Group protein structure prediction method based on Rosetta local reinforcement

Abstract

The invention discloses a group protein structure prediction method based on Rosetta local reinforcement. The prediction method includes the steps: firstly, dividing searching processes of a whole algorithm in structure prediction into four stages, setting fragment length for each stage, assembling fragments, and selecting different energy functions to measure weight of conformation individuals; secondly, generating testing conformations by the aid of different mutation strategies and loop area information based on secondary structure information, randomly exchanging the loop area information to achieve cross processes, keeping population diversities, and executing Rosetta local reinforcement for testing conformations and target conformations of the stages; finally, extracting characteristic vectors of the conformations to measure diversities of the conformation individuals, taking the energy functions as main measurement indexes, taking the diversities as auxiliary measurement indexes, and guiding conformation groups to update. The prediction method is high in searching capability and high in prediction accuracy, and group diversities can be kept by energy.

Description

technical field [0001] The present invention relates to the fields of biological informatics, intelligent optimization and computer application, in particular to a method for predicting population protein structure based on Rosetta local enhancement. Background technique [0002] Proteins are central to cellular function and are implicated in most core life processes. In fact, proteins can only produce their specific biological functions after being folded into a specific three-dimensional structure (ie, protein tertiary structure). Therefore, in order to understand the function of protein, it is necessary to obtain its three-dimensional space structure, so as to promote the development of functional material design and new drug development by understanding the three-dimensional structure of protein, and help people understand the basic process of life, including Alzheimer's disease, Awareness of protein folding diseases such as Parkinson's disease and type 2 diabetes. [...

AI Technical Summary

Problems solved by technology

However, since the protein energy model considers the bonding of molecular systems and non-bonding interactions such as van der Waals forces, electrostatics, hydrogen bonds, and hydrophobicity, the energy surface formed by it is extremely rough, and the number of local minimum solutions corresponding to conformations increases as the sequence length increases. Exponential growth, the prediction of these traditional methods seems powerless, the reason is that the huge conformation search space will lead to the gradual decline of the search ability of the algorithm in the prediction process, and at the same time, the diversity of the population becomes smaller and smaller, which leads to the loss of the algorithm. The power of the search, which affects the final prediction accuracy

[0005] Therefore, existing population protein structure prediction methods are deficient in search ability and population diversity maintenance, and need to be improved

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