scholarly journals KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination

2020 ◽  
Author(s):  
Dominique Sydow ◽  
Paula Schmiel ◽  
Jérémie Mortier ◽  
Andrea Volkamer
Keyword(s):  
Drug Targets ◽  
Combinatorial Library ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Chemical Space ◽  
Design Strategies ◽  
Lipinski’S Rule ◽  
Fragment Library ◽  
Lipinski’S Rule Of Five ◽  
Using Data

Protein kinases play a crucial role in many cell signaling processes, making them one of the most important families of drug targets. In this context, fragment-based drug design strategies have been successfully applied to develop novel kinase inhibitors, usually following a knowledge-driven approach to optimize a focused set of fragments to a potent kinase inhibitor. <br>Alternatively, KinFragLib is a new method that allows to explore and extend the chemical space of kinase inhibitors using data-driven fragmentation and recombination, built on available structural kinome data from the KLIFS database for over 2,500 kinase DFG-in complexes. The computational fragmentation method splits the co-crystallized non-covalent kinase inhibitors into fragments with respect to their 3D proximity to six predefined functionally relevant subpocket centers. The resulting fragment library consists of six subpocket pools with over 7,000 fragments, available at https://github.com/volkamerlab/KinFragLib.<br>KinFragLib offers two main applications: (i) In-depth analyses of the chemical space of known kinase inhibitors, subpocket characteristics and connections, as well as (ii) subpocket-informed recombination of fragments to generate potential novel inhibitors. The latter showed that recombining only a subset of 624 representative fragments generated a combinatorial library of 6.7 million molecules, containing, besides some known kinase inhibitors, more than 99% novel chemical matter compared to ChEMBL and 63% molecules compliant with Lipinski's rule of five. <br>

scholarly journals KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination

2020 ◽  
Author(s):  
Dominique Sydow ◽  
Paula Schmiel ◽  
Jérémie Mortier ◽  
Andrea Volkamer
Keyword(s):  
Drug Targets ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Chemical Space ◽  
American Chemical Society ◽  
Chemical Society ◽  
Chemical Information ◽  
Design Strategies ◽  
Lipinski’S Rule ◽  
Using Data

Protein kinases play a crucial role in many cell signaling processes, making them one of the most important families of drug targets. In this context, fragment-based drug design strategies have been successfully applied to develop novel kinase inhibitors, usually following a knowledge-driven approach to optimize a focused set of fragments to a potent kinase inhibitor. <br>Alternatively, KinFragLib is a new method that allows to explore and extend the chemical space of kinase inhibitors using data-driven fragmentation and recombination, built on available structural kinome data from the KLIFS database for over 2,500 kinase DFG-in complexes. The computational fragmentation method splits the co-crystallized non-covalent kinase inhibitors into fragments with respect to their 3D proximity to six predefined functionally relevant subpocket centers. The resulting fragment library consists of six subpocket pools with over 7,000 fragments, available at https://github.com/volkamerlab/KinFragLib.<br><div>KinFragLib offers two main applications: (i) In-depth analyses of the chemical space of known kinase inhibitors, subpocket characteristics and connections, as well as (ii) subpocket-informed recombination of fragments to generate potential novel inhibitors. The latter showed that recombining only a subset of 624 representative fragments generated a combinatorial library of 6.7 million molecules, containing, besides some known kinase inhibitors, more than 99% novel chemical matter compared to ChEMBL and 63% molecules compliant with Lipinski's rule of five.</div><div><b><br></b></div><div><b>Note: </b>This document is the unedited Author’s version of a Submitted Work that was subsequently accepted for publication in the Journal of Chemical Information and Modeling, copyright © American Chemical Society after peer review. To access the final edited and published work see https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c00839.<br> </div>

scholarly journals KinaFrag explores the kinase-ligand fragment interaction space for selective kinase inhibitor discovery

