AlphaFold2: Protein Folding Tutorial on Google Colab & ChimeraX

AlphaFold2, developed by DeepMind, a Google subsidiary, represents a significant advancement in the field of protein structure prediction. Prior to AlphaFold2, various methods were used for protein structure prediction, including homology modeling and de novo modeling. However, AlphaFold2 introduced a new paradigm by utilizing deep learning (DL) algorithms to predict protein structures directly from amino acid sequences.

One of the key strengths of AlphaFold2 lies in its ability to predict protein structures with atomic-level accuracy. This was demonstrated in the 13th and 14th Critical Assessment of Structure Prediction (CASP) competitions, where AlphaFold2 outperformed other methods. In CASP14, for instance, AlphaFold2 achieved a root-mean-square deviation (RMSD) of only 0.8Å compared to the experimental backbone structures, significantly better than the next best performing method at 2.8Å.

The architecture of AlphaFold2 is based on state-of-the-art deep learning algorithms, including the use of transformer models, which were originally developed for natural language processing. These models are adept at capturing the intrinsic features of amino acid sequences and utilize the concept of self-attention to improve prediction performance. The underlying principle of AlphaFold2 also takes into account the conservation of protein structures through evolution, recognizing that protein structures are often more conserved than their amino acid sequences.

AlphaFold2's success has been attributed to its novel neural network architectures, which incorporate evolutionary, physical, and geometric constraints of protein structures. These architectures involve multiple sequence alignments and the identification of proteins with similar structures to the input sequence. This comprehensive approach allows AlphaFold2 to predict protein structures with unprecedented accuracy.

In addition to its groundbreaking performance in protein structure prediction, AlphaFold2 is becoming an essential tool in structural biology. Its predicted structures can be used to determine domain boundaries for recombinant protein production, solve crystal structures by molecular replacement, interpret cryo-EM 3D reconstructions, and function as reliable templates for functional characterization of challenging samples.

Overall, AlphaFold2 represents a monumental leap forward in the field of protein structure prediction, impacting various areas of biology and medicine. Its ability to accurately predict protein structures, which was once considered a daunting challenge in computational biology, has opened new frontiers in scientific research and drug discovery

Google Collab: https://colab.research.google.com/git...


Github: https://github.com/deepmind/alphafold
Protein universe: https://www.nature.com/articles/d4158...
AlphaFold Protein Structure Database: https://alphafold.ebi.ac.uk
Paper: https://www.nature.com/articles/s4158...
Colab fold: https://github.com/sokrypton/ColabFold

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