AngularQA: Protein Model Quality Assessment with LSTM Networks

Abstract

Quality Assessment (QA) plays an important role in protein structure prediction. Traditional multimodel QA method usually suffer from searching databases or comparing with other models for making predictions, which usually fail when the poor quality models dominate the model pool. We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (Cα) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. An LSTM network is used to predict the quality by treating each amino acid as a time-step and consider the final value returned by the LSTM cells. To the best of our knowledge, this is the first time anyone has attempted to use an LSTM model on the QA problem; furthermore, we use a new representation which has not been studied for QA. In addition to angles, we make use of sequence properties like secondary structure parsed from protein structure at each time-step without using any database, which is different than all existed QA methods. Our model achieves an overall correlation of 0.651 on the CASP12 testing dataset. Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. The software is freely available at GitHub: https://github.com/caorenzhi/AngularQA

Machine Learning Approaches for Quality Assessment of Protein Structures

“…Recently, Conover et al [78] introduced a novel LSTM model called AngularQA for protein quality estimation. The core features of AngularQA are the angles between and within the protein residues.…”

“…The Tau, Theta, Phi, and Delta angles in this method are weakly correlated to the GDT TS score. Conover et al also considered the amino acid type, secondary structure, protein properties (hydrophobicity, polarity, charge), and proximity counts of the residues [78]. In each time step, the features of one residue are input to the LSTM network for evaluation.…”

“…The same trend could be observed in Stage 2. Despite its less than stellar results on CASP12, LSTM is a promising avenue in EMA research [78].…”

“…The number of new DL approaches increases each year, providing increasingly advanced ML approaches for EMA research. For example, AngularQA [78], which has been recently proposed for quality assessment of protein structures, is the first EMA method built with the LSTM architecture. Innovative ML approaches provide another avenue for improving current EMA methods.…”

Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method

“…Machine learning method is a good choice to uncover underlying patterns (Stephenson et al, 2019). It has been widely employed in bioinformatics (Cao et al, 2017;Bao et al, 2019;Conover et al, 2019;Moritz et al, 2019;Stephenson et al, 2019;Zou and Ma, 2019;Sun et al, 2020). The current work aims to develop a machine learning based method to diagnose HCC within-sample REOs.…”