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Description

Proteins, which are remarkably complex machines, play a crucial role not only in the biological functions of your body but also in every living organism's processes. They serve as the fundamental units of life. As of now, there are approximately 100 million identified proteins, with discoveries being made regularly. Each protein possesses a distinctive three-dimensional shape that is essential to its functionality and purpose. However, determining a protein's precise structure is often a costly and lengthy endeavor, resulting in an understanding of only a small percentage of the proteins recognized by science. Addressing this growing disparity and developing methods to predict the structures of millions of yet-to-be-discovered proteins could significantly advance our ability to combat diseases, expedite the discovery of new treatments, and potentially unveil the secrets of life's mechanisms. The implications of such advancements could transform both medicine and our understanding of biology.

Description

ESMFold2 builds upon its predecessor, ESMFold, by establishing a new benchmark in single-sequence structure prediction and facilitating the creation of novel functional proteins via exploration of the latent space within the ESMC model. This advanced model is capable of forecasting high-resolution, all-atom 3D structures of biomolecular complexes straight from the amino acid sequence, and it allows for the incorporation of multiple sequence alignments to improve accuracy on difficult targets. Tailored for predicting structures through both sequence and structure modalities, it employs ESM representations that drive a series of looped folding layers while a diffusion model translates pairwise representations into atomic-resolution outcomes. ESMFold2 excels in predicting protein structures from amino acid sequences, providing detailed structural data, including precise all-atom coordinates for both backbone and side chains, along with confidence metrics and optional distogram predictions for in-depth structural evaluation. Furthermore, its innovative approach enhances the understanding of protein folding dynamics and functional implications, making it a valuable tool for researchers in the field.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Biohub No 
Complete Yes 
Gemini Robotics Yes 
Python No 
WeatherNext Yes 

Integrations

Biohub Yes 
Complete No 
Gemini Robotics No 
Python Yes 
WeatherNext No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

DeepMind

Founded

2010

Country

United Kingdom

Website

deepmind.com/research/case-studies/alphafold

Vendor Details

Company Name

Biohub

Founded

2016

Country

United States

Website

biohub.ai/models/esmfold2

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Product Features

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