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What is ESMFold2?

Building upon its predecessor, ESMFold, ESMFold2 sets a new standard in the realm of single-sequence structure prediction while also enabling the design of novel functional proteins by delving into the latent space of the ESMC model. This sophisticated model can accurately predict high-resolution, all-atom 3D structures of biomolecular complexes directly from amino acid sequences and incorporates multiple sequence alignments to enhance accuracy for challenging targets. Designed to predict structures using both sequence and structural modalities, it utilizes ESM representations that power a sequence of looped folding layers, while a diffusion model converts pairwise representations into atomic-resolution results. ESMFold2 stands out in its ability to forecast protein structures from amino acid sequences, providing comprehensive structural information, including exact all-atom coordinates for backbone and side chains, as well as confidence metrics and optional distogram predictions for thorough structural analysis. In addition, its groundbreaking methodology deepens the understanding of protein folding dynamics and their functional implications, positioning it as an indispensable tool for researchers engaging in this area of study. Ultimately, ESMFold2 not only advances structural biology but also opens new avenues for the development of protein-based applications.

What is AlphaFold?

Proteins are extraordinary and intricate entities that form the bedrock of biological processes not just within your own body, but across all living organisms on our planet. They are essential to the very fabric of life. Currently, there are roughly 100 million identified unique proteins, with fresh discoveries occurring each year. Each of these proteins has a unique three-dimensional structure that influences its function and purpose. Nonetheless, pinpointing the exact structure of a protein is frequently a resource-intensive task, leading to only a limited number of proteins having their precise 3D configurations mapped by researchers. Tackling this expanding challenge and creating techniques to forecast the structures of numerous unidentified proteins could greatly improve our capacity to fight diseases and accelerate the development of innovative drugs. Furthermore, such breakthroughs might shed light on the fundamental nature of life itself, paving the way for transformative advancements in our comprehension of biological systems and yielding significant progress in the fields of medicine and biotechnology. The potential implications of these developments could be profound, influencing not only our health but also our understanding of the mechanisms that sustain life.

Media

Media

Integrations Supported

Biohub
Complete
Gemini Robotics
Python
WeatherNext

Integrations Supported

Biohub
Complete
Gemini Robotics
Python
WeatherNext

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Biohub

Date Founded

2016

Company Location

United States

Company Website

biohub.ai/models/esmfold2

Company Facts

Organization Name

DeepMind

Date Founded

2010

Company Location

United Kingdom

Company Website

deepmind.com/research/case-studies/alphafold

Categories and Features

Categories and Features

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