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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.
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Free
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Windows
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Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
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Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
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Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
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