Recently, the artificial intelligence company Shanghai Tianran Intelligent Technology Co., Ltd. announced that its self-developed deep learning protein folding prediction platform has achieved excellent results in the evaluation of the protein test set of the International Protein Structure Prediction Competition, ranking among the top teams of the same type in the world. When predicting a protein chain of 400 amino acids, the prediction platform only takes 16 seconds.

　　Scientists say that proteins are the main functional molecules in cells and perform a variety of functions in cells. For example, as an enzyme, it plays a catalytic role, participates in the regulation of metabolism in organisms, transports metabolites, is used in the formation of cytoskeleton, and participates in processes such as immunity, cell differentiation, and cell apoptosis. As the basic components of life, deciphering the function of protein is the golden key to uncover various life phenomena.

　　According to Dr. Xue Guirong, the founder of Skyland, in order to perform specific functions, proteins must be folded into a specific structure, and only a few proteins are in a natural unfolded state but still function. The three-dimensional structure of the protein also directly determines the function of the protein. Once the three-dimensional structure is destroyed, the protein function will be lost. Many diseases are caused by abnormalities in the structure of important proteins in the body. Therefore, the study of protein structure helps to understand the function and role of proteins, thereby bringing about improvements in healthcare, food sustainability, innovative biotechnology, etc., and advancing the development of life sciences, drug discovery, and synthetic biology.

　　In the field of life sciences, observing and analyzing protein structures has always been a fascinating topic, attracting many scientists to tackle difficult problems, but it is also faced with difficulties, high costs, and limited progress. There are three main methods for traditionally observing protein structures: nuclear magnetic resonance, X-rays, and cryo-electron microscopy. These methods rely on a lot of trial and error and expensive equipment, and the study of each structure often takes several years. Existing experimental methods are not enough to reveal some important protein structures, and more bioinformatics and computational biology methods are needed to explore. But using ordinary computer software to calculate the protein structure, the amount of calculation is quite amazing, even supercomputers can't bear it. For this reason, protein structure prediction has become an important branch of structural biology. Researchers develop related artificial intelligence algorithms to predict the spatial structure of proteins based on amino acid sequences.

　　"From artificial intelligence defeating the world champion of Go to urban traffic scheduling, artificial intelligence has shown amazing intelligent decision-making ability in solving complex system problems. Although protein structure prediction is a biological topic, it is also a problem in complex scenarios. It reflects the great potential of artificial intelligence in basic scientific research, and we don't want to miss this landscape." Xue Guirong said that such a comprehensive innovation project is very precious, covering interdisciplinary innovation, industry innovation, and basic science. Innovation, innovation in artificial intelligence algorithms and engineering capabilities.

　　These recent developments show that the application of artificial intelligence to the field of protein structure can solve some structures that cannot be resolved by traditional observation methods through prediction, and the reliability is relatively high, which is very close to the truth. This artificial intelligence structure prediction algorithm is expected to become a sharp weapon for scientists and accelerate the research and development in the field of life sciences.

　　At present, the folding prediction of individual proteins is only a starting point. Proteins usually function in pairs or groups in the form of complexes to undertake various functions required by life, and the structures of many protein complexes are still enigmatic. Xue Guirong believes that in the future, the universality and accuracy of artificial intelligence algorithms will be further improved, and contributions will be made in revealing the interactions between multiple proteins, so as to help humans find new methods for precise disease treatment.

　　Source: People's Daily