ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
S-PLM: Structure-Aware Protein Language Model via Contrastive Learning Between Sequence and Structure.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- Evaluating AlphaFold Tools and Related Scoring Functions for Protein-peptide Complex Prediction.Genomics, proteomics & bioinformatics · 2026Article
- Contrastive learning unites sequence and structure in a global representation of protein space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- CLASPP: A unified model for predicting post-translational modifications.PLoS computational biology · 2026Article
- CLASPP: A unified model for predicting post-translational modifications.bioRxiv : the preprint server for biology · 2026Article
- AI-enabled protein design facilitates future plant research and crop breeding.Plant physiology · 2026Review
- eSIG-Net: Accurate prediction of single-mutation induced perturbations on protein interactions using a language model.bioRxiv : the preprint server for biology · 2026Article
- Article
- BCRInsight: an antibody language model to decode biological signals from BCR sequences.Briefings in bioinformatics · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- DSSA-PPI: enhancing binding affinity change prediction upon protein mutations using disentangled structure-sequence aware attention.Chemical science · 2026Article
- Deciphering DEL pocket patterns through contrastive learning.Nature communications · 2026Article
- CACLENS: A Multitask Deep Learning System for Enzyme Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Learnable protein representations in computational biology for predicting drug-target affinity.Journal of cheminformatics · 2026Review
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
- pLM-SAV: a Δ-embedding approach for predicting pathogenic single amino acid variants.Bioinformatics advances · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- M3Site: multiclass multimodal learning for protein active site identification and classification.Briefings in bioinformatics · 2025Article
- A deep learning method for predicting interactions for intrinsically disordered regions of proteins.bioRxiv : the preprint server for biology · 2025Article
- MULoc-target: Targeting peptide classification and detection using a protein language model.Briefings in bioinformatics · 2025Article
- MTPrompt-PTM: A Multi-Task Method for Post-Translational Modification Prediction Using Prompt Tuning on a Structure-Aware Protein Language Model.Biomolecules · 2025Article
Corrections and comments
- Update of
Authors and funding
11 authors.
Funding
Abstract
Proteins play an essential role in various biological and engineering processes. Large protein language models (PLMs) present excellent potential to reshape protein research by accelerating the determination of protein functions and the design of proteins with the desired functions. The prediction and design capacity of PLMs relies on the representation gained from the protein sequences. However, the lack of crucial 3D structure information in most PLMs restricts the prediction capacity of PLMs in various applications, especially those heavily dependent on 3D structures. To address this issue, S-PLM is introduced as a 3D structure-aware PLM that utilizes multi-view contrastive learning to align the sequence and 3D structure of a protein in a coordinated latent space. S-PLM applies Swin-Transformer on AlphaFold-predicted protein structures to embed the structural information and fuses it into sequence-based embedding from ESM2. Additionally, a library of lightweight tuning tools is provided to adapt S-PLM for diverse downstream protein prediction tasks. The results demonstrate S-PLM's superior performance over sequence-only PLMs on all protein clustering and classification tasks, achieving competitiveness comparable to state-of-the-art methods requiring both sequence and structure inputs. S-PLM and its lightweight tuning tools are available at https://github.com/duolinwang/S-PLM/.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.