scholarly journals kinCSM: using graph-based signatures to predict small molecule CDK2 kinase inhibitors

2021 ◽  
Author(s):  
Yunzhuo Zhou ◽  
Raghad Al-Jarf ◽  
Azadeh Alavi ◽  
Thanh Binh Nguyen ◽  
Carlos H. M. Rodrigues ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Function ◽  
Molecular Mechanisms ◽  
Kinase Inhibitors ◽  
Correlation Coefficients ◽  
Cost Effective ◽  
Blind Test ◽  
Invaluable Tool ◽  
Inhibition Constants ◽  
Matthew’S Correlation Coefficient

Abstract Protein phosphorylation acts as an essential on/off switch in many cellular signalling pathways, regulating protein function. This has led to ongoing interest in targeting kinases for therapeutic intervention. Computer-aided drug discovery has been proven a useful and cost-effective approach for facilitating prioritisation and enrichment of screening libraries. Limited effort, however, has been devoted to developing and tailoring in silico tools to assist the development of kinase inhibitors and providing relevant insights on what makes potent inhibitors. To fill this gap, here we developed kinCSM, an integrative computational tool capable of accurately identifying potent cyclin-dependent kinase 2 (CDK2) inhibitors, quantitatively predicting CDK2 ligand-kinase inhibition constants (pKi) and classify inhibition modes without kinase information. kinCSM predictive models were built using supervised learning and leveraged the concept of graph-based signatures to capture both physicochemical properties and geometry properties of small molecules. CDK2 inhibitors were accurately identified with Matthew’s Correlation Coefficients of up to 0.74, and inhibition constants predicted with Pearson’s correlation of up to 0.76, both with consistent performances of 0.66 and 0.68 on non-redundant blind tests, respectively. kinCSM was also able to identify the potential type of inhibition for a given molecule, achieving Matthew’s Correlation Coefficient of up to 0.80 on cross-validation and 0.73 on blind test. Analysing the molecular composition of kinase inhibitors revealed enriched chemical fragments in potent CDK2 inhibitors and different types of inhibitors, which provides insights into the molecular mechanisms behind ligand-kinase interactions. We believe kinCSM will be an invaluable tool to guide future kinase drug discovery. To aid the fast and accurate screening of potent CDK2 kinase inhibitors, we made kinCSM freely available online at http://biosig.unimelb.edu.au/kin_csm/.

scholarly journals An integration of deep learning with feature embedding for protein–protein interaction prediction

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7126 ◽  
Author(s):  
Yu Yao ◽  
Xiuquan Du ◽  
Yanyu Diao ◽  
Huaixu Zhu
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Function ◽  
Molecular Mechanisms ◽  
Protein Protein Interactions ◽  
Model Combining ◽  
Representation Method ◽  
Structure Knowledge ◽  
Matthew’S Correlation Coefficient

Protein–protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein–protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein–protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline to infer protein–protein interactions, even when protein structure knowledge is entirely unknown. The proposed method DeepFE-PPI is evaluated on the S. Cerevisiae and human datasets. The experimental results show that DeepFE-PPI achieves 94.78% (accuracy), 92.99% (recall), 96.45% (precision), 89.62% (Matthew’s correlation coefficient, MCC) and 98.71% (accuracy), 98.54% (recall), 98.77% (precision), 97.43% (MCC), respectively. In addition, we also evaluate the performance of DeepFE-PPI on five independent species datasets and all the results are superior to the existing methods. The comparisons show that DeepFE-PPI is capable of predicting protein–protein interactions by a novel residue representation method and a deep learning classification framework in an acceptable level of accuracy. The codes along with instructions to reproduce this work are available from https://github.com/xal2019/DeepFE-PPI.

scholarly journals C. elegans PVD Neurons: A Platform for Functionally Validating and Characterizing Neuropsychiatric Risk Genes

2016 ◽  
Author(s):  
Cristina Aguirre-Chen ◽  
Nuri Kim ◽  
Olivia Mendivil Ramos ◽  
Melissa Kramer ◽  
W. Richard McCombie ◽  
...  
Keyword(s):  
Protein Function ◽  
Molecular Mechanisms ◽  
Neuronal Development ◽  
De Novo ◽  
Genetic Interaction ◽  
Cost Effective ◽  
Chromatin Remodelers ◽  
C Elegans ◽  
Effective Manner

