AIMC Topic: Protein Structure, Secondary

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Redundancy-weighting the PDB for detailed secondary structure prediction using deep-learning models.

Bioinformatics (Oxford, England)
MOTIVATION: The Protein Data Bank (PDB), the ultimate source for data in structural biology, is inherently imbalanced. To alleviate biases, virtually all structural biology studies use nonredundant (NR) subsets of the PDB, which include only a fracti...

MUFold-SSW: a new web server for predicting protein secondary structures, torsion angles and turns.

Bioinformatics (Oxford, England)
MOTIVATION: Protein secondary structure and backbone torsion angle prediction can provide important information for predicting protein 3D structures and protein functions. Our new methods MUFold-SS, MUFold-Angle, MUFold-BetaTurn and MUFold-GammaTurn,...

Prediction of mRNA subcellular localization using deep recurrent neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Messenger RNA subcellular localization mechanisms play a crucial role in post-transcriptional gene regulation. This trafficking is mediated by trans-acting RNA-binding proteins interacting with cis-regulatory elements called zipcodes. Whi...

Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Sequence-based prediction of one dimensional structural properties of proteins has been a long-standing subproblem of protein structure prediction. Recently, prediction accuracy has been significantly improved due to the rapid expansion o...

ConDo: protein domain boundary prediction using coevolutionary information.

Bioinformatics (Oxford, England)
MOTIVATION: Domain boundary prediction is one of the most important problems in the study of protein structure and function. Many sequence-based domain boundary prediction methods are either template-based or machine learning (ML) based. ML-based met...

Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks.

Journal of bioinformatics and computational biology
Protein secondary structure prediction (PSSP) is an important research field in bioinformatics. The representation of protein sequence features could be treated as a matrix, which includes the amino-acid residue (time-step) dimension and the feature ...

ComplexContact: a web server for inter-protein contact prediction using deep learning.

Nucleic acids research
ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how pro...

Protein threading using residue co-variation and deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Template-based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain...

Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

Briefings in bioinformatics
Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new method...

SVM-dependent pairwise HMM: an application to protein pairwise alignments.

Bioinformatics (Oxford, England)
MOTIVATION: Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondar...