BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

1.1. Uncharacterized Protein Segments Are a Source of Functional Novelty
Over the past decade, we have observed a massive increase in the amount of information describing protein sequences from a variety of organisms.1,2 While this may reflect the diversity in sequence space, and possibly also in function space,3 a large proportion of the sequences lacks any useful function annotation.4,5 Often these sequences are annotated as putative or hypothetical proteins, and for the majority their functions still remain unknown.6,7 Suggestions about potential protein function, primarily molecular function, often come from computational analysis of their sequences. For instance, homology detection allows for the transfer of information from well-characterized protein segments to those with similar sequences that lack annotation of molecular function.8−10 Other aspects of function, such as the biological processes proteins participate in, may come from genetic- and disease-association studies, expression and interaction network data, and comparative genomics approaches that investigate genomic context.11−17 Characterization of unannotated and uncharacterized protein segments is expected to lead to the discovery of novel functions as well as provide important insights into existing biological processes. In addition, it is likely to shed new light on molecular mechanisms of diseases that are not yet fully understood. Thus, uncharacterized protein segments are likely to be a large source of functional novelty relevant for discovering new biology.

/pdf/classification-of-intrinsically-disordered-regions-and-2mev7glgpx.pdf

Classification of intrinsically disordered regions and proteins.

Coronaviruses (CoVs) primarily cause enzootic infections in birds and mammals but, in the last few decades, have shown to be capable of infecting humans as well. The outbreak of severe acute respiratory syndrome (SARS) in 2003 and, more recently, Middle-East respiratory syndrome (MERS) has demonstrated the lethality of CoVs when they cross the species barrier and infect humans. A renewed interest in coronaviral research has led to the discovery of several novel human CoVs and since then much progress has been made in understanding the CoV life cycle. The CoV envelope (E) protein is a small, integral membrane protein involved in several aspects of the virus’ life cycle, such as assembly, budding, envelope formation, and pathogenesis. Recent studies have expanded on its structural motifs and topology, its functions as an ion-channelling viroporin, and its interactions with both other CoV proteins and host cell proteins. This review aims to establish the current knowledge on CoV E by highlighting the recent progress that has been made and comparing it to previous knowledge. It also compares E to other viral proteins of a similar nature to speculate the relevance of these new findings. Good progress has been made but much still remains unknown and this review has identified some gaps in the current knowledge and made suggestions for consideration in future research. The most progress has been made on SARS-CoV E, highlighting specific structural requirements for its functions in the CoV life cycle as well as mechanisms behind its pathogenesis. Data shows that E is involved in critical aspects of the viral life cycle and that CoVs lacking E make promising vaccine candidates. The high mortality rate of certain CoVs, along with their ease of transmission, underpins the need for more research into CoV molecular biology which can aid in the production of effective anti-coronaviral agents for both human CoVs and enzootic CoVs.

/pdf/coronavirus-envelope-protein-current-knowledge-ulubnioebv.pdf

Coronavirus envelope protein: current knowledge

Some remarks on protein attribute prediction and pseudo amino acid composition.

The Protein Data Bank (PDB) is the world-wide repository of macromolecular structure information. We present a series of databases that run parallel to the PDB. Each database holds one entry, if possible, for each PDB entry. DSSP holds the secondary structure of the proteins. PDBREPORT holds reports on the structure quality and lists errors. HSSP holds a multiple sequence alignment for all proteins. The PDBFINDER holds easy to parse summaries of the PDB file content, augmented with essentials from the other systems. PDB_REDO holds re-refined, and often improved, copies of all structures solved by X-ray. WHY_NOT summarizes why certain files could not be produced. All these systems are updated weekly. The data sets can be used for the analysis of properties of protein structures in areas ranging from structural genomics, to cancer biology and protein design.

/pdf/a-series-of-pdb-related-databases-for-everyday-needs-31gfx8ffvy.pdf

A series of PDB related databases for everyday needs.

Motivation: Intrinsically disordered proteins play a crucial role in numerous regulatory processes. Their abundance and ubiquity combined with a relatively low quantity of their annotations motivate research toward the development of computational models that predict disordered regions from protein sequences. Although the prediction quality of these methods continues to rise, novel and improved predictors are urgently needed.

Results: We propose a novel method, named MFDp (Multilayered Fusion-based Disorder predictor), that aims to improve over the current disorder predictors. MFDp is as an ensemble of 3 Support Vector Machines specialized for the prediction of short, long and generic disordered regions. It combines three complementary disorder predictors, sequence, sequence profiles, predicted secondary structure, solvent accessibility, backbone dihedral torsion angles, residue flexibility and B-factors. Our method utilizes a custom-designed set of features that are based on raw predictions and aggregated raw values and recognizes various types of disorder. The MFDp is compared at the residue level on two datasets against eight recent disorder predictors and top-performing methods from the most recent CASP8 experiment. In spite of using training chains with ≤25% similarity to the test sequences, our method consistently and significantly outperforms the other methods based on the MCC index. The MFDp outperforms modern disorder predictors for the binary disorder assignment and provides competitive real-valued predictions. The MFDp's outputs are also shown to outperform the other methods in the identification of proteins with long disordered regions.

