Research could lead to a plethora of drug targets – ScienceDaily
Researchers at UT Southwestern and the University of Washington led an international team that used artificial intelligence (AI) and evolutionary analysis to produce 3D models of eukaryotic protein interactions. The study, published in Science, identified for the first time more than 100 probable protein complexes and provided structural models for more than 700 previously uncharacterized complexes. Information on how pairs or groups of proteins fit together to effect cellular processes could lead to a host of new drug targets.
“Our results represent a significant advance in the new era of structural biology in which calculus plays a fundamental role,” said Qian Cong, Ph.D., assistant professor at the Eugene McDermott Center for Human Growth and Development with a secondary position. in biophysics. .
Dr Cong led the study with David Baker, Ph.D., professor of biochemistry and postdoctoral mentor to Dr Cong at the University of Washington prior to his recruitment at UT Southwestern. The study has four lead co-authors, including UT Southwestern computational biologist Jimin Pei, Ph.D.
Proteins often work in pairs or groups called complexes to perform all the tasks necessary to keep an organism alive, Dr. Cong explained. While some of these interactions are well studied, many remain a mystery. Building complete interactomes – or descriptions of the full set of molecular interactions in a cell – would shed light on many fundamental aspects of biology and give researchers a new starting point for developing drugs that either encourage or discourage such interactions. interactions. Dr Cong works in the emerging field of interactomics, which combines bioinformatics and biology.
Until recently, a major obstacle to building an interactome was the uncertainty about the structures of many proteins, a problem that scientists have been trying to solve for half a century. In 2020 and 2021, a company called DeepMind and Dr. Baker’s lab independently released two AI technologies called AlphaFold (AF) and RoseTTAFold (RF) that use different strategies to predict protein structures based on gene sequences that produce them.
In the present study, Dr Cong, Dr Baker and their colleagues developed these AI structure prediction tools by modeling numerous yeast protein complexes. Yeast is a common model organism for basic biological studies. To find proteins that could interact, the scientists first searched the genomes of related fungi for genes that had acquired mutations in a related manner. They then used the two AI technologies to determine if these proteins could be assembled into 3D structures.
Their work identified 1,505 probable protein complexes. Among these, 699 had already been characterized structurally, verifying the usefulness of their method. However, there was only limited experimental data supporting 700 of the predicted interactions, and another 106 had never been described.
To better understand these poorly characterized or unknown complexes, teams at the University of Washington and UT Southwestern worked with colleagues around the world who were already studying these or similar proteins. By combining the 3D models that the scientists in the present study had generated with the information from the collaborators, the teams were able to acquire new knowledge about the protein complexes involved in the maintenance and processing of genetic information, cell construction and transport systems, metabolism, DNA repair and other places. They also identified roles for proteins whose functions were previously unknown based on their newly identified interactions with other well-characterized proteins.
“The work described in our new article sets the stage for similar studies on the human interactome and could potentially help develop new treatments for human diseases,” added Dr. Cong.
Dr Cong noted that the complex predicted protein structures generated in this study can be downloaded from ModelArchive (https://modelarchive.org/doi/10.5452/ma-bak-cepc). These structures and others generated using this technology in future studies will be a rich source of research questions for years to come, she said.
Dr Cong is a Southwestern Medical Foundation Fellow in Biomedical Research. Other UTSW researchers who contributed to this study include Jing Zhang and Josep Rizo, Ph.D., Virginia Lazenby O’Hara Chair in Biochemistry.
Collaborating institutions include: Harvard University, Wayne State University, Cornell University, MRC Laboratory of Molecular Biology, Memorial Sloan Kettering Cancer Center, Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Fred Hutchinson Cancer Research Center, Columbia University, University of Würzburg in Germany, St Jude Children’s Research Hospital, FIRC Institute of Molecular Oncology in Milan, Italy, and National Research Council, Institute of Molecular Genetics in Rome, Italy.
This work was supported by the Southwestern Medical Foundation, the Cancer Prevention and Research Institute of Texas (CPRIT) (RP210041), Amgen, Microsoft, the Washington Research Foundation, Howard Hughes Medical Institute, National Science Foundation (DBI 1937533), National Institutes of Health (R35GM118026, R01CA221858, R35GM136258, R21AI156595), UK Medical Research Council (MRC_UP_1201 / 10), HHMI Gilliam Fellowship, Deutsche Forschungsgemeinschaft (KI-562 / 11-1, KI-562 / 7-1), researcher of the AIRC and Research Council Consolidator (IG23710 and 682190), Defense Threat Reduction Agency (HDTRA1-21-1-0007) and National Energy Research Scientific Computing Center.