This Artificial Intelligence Paper Introduces AF2Complex: A Deep Learning Tool Designed To Predict The Physical Interactions of Multiple Proteins

Large complex molecules called proteins are responsible for almost all life processes that occur within the bodies of living organisms. Because of this, although it is a relatively recent field of study, protein research and engineering has emerged as fundamental. A breakthrough in protein research was the introduction of AlphaFold and AlphaFold 2 by DeepMind, a subsidiary of Alphabet. AlphaFold is a machine learning tool that can accurately predict the three-dimensional structures of proteins. However, despite these advances, it is extremely challenging and time-consuming to experimentally analyze the folding and transport of biological proteins.

DeepMind researchers have once again demonstrated that their latest model, AF2Complex, can help solve this problem. AlphaFold 2 Complex, or simply AF2Complex, is a deep learning technique designed to predict physical interactions between several proteins. At an unprecedented level of detail, AF2Complex can predict which proteins will interact with each other to form functional complexes. The pace of protein research is being significantly accelerated by this model, which is heavily based on DeepMind’s AlphaFold 2, a machine learning tool that can predict the three-dimensional structures of proteins using only their amino acids. DeepMind’s work has also gained recognition in the famous biomedical scientific journal eLife.

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Using amino acids, AF2Complex can predict whether proteins can interact to form functional complexes, which parts of each structure are most likely to interact, and even which protein complexes are most likely to combine to generate supercomplexes. Supercomplexes are large groups of interdependent proteins required for biological functions. The successful creation of AF2Complex led to the belief that this method has great potential for finding and describing the collection of protein-protein interactions essential for life. Researchers studying proteins mainly perform computational studies to understand the atomic features of supercomplexes. AF2Complex, which acts as a “computer microscope” powered by deep learning and supercomputing, is essential to this research as it has significantly increased the speed of the investigation.

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Another area of ​​research focuses on elucidating the interactions between proteins in the pathway that transports a newly made protein from the inner to the outer membrane of bacteria. This complex process of protein synthesis and transport pathway is also being studied using AF2Complex. Discoveries during this research may provide new targets for antibiotic and treatment development and a basis for using AF2Complex to computationally accelerate this type of biomedical research in general.

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To convince the molecular biology community of the tool’s power and high impact, the researchers decided to apply AF2Complex to a pathway in Escherichia coli (E. coli), a model organism often used for experimental DNA manipulation and protein synthesis. because of his relatives. simplicity and rapid growth. The team examined the production and movement of outer membrane proteins (OMPs), which are found in gram-negative bacteria such as the outermost membrane of E. coli and are essential for nutrient exchange. Since proteins are made inside the cell, they must be translocated to reach the outer membrane.

The researchers performed several experimental evaluations to determine which pairs of proteins the tool predicted would interact and which pairs were likely to form supercomplexes. To this end, they compared several proteins essential for synthesizing and transporting OMPs with approximately 1,500 other proteins (all known proteins in the E. coli cell envelope). The researchers matched the tool’s predictions with previously published experimental data to validate their findings. Most of the previously known interacting pairs, and some unknown ones, were accurately predicted by AF2Complex. The accuracy of the tool was also strengthened by its ability to draw attention to the structural aspects of those interactions that explain data from previous tests.

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Researchers are optimistic that AF2Complex can significantly impact biomedical research. Key proteins studied by the vehicle on the road can serve as brand new targets for the development of new antibiotics. Predicting the structural model of a supercomplex is extremely complicated in contrast to predicting the structures of a single protein sequence. In this sense, AF2Complex can be a new computational tool for biologists to perform experiments using different combinations of proteins, accelerating the pace and efficiency of this particular field of biological research.

Look Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to join Our Reddit page, Channel DiscordAND Email newsletterwhere we share the latest AI research news, cool AI projects and more.

Khushboo Gupta is a Consulting Intern at MarktechPost. She is currently pursuing B.Tech from Indian Institute of Technology (IIT), Goa. She is passionate about the fields of Machine Learning, Natural Language Processing and Web Development. She enjoys learning more about the technical field by participating in several challenges.


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