AbstractOne of the primary challenges in the field of psychiatric genetics is the lack of an in vivo model system in which to functionally validate candidate neuropsychiatric risk genes (NRGs) in a rapid and cost-effective manner1−3. To overcome this obstacle, we performed a candidate-based RNAi screen in which C. elegans orthologs of human NRGs were assayed for dendritic arborization and cell specification defects using C. elegans PVD neurons. Of 66 NRGs, identified via exome sequencing of autism (ASD)4 or schizophrenia (SCZ)5−9 probands and whose mutations are de novo and predicted to result in a complete or partial loss of protein function, the C. elegans orthologs of 7 NRGs were found to be required for proper neuronal development and represent a variety of functional classes, including transcriptional regulators and chromatin remodelers, molecular chaperones, and cytoskeleton-related proteins. Notably, the positive hit rate, when selectively assaying C. elegans orthologs of ASD and SCZ NRGs, is enriched >14-fold as compared to unbiased RNAi screening10. Furthermore, we find that RNAi phenotypes associated with the depletion of NRG orthologs is recapitulated in genetic mutant animals, and, via genetic interaction studies, we show that the NRG ortholog of ANK2, unc-44, is required for SAX-7/MNR-1/DMA-1 signaling. Collectively, our studies demonstrate that C. elegans PVD neurons are a tractable model in which to discover and dissect the fundamental molecular mechanisms underlying neuropsychiatric disease pathogenesis.

scholarly journals Tackling Acute Lymphoblastic Leukemia—One Fish at a Time

2019 ◽  
Vol 20 (21) ◽  
pp. 5313
Author(s):  
Arpan A. Sinha ◽  
Gilseung Park ◽  
J. Kimble Frazer
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Drug Discovery ◽  
Molecular Mechanisms ◽  
Genetic Manipulation ◽  
Lymphoblastic Leukemia ◽  
In Vivo Studies ◽  
Invaluable Tool ◽  
Molecular Landscape ◽  
Combination Regimens

Despite advancements in the diagnosis and treatment of acute lymphoblastic leukemia (ALL), a need for improved strategies to decrease morbidity and improve cure rates in relapsed/refractory ALL still exists. Such approaches include the identification and implementation of novel targeted combination regimens, and more precise upfront patient risk stratification to guide therapy. New curative strategies rely on an understanding of the pathobiology that derives from systematically dissecting each cancer’s genetic and molecular landscape. Zebrafish models provide a powerful system to simulate human diseases, including leukemias and ALL specifically. They are also an invaluable tool for genetic manipulation, in vivo studies, and drug discovery. Here, we highlight and summarize contributions made by several zebrafish T-ALL models and newer zebrafish B-ALL models in translating the underlying genetic and molecular mechanisms operative in ALL, and also highlight their potential utility for drug discovery. These models have laid the groundwork for increasing our understanding of the molecular basis of ALL to further translational and clinical research endeavors that seek to improve outcomes in this important cancer.

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

scholarly journals The impact of FGF19/FGFR4 signaling inhibition in antitumor activity of multi-kinase inhibitors in hepatocellular carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroaki Kanzaki ◽  
Tetsuhiro Chiba ◽  
Junjie Ao ◽  
Keisuke Koroki ◽  
Kengo Kanayama ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Molecular Mechanisms ◽  
Kinase Inhibitors ◽  
Progression Free Survival ◽  
Erk Signaling ◽  
Enzyme Linked Immunosorbent Assay ◽  
Antitumor Effects ◽  
The Impact

