AIMC Topic: Proteins

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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...

Correct machine learning on protein sequences: a peer-reviewing perspective.

Briefings in bioinformatics
Machine learning methods are becoming increasingly popular to predict protein features from sequences. Machine learning in bioinformatics can be powerful but carries also the risk of introducing unexpected biases, which may lead to an overestimation ...

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 ...

IAS: Interaction Specific GO Term Associations for Predicting Protein-Protein Interaction Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Proteins carry out their function in a cell through interactions with other proteins. A large scale protein-protein interaction (PPI) network of an organism provides static yet an essential structure of interactions, which is valuable clue for unders...

Sparse Markov chain-based semi-supervised multi-instance multi-label method for protein function prediction.

Journal of bioinformatics and computational biology
Automated assignment of protein function has received considerable attention in recent years for genome-wide study. With the rapid accumulation of genome sequencing data produced by high-throughput experimental techniques, the process of manually pre...

Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME.

Database : the journal of biological databases and curation
Today's large, public databases of protein-small molecule interaction data are creating important new opportunities for data mining and integration. At the same time, new graphical user interface-based workflow tools offer facile alternatives to cust...

APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction.

Computational biology and chemistry
Tertiary protein structure prediction is one of the most challenging problems in structural bioinformatics. Despite the advances in algorithm development and computational strategies, predicting the folded structure of a protein only from its amino a...

Predicting protein function and other biomedical characteristics with heterogeneous ensembles.

Methods (San Diego, Calif.)
Prediction problems in biomedical sciences, including protein function prediction (PFP), are generally quite difficult. This is due in part to incomplete knowledge of the cellular phenomenon of interest, the appropriateness and data quality of the va...

Enhancing protein function prediction with taxonomic constraints--The Argot2.5 web server.

Methods (San Diego, Calif.)
Argot2.5 (Annotation Retrieval of Gene Ontology Terms) is a web server designed to predict protein function. It is an updated version of the previous Argot2 enriched with new features in order to enhance its usability and its overall performance. The...

LncRNA-ID: Long non-coding RNA IDentification using balanced random forests.

Bioinformatics (Oxford, England)
MOTIVATION: Long non-coding RNAs (lncRNAs), which are non-coding RNAs of length above 200 nucleotides, play important biological functions such as gene expression regulation. To fully reveal the functions of lncRNAs, a fundamental step is to annotate...