2021 ◽  
Author(s):  
Zhi-Zheng Wang ◽  
Xing-Xing Shi ◽  
Fan Wang ◽  
Ge-Fei Hao ◽  
Guang-Fu Yang
Keyword(s):  
Drug Targets ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Structural Information ◽  
3D Structure ◽  
Interaction Space ◽  
Design Strategies ◽  
Growing Strategy ◽  
Fragment Interaction

Protein kinases play a crucial role in many cellular signaling processes, making them one of the most important families of drug targets. But selectivity put a barrier at the design of kinase inhibitors. Fragment-based drug design strategies have been successfully applied to develop novel selective kinase inhibitors. However, the complicate kinase-fragment interaction and fragment-to-lead process pose challenges to fragment-based kinase discovery. Here, we developed a web source KinaFrag to investigate kinase-fragment interaction space and perform fragment-to-lead optimization. KinaFrag contained 31464 fragments from reported kinase inhibitors, which involved 3244 crystal fragment structures and 7783 crystal kinase-fragment complexes. These crystal fragments were classified by their binding cleft and subpockets, and their 3D structure and interactions were displayed in KinaFrag. In addition, the structural information, physicochemical information, similarity information, and substructure relationship information were contained in KinaFrag. Moreover, a computational fragment growing strategy obviously developed by our group was implemented in the KinaFrag. We test this fragment growing strategy using our fragment libraries, and obtained a lead compound of c-Met with ~1000-fold in vitro activity improvement compared with the hit compound. We hope KinaFrag could become a powerful tool for the fragment-based kinase inhibitor design. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

scholarly journals Targeting USP47 overcomes tyrosine kinase inhibitor resistance and eradicates leukemia stem/progenitor cells in chronic myelogenous leukemia

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hu Lei ◽  
Han-Zhang Xu ◽  
Hui-Zhuang Shan ◽  
Meng Liu ◽  
Ying Lu ◽  
...  
Keyword(s):  
Tyrosine Kinase ◽  
Progenitor Cells ◽  
Chronic Myelogenous Leukemia ◽  
Drug Targets ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Myelogenous Leukemia ◽  
Tki Resistance

AbstractIdentifying novel drug targets to overcome resistance to tyrosine kinase inhibitors (TKIs) and eradicating leukemia stem/progenitor cells are required for the treatment of chronic myelogenous leukemia (CML). Here, we show that ubiquitin-specific peptidase 47 (USP47) is a potential target to overcome TKI resistance. Functional analysis shows that USP47 knockdown represses proliferation of CML cells sensitive or resistant to imatinib in vitro and in vivo. The knockout of Usp47 significantly inhibits BCR-ABL and BCR-ABLT315I-induced CML in mice with the reduction of Lin−Sca1+c-Kit+ CML stem/progenitor cells. Mechanistic studies show that stabilizing Y-box binding protein 1 contributes to USP47-mediated DNA damage repair in CML cells. Inhibiting USP47 by P22077 exerts cytotoxicity to CML cells with or without TKI resistance in vitro and in vivo. Moreover, P22077 eliminates leukemia stem/progenitor cells in CML mice. Together, targeting USP47 is a promising strategy to overcome TKI resistance and eradicate leukemia stem/progenitor cells in CML.

scholarly journals Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging

2021 ◽  
Author(s):  
Grigorii V. Andrianov ◽  
Wern Juin Gabriel Ong ◽  
Ilya Serebriiskii ◽  
John Karanicolas
Keyword(s):  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Chemical Space ◽  
Early Stage ◽  
Building Blocks ◽  
Lead Optimization ◽  
Chemical Transformations ◽  
Interaction Energies ◽  
The Individual ◽  
New Compounds

