ArticleScientific reports2022
Reaching alignment-profile-based accuracy in predicting protein secondary and tertiary structural properties without alignment.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 50 citations in OpenAlex.
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- PolyProline Predictor: A web server for empirical sequence-based prediction of polyproline II helices.Protein science : a publication of the Protein Society · 2026Article
- Improved prediction of microbial optimal growth temperatures with neural networks and protein language models.Frontiers in genetics · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- TruMPET: A New Method for Protein Secondary Structure Prediction Using Neural Networks Trained on Multiple Pre-Selected Physicochemical and Structural Features.International journal of molecular sciences · 2025Article
- MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction.Briefings in bioinformatics · 2025Article
- Sequence-Based Prediction for Protein Solvent Accessibility.International journal of molecular sciences · 2025Article
- Carmna: classification and regression models for nitrogenase activity based on a pretrained large protein language model.Briefings in bioinformatics · 2025Article
- PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs).Biomolecules · 2025Article
- Advancements in one-dimensional protein structure prediction using machine learning and deep learning.Computational and structural biotechnology journal · 2025Review
- DeepPredict: a state-of-the-art web server for protein secondary structure and relative solvent accessibility prediction.Frontiers in bioinformatics · 2025Article
- Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Porter 6: Protein Secondary Structure Prediction by Leveraging Pre-Trained Language Models (PLMs).International journal of molecular sciences · 2024Article
- MHTAPred-SS: A Highly Targeted Autoencoder-Driven Deep Multi-Task Learning Framework for Accurate Protein Secondary Structure Prediction.International journal of molecular sciences · 2024Article
- Parameterized hypercomplex convolutional network for accurate protein backbone torsion angle prediction.Scientific reports · 2024Article
- Article
- Exploring protein functions from structural flexibility using CABS-flex modeling.Protein science : a publication of the Protein Society · 2024Review
- PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial peptide identification with remarkable accuracy.Bioinformatics (Oxford, England) · 2024Article
- DeepSS2GO: protein function prediction from secondary structure.Briefings in bioinformatics · 2024Article
- SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model.NAR genomics and bioinformatics · 2024Article
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Authors and funding
5 authors at 1 institution in 2 countries.
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Abstract
Protein language models have emerged as an alternative to multiple sequence alignment for enriching sequence information and improving downstream prediction tasks such as biophysical, structural, and functional properties. Here we show that a method called SPOT-1D-LM combines traditional one-hot encoding with the embeddings from two different language models (ProtTrans and ESM-1b) for the input and yields a leap in accuracy over single-sequence-based techniques in predicting protein 1D secondary and tertiary structural properties, including backbone torsion angles, solvent accessibility and contact numbers for all six test sets (TEST2018, TEST2020, Neff1-2020, CASP12-FM, CASP13-FM and CASP14-FM). More significantly, it has a performance comparable to profile-based methods for those proteins with homologous sequences. For example, the accuracy for three-state secondary structure (SS3) prediction for TEST2018 and TEST2020 proteins are 86.7% and 79.8% by SPOT-1D-LM, compared to 74.3% and 73.4% by the single-sequence-based method SPOT-1D-Single and 86.2% and 80.5% by the profile-based method SPOT-1D, respectively. For proteins without homologous sequences (Neff1-2020) SS3 is 80.41% by SPOT-1D-LM which is 3.8% and 8.3% higher than SPOT-1D-Single and SPOT-1D, respectively. SPOT-1D-LM is expected to be useful for genome-wide analysis given its fast performance. Moreover, high-accuracy prediction of both secondary and tertiary structural properties such as backbone angles and solvent accessibility without sequence alignment suggests that highly accurate prediction of protein structures may be made without homologous sequences, the remaining obstacle in the post AlphaFold2 era.
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