AIMC Topic: Amino Acid Sequence

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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...

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...

DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy and interpretability.

Multifaceted protein-protein interaction prediction based on Siamese residual RCNN.

Bioinformatics (Oxford, England)
MOTIVATION: Sequence-based protein-protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Base...

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...

The PSIPRED Protein Analysis Workbench: 20 years on.

Nucleic acids research
The PSIPRED Workbench is a web server offering a range of predictive methods to the bioscience community for 20 years. Here, we present the work we have completed to update the PSIPRED Protein Analysis Workbench and make it ready for the next 20 year...

PrankWeb: a web server for ligand binding site prediction and visualization.

Nucleic acids research
PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability cent...

NetGO: improving large-scale protein function prediction with massive network information.

Nucleic acids research
Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Bas...

DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Protein structure determination has primarily been performed using X-ray crystallography. To overcome the expensive cost, high attrition rate and series of trial-and-error settings, many in-silico methods have been developed to predict cr...

Bastion3: a two-layer ensemble predictor of type III secreted effectors.

Bioinformatics (Oxford, England)
MOTIVATION: Type III secreted effectors (T3SEs) can be injected into host cell cytoplasm via type III secretion systems (T3SSs) to modulate interactions between Gram-negative bacterial pathogens and their hosts. Due to their relevance in pathogen-hos...