Protein Folding Models

Top 5

AlphaFold 2

AlphaFold 2 is an AI model developed by Google’s DeepMind is a deep learning approach that includes physical and biological knowledge about protein structure and multiple sequence alignments (MSA). The model won at the 14th critical assessment of protein structure prediction (CASP14) held in November 2020 and earned the position of being one of the best protein folding models.


RoseTTAFold is a software tool using deep learning to predict protein structures developed by Minkyung Beak, Ph.D. at Baker lab. It is insightful towards protein function without a determined structure, making it faster to generate accurate protein-protein complexes. The software allows the network to directly collect reasons and patterns in the relationships between peptides and folded architecture.


ESMFold is a high-accuracy end-to-end atomic-level protein structure prediction model developed by Meta AI Research. It uses a transformer-based language model ESM-2, which is an updated version of the evolutionary scale modeling (ESM) model. The ESM model is capable of learning the interactions of bonds between amino acids in a protein sequence.


D-I-TASSER is a distance-guided iterative threading assembly refinement model uprooted from the I-TASSER method developed by Zhang lab. It is a high-accuracy protein structure and function prediction model built using the integration of threading and deep learning. The working of D-I-TASSER starts with a query sequence.


OmegaFold is a high-resolution de novo structure prediction model from a primary sequence launched by HeliXon, a Chinese biotech firm, in July 2022. It works on divergent sequences instead of multiple sequence alignments preprocessing that other protein folding models, including AlphaFold 2, RoseTTAFold, and more work on.



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