ArticleBioinformatics (Oxford, England)2018
DeepSF: deep convolutional neural network for mapping protein sequences to folds.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.
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Who cites it
65 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- ProtDML: label-aware representation learning for broad-spectrum protein function prediction.Briefings in bioinformatics · 2026Article
- Evolutionary profile enhancement improves protein function annotation for remote homologs.bioRxiv : the preprint server for biology · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Boosting AlphaFold protein tertiary structure prediction through MSA engineering and extensive model sampling and ranking in CASP16.Communications biology · 2025Article
- Endowing protein language models with structural knowledge.Bioinformatics (Oxford, England) · 2025Article
- SLAE: Strictly Local All-atom Environment for Protein Representation.bioRxiv : the preprint server for biology · 2025Article
- DeepProtein: deep learning library and benchmark for protein sequence learning.Bioinformatics (Oxford, England) · 2025Article
- Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment.BMC genomics · 2025Article
- $\mathcal{S}$ able: bridging the gap in protein structure understanding with an empowering and versatile pre-training paradigm.Briefings in bioinformatics · 2025Article
- Deep learning-based prediction of autoimmune diseases.Scientific reports · 2025Article
- S-PLM: Structure-Aware Protein Language Model via Contrastive Learning Between Sequence and Structure.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Convolutional-LSTM approach for temporal catch hotspots (CATCH): an AI-driven model for spatiotemporal forecasting of fisheries catch probability densities.Biology methods & protocols · 2025Article
- AllerTrans: a deep learning method for predicting the allergenicity of protein sequences.Biology methods & protocols · 2025Article
- Improving compound-protein interaction prediction by focusing on intra-modality and inter-modality dynamics with a multimodal tensor fusion strategy.Computational and structural biotechnology journal · 2024Article
- Generative β-hairpin design using a residue-based physicochemical property landscape.Biophysical journal · 2024Article
- PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications.Journal of cheminformatics · 2024Article
- Article
- Effect of tokenization on transformers for biological sequences.Bioinformatics (Oxford, England) · 2024Article
5 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
Motivation: Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice. Results: We develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein sequence into one of 1195 known folds, which is useful for both fold recognition and the study of sequence-structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and maps it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding an average classification accuracy of 75.3%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 73.0%. We compare our method with a top profile-profile alignment method-HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 12.63-26.32% higher than HHSearch on template-free modeling targets and 3.39-17.09% higher on hard template-based modeling targets for top 1, 5 and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking. Availability and implementation: The DeepSF server is publicly available at: http://iris.rnet.missouri.edu/DeepSF/. Contact: chengji@missouri.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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