ReviewCurrent opinion in structural biology2021
Data-driven computational protein design.
Review in Current opinion in structural biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.bioRxiv : the preprint server for biology · 2026Article
- Evolutionary discovery and characterization of fungal transcriptional activators using active learning.bioRxiv : the preprint server for biology · 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
- Enhancing stability of enzymes for industrial applications: molecular insights and emerging approaches.World journal of microbiology & biotechnology · 2025Review
- Deep mutational scanning reveals a de novo disulfide bond and combinatorial mutations for engineering thermostable myoglobin.Protein science : a publication of the Protein Society · 2025Article
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Deep-learning-based design of synthetic orthologs of SH3 signaling domains.Cell systems · 2024Article
- Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families.Protein science : a publication of the Protein Society · 2024Article
- Computational Protein Design - Where it goes?Current medicinal chemistry · 2024Review
- What Strengthens Protein-Protein Interactions: Analysis and Applications of Residue Correlation Networks.Journal of molecular biology · 2023Article
- ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design.ACS synthetic biology · 2023Article
- Protein engineering via sequence-performance mapping.Cell systems · 2023Review
- Article
- Structural and computational design of a SARS-CoV-2 spike antigen with improved expression and immunogenicity.Science advances · 2023Article
- Data-driven strategies for the computational design of enzyme thermal stability: trends, perspectives, and prospects.Acta biochimica et biophysica Sinica · 2023Review
- Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.Protein science : a publication of the Protein Society · 2023Article
- Accelerating therapeutic protein design with computational approaches toward the clinical stage.Computational and structural biotechnology journal · 2023Review
- Bioprospecting of microbial enzymes: current trends in industry and healthcare.Applied microbiology and biotechnology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design.
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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.