Molecular Biology and Protein Science Standards

The design and production of proteins is one of the foundational building blocks of modern drug discovery. The current Design-Make-Test-Analyse Cycle (DMTA) for recombinant protein generation is empirical, iterative, and can be extremely time and resource intensive. This is especially true as both industry and academic laboratories move into less well-known regions of the human proteome. Recent advances in machine learning and artificial intelligence offer an opportunity to apply these techniques to protein engineering to reduce or even “break” the current DMTA cycle for recombinant protein generation. AI/ML can predict protein structure from primary sequence and, as we have heard during this conference, putative novel functional proteins from known functional proteins. We generate a lot of positive and negative data during recombinant protein generation – how can we utilize these data sets to generate predictive protein sequences and expression systems?. Please join us in developing this SIG to help us get the data we need to continue to explore AI/ML for molecular biology and protein engineering.