BioStrand Blog Beyond AlphaFold 2: The next frontier in macromolecular structure prediction

IntroductionAs a testament to the recent breakthrough of deep-learning technologies in the field of (structural) bioinformatics, half of the Nobel Prize in Chemistry 2024 [1] has been awarded to John Jumper and Demis Hassabis, the main contributors to AlphaFold 2, the other half to Prof. David Baker (University of Washington, Seattle). Speaking about breakthroughs is not an understatement: as of this time of writing, the original AlphaFold2 publication [2] has been cited more than 27,800 times (according to Google Scholar [3]). For comparison, on Feb 21, 2023 (roughly 1.5 years ago), the number of citations was just 8,783. AlphaFold 2 is a solution to the protein folding problem and can predict with near experimental accuracy the structure of proteins as long as their primary structures (the sequence of amino acids along the protein chain) are known. This technology has been integrated with the LENSᵃⁱ in silico discovery platform, and we have discussed it in length in different blog posts since the public release of AlphaFold2 [4, 5, 6]. In this new blog post, we will review how these latest developments are impacting drug discovery, what can be technically achieved with current technology, and assess limitations that hinder discovery processes and future outcomes. Finally, we will briefly present how these breakthrough technologies are integrated within the BioStrand LENSai platform.