ArticleProteins2014
Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles.
Article in Proteins, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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.
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, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- ProtSeqGen: a novel deep learning model for protein sequence design.BMC bioinformatics · 2026Article
- DivPro: diverse protein sequence design with direct structure recovery guidance.Bioinformatics (Oxford, England) · 2025Article
- How well do contextual protein encodings learn structure, function, and evolutionary context?Cell systems · 2025Article
- $\mathcal{S}$ able: bridging the gap in protein structure understanding with an empowering and versatile pre-training paradigm.Briefings in bioinformatics · 2025Article
- ScFold: a GNN-based model for efficient inverse folding of short-chain proteins via spatial reduction.Briefings in bioinformatics · 2025Article
- AntiFold: improved structure-based antibody design using inverse folding.Bioinformatics advances · 2025Article
- DIProT: A deep learning based interactive toolkit for efficient and effective Protein design.Synthetic and systems biotechnology · 2024Article
- A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation.Briefings in bioinformatics · 2024Article
- SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.Briefings in bioinformatics · 2024Article
- 3 = 1 + 2: how the divide conquered de novo protein structure prediction and what is next?National science review · 2023Article
- SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network.PLoS computational biology · 2023Article
- ProRefiner: an entropy-based refining strategy for inverse protein folding with global graph attention.Nature communications · 2023Article
- Contextual protein and antibody encodings from equivariant graph transformers.bioRxiv : the preprint server for biology · 2023Article
- From sequence to function through structure: Deep learning for protein design.Computational and structural biotechnology journal · 2023Review
- Protein sequence design with a learned potential.Nature communications · 2022Article
- Structure-based protein design with deep learning.Current opinion in chemical biology · 2021Review
- Protein Design with Deep Learning.International journal of molecular sciences · 2021Review
- Data-driven computational protein design.Current opinion in structural biology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Locating sequences compatible with a protein structural fold is the well-known inverse protein-folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy-optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment-derived sequence profiles and structure-derived energy profiles. SPIN improves over the fragment-derived profile by 6.7% (from 23.6 to 30.3%) in sequence identity between predicted and wild-type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significantly better balance of hydrophilic and hydrophobic residues at protein surface. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single-body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks-lab.org.
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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.