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HARRISON FORD

Revolutionizing Protein Function Prediction from Sequence with Statistics-Informed Graph Networks

PhiGnet predicts protein functions using sequence data, closing gaps in annotations without structural information.

Understanding the complex mechanisms behind many essential biological activities is essential for developing new drugs and has broad ramifications in the disciplines of biotechnology, medicine, and drug development. Still, about 200 million proteins are uncharacterized, and computational attempts foresee annotations of different quality, mostly depending on protein structural information. Here, scientists describe a method for predicting protein activities just based on the sequence by using graph networks informed by statistics. PhiGnet is a quantitative evaluation technique that allows for the quantitative evaluation of particular functions by characterizing evolutionary signatures. Even in the absence of structural information, it reduces the sequence-function gap. In research and biology, this approach identifies functional sites at the residue level, offering significant assistance in the interpretation of the characteristics and novel functions of proteins.

The prediction of protein structure has benefited greatly from the evolutionary knowledge found in protein sequences that have been obtained through genome sequencing. Protein functional sites have been characterized by the couplings between paired residues, which capture interactions between residues that support particular functionalities. 

The identification of disease variations, allosteric mechanisms in proteins, and metamorphism in proteins—reversible transitions between distinct folds, frequently accompanied by diverse functions—have all been made possible by this data. Comprehending the molecular functioning of living creatures, health, sickness, and evolution requires an understanding of protein function.

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