Huge News!Announcing our $40M Series B led by Abstract Ventures.Learn More
Socket
Sign inDemoInstall
Socket

trill-proteins

Package Overview
Dependencies
Maintainers
1
Alerts
File Explorer

Advanced tools

Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

trill-proteins

Sandbox for Computational Protein Design

  • 1.8.2
  • PyPI
  • Socket score

Maintainers
1
                          _____________________.___.____    .____     
                          \__    ___/\______   \   |    |   |    |    
                            |    |    |       _/   |    |   |    |    
                            |    |    |    |   \   |    |___|    |___ 
                            |____|    |____|_  /___|_______ \_______ \
                                             \/            \/       \/

pypi version Downloads license Documentation Status

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 v1.8.0:

Breakdown of TRILL's Commands

CommandFunctionAvailable Models
EmbedGenerates numerical representations or "embeddings" of protein sequences for quantitative analysis and comparison.ESM2, ProtT5-XL, ProstT5, Ankh
VisualizeCreates interactive 2D visualizations of embeddings for exploratory data analysis.PCA, t-SNE, UMAP
FinetuneFinetunes protein language models for specific tasks.ESM2, ProtGPT2, ZymCTRL
Language Model Protein GenerationGenerates proteins using pretrained language models.ESM2, ProtGPT2, ZymCTRL
Inverse Folding Protein GenerationDesigns proteins to fold into specific 3D structures.ESM-IF1, LigandMPNN, ProstT5
Diffusion Based Protein GenerationUses denoising diffusion models to generate proteins.RFDiffusion
FoldPredicts 3D protein structures.ESMFold, ProstT5
DockSimulates protein-ligand interactions.DiffDock, Smina, Autodock Vina, Lightdock, GeoDock
ClassifyPredicts protein properties with pretrained models or train custom classifiersTemStaPro, EpHod, ECPICK, LightGBM, XGBoost, Isolation Forest
RegressTrain custom regression models.LightGBM, Linear
SimulateUses molecular dynamics to simulate protein-ligand interactions.OpenMM
ScoreUtilize ESM1v or ESM2 to score protein sequences or ProteinMPNN to score protein structures in a zero-shot manner.COMPSS

Documentation

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

Keywords

FAQs


Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

SocketSocket SOC 2 Logo

Product

  • Package Alerts
  • Integrations
  • Docs
  • Pricing
  • FAQ
  • Roadmap
  • Changelog

Packages

npm

Stay in touch

Get open source security insights delivered straight into your inbox.


  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc