To understand a protein’s structure is to understand its function, says structural and chemical biologist Jens Meiler, PhD, distinguished research professor of Chemistry.
It can take a PhD student up to five sleep-deprived years to determine the structure of a single protein, and of the 20,000 human proteins, only about 17% are considered to have had their structure determined experimentally with very high accuracy.
Meanwhile, AlphaFold 2, a deep learning program owned by Google’s parent company, can in minutes compute a protein’s structure with an accuracy competitive with experiment.
Meiler, who for decades has used machine learning (ML) to predict protein structure, notes that an excess of biomolecular data amassed over two decades by experimentalists was used to train AlphaFold 2, and he acknowledges that the resulting performance is indeed impressive.
“But the real hard problems are problems of limited data,” said Meiler, who in addition to his Vanderbilt faculty appointment holds an Alexander von Humboldt Professorship at Leipzig University in Germany. Meiler is collaborating with other Vanderbilt researchers to advance precision medicine, where treatment is to be tailored more than ever to individual differences among patients, including down to the molecular level.
And he’s using machine learning to do it.
Brad Malin, Colin Walsh, Cosmin “Adi” Bejan, Michael Matheny, You Chen and other VUMC researchers were featured in the article to discuss various uses of machine learning and the pros/cons of ML.