deep learning applied to sa prediction hai'e gong mar 22ndcasp/temp/20160322.pdf · mat lab...

Deep learning applied to SA prediction

Hai'e GongMar 22nd, 2016

● Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

● Rhys Heffernan, Kuldip Paliwal, James Lyons, Abdollah Dehzangi, Alok Sharma, Jihua Wang, Abdul Sattar, Yuedong Yang, Yaoqi Zhou

● Scientific Reports

Abstract

Protein structure prediction

Secondary structure

Angles

ASA

The contribution of iteration

Related work

This method: SPIDER2

Datasets

Model

Architechture

Iterative algorithm

Iteration performace

Iteration performace on SS

Iteration performace on prediction accuracy of angles

RMSD of constructed structures by angles

Comparision with other tools

Q3 and its misclassifications

Comparision with models in CASP 11

Contructed structures by angles

The contribution of other structural info except for predicted SS

Applications of the prediction results

Summary

● Use angles as features when predicting strutural information.

● Apply architecture of iterative algorithms to our work.

● Use bigger dataset.● Broaden the window size around the target

residue.