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Solving the half-century old protein structure prediction problem by DeepMind’s AlphaFold is certainly one of the greatest breakthroughs in biology in the twenty first century. This breakthrough paved the way for tackling some previously highly challenging or even infeasible problems in structural biology. In this study, we propose strategies to use AlphaFold to address several fundamental problems: (1) protein engineering by predicting the experimentally measured stability changes using the representations extracted from AlphaFold models; (2) estimating the designability of a given protein structure by combining a protein design method (e.g. ProDCoNN), sequential Monte Carlo, and AlphaFold. The designability of a protein structure is defined as the number of sequences that encode that protein structure.; (3) predicting protein stabilities using natural sequences and designed sequences as training data, and representations extracted from AlphaFold models as input features; and (4) understanding the sequence-structure relationship of proteins by computational mutagenesis and testing the foldability of the mutants by AlphaFold. We found the representations extracted from AlphaFold models can be used to predict the experimentally measured stability changes accurately. For the first time, we have estimated the designability for a few real proteins. For example, the designability of chain A of FLT3 ligand (PDB ID: 1ETE) with 134 residues was estimated as 3.12±2.14E85.

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