trill-proteins 1.5.2

Sandbox for Computational Protein Design

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Intro

TRILL (TRaining and Inference using the Language of Life) is a sandbox for creative protein engineering and discovery. As a bioengineer myself, deep-learning based approaches for protein design and analysis are of great interest to me. However, many of these deep-learning models are rather unwieldy, especially for non ML-practitioners due to their sheer size. Not only does TRILL allow researchers to perform inference on their proteins of interest using a variety of models, but it also democratizes the efficient fine-tuning of large-language models. Whether using Google Colab with one GPU or a supercomputer with many, TRILL empowers scientists to leverage models with millions to billions of parameters without worrying (too much) about hardware constraints. Currently, TRILL supports using these models as of v1.5.0:

Breakdown of TRILL's Commands

Command	Function	Available Models
Embed	Generates numerical representations or "embeddings" of protein sequences for quantitative analysis and comparison.	ESM2, ProtT5-XL, ProstT5
Visualize	Creates interactive 2D visualizations of embeddings for exploratory data analysis.	PCA, t-SNE, UMAP
Finetune	Finetunes protein language models for specific tasks.	ESM2, ProtGPT2, ZymCTRL
Language Model Protein Generation	Generates proteins using pretrained language models.	ESM2, ProtGPT2, ZymCTRL
Inverse Folding Protein Generation	Designs proteins to fold into specific 3D structures.	ESM-IF1, ProteinMPNN, ProstT5
Diffusion Based Protein Generation	Uses denoising diffusion models to generate proteins.	RFDiffusion
Fold	Predicts 3D protein structures.	ESMFold, ProstT5
Dock	Simulates protein-ligand interactions.	DiffDock, Smina, Autodock Vina, Lightdock
Classify	Predicts protein properties at high throughput.	TemStaPro, EpHod
Simulate	Uses molecular dynamics with the AMBER force field to relax structures.	OpenMM

Documentation

Check out the documentation and examples at https://trill.readthedocs.io/en/latest/index.html