ArticleBioinformatics (Oxford, England)2022
SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- 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
- Learnable protein representations in computational biology for predicting drug-target affinity.Journal of cheminformatics · 2026Review
- Root-associated protein prediction using a protein large language model and hypergraph convolutional networks.Scientific reports · 2026Article
- Advancing the Accuracy of Anti-MRSA Peptide Prediction Through Integrating Multi-Source Protein Language Models.Interdisciplinary sciences, computational life sciences · 2025Article
- A deep learning method for predicting interactions for intrinsically disordered regions of proteins.bioRxiv : the preprint server for biology · 2025Article
- Shared-weight graph framework for comprehensive protein stability prediction across diverse mutation types.Briefings in bioinformatics · 2025Article
- ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks.ACS omega · 2025Article
- GRACE: Generative Redesign in Artificial Computational Enzymology.ACS synthetic biology · 2024Article
- PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.BMC bioinformatics · 2024Article
- Application of Transformers in Cheminformatics.Journal of chemical information and modeling · 2024Review
- Freeprotmap: waiting-free prediction method for protein distance map.BMC bioinformatics · 2024Article
- SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model.NAR genomics and bioinformatics · 2024Article
- Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins.Nature protocols · 2023Review
- Review
- SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction.Bioinformatics advances · 2023Article
- E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants.Bioinformatics (Oxford, England) · 2022Article
- Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction.Biology · 2022Article
- BIAPSS: A Comprehensive Physicochemical Analyzer of Proteins Undergoing Liquid-Liquid Phase Separation.International journal of molecular sciences · 2022Article
- Inter-Residue Distance Prediction From Duet Deep Learning Models.Frontiers in genetics · 2022Article
- Deep learning for protein secondary structure prediction: Pre and post-AlphaFold.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationAccurate prediction of protein contact-map is essential for accurate protein structure and function prediction. As a result, many methods have been developed for protein contact map prediction. However, most methods rely on protein-sequence-evolutionary information, which may not exist for many proteins due to lack of naturally occurring homologous sequences. Moreover, generating evolutionary profiles is computationally intensive. Here, we developed a contact-map predictor utilizing the output of a pre-trained language model ESM-1b as an input along with a large training set and an ensemble of residual neural networks.
resultsWe showed that the proposed method makes a significant improvement over a single-sequence-based predictor SSCpred with 15% improvement in the F1-score for the independent CASP14-FM test set. It also outperforms evolutionary-profile-based methods trRosetta and SPOT-Contact with 48.7% and 48.5% respective improvement in the F1-score on the proteins without homologs (Neff = 1) in the independent SPOT-2018 set. The new method provides a much faster and reasonably accurate alternative to evolution-based methods, useful for large-scale prediction. AVAILABILITY AND IMPLEMENTATION: Stand-alone-version of SPOT-Contact-LM is available at https://github.com/jas-preet/SPOT-Contact-Single. Direct prediction can also be made at https://sparks-lab.org/server/spot-contact-single. The datasets used in this research can also be downloaded from the GitHub. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.