ArticleACS biomaterials science & engineering2022
End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins.
Article in ACS biomaterials science & engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Artificial intelligence for design strategies of tissue engineering materials.Fundamental research · 2026Review
- Identification and Validation of Ferroptosis-Related Biomarkers and Therapeutic Targets in ARDS: A Bioinformatics and Experimental Study.Journal of inflammation research · 2026Article
- Systematic evaluation of predictors for binding free energy changes upon mutations in protein complexes.Briefings in bioinformatics · 2025Article
- GRACE: Generative Redesign in Artificial Computational Enzymology.ACS synthetic biology · 2024Article
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- Diffusion model assisted designing self-assembling collagen mimetic peptides as biocompatible materials.Briefings in bioinformatics · 2024Article
- Progress in Multiscale Modeling of Silk Materials.Biomacromolecules · 2024Review
- ProtAgents: protein discoveryDigital discovery · 2024Article
- Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design.ACS engineering Au · 2024Article
- ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model.Science advances · 2024Article
- Untapped Potential of Deep Eutectic Solvents for the Synthesis of Bioinspired Inorganic-Organic Materials.Chemistry of materials : a publication of the American Chemical Society · 2023Review
- Generative models for protein sequence modeling: recent advances and future directions.Briefings in bioinformatics · 2023Review
- Generative design ofChem · 2023Article
- Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins.Computational and structural biotechnology journal · 2023Article
- qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids.Protein science : a publication of the Protein Society · 2023Article
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7 authors.
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Abstract
Structural proteins are the basis of many biomaterials and key construction and functional components of all life. Further, it is well-known that the diversity of proteins' function relies on their local structures derived from their primary amino acid sequences. Here, we report a deep learning model to predict the secondary structure content of proteins directly from primary sequences, with high computational efficiency. Understanding the secondary structure content of proteins is crucial to designing proteins with targeted material functions, especially mechanical properties. Using convolutional and recurrent architectures and natural language models, our deep learning model predicts the content of two essential types of secondary structures, the α-helix and the β-sheet. The training data are collected from the Protein Data Bank and contain many existing protein geometries. We find that our model can learn the hidden features as patterns of input sequences that can then be directly related to secondary structure content. The α-helix and β-sheet content predictions show excellent agreement with training data and newly deposited protein structures that were recently identified and that were not included in the original training set. We further demonstrate the features of the model by a search for de novo protein sequences that optimize max/min α-helix/β-sheet content and compare the predictions with folded models of these sequences based on
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