Exploring Deep Learning in Computational Biology and Bioinformatics: A Tutorial Covering DNA, Protein Folding, and Alphafold2

Finally, the AlphaFold 2 paper and code have been released, inspiring new generations of Machine Learning (ML) engineers to focus on foundational biological problems. This post is a collection of core concepts designed to provide an in-depth understanding of AlphaFold2 and related concepts. The article covers a wide range of topics, including the central dogma of biology, proteins, amino acids, nucleotides, codons, protein structure characteristics, distograms, phenotypes, genotypes, multiple sequence alignment, biology tasks that can be approached with ML, and the association of biology and ML model design. The aim of this article is to bridge the gap between biology and bioinformatics and machine/deep learning and inspire new perspectives. Included are links to official resources, blog posts, and other materials for further reading.