Dataset Information

Machine Learning Models to Interrogate Proteome-wide Cysteine Ligandabilities.

ABSTRACT: Machine learning (ML) identification of covalently ligandable sites may significantly accelerate targeted covalent inhibitor discoveries and expand the druggable proteome space. Here we report the development of the tree-based models and convolutional neural networks trained on a newly curated database (LigCys3D) of over 1,000 liganded cysteines in nearly 800 proteins represented by over 10,000 X-ray structures as reported in the protein data bank (PDB). The unseen tests yielded 94% AUC (area under the receiver operating characteristic curve), demonstrating the highly predictive power of the models. Interestingly, application to the proteins evaluated by the activity-based protein profiling (ABPP) experiments in cell lines gave a lower AUC of 72%. Analysis revealed significant discrepancies in the structural environment of the ligandable cysteines captured by X-ray crystallography and those determined by ABPP. This surprising finding warrants further investigations and may have implications for future drug discoveries. We discuss ways to improve the models and project future directions. Our work represents a first step towards the ML-led integration of big genome data, structure models, and chemoproteomic experiments to annotate the human proteome space for the next-generation drug discoveries.

SUBMITTER: Liu R

PROVIDER: S-EPMC10473668 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

Machine Learning Models to Interrogate Proteomewide Covalent Ligandabilities Directed at Cysteines.

Liu Ruibin R Clayton Joseph J Shen Mingzhe M Bhatnagar Shubham S Shen Jana J

bioRxiv : the preprint server for biology 20240107

Machine learning (ML) identification of covalently ligandable sites may accelerate targeted covalent inhibitor design and help expand the druggable proteome space. Here we report the rigorous development and validation of the tree-based models and convolutional neural networks (CNNs) trained on a newly curated database (LigCys3D) of over 1,000 liganded cysteines in nearly 800 proteins represented by over 10,000 three-dimensional structures in the protein data bank. The unseen tests yielded 94% a ...[more]

PMID: 37662346

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Project description:BACKGROUND AND PURPOSE:MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. MATERIALS AND METHODS:We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS:Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%). CONCLUSIONS:Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

Dataset Information

Machine Learning Models to Interrogate Proteome-wide Cysteine Ligandabilities.

Publications

Machine Learning Models to Interrogate Proteomewide Covalent Ligandabilities Directed at Cysteines.

Similar Datasets

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