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Gonzalez2021 - QSAR Prediction Model for CYP2D6 Inhibitor and Substrate

ABSTRACT: Analysis of metabolic stability, determining the inhibition of CYP2D6 activity and whether the compounds are a substrate for the CYP2D6 enzyme. The data used to build these models has been publicly available at PubChem (AID1645840) by ADME@NCATS. Model Type: Predictive machine learning model. Model Relevance: Probability of inhibiting the CYP2D6 enzyme and probability of being a substrate of the enzyme Model Encoded by: Zakia Yahya (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos7nno

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2404160003 | BioModels | 2024-05-10

REPOSITORIES: BioModels

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Publications

Development of Robust Quantitative Structure-Activity Relationship Models for CYP2C9, CYP2D6, and CYP3A4 Catalysis and Inhibition.

Gonzalez Eric E Jain Sankalp S Shah Pranav P Torimoto-Katori Nao N Zakharov Alexey A Nguyễn Ðắc-Trung ÐT Sakamuru Srilatha S Huang Ruili R Xia Menghang M Obach R Scott RS Hop Cornelis E C A CECA Simeonov Anton A Xu Xin X

Drug metabolism and disposition: the biological fate of chemicals 20210628 9

Cytochrome P450 enzymes are responsible for the metabolism of >75% of marketed drugs, making it essential to identify the contributions of individual cytochromes P450 to the total clearance of a new candidate drug. Overreliance on one cytochrome P450 for clearance levies a high risk of drug-drug interactions; and considering that several human cytochrome P450 enzymes are polymorphic, it can also lead to highly variable pharmacokinetics in the clinic. Thus, it would be advantageous to understand ...[more]

PMID: 34183376

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Gonzalez2021 - QSAR Prediction Model for CYP3A4 Inhibitor and Substrate

Project description:Analysis of metabolic stability, determining the inhibition of CYP3A4 activity and whether the compounds are a substrate for the CYP3A4 enzyme. The data used to build these models has been publicly available at PubChem (AID1645841) by ADME@NCATS. Model Type: Predictive machine learning model. Model Relevance: The model predicts a chemical compound as an inihibitor and substrate of the CYP3A4 enzymes. Model Encoded by: Zakia Yahya (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos3ev6

2024-05-10 | MODEL2404160001 | BioModels

Gonzalez2021 - QSAR Prediction Model for CYP2C9 Inhibitor and Substrate

Project description:Analysis of metabolic stability, determining the inhibition of CYP2C9 activity and whether the compounds are a substrate for the CYP2C9 enzyme. The data used to build these models has been publicly available at PubChem (AID1645842) by ADME@NCATS. Model Type: Predictive machine learning model. Model Relevance: The model predicts a chemical compound as an inihibitor and substrate of the CYP2C9 enzymes Model Encoded by: Zakia Yahya (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos5jz9

2024-04-16 | MODEL2404160002 | BioModels

Gonzalez2021 - QSAR Prediction Model for CYP450 enzymes Inhibitor and Substrate

Project description:Analysis of metabolic stability, determining the inhibition of CYP450 activity and whether the compounds are a substrate for the CYP450 enzymes. The data used to build these models is publicly available at PubChem (AID1645840, AID1645841, AID1645842). The tested cyps include CYP2C9, CYP2D6 and CYP3A4. Model Type: Predictive machine learning model. Model Relevance: The model predicts a chemical compound as an inihibitor and substrate of the CYP450 enzymes. Model Encoded by: Zakia Yahya (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos44zp