AbstractFGF19/FGFR4 autocrine signaling is one of the main targets for multi-kinase inhibitors (MKIs). However, the molecular mechanisms underlying FGF19/FGFR4 signaling in the antitumor effects to MKIs in hepatocellular carcinoma (HCC) remain unclear. In this study, the impact of FGFR4/ERK signaling inhibition on HCC following MKI treatment was analyzed in vitro and in vivo assays. Serum FGF19 in HCC patients treated using MKIs, such as sorafenib (n = 173) and lenvatinib (n = 40), was measured by enzyme-linked immunosorbent assay. Lenvatinib strongly inhibited the phosphorylation of FRS2 and ERK, the downstream signaling molecules of FGFR4, compared with sorafenib and regorafenib. Additional use of a selective FGFR4 inhibitor with sorafenib further suppressed FGFR4/ERK signaling and synergistically inhibited HCC cell growth in culture and xenograft subcutaneous tumors. Although serum FGF19high (n = 68) patients treated using sorafenib exhibited a significantly shorter progression-free survival and overall survival than FGF19low (n = 105) patients, there were no significant differences between FGF19high (n = 21) and FGF19low (n = 19) patients treated using lenvatinib. In conclusion, robust inhibition of FGF19/FGFR4 is of importance for the exertion of antitumor effects of MKIs. Serum FGF19 levels may function as a predictive marker for drug response and survival in HCC patients treated using sorafenib.

scholarly journals Druggable Transient Pockets in Protein Kinases

Molecules ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 651
Author(s):  
Koji Umezawa ◽  
Isao Kii
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Protein Kinases ◽  
Binding Sites ◽  
Kinase Inhibitors ◽  
Screening Methods ◽  
Research Attention ◽  
Folding Process ◽  
Chemical Screening ◽  
Inhibitory Mechanisms

Drug discovery using small molecule inhibitors is reaching a stalemate due to low selectivity, adverse off-target effects and inevitable failures in clinical trials. Conventional chemical screening methods may miss potent small molecules because of their use of simple but outdated kits composed of recombinant enzyme proteins. Non-canonical inhibitors targeting a hidden pocket in a protein have received considerable research attention. Kii and colleagues identified an inhibitor targeting a transient pocket in the kinase DYRK1A during its folding process and termed it FINDY. FINDY exhibits a unique inhibitory profile; that is, FINDY does not inhibit the fully folded form of DYRK1A, indicating that the FINDY-binding pocket is hidden in the folded form. This intriguing pocket opens during the folding process and then closes upon completion of folding. In this review, we discuss previously established kinase inhibitors and their inhibitory mechanisms in comparison with FINDY. We also compare the inhibitory mechanisms with the growing concept of “cryptic inhibitor-binding sites.” These sites are buried on the inhibitor-unbound surface but become apparent when the inhibitor is bound. In addition, an alternative method based on cell-free protein synthesis of protein kinases may allow the discovery of small molecules that occupy these mysterious binding sites. Transitional folding intermediates would become alternative targets in drug discovery, enabling the efficient development of potent kinase inhibitors.

scholarly journals Drug Tolerance to EGFR Tyrosine Kinase Inhibitors in Lung Cancers with EGFR Mutations

Cells ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1590
Author(s):  
Kenichi Suda ◽  
Tetsuya Mitsudomi
Keyword(s):  
Lung Cancer ◽  
Tyrosine Kinase ◽  
Tyrosine Kinase Inhibitors ◽  
Molecular Mechanisms ◽  
Kinase Inhibitors ◽  
Drug Tolerance ◽  
Lung Cancers ◽  
Egfr Tyrosine Kinase ◽  
Egfr Tyrosine Kinase Inhibitors ◽  
Egfr Mutated

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) serve as the standard of care for the first-line treatment of patients with lung cancers with EGFR-activating mutations. However, the acquisition of resistance to EGFR TKIs is almost inevitable, with extremely rare exceptions, and drug-tolerant cells (DTCs) that demonstrate reversible drug insensitivity and that survive the early phase of TKI exposure are hypothesized to be an important source of cancer cells that eventually acquire irreversible resistance. Numerous studies on the molecular mechanisms of drug tolerance of EGFR-mutated lung cancers employ lung cancer cell lines as models. Here, we reviewed these studies to generally describe the features, potential origins, and candidate molecular mechanisms of DTCs. The rapid development of an optimal treatment for EGFR-mutated lung cancer will require a better understanding of the underlying molecular mechanisms of the drug insensitivity of DTCs.

