More Like this

1. Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

   https://doi.org/10.1038/s41598-021-90303-6  

   Chen, Xiao; Liu, Jian; Guo, Zhiye; Wu, Tianqi; Hou, Jie; Cheng, Jianlin (December 2021, Scientific Reports)

   null (Ed.)

   Abstract The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, during the 2020 CASP14 experiment, we integrated several new inter-residue distance features with the existing model quality assessment features in several deep learning methods to predict the quality of protein structural models. According to the evaluation of performance in selecting the best model from the models of CASP14 targets, our three multi-model predictors of estimating model accuracy (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) achieve the averaged loss of 0.073, 0.079, and 0.081, respectively, in terms of the global distance test score (GDT-TS). The three methods are ranked first, second, and third out of all 68 CASP14 predictors. MULTICOM-DEEP, the single-model predictor of estimating model accuracy (EMA), is ranked within top 10 among all the single-model EMA methods according to GDT-TS score loss. The results demonstrate that inter-residue distance features are valuable inputs for deep learning to predict the quality of protein structural models. However, larger training datasets and better ways of leveraging inter-residue distance information are needed to fully explore its potentials. 

   more » « less
2. Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

   https://doi.org/10.1002/prot.26186  

   Liu, Jian; Wu, Tianqi; Guo, Zhiye; Hou, Jie; Cheng, Jianlin (July 2021, Proteins: Structure, Function, and Bioinformatics)

   Abstract Substantial progresses in protein structure prediction have been made by utilizing deep‐learning and residue‐residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by incorporating three new components: (a) a new deep learning‐based protein inter‐residue distance predictor to improve template‐free (ab initio) tertiary structure prediction, (b) an enhanced template‐based tertiary structure prediction method, and (c) distance‐based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked seventh out of 146 predictors in tertiary structure prediction and ranked third out of 136 predictors in inter‐domain structure prediction. The results demonstrate that the template‐free modeling based on deep learning and residue‐residue distance prediction can predict the correct topology for almost all template‐based modeling targets and a majority of hard targets (template‐free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. Moreover, the template‐free modeling performs better than the template‐based modeling on not only hard targets but also the targets that have homologous templates. The performance of the template‐free modeling largely depends on the accuracy of distance prediction closely related to the quality of multiple sequence alignments. The structural model quality assessment works well on targets for which enough good models can be predicted, but it may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed. MULTICOM is available athttps://github.com/jianlin-cheng/MULTICOM_Human_CASP14/tree/CASP14_DeepRank3andhttps://github.com/multicom-toolbox/multicom/tree/multicom_v2.0. 

   more » « less
3. Small angle X‐ray scattering‐assisted protein structure prediction in CASP13 and emergence of solution structure differences

   https://doi.org/10.1002/prot.25827  

   Hura, Greg L.; Hodge, Curtis D.; Rosenberg, Daniel; Guzenko, Dmytro; Duarte, Jose M.; Monastyrskyy, Bohdan; Grudinin, Sergei; Kryshtafovych, Andriy; Tainer, John A.; Fidelis, Krzysztof; et al (October 2019, Proteins: Structure, Function, and Bioinformatics)

   Abstract Small angle X‐ray scattering (SAXS) measures comprehensive distance information on a protein's structure, which can constrain and guide computational structure prediction algorithms. Here, we evaluate structure predictions of 11 monomeric and oligomeric proteins for which SAXS data were collected and provided to predictors in the 13th round of the Critical Assessment of protein Structure Prediction (CASP13). The category for SAXS‐assisted predictions made gains in certain areas for CASP13 compared to CASP12. Improvements included higher quality data with size exclusion chromatography‐SAXS (SEC‐SAXS) and better selection of targets and communication of results by CASP organizers. In several cases, we can track improvements in model accuracy with use of SAXS data. For hard multimeric targets where regular folding algorithms were unsuccessful, SAXS data helped predictors to build models better resembling the global shape of the target. For most models, however, no significant improvement in model accuracy at the domain level was registered from use of SAXS data, when rigorously comparing SAXS‐assisted models to the best regular server predictions. To promote future progress in this category, we identify successes, challenges, and opportunities for improved strategies in prediction, assessment, and communication of SAXS data to predictors. An important observation is that, for many targets, SAXS data were inconsistent with crystal structures, suggesting that these proteins adopt different conformation(s) in solution. This CASP13 result, if representative of PDB structures and future CASP targets, may have substantive implications for the structure training databases used for machine learning, CASP, and use of prediction models for biology. 

   more » « less
4. Protein structure accuracy estimation using geometry‐complete perceptron networks

   https://doi.org/10.1002/pro.4932  

   Morehead, Alex; Liu, Jian; Cheng, Jianlin (March 2024, Protein Science)

   Abstract Estimating the accuracy of protein structural models is a critical task in protein bioinformatics. The need for robust methods in the estimation of protein model accuracy (EMA) is prevalent in the field of protein structure prediction, where computationally‐predicted structures need to be screened rapidly for the reliability of the positions predicted for each of their amino acid residues and their overall quality. Current methods proposed for EMA are either coupled tightly to existing protein structure prediction methods or evaluate protein structures without sufficiently leveraging the rich, geometric information available in such structures to guide accuracy estimation. In this work, we propose a geometric message passing neural network referred to as the geometry‐complete perceptron network for protein structure EMA (GCPNet‐EMA), where we demonstrate through rigorous computational benchmarks that GCPNet‐EMA's accuracy estimations are 47% faster and more than 10% (6%) more correlated with ground‐truth measures of per‐residue (per‐target) structural accuracy compared to baseline state‐of‐the‐art methods for tertiary (multimer) structure EMA including AlphaFold 2. The source code and data for GCPNet‐EMA are available on GitHub, and a public web server implementation is freely available. 

   more » « less
5. DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

   https://doi.org/10.1186/s12859-021-03960-9  

   Wu, Tianqi; Guo, Zhiye; Hou, Jie; Cheng, Jianlin (December 2021, BMC Bioinformatics)

   null (Ed.)

   Abstract Background Driven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently, most of the distance prediction methods classify inter-residue distances into multiple distance intervals instead of directly predicting real-value distances. The output of the former has to be converted into real-value distances to be used in tertiary structure prediction. Results To explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction. The average mean square error (MSE) of DeepDist’s real-value distance prediction is 0.896 Å 2 when filtering out the predicted distance ≥ 16 Å, which is lower than 1.003 Å 2 of DeepDist’s multi-class distance prediction. When distance predictions are converted into contact predictions at 8 Å threshold (the standard threshold in the field), the precision of top L/5 and L/2 contact predictions of DeepDist’s multi-class distance prediction is 79.3% and 66.1%, respectively, higher than 78.6% and 64.5% of its real-value distance prediction and the best results in the CASP13 experiment. Conclusions DeepDist can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE. Finally, we demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone. 

   more » « less