The goal of this competition is to predict the thermostability of enzyme variants. The experimentally measured thermostability (melting temperature) data includes natural sequences, as well as engineered sequences with single or multiple mutations upon the natural sequences.
Computational protein stability prediction based on physics principles have made remarkable progress thanks to advanced physics-based methods such as FoldX, Rosetta, and others. Recently, many machine learning methods were proposed to predict the stability impact of mutations on protein based on the pattern of variation in natural sequences and their three dimensional structures. More and more protein structures are being solved thanks to the recent breakthrough of AlphaFold2. However, accurate prediction of protein thermal stability remains a great challenge


1st Place - $ 12,000
2nd Place - $ 8,000
3rd Place - $ 5,000