Protein Structure Prediction Of Fusarium Pseudograminearum Base On Deep Learning

In nature,living objects are called organisms,and nucleic acids and proteins together constitute the basic material composition of organisms.Biology is a branch of the natural sciences.The scale of its research has gradually shifted from experimental analysis and data accumulation to the analysis of existing biological data,as well as the experimental confirmation under its guidance.With the development of life sciences,thinking has gradually changed from analysis to system.The integrated thinking and finding some powerful data analysis methods and tools have gradually become the focus of the development of life sciences.At present,computer science technology and machine learning algorithms based on the processing and analysis of biological data have achieved outstanding results in the field of bioinformatics.The combination of technology and algorithms has gradually penetrated into every corner of the field of bioinformatics.The protein model structure simulated by the computer prediction method plays an important role in the study of protein structure characteristics,molecular interaction mechanisms,and the design of anti-bacterial drugs.The main research object of this article is Fusarium pseudograss.If you want to solve the genetic mystery of a strain,it is often not enough to explore the arrangement rules of the bases in its genome.It is also necessary to understand the product of the strain's gene-protein.The direction of the article is to explore the protein level of the target strain,and based on the deep learning method to study the secondary structure of the target strain Fusarium pseudograminum and predict it,which will help to further understand and explore the special properties and three-dimensional structure of the protein.And the important functions closely related to it and how they interact with each other.Up to now,through crystal X-ray diffraction methods and nuclear magnetic resonance test methods,the protein conformation can usually be obtained when a certain standard is reached.However,these analytical methods have high requirements on the premise of the ontology,harsh experimental conditions,and expensive operation costs.Professionals must be required to operate,the experiment takes a long time,and the result is often not guaranteed to be able to resolve the structure,so it is very necessary to estimate the structure by means of fusion algorithms.This paper uses the convolutional neural network algorithm in deep learning,which can process the selected public data sets Cull PDB 6133 and CB513,effectively learn the corresponding features from a large number of samples,and obtain the expected prediction accuracy,and finally can use CNN training A good model predicts your own experimental data and achieves the desired result.