2-Asp and Et2-Glu oligomerizations, respectively, is consistent with experimental results.
| S-EPMC7066554 | biostudies-literature
Project description:The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure-activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model's performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23.
| S-EPMC6151389 | biostudies-literature
Project description:Kinases play central roles in signaling pathways and are promising therapeutic targets for many diseases. Designing selective kinase inhibitors is an emergent and challenging task, because kinases share an evolutionary conserved ATP-binding site. KIDFamMap (http://gemdock.life.nctu.edu.tw/KIDFamMap/) is the first database to explore kinase-inhibitor families (KIFs) and kinase-inhibitor-disease (KID) relationships for kinase inhibitor selectivity and mechanisms. This database includes 1208 KIFs, 962 KIDs, 55 603 kinase-inhibitor interactions (KIIs), 35 788 kinase inhibitors, 399 human protein kinases, 339 diseases and 638 disease allelic variants. Here, a KIF can be defined as follows: (i) the kinases in the KIF with significant sequence similarity, (ii) the inhibitors in the KIF with significant topology similarity and (iii) the KIIs in the KIF with significant interaction similarity. The KIIs within a KIF are often conserved on some consensus KIDFamMap anchors, which represent conserved interactions between the kinase subsites and consensus moieties of their inhibitors. Our experimental results reveal that the members of a KIF often possess similar inhibition profiles. The KIDFamMap anchors can reflect kinase conformations types, kinase functions and kinase inhibitor selectivity. We believe that KIDFamMap provides biological insights into kinase inhibitor selectivity and binding mechanisms.
| S-EPMC3531076 | biostudies-literature
Project description:Small-molecule protein kinase inhibitors are widely used to elucidate cellular signaling pathways and are promising therapeutic agents. Owing to evolutionary conservation of the ATP-binding site, most kinase inhibitors that target this site promiscuously inhibit multiple kinases. Interpretation of experiments that use these compounds is confounded by a lack of data on the comprehensive kinase selectivity of most inhibitors. Here we used functional assays to profile the activity of 178 commercially available kinase inhibitors against a panel of 300 recombinant protein kinases. Quantitative analysis revealed complex and often unexpected interactions between protein kinases and kinase inhibitors, with a wide spectrum of promiscuity. Many off-target interactions occur with seemingly unrelated kinases, revealing how large-scale profiling can identify multitargeted inhibitors of specific, diverse kinases. The results have implications for drug development and provide a resource for selecting compounds to elucidate kinase function and for interpreting the results of experiments involving kinase inhibitors.
| S-EPMC3230241 | biostudies-literature
Project description:Deubiquitinylases (DUBs) are highly specialized enzymes which are responsible for removal of covalently attached ubiquitin(s) from the targeted proteins. DUBs play an important role in maintaining the protein homeodynamics. Recently, DUBs have emerged as novel therapeutic targets in cancer, inflammation, diabetes, and neurodegeneration. Among the different families of DUBs, the metalloprotease group or JAB1/MOV34/MPR1 (JAMMs) proteases are unique in terms of catalytic mechanism. JAMMs exhibit a Zn2+-dependent deubiquitinylase activity. Within the JAMM family, deubiquitinylases Rpn11 and CSN5 are constituents of large bimolecular complexes, namely the 26S proteasome and COP9 signalosome (CSN), respectively. Rpn11 and CSN5 are potential drug targets in cancer and selective inhibitors of both proteins have been reported in the literature. However, the selectivity of JAMM inhibitors (capzimin for RPN11 and CSN5i-3 for CSN5) has not been structurally resolved yet. In the present work, we have explored the binding modes of capzimin and CSN5i-3 and rationalize their selectivity for Rpn11 and CSN5 targets. We found that capzimin interacts with the active site Zn+2 of Rpn11 in a bidentate manner and also interacts with the residues in the distal ubiquitin binding site. MD simulations studies and binding energy analysis revealed that the selective binding of the inhibitors can be only explained by the consideration of larger heterodimeric complexes of Rpn11 (Rpn8-Rpn11) and CSN5 (CSN5-CSN6). Simulation of these protein-protein complexes is necessary to avoid unrealistic large conformational changes. The selective binding of inhibitors is mainly governed by residues in the distal ubiquitin binding site. This study demonstrates that selective inhibitor binding design for Rpn11 and CSN5 JAMM proteases requires consideration of heterodimeric protein-protein target structures.