scholarly journals Examination of the Transcriptional Response to LaMIR166a Overexpression in Larix kaempferi (Lamb.) Carr

Biology ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 576
Author(s):  
Yanru Fan ◽  
Wanfeng Li ◽  
Zhexin Li ◽  
Shaofei Dang ◽  
Suying Han ◽  
...  
Keyword(s):  
Cell Lines ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
De Novo ◽  
Pearson Correlation ◽  
Transcriptional Response ◽  
Correlation Coefficients ◽  
Embryonic Cell ◽  
Larix Kaempferi ◽  
Transgenic Cell

The study of somatic embryogenesis can provide insight into early plant development. We previously obtained LaMIR166a-overexpressing embryonic cell lines of Larix kaempferi (Lamb.) Carr. To further elucidate the molecular mechanisms associated with miR166 in this species, the transcriptional profiles of wild-type (WT) and three LaMIR166a-overexpressing transgenic cell lines were subjected to RNA sequencing using the Illumina NovaSeq 6000 system. In total, 203,256 unigenes were generated using Trinity de novo assembly, and 2467 differentially expressed genes were obtained by comparing transgenic and WT lines. In addition, we analyzed the cleaved degree of LaMIR166a target genes LaHDZ31–34 in different transgenic cell lines by detecting the expression pattern of LaHdZ31–34, and their cleaved degree in transgenic cell lines was higher than that in WT. The downstream genes of LaHDZ31–34 were identified using Pearson correlation coefficients. Yeast one-hybrid and dual-luciferase report assays revealed that the transcription factors LaHDZ31–34 could bind to the promoters of LaPAP, LaPP1, LaZFP5, and LaPHO1. This is the first report of gene expression changes caused by LaMIR166a overexpression in Japanese larch. These findings lay a foundation for future studies on the regulatory mechanism of miR166.

scholarly journals Reproducibility and relative validity of a semi-quantitative food frequency questionnaire for the Chinese lactating mothers

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ye Ding ◽  
Fang Li ◽  
Ping Hu ◽  
Mei Ye ◽  
Fangping Xu ◽  
...  
Keyword(s):  
Food Frequency Questionnaire ◽  
Dietary Assessment ◽  
Correlation Coefficients ◽  
Cost Effective ◽  
Assessment Method ◽  
Food Frequency ◽  
Relative Validity ◽  
Lactating Mothers ◽  
Dietary Record ◽  
Spearman’S Correlation

Abstract Background The dietary nutritional status of the lactating mothers is related to maternal health and has a significant impact on the growth and development of infants through the secretion of breast milk. The food frequency questionnaire (FFQ) is the most cost-effective dietary assessment method that can help obtain information on the usual dietary pattern of participants. Until now, the FFQs have been used for different populations in China, but there are few FFQs available for the lactating mothers. We aimed to develop a semi-quantitative, 156-item FFQ for the Chinese lactating mothers, and evaluate its reproducibility and relative validity. Methods A total of 112 lactating mothers completed two FFQs and one 3-d dietary record (3DR). The first FFQ (FFQ1) was conducted during postpartum at 60–65 days and the second FFQ (FFQ2) during subsequent follow-up at 5 weeks. The 3DR was completed with portion sizes assessed using photographs taken by the respondent before and after eating (instant photography) 1 week after FFQ1. Results For reproducibility, the Spearman’s correlation coefficients for food ranged from 0.34 to 0.68, and for nutrients from 0.25 to 0.61. Meanwhile, the intra-class correlation coefficients for food ranged from 0.48 to 0.87, and for nutrients from 0.27 to 0.70. For relative validity, the Spearman’s correlation coefficients for food ranged from 0.32 to 0.56, and for nutrients from 0.23 to 0.72. The energy-adjusted coefficients for food ranged from 0.26 to 0.55, and for nutrients from 0.22 to 0.47. Moreover, the de-attenuation coefficients for food ranged from 0.34 to 0.67, and for nutrients from 0.28 to 0.77. The Bland-Altman plots also showed reasonably acceptable agreement between the two methods. Conclusions This FFQ is a reasonably reproducible and a relative valid tool for assessing dietary intake of the Chinese lactating mothers.

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