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Related Experiment Video

Updated: Jul 10, 2025

Expression and Purification of the Human Lipid-sensitive Cation Channel TRPC3 for Structural Determination by Single-particle Cryo-electron Microscopy

Expression and Purification of the Human Lipid-sensitive Cation Channel TRPC3 for Structural Determination by Single-particle Cryo-electron Microscopy

Published on: January 7, 2019

TRILL: ORCHESTRATING MODULAR DEEP-LEARNING WORKFLOWS FOR DEMOCRATIZED, SCALABLE PROTEIN ANALYSIS AND ENGINEERING.

Zachary A Martinez¹, Richard M Murray¹, Matt W Thomson¹

¹Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91125.

Biorxiv : the Preprint Server for Biology

|November 21, 2023

Summary

This summary is machine-generated.

TRILL is a platform that simplifies protein design and discovery using deep learning. It enables scientists to create novel proteins and analyze biological data by integrating advanced AI models, making complex methods more accessible.

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Related Experiment Videos

Last Updated: Jul 10, 2025

Expression and Purification of the Human Lipid-sensitive Cation Channel TRPC3 for Structural Determination by Single-particle Cryo-electron Microscopy

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Published on: January 7, 2019

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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Published on: November 15, 2017

Area of Science:

Computational Biology
Artificial Intelligence in Biology
Protein Engineering

Background:

Deep learning models are increasingly used in biology due to vast sequencing data.
Large, complex models pose challenges for researchers lacking machine learning expertise.
Protein language models learn biological patterns from extensive sequence data.

Conclusions:

TRILL democratizes access to powerful deep learning tools for biological research.
It bridges the gap between complex AI models and practical scientific application.
The platform simplifies the integration and use of advanced computational methods in biology.