| S-EPMC6189316 | biostudies-literature
Project description:The central role of kinases in virtually all signal transduction networks is the driving motivation for the development of compounds modulating their activity. ATP-mimetic inhibitors are essential tools for elucidating signaling pathways and are emerging as promising therapeutic agents. However, off-target ligand binding and complex and sometimes unexpected kinase/inhibitor relationships can occur for seemingly unrelated kinases, stressing that computational approaches are needed for learning the interaction determinants and for the inference of the effect of small compounds on a given kinase. Recently published high-throughput profiling studies assessed the effects of thousands of small compound inhibitors, covering a substantial portion of the kinome. This wealth of data paved the road for computational resources and methods that can offer a major contribution in understanding the reasons of the inhibition, helping in the rational design of more specific molecules, in the in silico prediction of inhibition for those neglected kinases for which no systematic analysis has been carried yet, in the selection of novel inhibitors with desired selectivity, and offering novel avenues of personalized therapies.
| S-EPMC4075008 | biostudies-literature
Project description:Signal transduction and gene regulation determine a major reorganization of metabolic activities in order to support cell proliferation. Protein Kinase B (PKB), also known as Akt, participates in the PI3K/Akt/mTOR pathway, a master regulator of aerobic glycolysis and cellular biosynthesis, two activities shown by both normal and cancer proliferating cells. Not surprisingly considering its relevance for cellular metabolism, Akt/PKB is often found hyperactive in cancer cells. In the last decade, many efforts have been made to improve the understanding of the control of glucose metabolism and the identification of a therapeutic window between proliferating cancer cells and proliferating normal cells. In this context, we have modeled the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production, and nucleotide biosynthesis. We used a computational model to compare two metabolic states generated by two different levels of signaling through the PI3K/Akt/mTOR pathway: one of the two states represents the metabolism of a growing cancer cell characterized by aerobic glycolysis and cellular biosynthesis, while the other state represents the same metabolic network with a reduced glycolytic rate and a higher mitochondrial pyruvate metabolism. Biochemical reactions that link glycolysis and pentose phosphate pathway revealed their importance for controlling the dynamics of cancer glucose metabolism.
| S-EPMC3502886 | biostudies-literature
Project description:IntroductionDeucravacitinib, a novel, oral, selective inhibitor of tyrosine kinase 2 (TYK2) signaling, acts via an allosteric mechanism by binding to the enzyme's regulatory domain instead of the catalytic domain. This unique binding provides high functional selectivity for TYK2 versus the closely related Janus kinases (JAKs) 1/2/3. Deucravacitinib was efficacious in phase 2 and 3 psoriasis trials, without clinical or laboratory parameters indicative of JAK 1/2/3 inhibition being observed. This analysis compared the kinase specificities of deucravacitinib versus JAK 1/2/3 inhibitors at therapeutic exposures.MethodsSignaling via JAK 1/3, JAK 2/2, and TYK2/JAK 2 dimers was measured in in vitro whole blood assays. Concentrations providing half-maximal inhibition (IC50) in these assays were determined for deucravacitinib and the JAK 1/2/3 inhibitors tofacitinib, upadacitinib, and baricitinib. Newly derived whole blood IC50 values were plotted against available pharmacokinetic profiles using doses evaluated in phase 2/3 trials. Simulated average daily inhibition and durations over which concentrations exceeded IC50 were evaluated.ResultsAt clinically relevant exposures, projected steady-state deucravacitinib plasma concentrations were higher than TYK2 IC50 for approximately 9-18 h. Maximal plasma concentrations (Cmax) of deucravacitinib were 8- to 17-fold lower than JAK 1/3 IC50 and > 48- to > 102-fold lower than JAK 2/2 IC50. Simulated daily average TYK2 inhibition by deucravacitinib ranged from 50% to 69%. Simulations indicated that tofacitinib, upadacitinib, and baricitinib at steady state exhibited varying degrees of JAK 1/3 (daily average inhibition, 70-94%) and JAK 2/2 (23%-67%) inhibition at therapeutic concentrations, with Cmax values 17- to 33-fold lower than their TYK2 IC50 levels.ConclusionAt clinically relevant doses and exposures, deucravacitinib demonstrates highly selective inhibition of TYK2 and not JAK 1/2/3. Tofacitinib, upadacitinib, and baricitinib variably inhibit JAK 1/2/3 but not TYK2. These results indicate that deucravacitinib is a distinct class of kinase inhibitor compared with JAK 1/2/3 inhibitors.
| S-EPMC8484413 | biostudies-literature