In early stage drug discovery, the stage of hit-to-lead optimization (or "hit expansion") entails starting from a newly-identified active compound, and improving its potency or other properties. Traditionally this process relies on synthesizing and evaluating a series of analogs to build up structure-activity relationships. Here, we describe a computational strategy focused on kinase inhibitors, intended to expedite the process of identifying analogs with improved potency. Our protocol begins from an inhibitor of the target kinase, and generalizes the synthetic route used to access it. By searching for commercially-available replacements for the individual building blocks used to make the parent inhibitor, we compile an enumerated library of compounds that can be accessed using the same chemical transformations; these huge libraries can exceed many millions - or billions - of compounds. Because the resulting libraries are much too large for explicit virtual screening, we instead consider alternate approaches to identify the top-scoring compounds. We find that contributions from individual substituents are well-described by a pairwise additivity approximation, provided that the corresponding fragments position their shared core in precisely the same way relative to the binding site. This key insight allows us to determine which fragments are suitable for merging into a single new compounds, and which are not. Further, the use of the pairwise approximation allows interaction energies to be assigned to each compound in the library, without the need for any further structure-based modeling: interaction energies instead can be reliably estimated from the energies of the component fragments. We demonstrate this protocol using libraries built from five representative kinase inhibitors drawn from the literature, which target four different kinases: CDK9, CHK1, CDK2, and ACK1. In each example, the enumerated library includes additional analogs reported by the original study to have activity, and these analogs are successfully prioritized within the library. We envision that the insights from this work can facilitate the rapid assembly and screening of increasingly large libraries for focused hit-to-lead optimization. To enable adoption of these methods and to encourage further analyses, we disseminate the computational tools needed to deploy this protocol.

scholarly journals Comparative Assessment of Protein Kinase Inhibitors in Public Databases and in PKIDB

Molecules ◽  
2020 ◽  
Vol 25 (14) ◽  
pp. 3226 ◽  
Author(s):  
Colin Bournez ◽  
Fabrice Carles ◽  
Gautier Peyrat ◽  
Samia Aci-Sèche ◽  
Stéphane Bourg ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Protein Kinase ◽  
Protein Kinases ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Chemical Space ◽  
Principal Component ◽  
Great Majority ◽  
Protein Kinase Inhibitors ◽  
Principal Moments Of Inertia

Since the first approval of a protein kinase inhibitor (PKI) by the Food and Drug Administration (FDA) in 2001, 55 new PKIs have reached the market, and many inhibitors are currently being evaluated in clinical trials. This is a clear indication that protein kinases still represent major drug targets for the pharmaceutical industry. In a previous work, we have introduced PKIDB, a publicly available database, gathering PKIs that have already been approved (Phase 4), as well as those currently in clinical trials (Phases 0 to 3). This database is updated frequently, and an analysis of the new data is presented here. In addition, we compared the set of PKIs present in PKIDB with the PKIs in early preclinical studies found in ChEMBL, the largest publicly available chemical database. For each dataset, the distribution of physicochemical descriptors related to drug-likeness is presented. From these results, updated guidelines to prioritize compounds for targeting protein kinases are proposed. The results of a principal component analysis (PCA) show that the PKIDB dataset is fully encompassed within all PKIs found in the public database. This observation is reinforced by a principal moments of inertia (PMI) analysis of all molecules. Interestingly, we notice that PKIs in clinical trials tend to explore new 3D chemical space. While a great majority of PKIs is located on the area of “flatland”, we find few compounds exploring the 3D structural space. Finally, a scaffold diversity analysis of the two datasets, based on frequency counts was performed. The results give insight into the chemical space of PKIs, and can guide researchers to reach out new unexplored areas. PKIDB is freely accessible from the following website: http://www.icoa.fr/pkidb.

scholarly journals Development of an Antivirulence Drug againstStreptococcus mutans: Repression of Biofilm Formation, Acid Tolerance, and Competence by a Histidine Kinase Inhibitor, Walkmycin C

2011 ◽  
Vol 55 (4) ◽  
pp. 1475-1484 ◽  
Author(s):  
Yoko Eguchi ◽  
Norihiro Kubo ◽  
Hiroko Matsunaga ◽  
Masayuki Igarashi ◽  
Ryutaro Utsumi
Keyword(s):  
Histidine Kinase ◽  
Biofilm Formation ◽  
Drug Targets ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Acid Tolerance ◽  
Gene Clusters ◽  
Pathogenic Potential ◽  
Acid Sensitivity