Availability: http://biomine.ece.ualberta.ca/MFDp.html 

Supplementary information:Supplementary data are available at Bioinformatics online.

Contact: lkurgan@ece.ualberta.ca

/pdf/improved-sequence-based-prediction-of-disordered-regions-36z7j79hzi.pdf

Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources

Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although existing structural class prediction methods applied virtually all state-of-the-art classifiers, many of them use a rela- tively simple protein sequence representation that often includes amino acid (AA) composition. To this end, we pro- pose a novel sequence representation that incorporates evolutionary information encoded using PSI-BLAST profile- based collocation of AA pairs. We used six benchmark datasets and five representative classifiers to quantify and compare the quality of the structural class prediction with the proposed representation. The best, classifier support vector machine achieved 61-96% accuracy on the six datasets. These predictions were comprehensively compared with a wide range of recently proposed methods for prediction of structural classes. Our comprehensive comparison shows superiority of the proposed representation, which results in error rate reductions that range between 14% and 26% when compared with predictions of the best-performing, previously published classifiers on the considered data- sets. The study also shows that, for the benchmark dataset that includes sequences characterized by low identity (i.e., 25%, 30%, and 40%), the prediction accuracies are 20-35% lower than for the other three datasets that include sequences with a higher degree of similarity. In conclusion, the proposed representation is shown to substantially improve the accuracy of the structural class prediction. A web server that implements the presented prediction method is freely available at http://biomine.ece.ualberta.ca/Structural_Class/SCEC.html.

Prediction of protein structural class using novel evolutionary collocation-based sequence representation.

Background
Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction.

/pdf/scpred-accurate-prediction-of-protein-structural-class-for-2nqkci49hf.pdf

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

Motivation: The number of protein families has been estimated to be as small as 1000. Recent study shows that the growth in discovery of novel structures that are deposited into PDB and the related rate of increase of SCOP categories are slowing down. This indicates that the protein structure space will be soon covered and thus we may be able to derive most of remaining structures by using the known folding patterns. Present tertiary structure prediction methods behave well when a homologous structure is predicted, but give poorer results when no homologous templates are available. At the same time, some proteins that share twilight-zone sequence identity can form similar folds. Therefore, determination of structural similarity without sequence similarity would be beneficial for prediction of tertiary structures. Results: The proposed PFRES method for automated protein fold classification from low identity (535%) sequences obtains 66.4% and 68.4% accuracy for two test sets, respectively. PFRES obtains 6.3–12.4% higher accuracy than the existing methods. The prediction accuracy of PFRES is shown to be statistically significantly better than the accuracy of competing methods. Our method adopts a carefully designed, ensemble-based classifier, and a novel, compact and custom-designed feature representation that includes nearly 90% less features than the representation of the most accurate competing method (36 versus 283). The proposed representation combines evolutionary information by using the PSI-BLAST profile-based composition vector and information extracted from the secondary structure predicted with PSI-PRED. Availability: The method is freely available from the authors upon request.

/pdf/pfres-protein-fold-classification-by-using-evolutionary-98lou53jdk.pdf

PFRES: protein fold classification by using evolutionary information and predicted secondary structure.

Motivation: Nucleotides are multifunctional molecules that are essential for numerous biological processes. They serve as sources for chemical energy, participate in the cellular signaling and they are involved in the enzymatic reactions. The knowledge of the nucleotide–protein interactions helps with annotation of protein functions and finds applications in drug design.

Results: We propose a novel ensemble of accurate high-throughput predictors of binding residues from the protein sequence for ATP, ADP, AMP, GTP and GDP. Empirical tests show that our NsitePred method significantly outperforms existing predictors and approaches based on sequence alignment and residue conservation scoring. The NsitePred accurately finds more binding residues and binding sites and it performs particularly well for the sites with residues that are clustered close together in the sequence. The high predictive quality stems from the usage of novel, comprehensive and custom-designed inputs that utilize information extracted from the sequence, evolutionary profiles, several sequence-predicted structural descriptors and sequence alignment. Analysis of the predictive model reveals several sequence-derived hallmarks of nucleotide-binding residues; they are usually conserved and flanked by less conserved residues, and they are associated with certain arrangements of secondary structures and amino acid pairs in the specific neighboring positions in the sequence.

Availability: http://biomine.ece.ualberta.ca/nSITEpred/ 

Contact: lkurgan@ece.ualberta.ca

Supplementary information:Supplementary data are available at Bioinformatics online.

/pdf/prediction-and-analysis-of-nucleotide-binding-residues-using-y7ex484ly8.pdf

Ke Chen

Papers

Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources

Prediction of protein structural class using novel evolutionary collocation-based sequence representation.

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

PFRES: protein fold classification by using evolutionary information and predicted secondary structure.

Prediction and analysis of nucleotide-binding residues using sequence and sequence-derived structural descriptors