ArticleProteins2019
Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13.
Article in Proteins, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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55 citing papers in PubMed.
- Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs, Model Ranking, and Deep Learning.Proteins · 2026Article
- Protein domain embeddings for fast and accurate similarity search.Genome research · 2024Article
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
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- Microbiome-based enrichment pattern mining has enabled a deeper understanding of the biome-species-function relationship.Communications biology · 2023Article
- Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks.Nature communications · 2022Article
- I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.Nature protocols · 2022Review
- Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).The protein journal · 2022Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Evolution of a chordate-specific mechanism for myoblast fusion.Science advances · 2022Article
- cpxDeepMSA: A Deep Cascade Algorithm for Constructing Multiple Sequence Alignments of Protein-Protein Interactions.International journal of molecular sciences · 2022Article
- Soluble CD13 induces inflammatory arthritis by activating the bradykinin receptor B1.The Journal of clinical investigation · 2022Article
- Reaching alignment-profile-based accuracy in predicting protein secondary and tertiary structural properties without alignment.Scientific reports · 2022Article
- Deep learning methods for 3D structural proteome and interactome modeling.Current opinion in structural biology · 2022Review
- Progressive assembly of multi-domain protein structures from cryo-EM density maps.Nature computational science · 2022Article
- SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model.Bioinformatics (Oxford, England) · 2022Article
- Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins.PLoS computational biology · 2022Article
- Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Using metagenomic data to boost protein structure prediction and discovery.Computational and structural biotechnology journal · 2022Review
- Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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5 authors.
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Abstract
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 top L/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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