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1. Protein structure prediction using deep learning distance and hydrogen‐bonding restraints in CASP14

   https://doi.org/10.1002/prot.26193  

   Zheng, Wei ; Li, Yang ; Zhang, Chengxin ; Zhou, Xiaogen ; Pearce, Robin ; Bell, Eric W. ; Huang, Xiaoqiang ; Zhang, Yang ( August 2021 , Proteins: Structure, Function, and Bioinformatics)

   In this article, we report 3D structure prediction results by two of our best server groups (“Zhang‐Server” and “QUARK”) in CASP14. These two servers were built based on the D‐I‐TASSER and D‐QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I‐TASSER and QUARK, respectively. The new components include: (a) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (b) a contact‐based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (c) a residual convolutional neural network‐based method, DeepPotential, to predict multiple spatial restraints by co‐evolutionary features derived from the MSA; and (d) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM‐scores of the first models produced by D‐I‐TASSER and D‐QUARK were 96% and 112% higher than those constructed by I‐TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well‐tuned force field that combines spatial restraints, threading templates, and generic knowledge‐based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi‐domain proteins due to low accuracy in inter‐domain distance prediction and modeling protein domains from oligomer complexes, as the co‐evolutionary analysis cannot distinguish inter‐chain and intra‐chain distances. Specifically tuning the deep learning‐based predictors for multi‐domain targets and protein complexes may be helpful to address these issues.

    

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2. Ensembling multiple raw coevolutionary features with deep residual neural networks for contact‐map prediction in CASP13

   https://doi.org/10.1002/prot.25798  

   Li, Yang ; Zhang, Chengxin ; Bell, Eric W. ; Yu, Dong‐Jun ; Zhang, Yang ( August 2019 , Proteins: Structure, Function, and Bioinformatics)

   We report the results of residue‐residue contact prediction of a new pipeline built purely on the learning of coevolutionary features in the CASP13 experiment. For a query sequence, the pipeline starts with the collection of multiple sequence alignments (MSAs) from multiple genome and metagenome sequence databases using two complementary Hidden Markov Model (HMM)‐based searching tools. Three profile matrices, built on covariance, precision, and pseudolikelihood maximization respectively, are then created from the MSAs, which are used as the input features of a deep residual convolutional neural network architecture for contact‐map training and prediction. Two ensembling strategies have been proposed to integrate the matrix features through end‐to‐end training and stacking, resulting in two complementary programs called TripletRes and ResTriplet, respectively. For the 31 free‐modeling domains that do not have homologous templates in the PDB, TripletRes and ResTriplet generated comparable results with an average accuracy of 0.640 and 0.646, respectively, for the topL/5 long‐range predictions, where 71% and 74% of the cases have an accuracy above 0.5. Detailed data analyses showed that the strength of the pipeline is due to the sensitive MSA construction and the advanced strategies for coevolutionary feature ensembling. Domain splitting was also found to help enhance the contact prediction performance. Nevertheless, contact models for tail regions, which often involve a high number of alignment gaps, and for targets with few homologous sequences are still suboptimal. Development of new approaches where the model is specifically trained on these regions and targets might help address these problems.

    

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3. Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions

   https://doi.org/10.1038/s41467-021-25316-w  

   Mortuza, S. M. ; Zheng, Wei ; Zhang, Chengxin ; Li, Yang ; Pearce, Robin ; Zhang, Yang ( August 2021 , Nature Communications)

   Sequence-based contact prediction has shown considerable promise in assisting non-homologous structure modeling, but it often requires many homologous sequences and a sufficient number of correct contacts to achieve correct folds. Here, we developed a method, C-QUARK, that integrates multiple deep-learning and coevolution-based contact-maps to guide the replica-exchange Monte Carlo fragment assembly simulations. The method was tested on 247 non-redundant proteins, where C-QUARK could fold 75% of the cases with TM-scores (template-modeling scores) ≥0.5, which was 2.6 times more than that achieved by QUARK. For the 59 cases that had either low contact accuracy or few homologous sequences, C-QUARK correctly folded 6 times more proteins than other contact-based folding methods. C-QUARK was also tested on 64 free-modeling targets from the 13th CASP (critical assessment of protein structure prediction) experiment and had an average GDT_TS (global distance test) score that was 5% higher than the best CASP predictors. These data demonstrate, in a robust manner, the progress in modeling non-homologous protein structures using low-accuracy and sparse contact-map predictions.

    

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4. Integrating deep learning, threading alignments, and a multi‐MSA strategy for high‐quality protein monomer and complex structure prediction in CASP15

   https://doi.org/10.1002/prot.26585  

   Zheng, Wei ; Wuyun, Qiqige ; Freddolino, Peter L. ; Zhang, Yang ( August 2023 , Proteins: Structure, Function, and Bioinformatics)

   We report the results of the “UM‐TBM” and “Zheng” groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were obtained using the D‐I‐TASSER and DMFold‐Multimer algorithms, respectively. For monomer structure prediction, D‐I‐TASSER introduced four new features during CASP15: (i) a multiple sequence alignment (MSA) generation protocol that combines multi‐source MSA searching and a structural modeling‐based MSA ranker; (ii) attention‐network based spatial restraints; (iii) a multi‐domain module containing domain partition and arrangement for domain‐level templates and spatial restraints; (iv) an optimized I‐TASSER‐based folding simulation system for full‐length model creation guided by a combination of deep learning restraints, threading alignments, and knowledge‐based potentials. For 47 free modeling targets in CASP15, the final models predicted by D‐I‐TASSER showed average TM‐score 19% higher than the standard AlphaFold2 program. We thus showed that traditional Monte Carlo‐based folding simulations, when appropriately coupled with deep learning algorithms, can generate models with improved accuracy over end‐to‐end deep learning methods alone. For protein complex structure prediction, DMFold‐Multimer generated models by integrating a new MSA generation algorithm (DeepMSA2) with the end‐to‐end modeling module from AlphaFold2‐Multimer. For the 38 complex targets, DMFold‐Multimer generated models with an average TM‐score of 0.83 and Interface Contact Score of 0.60, both significantly higher than those of competing complex prediction tools. Our analyses on complexes highlighted the critical role played by MSA generating, ranking, and pairing in protein complex structure prediction. We also discuss future room for improvement in the areas of viral protein modeling and complex model ranking.

    

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5. 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)

   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.

    

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