2024-05-10 | MODEL2404160004 | BioModels

Proteomic QSAR analysis of calpain substrate specificity

Project description:Calpains are intracellular Ca2+-regulated cysteine proteases that are essential for various cellular functions. Mammalian conventional calpains (calpain-1 and calpain-2) modulate the structure and function of their substrates by limited proteolysis; however, their substrate specificity remains unclear because the amino acid (aa) sequences around their cleavage sites are very diverse. To clarify calpains’ substrate specificities, 84 20-mer oligopeptides, corresponding to P10-P10’ of reported cleavage site sequences, were proteolyzed by calpains, and the catalytic efficiencies (kcat/Km) were globally determined by LC/MS. This analysis revealed 483 cleavage site sequences, including 360 novel ones. The kcat/Kms for 119 sites ranged from 12.5~1,710 M-1s-1. Most sites were cleaved by both calpain-1 and -2 with a similar kcat/Km. The aa compositions of the novel sites were not significantly different from the 420 previously reported sites, suggesting calpains have a strict implicit rule for sequence specificity, and that the limited proteolysis of intact substrates is due to the substrates’ higher-order structures. Cleavage position frequencies indicated that longer sequences N-terminal to the cleavage site (P-sites) than C-terminal (P’-sites) were preferred for proteolysis. Quantitative structure-activity relationship (QSAR) analyses using partial least-squares regression and >1,300 aa descriptors achieved kcat/Km prediction with r=0.834, and binary-QSAR modeling attained 64.8% prediction accuracy for 132 reported calpain cleavage sites independent of our model construction. These results outperformed previous calpain cleavage predictors, and revealed the importance of the P2, P3’, P4’, and P1-P2 contexts. This study increases our understanding of calpain substrate specificities, and opens calpains to “next-generation,” i.e., activity-related quantitative and context-dependent analyses.

2016-07-19 | PXD002532 | Pride

substrate films

Project description:substrate films Raw sequence reads

| PRJNA792343 | ENA

Substrate sample

Project description:Substrate soil microbial community diversity

| PRJNA593085 | ENA

Molecular basis for substrate recruitment to the PRMT5 methylosome

Project description:PRMT5 is an essential arginine methyltransferase and a therapeutic target in MTAP null cancers. PRMT5 utilizes adaptor proteins for substrate recruitment through a previously undefined mechanism. Here, we identify an evolutionarily conserved peptide sequence shared among the three known substrate adaptors (CLNS1A, RIOK1 and COPR5) and show it is necessary and sufficient for interaction with PRMT5. We demonstrate that PRMT5 uses modular adaptor proteins containing a common binding motif for substrate recruitment, comparable to other enzyme classes such as kinases and E3 ligases. We structurally resolve the interface with PRMT5 and show via genetic perturbation that it is required for methylation of adaptor-recruited substrates including the spliceosome, histones, and ribosome assembly complexes. Further, disruption of this site affects Sm spliceosome stability and activity, leading to intron retention. Genetic disruption of the PRMT5-substrate adaptor interface leads to a hypomorphic decrease in growth of MTAP null tumor cells in vitro and in vivo, and is thus a novel site for development of therapeutic inhibitors of PRMT5.

2021-06-12 | GSE177036 | GEO

Aspartate Residues Far From the Active Site Drive O-GlcNAc Transferase Substrate Selection

Project description:Abstract: O-GlcNAc is an abundant post-translational modification found on nuclear and cytoplasmic proteins in all metazoans. This modification regulates a wide variety of cellular processes, and elevated O-GlcNAc levels have been implicated in cancer progression. A single essential enzyme, O-GlcNAc transferase (OGT), is responsible for all nucleocytoplasmic O-GlcNAcylation. Understanding how this enzyme chooses its substrates is critical for understanding, and potentially manipulating, its functions. Here we use protein microarray technology and proteome-wide glycosylation profiling to show that conserved aspartate residues in the tetratricopeptide repeat (TPR) lumen of OGT drive substrate selection. Changing these residues to alanines alters substrate selectivity and unexpectedly increases rates of protein glycosylation. Our findings support a model where sites of glycosylation for many OGT substrates are determined by TPR domain contacts to substrate side chains five to fifteen residues C-terminal to the glycosite. In addition to guiding design of inhibitors that target OGT's TPR domain, this information will inform efforts to engineer substrates to explore biological functions.

2019-07-24 | GSE132205 | GEO

Green roof substrate

Project description:Microbial community on modified green roof substrate

| PRJNA1178817 | ENA

Impact of substrate digestibility on microbial community

Project description:substrate digestibility

| PRJEB79632 | ENA