ABSTRACTTwo-component signal transduction systems (TCSs) in prokaryotes often regulate gene clusters that induce pathogenicity, and thus they have frequently been proposed as potential drug targets for attenuating the virulence of pathogens. The pathogenic potential ofStreptococcus mutans, the major etiological pathogen of dental caries, is also regulated by its TCSs. The object of this study was to evaluate the effect of a histidine kinase (HK) inhibitor against two major virulence factors ofS. mutans: biofilm formation and acid tolerance. Walkmycin C (WKM C), an HK inhibitor isolated from the screening of inhibitors against WalK HK inBacillus subtilis, inhibited thein vitroautophosphorylation activity of three purifiedS. mutansHKs, i.e., VicK, CiaH, and LiaS. AlthoughS. mutansdoes not have any essential HK but only an essential response regulator, VicR, WKM C showed an MIC of 6.25 μg/ml. This inhibitory effect of WKM C suggests that blocking the autophosphorylation of multiple HKs may inhibit phosphotransfer to VicR from VicK and other HKs. When WKM C was added at sub-MIC levels, the cells formed abnormal biofilms and also showed a defect in competence. When the cells were pretreated with WKM C, an increase in acid sensitivity was observed. Our results show that WKM C represses two pathogenic phenotypes ofS. mutans, indicating the possibility of developing histidine kinase inhibitors into antivirulence drugs.

scholarly journals The Clinical Kinase Index: Prioritizing Understudied Kinases as Targets for the Treatment of Cancer

2020 ◽  
Author(s):  
Derek Essegian ◽  
Rimpi Khurana ◽  
Vasileios Stathias ◽  
Stephan C. Schürer
Keyword(s):  
Drug Targets ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
The Cancer Genome Atlas ◽  
Clinical Value ◽  
Target Class ◽  
Hotspot Analysis ◽  
Oncological Treatment ◽  
Cancer Genome Atlas ◽  
Differential Gene

AbstractThe approval of the first kinase inhibitor, Gleevec, in 2001, ushered in a paradigm shift for oncological treatment—the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the established role deregulated kinase activity plays in cancer, only 8% of the entire kinome has been effectively “drugged”. Moreover, a quarter of the 634 human kinases are vastly understudied. We have developed a comprehensive scoring system which utilizes differential gene expression, clinical and pathological parameters, overall survival and mutational hotspot analysis to rank and prioritize clinically-relevant kinase targets across 17 solid tumor cancers from The Cancer Genome Atlas (TCGA). Collectively, we report that dark kinases have potential clinical value as biomarkers or as new drug targets that warrant further study.

scholarly journals The Use of Traditional Chinese Medicine in Relieving EGFR-TKI-Associated Diarrhea Based on Network Pharmacology and Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shuaihang Hu ◽  
Wenchao Dan ◽  
Jinlei Liu ◽  
Peng Ha ◽  
Tong Zhou ◽  
...  
Keyword(s):  
Data Mining ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Drug Targets ◽  
Kinase Inhibitor ◽  
Molecular Structures ◽  
Network Pharmacology ◽  
Lipinski’S Rule ◽  
The Core ◽  
Egfr Tki

