ArticleProteins2019
Recent developments in deep learning applied to protein structure prediction.
Article in Proteins, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features.Scientific reports · 2023Article
- Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.Neural computing & applications · 2023Article
- Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications.Computational and structural biotechnology journal · 2023Review
- Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing.Biodesign research · 2022Review
- Mimetic Neural Networks: A Unified Framework for Protein Design and Folding.Frontiers in bioinformatics · 2022Article
- Structure-based protein design with deep learning.Current opinion in chemical biology · 2021Review
- Fold recognition by scoring protein maps using the congruence coefficient.Bioinformatics (Oxford, England) · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy.International journal of molecular sciences · 2021Article
- PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design.Biophysicist (Rockville, Md.) · 2021Article
- Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins.PLoS computational biology · 2021Article
- Advances in integrative structural biology: Towards understanding protein complexes in their cellular context.Computational and structural biotechnology journal · 2021Review
- Deep Learning in Protein Structural Modeling and Design.Patterns (New York, N.Y.) · 2020Review
- Deep Learning in Proteomics.Proteomics · 2020Review
- Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks.PLoS computational biology · 2020Article
- AllesTM: predicting multiple structural features of transmembrane proteins.BMC bioinformatics · 2020Article
- Enhancing fragment-based protein structure prediction by customising fragment cardinality according to local secondary structure.BMC bioinformatics · 2020Article
- Machine Learning Approaches for Quality Assessment of Protein Structures.Biomolecules · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Although many structural bioinformatics tools have been using neural network models for a long time, deep neural network (DNN) models have attracted considerable interest in recent years. Methods employing DNNs have had a significant impact in recent CASP experiments, notably in CASP12 and especially CASP13. In this article, we offer a brief introduction to some of the key principles and properties of DNN models and discuss why they are naturally suited to certain problems in structural bioinformatics. We also briefly discuss methodological improvements that have enabled these successes. Using the contact prediction task as an example, we also speculate why DNN models are able to produce reasonably accurate predictions even in the absence of many homologues for a given target sequence, a result that can at first glance appear surprising given the lack of input information. We end on some thoughts about how and why these types of models can be so effective, as well as a discussion on potential pitfalls.
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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.