AIMC Topic: Proteins

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CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision.

Bioinformatics (Oxford, England)
MOTIVATION: Information extraction by mining the scientific literature is key to uncovering relations between biomedical entities. Most existing approaches based on natural language processing extract relations from single sentence-level co-mentions,...

Protein contact prediction using metagenome sequence data and residual neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Almost all protein residue contact prediction methods rely on the availability of deep multiple sequence alignments (MSAs). However, many proteins from the poorly populated families do not have sufficient number of homologs in the convent...

DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine-learning methods are widely used to predict domain boundary, these approaches often ignore long-ran...

evERdock BAI: Machine-learning-guided selection of protein-protein complex structure.

The Journal of chemical physics
Computational techniques for accurate and efficient prediction of protein-protein complex structures are widely used for elucidating protein-protein interactions, which play important roles in biological systems. Recently, it has been reported that s...

A computational method for design of connected catalytic networks in proteins.

Protein science : a publication of the Protein Society
Computational design of new active sites has generally proceeded by geometrically defining interactions between the reaction transition state(s) and surrounding side-chain functional groups which maximize transition-state stabilization, and then sear...

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13).

Proteins
We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13. Submissions were made by three free-modeling (FM) methods which combine the predictions of three neural networks. All three systems were guide...

Machine learning can be used to distinguish protein families and generate new proteins belonging to those families.

The Journal of chemical physics
Proteins are classified into families based on evolutionary relationships and common structure-function characteristics. Availability of large data sets of gene-derived protein sequences drives this classification. Sequence space is exponentially lar...

ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do...

Develop machine learning-based regression predictive models for engineering protein solubility.

Bioinformatics (Oxford, England)
MOTIVATION: Protein activity is a significant characteristic for recombinant proteins which can be used as biocatalysts. High activity of proteins reduces the cost of biocatalysts. A model that can predict protein activity from amino acid sequence is...

Neural networks with circular filters enable data efficient inference of sequence motifs.

Bioinformatics (Oxford, England)
MOTIVATION: Nucleic acids and proteins often have localized sequence motifs that enable highly specific interactions. Due to the biological relevance of sequence motifs, numerous inference methods have been developed. Recently, convolutional neural n...