Classifying Metal-Binding Sites with Neural Networks

Oostrom, Marjolein

doi:10.25584/1923007

Title: Classifying Metal-Binding Sites with Neural Networks

Dataset
Other Related Research

Abstract

A pre-trained convolutional neural network was fine-tuned for three separate classification tasks, distinguishing 2D images of: 1) single amino acids, 2) protein structural ball and stick images of metalloproteins, and 3) protein structural ball and stick images of metalloenzymes with the metal cofactors removed. Images used in the training, testing and validation are shared here.

Authors:

PNNL

Publication Date:: Wed Sep 07 04:00:00 UTC 2022

DOE Contract Number:: AC05-76RL01830

Research Org.:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE Office of Science (SC)

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

OSTI Identifier:: 1923007

DOI:: https://doi.org/10.25584/1923007

Citation Formats


                    Oostrom, Marjolein. Classifying Metal-Binding Sites with Neural Networks.  United States: N. p., 2022. 
        Web.  doi:10.25584/1923007.

Copy to clipboard


                    Oostrom, Marjolein. Classifying Metal-Binding Sites with Neural Networks.  United States.  doi:https://doi.org/10.25584/1923007

Copy to clipboard


                    Oostrom, Marjolein. 2022.  
"Classifying Metal-Binding Sites with Neural Networks".  United States.  doi:https://doi.org/10.25584/1923007.  https://www.osti.gov/servlets/purl/1923007. Pub date:Wed Sep 07 04:00:00 UTC 2022

Copy to clipboard


                    
@article{osti_1923007,

  title        = {Classifying Metal-Binding Sites with Neural Networks},

  author       = {Oostrom, Marjolein},

  abstractNote = {A pre-trained convolutional neural network was fine-tuned for three separate classification tasks, distinguishing 2D images of: 1) single amino acids, 2) protein structural ball and stick images of metalloproteins, and 3) protein structural ball and stick images of metalloenzymes with the metal cofactors removed. Images used in the training, testing and validation are shared here.},

  doi          = {10.25584/1923007},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Wed Sep 07 04:00:00 UTC 2022},

  month        = {Wed Sep 07 04:00:00 UTC 2022}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.25584/1923007

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

Classifying metal-binding sites with neural networks

Journal Article Oostrom, Marjolein ; Akers, Sarah ; Garrett, Noah ; ... - Protein Science

To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal-binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification. First,more »« less
Classifying metal‐binding sites with neural networks

Journal Article Oostrom, Marjolein ; Akers, Sarah ; Garrett, Noah ; ... - Protein Science

Abstract To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal‐binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification.more »« less
Classifying metal‐binding sites with neural networks

Journal Article Oostrom, Marjolein ; Akers, Sarah ; Garrett, Noah ; ... - Protein Science

Abstract To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal‐binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification.more »« less
Genome-wide Transcription Factor DNA Binding Sites and Gene Regulatory Networks in Clostridium thermocellum

Dataset Chou, Katherine ; Hebdon, Skyler ; Gerritsen, Alida

Clostridium thermocellum is a thermophilic bacterium recognized for its natural ability to effectively deconstruct cellulosic biomass. While there is a large body of studies on the genetic engineering of this bacterium and its physiology to-date, there is limited knowledge in the transcriptional regulation in this organism and thermophilic bacteria in general. The study herein is the first report of a high-throughput application of DNA-affinity purification sequencing (DAP-seq) to transcription factors (TFs) from a thermophile. We applied DAP-seq to >90 TFs in C. thermocellum and detected genome-wide binding sites for 11 of them. We then compiled and aligned DNA binding sequencesmore »« less
A neural network for cloud masking in WorldView satellite imagery

Dataset Rumpca, Collin ; Abolt, Charles ; Atchley, Adam ; ...

This repository includes the cloud detection algorithm used in the manuscript 'Topography controls variability in circumpolar permafrost thaw pond expansion' by Abolt et al. It also includes a demonstration of the algorithm's use at a survey area in northern Alaska and a demonstration of training the algorithm. This dataset includes .pt place-text, .py python, .tif image, .ipynb Jupyter notebook, and .txt text files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Departmentmore »« less

Similar Records