AIMC Topic: Sequence Analysis, Protein

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Prediction the Substrate Specificities of Membrane Transport Proteins Based on Support Vector Machine and Hybrid Features.

IEEE/ACM transactions on computational biology and bioinformatics
Membrane transport proteins and their substrate specificities play crucial roles in a variety of cellular functions. Identifying the substrate specificities of membrane transport proteins is closely related to the protein-target interaction predictio...

Unsupervised learning assisted robust prediction of bioluminescent proteins.

Computers in biology and medicine
Bioluminescence plays an important role in nature, for example, it is used for intracellular chemical signalling in bacteria. It is also used as a useful reagent for various analytical research methods ranging from cellular imaging to gene expression...

Computational probing protein-protein interactions targeting small molecules.

Bioinformatics (Oxford, England)
MOTIVATION: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug target...

MDD-SOH: exploiting maximal dependence decomposition to identify S-sulfenylation sites with substrate motifs.

Bioinformatics (Oxford, England)
UNLABELLED: S-sulfenylation (S-sulphenylation, or sulfenic acid), the covalent attachment of S-hydroxyl (-SOH) to cysteine thiol, plays a significant role in redox regulation of protein functions. Although sulfenic acid is transient and labile, most ...

DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.

Nucleic acids research
There are currently 151 plants with draft genomes available but levels of functional annotation for putative protein products are low. Therefore, accurate computational predictions are essential to annotate genomes in the first instance, and to provi...

Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning.

Bioinformatics (Oxford, England)
MOTIVATION: Protein contact prediction is important for protein structure and functional study. Both evolutionary coupling (EC) analysis and supervised machine learning methods have been developed, making use of different information sources. However...

GoFDR: A sequence alignment based method for predicting protein functions.

Methods (San Diego, Calif.)
In this study, we developed a method named GoFDR for predicting Gene Ontology (GO)-based protein functions. The input for GoFDR is simply a query sequence-based multiple sequence alignment (MSA) produced by PSI-BLAST. For each GO term annotated to th...

MetazSecKB: the human and animal secretome and subcellular proteome knowledgebase.

Database : the journal of biological databases and curation
The subcellular location of a protein is a key factor in determining the molecular function of the protein in an organism. MetazSecKB is a secretome and subcellular proteome knowledgebase specifically designed for metazoan, i.e. human and animals. Th...

Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC.

Computer methods and programs in biomedicine
Heat Shock Proteins (HSPs) are the substantial ingredients for cell growth and viability, which are found in all living organisms. HSPs manage the process of folding and unfolding of proteins, the quality of newly synthesized proteins and protecting ...

TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins.

Bioinformatics (Oxford, England)
MOTIVATION: Molecular recognition of N-terminal targeting peptides is the most common mechanism controlling the import of nuclear-encoded proteins into mitochondria and chloroplasts. When experimental information is lacking, computational methods can...