In this study, the role of traditional Chinese medicine (TCM) in relieving epidermal growth factor receptor-tyrosine kinase inhibitor- (EGFR-TKI-) associated diarrhea was discussed by network pharmacology and data mining. Prediction of drug targets by introducing the EGFR-TKI molecular structures into the SwissTargetPrediction platform and diarrhea-related targets in the DrugBank, GeneCards, DisGeNET, and OMIM databases were obtained. Compounds in the drug-disease target intersection were screened by absorption, distribution, metabolism, and excretion parameters and Lipinski’s rule in Traditional Chinese Medicine Systems Pharmacology. TCM-containing compounds were selected, and information on the property, taste, and meridian tropism of these TCMs was summarized and analyzed. A target-compound-TCM network diagram was constructed, and core targets, compounds, and TCMs were selected. The core targets and components were docked by AutoDock Vina (Version 1.1.2) to explore the target combinations of related compounds and evaluate the docking activity of related targets and compounds. Twenty-three potential therapeutic TCM targets for the treatment of EGFR-TKI-related diarrhea were obtained. There were 339 compounds acting on potential therapeutic targets, involving a total of 402 TCMs. The results of molecular docking showed good binding between the core targets and compounds, and the binding between the core targets and compounds was similar to that of the core target and the recommended drug loperamide. TCMs have multitarget characteristics and are present in a variety of compounds used for relieving EGFR-TKI-associated diarrhea. Antitumor activity and the efficacy of alleviating diarrhea are the pharmacological basis of combining TCMs with EGFR-TKI in the treatment of non-small-cell lung cancer. The core targets, compounds, and TCMs can provide data to support experimental and clinical studies on the relief of EGFR-TKI-associated diarrhea in the future.

scholarly journals Survival impact of adherence to tyrosine kinase inhibitor in chronic myeloid leukemia

2021 ◽  
Vol 36 (6) ◽  
pp. 1450-1458
Author(s):  
Yundeok Kim ◽  
Tae-Hwa Go ◽  
Jaeyeon Jang ◽  
Jii Bum Lee ◽  
Seung Taek Lim ◽  
...  
Keyword(s):  
Chronic Myeloid Leukemia ◽  
Tyrosine Kinase ◽  
Myeloid Leukemia ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Medication Possession Ratio ◽  
High Income ◽  
High Adherence ◽  
Male Sex ◽  
Using Data

Background/Aims: Adherence to tyrosine kinase inhibitors (TKIs) has become a critical aspect of care in chronic myeloid leukemia (CML). We aimed to examine the association of TKI adherence with overall survival (OS) outcomes in Korean patients diagnosed with CML and treated with TKIs using data from the National Health Information Database.Methods: This study included 2,870 CML patients diagnosed between 2005 and 2013. Drug adherence was evaluated according to the medication possession ratio (MPR) and classified as high adherence (i.e., MPR ≥ 0.95 [upper 50%]), moderate adherence (i.e., MPR ≥ 0.68 and < 0.95 [middle 25%]), and low adherence (i.e., MPR < 0.68 [lower 25%]).Results: The median MPR was 0.95 (range, 0 to 4.67). Male sex (p = 0.003), age < 70 years (p < 0.001), high income (≥ 30%, p < 0.001), and maintaining frontline TKI (< 0.001) were associated with better adherence. Adherence to dasatinib was the lowest (vs. imatinib or nilotinib, p < 0.001). Compared with high MPR patients, those with moderate MPR (hazard ratio [HR], 4.90; 95% confidence interval [CI], 3.87 to 6.19; p < 0.001) and low MPR (HR, 11.6; 95% CI, 9.35 to 14.42; p < 0.001) had poorer OS.Conclusions: Adherence to TKI treatment is an important factor predicting survival outcomes in Korean CML patients. Male sex, age < 70 years, high income, and maintaining frontline TKI are associated with high adherence to TKI. Thus, those without these characteristics should be closely monitored for treatment adherence.

Towards a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning

2019 ◽  
Author(s):  
Seoin Back ◽  
Kevin Tran ◽  
Zachary Ulissi
Keyword(s):  
Machine Learning ◽  
Oxygen Evolution ◽  
Active Sites ◽  
Density Functional ◽  
Chemical Space ◽  
Density Functional Theory Calculations ◽  
Catalyst Design ◽  
Transition Metal Catalysts ◽  
Oxide Materials ◽  
Design Strategies

<div> <div> <div> <div><p>Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we pro- vide catalyst design strategies to improve catalytic activity of Ir based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.<br></p></div></div></div></div><div><div><div> </div> </div> </div>

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