iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach.

Information about the interactions of drug compounds with proteins in cellular networking is very important for drug development. Unfortunately, all the existing predictors for identifying drug-protein interactions were trained by a skewed benchmark data-set where the number of non-interactive drug-protein pairs is overwhelmingly larger than that of the interactive ones. Using this kind of highly unbalanced benchmark data-set to train predictors would lead to the outcome that many interactive drug-protein pairs might be mispredicted as non-interactive.

A new multi-label classifier in identifying the functional types of human membrane proteins.

Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their type. Given a membrane protein sequence, how can we identify its type(s)? Particularly, how can we deal with the multi-type problem since one membrane protein may simultaneously belong to two or more different types? To address these problems, which are obviously very important to both basic research and drug development, a new multi-label classifier was developed based on pseudo amino acid composition with multi-label k-nearest neighbor algorithm. The success rate achieved by the new predictor on the benchmark dataset by jackknife test is 73.

iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model.

As one of the most important posttranslational modifications (PTMs), ubiquitination plays an important role in regulating varieties of biological processes, such as signal transduction, cell division, apoptosis, and immune response. Ubiquitination is also named "lysine ubiquitination" because it occurs when an ubiquitin is covalently attached to lysine (K) residues of targeting proteins. Given an uncharacterized protein sequence that contains many lysine residues, which one of them is the ubiquitination site, and which one is of non-ubiquitination site? With the avalanche of protein sequences generated in the postgenomic age, it is highly desired for both basic research and drug development to develop an automated method for rapidly and accurately annotating the ubiquitination sites in proteins.

A multi-label classifier for prediction membrane protein functional types in animal.

Membrane protein is an important composition of cell membrane. Given a membrane protein sequence, how can we identify its type(s) is very important because the type keeps a close correlation with its functions. According to previous studies, membrane protein can be divided into the following eight types: single-pass type I, single-pass type II, single-pass type III, single-pass type IV, multipass, lipid-anchor, GPI-anchor, peripheral membrane protein.

iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.

Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called "iNR-Drug" was developed.

iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components.

Meiosis and recombination are the two opposite aspects that coexist in a DNA system. As a driving force for evolution by generating natural genetic variations, meiotic recombination plays a very important role in the formation of eggs and sperm. Interestingly, the recombination does not occur randomly across a genome, but with higher probability in some genomic regions called "hotspots", while with lower probability in so-called "coldspots".

iEzy-drug: a web server for identifying the interaction between enzymes and drugs in cellular networking.

With the features of extremely high selectivity and efficiency in catalyzing almost all the chemical reactions in cells, enzymes play vitally important roles for the life of an organism and hence have become frequent targets for drug design. An essential step in developing drugs by targeting enzymes is to identify drug-enzyme interactions in cells. It is both time-consuming and costly to do this purely by means of experimental techniques alone.

iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.

Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown.

iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints.

Many crucial functions in life, such as heartbeat, sensory transduction and central nervous system response, are controlled by cell signalings via various ion channels. Therefore, ion channels have become an excellent drug target, and study of ion channel-drug interaction networks is an important topic for drug development. However, it is both time-consuming and costly to determine whether a drug and a protein ion channel are interacting with each other in a cellular network by means of experimental techniques.

Recent advances in predicting protein classification and their applications to drug development.

With the explosion of protein sequences generated in the postgenomic era, the gap between the number of attribute- known proteins and that of uncharacterized ones has become increasingly large. Knowing the key attributes of proteins is a shortcut for prioritizing drug targets and developing novel new drugs. Unfortunately, it is both time-consuming and costly to acquire these kinds of information by purely conducting biological experiments.

Predict drug-protein interaction in cellular networking.

Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs".

Recent progresses in identifying nuclear receptors and their families.

Nuclear receptors (NRs) are members of a large superfamily of evolutionarily related DNA-binding transcription factors. They regulate diverse functions, such as homeostasis, reproduction, development and metabolism. As nuclear receptors bind small molecules that can easily be modified by drug design, and control functions associated with major diseases (e.

iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types.

Antimicrobial peptides (AMPs), also called host defense peptides, are an evolutionarily conserved component of the innate immune response and are found among all classes of life. According to their special functions, AMPs are generally classified into ten categories: Antibacterial Peptides, Anticancer/tumor Peptides, Antifungal Peptides, Anti-HIV Peptides, Antiviral Peptides, Antiparasital Peptides, Anti-protist Peptides, AMPs with Chemotactic Activity, Insecticidal Peptides, and Spermicidal Peptides. Given a query peptide, how can we identify whether it is an AMP or non-AMP? If it is, can we identify which functional type or types it belong to? Particularly, how can we deal with the multi-type problem since an AMP may belong to two or more functional types? To address these problems, which are obviously very important to both basic research and drug development, a multi-label classifier was developed based on the pseudo amino acid composition (PseAAC) and fuzzy K-nearest neighbor (FKNN) algorithm, where the components of PseAAC were featured by incorporating five physicochemical properties.

Swfoldrate: predicting protein folding rates from amino acid sequence with sliding window method.

Protein folding is the process by which a protein processes from its denatured state to its specific biologically active conformation. Understanding the relationship between sequences and the folding rates of proteins remains an important challenge. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input.

iNR-PhysChem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrix.

Nuclear receptors (NRs) form a family of ligand-activated transcription factors that regulate a wide variety of biological processes, such as homeostasis, reproduction, development, and metabolism. Human genome contains 48 genes encoding NRs. These receptors have become one of the most important targets for therapeutic drug development.

iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites.

Predicting protein subcellular localization is a challenging problem, particularly when query proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing methods can only be used to deal with the single-location proteins. Actually, multiple-location proteins should not be ignored because they usually bear some special functions worthy of our notice.

NR-2L: a two-level predictor for identifying nuclear receptor subfamilies based on sequence-derived features.

Nuclear receptors (NRs) are one of the most abundant classes of transcriptional regulators in animals. They regulate diverse functions, such as homeostasis, reproduction, development and metabolism. Therefore, NRs are a very important target for drug development.

Cellular automata and its applications in protein bioinformatics.

With the explosion of protein sequences generated in the postgenomic era, it is highly desirable to develop high-throughput tools for rapidly and reliably identifying various attributes of uncharacterized proteins based on their sequence information alone. The knowledge thus obtained can help us timely utilize these newly found protein sequences for both basic research and drug discovery. Many bioinformatics tools have been developed by means of machine learning methods.

A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites.

Prediction of protein subcellular localization is a challenging problem, particularly when the system concerned contains both singleplex and multiplex proteins. In this paper, by introducing the "multi-label scale" and hybridizing the information of gene ontology with the sequential evolution information, a novel predictor called iLoc-Gneg is developed for predicting the subcellular localization of gram-positive bacterial proteins with both single-location and multiple-location sites. For facilitating comparison, the same stringent benchmark dataset used to estimate the accuracy of Gneg-mPLoc was adopted to demonstrate the power of iLoc-Gneg.

iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites.

In the last two decades or so, although many computational methods were developed for predicting the subcellular locations of proteins according to their sequence information, it is still remains as a challenging problem, particularly when the system concerned contains both single- and multiple-location proteins. Also, among the existing methods, very few were developed specialized for dealing with viral proteins, those generated by viruses. Actually, knowledge of the subcellular localization of viral proteins in a host cell or virus-infected cell is very important because it is closely related to their destructive tendencies and consequences.

GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions.

G protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. With the avalanche of newly generated protein sequences in the post genomic age, to expedite the process of drug discovery, it is highly desirable to develop an automated method to rapidly identify GPCRs and their types. A new predictor was developed by hybridizing two different modes of pseudo-amino acid composition (PseAAC): the functional domain PseAAC and the low-frequency Fourier spectrum PseAAC.

2D-MH: A web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids.

Introduction of graphic representation for biological sequences can provide intuitive overall pictures as well as useful insights for performing large-scale analysis. Here, a new two-dimensional graph, called "2D-MH", is proposed to represent protein sequences. It is formed by incorporating the information of the side-chain mass of each of the constituent amino acids and its hydrophobicity.

Using adaptive K-nearest neighbor algorithm and cellular automata images to predicting G-Protein-Coupled Receptor classes.

G-Protein-Coupled Receptors (GPCRs) are the largest of cell surface receptor, accounting for >1% of the human genome. They play a key role in cellular signaling networks that regulate various physiological processes. The functions of many of GPCRs are unknown, because they are difficult to crystallize and most of them will not dissolve in normal solvents.

Quat-2L: a web-server for predicting protein quaternary structural attributes.

By hybridizing the functional-domain and sequence-correlated pseudo amino acid composition approaches, a 2-layer predictor called "Quat-2L" was developed for predicting the quaternary structural attribute of a protein according to its sequence information alone. The 1st layer is to identify the query protein as monomer, homo-oligomer, or hetero-oligomer. If the result thus obtained turns out to be homo-oligomer or hetero-oligomer, then the prediction will be automatically continued to further identify it belonging to one of the following six subtypes: (1) dimer, (2) trimer, (3) tetramer, (4) pentamer, (5) hexamer, and (6) octamer.