PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids.

The biological process known as post-translational modification (PTM) contributes to diversifying the proteome hence affecting many aspects of normal cell biology and pathogenesis. There have been many recently reported PTMs, but lysine phosphoglycerylation has emerged as the most recent subject of interest. Despite a large number of proteins being sequenced, the experimental method for detection of phosphoglycerylated residues remains an expensive, time-consuming and inefficient endeavor in the post-genomic era.

iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition.

The promoter is a regulatory DNA region about 81-1000 base pairs long, usually located near the transcription start site (TSS) along upstream of a given gene. By combining a certain protein called transcription factor, the promoter provides the starting point for regulated gene transcription, and hence plays a vitally important role in gene transcriptional regulation. With explosive growth of DNA sequences in the post-genomic age, it has become an urgent challenge to develop computational method for effectively identifying promoters because the information thus obtained is very useful for both basic research and drug development.

pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset.

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called "pLoc-mPlant" was developed for identifying the subcellular localization of plant proteins.

pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC.

One of the hottest topics in molecular cell biology is to determine the subcellular localization of proteins from various different organisms. This is because it is crucially important for both basic research and drug development. Recently, a predictor called "pLoc-mGneg" was developed for identifying the subcellular localization of Gram-negative bacterial proteins.

pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites.

Motivation: Cells are deemed the basic unit of life. However, many important functions of cells as well as their growth and reproduction are performed via the protein molecules located at their different organelles or locations. Facing explosive growth of protein sequences, we are challenged to develop fast and effective method to annotate their subcellular localization.

pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC.

Information of the proteins' subcellular localization is crucially important for revealing their biological functions in a cell, the basic unit of life. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop computational tools for timely identifying their subcellular locations based on the sequence information alone. The current study is focused on the Gram-negative bacterial proteins.

iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC.

Motivation: Being responsible for initiating transaction of a particular gene in genome, promoter is a short region of DNA. Promoters have various types with different functions. Owing to their importance in biological process, it is highly desired to develop computational tools for timely identifying promoters and their types.

pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC.

Many efforts have been made in predicting the subcellular localization of eukaryotic proteins, but most of the existing methods have the following two limitations: (1) their coverage scope is less than ten locations and hence many organelles in an eukaryotic cell cannot be covered, and (2) they can only be used to deal with single-label systems in which each of the constituent proteins has one and only one location. Actually, proteins with multiple locations are particularly interesting since they may have some exceptional functions very important for in-depth understanding the biological process in a cell and for selecting drug target as well. Although several predictors (such as "Euk-mPLoc", "Euk-PLoc 2.

pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC.

Knowledge of subcellular locations of proteins is crucially important for in-depth understanding their functions in a cell. With the explosive growth of protein sequences generated in the postgenomic age, it is highly demanded to develop computational tools for timely annotating their subcellular locations based on the sequence information alone. The current study is focused on virus proteins.