Two New Heuristic Methods for Protein Model Quality Assessment

MUfoldQA_G: High-accuracy protein model QA via retraining and transformation

Protein tertiary structure prediction is an active research area and has attracted significant attention recently due to the success of AlphaFold from DeepMind. Methods capable of accurately evaluating the quality of predicted models are of great importance. In the past, although many model quality assessment (QA) methods have been developed, their accuracies are not consistently high across different QA performance metrics for diverse target proteins. In this paper, we propose MUfoldQA_G, a new multi-model QA method that aims at simultaneously optimizing Pearson correlation and average GDT-TS difference, two commonly used QA performance metrics. This method is based on two new algorithms MUfoldQA_Gp and MUfoldQA_Gr. MUfoldQA_Gp uses a new technique to combine information from protein templates and reference protein models to maximize the Pearson correlation QA metric. MUfoldQA_Gr employs a new machine learning technique that resamples training data and retrains adaptively to learn a consensus model that is better than naïve consensus while minimizing average GDT-TS difference. MUfoldQA_G uses a new method to combine the results of MUfoldQA_Gr and MUfoldQA_Gp so that the final QA prediction results achieve low average GDT-TS difference that is close to the results from MUfoldQA_Gr, while maintaining high Pearson correlation that is the same as the results from MUfoldQA_Gp. In CASP14 QA categories, MUfoldQA_G ranked No. 1 in Pearson correlation and No. 2 in average GDT-TS difference.

Probabilistic divergence of a template-based modelling methodology from the ideal protocol

Protein structural information is essential for the detailed mapping of a functional protein network. For a higher modelling accuracy and quicker implementation, template-based algorithms have been extensively deployed and redefined. The methods only assess the predicted structure against its native state/template and do not estimate the accuracy for each modelling step. A divergence measure is therefore postulated to estimate the modelling accuracy against its theoretical optimal benchmark. By freezing the domain boundaries, the divergence measures are predicted for the most crucial steps of a modelling algorithm. To precisely refine the score using weighting constants, big data analysis could further be deployed.

PSICA: a fast and accurate web service for protein model quality analysis

This paper presents a new fast and accurate web service for protein model quality analysis, called PSICA (Protein Structural Information Conformity Analysis). It is designed to evaluate how much a tertiary model of a given protein primary sequence conforms to the known protein structures of similar protein sequences, and to evaluate the quality of predicted protein models. PSICA implements the MUfoldQA_S method, an efficient state-of-the-art protein model quality assessment (QA) method. In CASP12, MUfoldQA_S ranked No. 1 in the protein model QA select-20 category in terms of the difference between the predicted and true GDT-TS value of each model. For a given predicted 3D model, PSICA generates (i) predicted global GDT-TS value; (ii) interactive comparison between the model and other known protein structures; (iii) visualization of the predicted local quality of the model; and (iv) JSmol rendering of the model. Additionally, PSICA implements MUfoldQA_C, a new consensus method based on MUfoldQA_S. In CASP12, MUfoldQA_C ranked No. 1 in top 1 model GDT-TS loss on the select-20 QA category and No. 2 in the average difference between the predicted and true GDT-TS value of each model for both select-20 and best-150 QA categories. The PSICA server is freely available at http://qas.wangwb.com/∼wwr34/mufoldqa/index.html.

Machine Learning Approaches for Quality Assessment of Protein Structures

Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly and time-consuming, and computational predictions of protein structures have not been perfected. Methods that assess the quality of protein models can help in selecting the most accurate candidates for further work. Driven by this demand, many structural bioinformatics laboratories have developed methods for estimating model accuracy (EMA). In recent years, EMA by machine learning (ML) have consistently ranked among the top-performing methods in the community-wide CASP challenge. Accordingly, we systematically review all the major ML-based EMA methods developed within the past ten years. The methods are grouped by their employed ML approach-support vector machine, artificial neural networks, ensemble learning, or Bayesian learning-and their significances are discussed from a methodology viewpoint. To orient the reader, we also briefly describe the background of EMA, including the CASP challenge and its evaluation metrics, and introduce the major ML/DL techniques. Overall, this review provides an introductory guide to modern research on protein quality assessment and directions for future research in this area.