Speaker: Juan Ignacio Álvarez Trejos.

Abstract: Based on https://www.nature.com/articles/s41586-021-03819-2.

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort, the structures of around 100,000 unique proteins have been determined, but this represents a small fraction of the billions of known protein sequences. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem—has been an important open research problem for more than 50 years.

In this talk, the winning model based on neural networks of the CASP14 challenge is presented.

That model demonstrates accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods.