Massive human proteome database accelerates precision medicine research opportunities
Precision medicine is a modern approach to medical treatment that provides medical care based on the specific traits of each individual, instead of providing the same treatments to everyone. Precision medicine has traditionally been based primarily on genetic variations; however, recent advances in the field have increased the understanding that there are many potential variations between individuals that may not be exclusively genetically based.
As a complement to genetics, another important area of precision medicine has recently gained attention; that is the field of proteomics. By studying the human proteome, researchers can see the entire set of proteins that make up an individual’s genome.
This is important because proteins are the functional units that each gene creates. Individual differences in proteins are what is actually physically responsible for the differences caused by variations in genes.
A confounding problem in proteomics, however, has been understanding how the one-dimensional chain of amino acids that genes make ultimately folds into three-dimensional, functioning proteins. This question has been a barrier to understanding proteomics for over five decades, making it an essential area of research.
A breakthrough in understanding protein folding occurred in December 2020, when a Google-developed AI network, DeepMind, announced it could predict a protein’s shape based on its amino acid sequence. DeepMind’s program, called AlphaFold, was found to be highly accurate in its predictions of how a protein folds.
Then, in another interesting development in July 2021, DeepMind announced a partnership with the European Molecular Biology Laboratory (EMBL) to provide a free, open database of predicted protein structures for anyone in the scientific community to access. Expanding on the developing impact of proteomics, the EMBL partnership allows a vast first look into the actual protein structures that genetics creates.
Advances in Proteomics Will Aid in Drug Discovery
“Our goal at DeepMind has always been to build AI and then use it as a tool to help accelerate the pace of scientific discovery itself, thereby advancing our understanding of the world around us,” said DeepMind Founder and CEO Demis Hassabis, PhD. “We used AlphaFold to generate the most complete and accurate picture of the human proteome. We believe this represents the most significant contribution AI has made to advancing scientific knowledge to date, and is a great illustration of the sorts of benefits AI can bring to society.”
The AlphaFold database is still being assembled but is already being used by Drugs for Neglected Diseases Initiative (DNDi), an organization that develops treatments for diseases that disproportionately affect developing countries.
“We need to supercharge the discovery of new drugs for the millions of people at risk of neglected diseases around the world,” said Ben Perry, PhD, Discovery Open Innovation Leader at DNDi, in a statement. “AI can be a game changer: by quickly and accurately predicting protein structures, AlphaFold opens new research horizons, improving both the scope and efficiency of R&D and facilitating our research in endemic countries. It is inspiring to see powerful cutting-edge AI enabling work on diseases which are concentrated almost exclusively in impoverished populations.”
Added Pushmeet Kohli, PhD, Head of AI for Science at DeepMind, “This database and AlphaFold have the potential to open up new avenues of scientific inquiry that will ultimately advance our understanding of many areas of biology and life itself. We believe that this will have a transformative impact for research on problems related to health and disease, the drug design process and environmental sustainability, and are very excited to see what applications are developed in the coming months and years.”
Advancing proteomics will undoubtedly lead to advances in precision medicine. Hospitals engaged in research involving proteomics will find the availability of this new database immediately impactful, while others will see benefits as new discoveries occur.