ArticleBioMed research international2015
AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Structure-informed evolutionary analysis of the meiotic recombination machinery.bioRxiv : the preprint server for biology · 2025Article
- PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs).Biomolecules · 2025Article
- Advancements in one-dimensional protein structure prediction using machine learning and deep learning.Computational and structural biotechnology journal · 2025Review
- Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins.PLoS computational biology · 2022Article
- Estimating the Influence of Physicochemical and Biochemical Property Indexes on Selection for Amino Acids Usage in Eukaryotic Cells.Journal of molecular evolution · 2021Article
- Solvent Accessibility of Residues Undergoing Pathogenic Variations in Humans: From Protein Structures to Protein Sequences.Frontiers in molecular biosciences · 2020Article
- ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank.Database : the journal of biological databases and curation · 2019Article
- ComplexContact: a web server for inter-protein contact prediction using deep learning.Nucleic acids research · 2018Article
- A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.BMC bioinformatics · 2017Article
- Conserved salt-bridge competition triggered by phosphorylation regulates the protein interactome.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Improving prediction of burial state of residues by exploiting correlation among residues.BMC bioinformatics · 2017Article
- Accurate prediction of protein relative solvent accessibility using a balanced model.BioData mining · 2017Article
- AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling.Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) · 2016Article
- AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields.Bioinformatics (Oxford, England) · 2016Article
- CoinFold: a web server for protein contact prediction and contact-assisted protein folding.Nucleic acids research · 2016Article
- RaptorX-Property: a web server for protein structure property prediction.Nucleic acids research · 2016Article
- Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins.Journal of chemical information and modeling · 2016Article
- DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields.International journal of molecular sciences · 2015Article
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2 authors.
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Abstract
motivationThe solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction.
methodWe present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields) model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels.
resultsTrained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.
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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.