Protein-biomolecule interactions play pivotal roles in almost all biological processes.For a biomolecule of interest,the identification of the interacting protein(s)is essential.For this need,although many assays are ...Protein-biomolecule interactions play pivotal roles in almost all biological processes.For a biomolecule of interest,the identification of the interacting protein(s)is essential.For this need,although many assays are available,highly robust and reliable methods are always desired.By combining a substrate-based proximity labeling activity from the pupylation pathway of Mycobacterium tuberculosis and the streptavidin(SA)-biotin system,we developed the Specific Pupylation as IDEntity Reporter(SPIDER)method for identifying protein-biomolecule interactions.Using SPIDER,we validated the interactions between the known binding proteins of protein,DNA,RNA,and small molecule.We successfully applied SPIDER to construct the global protein interactome for m^(6)A and m RNA,identified a variety of uncharacterized m^(6)A binding proteins,and validated SRSF7 as a potential m^(6)A reader.We globally identified the binding proteins for lenalidomide and Cob B.Moreover,we identified SARS-CoV-2-specific receptors on the cell membrane.Overall,SPIDER is powerful and highly accessible for the study of proteinbiomolecule interactions.展开更多
Proteins function as integral actors in essential life processes,rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investig...Proteins function as integral actors in essential life processes,rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation.Within the context of protein research,an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings.Due to the exorbitant costs and limited throughput inherent in experimental investigations,computational models offer a promising alternative to accelerate protein function annotation.In recent years,protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks.This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction.In this review,we elucidate the historical evolution and research paradigms of computational methods for predicting protein function.Subsequently,we summarize the progress in protein and molecule representation as well as feature extraction techniques.Furthermore,we assess the performance of machine learning-based algorithms across various objectives in protein function prediction,thereby offering a comprehensive perspective on the progress within this field.展开更多
基金supported by the National Key Research and Development Program of China(2020YFE0202200)the National Natural Science Foundation of China(31900112,21907065,31970130 and 31670831)。
文摘Protein-biomolecule interactions play pivotal roles in almost all biological processes.For a biomolecule of interest,the identification of the interacting protein(s)is essential.For this need,although many assays are available,highly robust and reliable methods are always desired.By combining a substrate-based proximity labeling activity from the pupylation pathway of Mycobacterium tuberculosis and the streptavidin(SA)-biotin system,we developed the Specific Pupylation as IDEntity Reporter(SPIDER)method for identifying protein-biomolecule interactions.Using SPIDER,we validated the interactions between the known binding proteins of protein,DNA,RNA,and small molecule.We successfully applied SPIDER to construct the global protein interactome for m^(6)A and m RNA,identified a variety of uncharacterized m^(6)A binding proteins,and validated SRSF7 as a potential m^(6)A reader.We globally identified the binding proteins for lenalidomide and Cob B.Moreover,we identified SARS-CoV-2-specific receptors on the cell membrane.Overall,SPIDER is powerful and highly accessible for the study of proteinbiomolecule interactions.
基金supported in part by the National Natural Science Foundation of China(22033001)the National Key R&D Program of China(2022YFA1303700)the Chinese Academy of Medical Sciences(2021-I2M-5-014).
文摘Proteins function as integral actors in essential life processes,rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation.Within the context of protein research,an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings.Due to the exorbitant costs and limited throughput inherent in experimental investigations,computational models offer a promising alternative to accelerate protein function annotation.In recent years,protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks.This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction.In this review,we elucidate the historical evolution and research paradigms of computational methods for predicting protein function.Subsequently,we summarize the progress in protein and molecule representation as well as feature extraction techniques.Furthermore,we assess the performance of machine learning-based algorithms across various objectives in protein function prediction,thereby offering a comprehensive perspective on the progress within this field.
