ArticleScientific reports2018
Design of metalloproteins and novel protein folds using variational autoencoders.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
What it found
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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.
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Who cites it
55 citing papers in PubMed, 140 citations in OpenAlex.
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- De novo functional protein sequence generation: overcoming data scarcity through regeneration and large language models.Briefings in bioinformatics · 2026Article
- PHA synthase variant design using a conditional variational autoencoder.PLoS computational biology · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- AI-driven genotype-phenotype modeling: a framework integrating multi-modal single-cell genomics and reverse vaccinology forFrontiers in genetics · 2026Review
- Multimodal diffusion for joint design of protein sequence and structure.Protein science : a publication of the Protein Society · 2025Article
- ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Generative Landscapes and Dynamics to Design Functional Multidomain Artificial Transmembrane Transporters.ACS central science · 2025Article
- Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.Molecules and cells · 2025Review
- ScFold: a GNN-based model for efficient inverse folding of short-chain proteins via spatial reduction.Briefings in bioinformatics · 2025Article
- Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review.Computational and structural biotechnology journal · 2024Review
- ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models.Structural dynamics (Melville, N.Y.) · 2024Article
- Deep learning in template-free de novo biosynthetic pathway design of natural products.Briefings in bioinformatics · 2024Review
- Deep-learning-based design of synthetic orthologs of SH3 signaling domains.Cell systems · 2024Article
- Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.International journal of molecular sciences · 2024Article
- ProtAgents: protein discoveryDigital discovery · 2024Article
- Diffusion models in bioinformatics and computational biology.Nature reviews bioengineering · 2024Article
- Sparks of function by de novo protein design.Nature biotechnology · 2024Review
- Forecasting SARS-CoV-2 spike protein evolution from small data by deep learning and regression.Frontiers in systems biology · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
The design of novel proteins has many applications but remains an attritional process with success in isolated cases. Meanwhile, deep learning technologies have exploded in popularity in recent years and are increasingly applicable to biology due to the rise in available data. We attempt to link protein design and deep learning by using variational autoencoders to generate protein sequences conditioned on desired properties. Potential copper and calcium binding sites are added to non-metal binding proteins without human intervention and compared to a hidden Markov model. In another use case, a grammar of protein structures is developed and used to produce sequences for a novel protein topology. One candidate structure is found to be stable by molecular dynamics simulation. The ability of our model to confine the vast search space of protein sequences and to scale easily has the potential to assist in a variety of protein design tasks.
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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.