Protein torsion angle prediction method based on lightweight deep convolutional network

Abstract

The invention discloses a protein torsion angle prediction method based on a lightweight deep convolutional network. The method comprises the following steps: constructing a protein torsion angle data set based on a PISCES server; extracting torsion angles Phi and Psi corresponding to each amino acid residue in each protein sequence from an RCSB PDB database, and adding the torsion angles Phi and Psi into a data set; performing multi-sequence comparison on the uniref90 database and each protein sequence to generate a PSSM spectrum matrix of the corresponding protein sequence, and constructing protein sequence characteristics based on the PSSM spectrum matrix and the physicochemical characteristics of amino acid; designing a residual module, and constructing a deep convolutional network model for predicting a protein torsion angle based on the module; constructing a loss function of the training network model; training a network model based on the constructed loss function; and predicting the protein torsion angle based on the trained network model. The method not only can accurately predict the protein torsion angle, but also has the advantages of small model and high prediction speed.

Description

technical field [0001] The invention belongs to the technical field of bioinformatics, and in particular relates to a protein torsion angle prediction method based on a lightweight deep convolutional network. Background technique [0002] Predicting torsion angles based on amino acid sequences of proteins is an important task in computational molecular biology. The function of a protein is determined by its structure. However, determining protein structures using experimental methods such as X-ray crystallography and NMR is extremely expensive and time-consuming. Therefore, it is very necessary to determine the structure of proteins by calculation. For a protein chain consisting of L amino acid residues, its protein backbone is a repeating sequence consisting of nitrogen atoms, α-carbon atoms and carbon atoms: N (1) , C (1) , N (2) , C (2) ,...,N (L) 、C ( α L) 、C (L) . In particular, the twist angle Psi is given by N (i) , and C (i) determined plane with ...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems of many parameters, large network model and long prediction time in the existing protein torsion angle prediction model, the present invention proposes a protein torsion angle prediction method based on lightweight deep convolutional network, which uses the physical and chemical properties of protein amino acids Properties and PSSM spectral matrix represent protein sequence features, and the lightweight convolutional network designed based on depth separable convolution realizes the prediction of protein torsion angle. The method proposed in the present invention can not only accurately predict protein torsion angle, but also has a small model, Advantages of Fast Forecasting

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