trill-proteins 1.9.0

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 v1.9.0:

Breakdown of TRILL's Commands

Command	Function	Available Models
Embed	Generates numerical representations or "embeddings" of biological sequences for quantitative analysis and comparison. Can be Small-Molecule SMILES/RNA/DNA/Proteins dpending on the model.	ESM2, MMELLON, MolT5, ProtT5-XL, ProstT5, Ankh, CaLM, mRNA-FM/RNA-FM, SaProt, SELFIES-TED, SMI-TED
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, ProGen2
Language Model Protein Generation	Generates proteins using pretrained language models.	ESM2, ProtGPT2, ZymCTRL, ProGen2
Inverse Folding Protein Generation	Designs proteins to fold into specific 3D structures.	ESM-IF1, LigandMPNN, ProstT5
Diffusion Based Protein Generation	Uses denoising diffusion models to generate proteins.	Genie2, RFDiffusion
Fold	Predicts 3D protein structures.	ESMFold, ProstT5, Chai-1, Boltz-2
Dock	Simulates protein-ligand interactions.	DiffDock-L, Smina, Autodock Vina, Gnina, Lightdock, GeoDock
Classify	Predicts properties with pretrained models or train custom classifiers	CataPro, CatPred, M-Ionic, PSICHIC, PSALM, TemStaPro, EpHod, ECPICK, LightGBM, XGBoost, Isolation Forest, End-to-End Finetuning of ESM2 with a Multilayer perceptron head
Regress	Train custom regression models.	LightGBM, Linear
Simulate	Uses molecular dynamics to simulate biomolecular interactions followed by automated scoring	OpenMM, MMGBSA, ProLIF
Score	Utilize ESM1v or ESM2 to score protein sequences or ProteinMPNN/LigandMPNN/SCASA to score protein structures/complexes in a zero-shot manner.	COMPSS, SC
Workflow	Automated protein design workflows.	Foldtuning

Documentation

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