Ab initio and homology based prediction of protein domains by recursive neural networks

Background  

Proteins, especially larger ones, are often composed of individual evolutionary units, domains, which have their own function and structural fold. Predicting domains is an important intermediate step in protein analyses, including the prediction of protein structures.     

Improved protein structure selection using decoy-dependent discriminatory functions

Background  

A key component in protein structure prediction is a scoring or discriminatory function that can distinguish near-native conformations from misfolded ones. Various types of scoring functions have been developed to accomplish this goal, but their performance is not adequate to solve the structure selection problem. In addition, there is poor correlation between the scores and the accuracy of the generated conformations.     

Analysis of substructural variation in families of enzymatic proteins with applications to protein function prediction

Background  

Structural variations caused by a wide range of physico-chemical and biological sources directly influence the function of a protein. For enzymatic proteins, the structure and chemistry of the catalytic binding site residues can be loosely defined as a substructure of the protein. Comparative analysis of drug-receptor substructures across and within species has been used for lead evaluation. Substructure-level similarity between the binding sites of functionally similar proteins has also been used to identify instances of convergent evolution among proteins. In functionally homologous protein families, shared chemistry and geometry at catalytic sites provide a common, local point of comparison among proteins that may differ significantly at the sequence, fold, or domain topology levels.     

Multiclass discovery in array data

Background  

A routine goal in the analysis of microarray data is to identify genes with expression levels that correlate with known classes of experiments. In a growing number of array data sets, it has been shown that there is an over-abundance of genes that discriminate between known classes as compared to expectations for random classes. Therefore, one can search for novel classes in array data by looking for partitions of experiments for which there are an over-abundance of discriminatory genes. We have previously used such an approach in a breast cancer study.     

Columba: an integrated database of proteins,structures, and annotations

Background  

Structural and functional research often requires the computation of sets of protein structures based on certain properties of the proteins, such as sequence features, fold classification, or functional annotation. Compiling such sets using current web resources is tedious because the necessary data are spread over many different databases. To facilitate this task, we have created COLUMBA, an integrated database of annotations of protein structures.     

Perceived discrimination and self-rated health in South Korea: a nationally representative survey

Background

There is mounting evidence that discriminatory experiences can harm health. However, previous research has mainly focused on the health effects of racial discrimination in U.S. or European countries although there is pervasive discrimination by gender, age, education and other factors in Asian countries.

Methods

We analyzed the data from the 7th wave of Korean Labor and Income Panel Study to investigate the association between perceived discriminatory experience and poor self-rated health in South Korea. Perceived discriminatory experiences were measured in eight situations through a modified Experience of Discrimination questionnaire. In each of eight situations, the lifetime prevalence of perceived discriminatory experience was compared between men and women and the main causes of those experiences were identified separately by gender. After adjusting for potential confounders, we examined the association between perceived discriminatory experience and poor self-rated health in each of eight social situations and also checked the association using the number of situations of perceived discriminatory experiences.

Results

For both men and women, education level and age were the main sources of work-related perceived discriminatory experiences. Gender was one of the main causes among women across eight situations and more than 90% of women reported their gender as a main cause of discriminatory experience in getting higher education and at home. Discriminatory experiences in four situations were positively associated with poor self-rated health. The odds ratio for poor self-rated health for those exposed to one, two, three or four or more social situations of perceived discrimination were respectively 1.06 (95% CI : 0.87–1.29), 1.15 (95% CI : 0.96–1.55), 1.59 (95% CI : 1.19–2.14), and 1.78 (95% CI :1.26–2.51).

Conclusion

There is consistent association between perceived discriminatory experience and poor self-rated health across eight social situations in South Korea.     

Local protein structure prediction using discriminative models

Background  

In recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results.     

Directionality in protein fold prediction

Background  

Ever since the ground-breaking work of Anfinsen et al. in which a denatured protein was found to refold to its native state, it has been frequently stated by the protein fold prediction community that all the information required for protein folding lies in the amino acid sequence. Recent in vitro experiments and in silico computational studies, however, have shown that cotranslation may affect the folding pathway of some proteins, especially those of ancient folds. In this paper aspects of cotranslational folding have been incorporated into a protein structure prediction algorithm by adapting the Rosetta program to fold proteins as the nascent chain elongates. This makes it possible to conduct a pairwise comparison of folding accuracy, by comparing folds created sequentially from each end of the protein.     

Potentials 'R'Us web-server for protein energy estimations with coarse-grained knowledge-based potentials

Background  

Knowledge-based potentials have been widely used in the last 20 years for fold recognition, protein structure prediction from amino acid sequence, ligand binding, protein design, and many other purposes. However generally these are not readily accessible online.     

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

Background  

Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of C _β atoms in other residues within a sphere around the C _β atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence.     

Novel methods for secondary structure determination using low wavelength (VUV) circular dichroism spectroscopic data

Background  

Circular Dichroism (CD) spectroscopy is a widely used method for studying protein structures in solution. Modern synchrotron radiation CD (SRCD) instruments have considerably higher photon fluxes than do conventional lab-based CD instruments, and hence have the ability to routinely measure CD data to much lower wavelengths. Recently a new reference dataset of SRCD spectra of proteins of known structure, designed to cover secondary structure and fold space, has been produced which includes low wavelength (vacuum ultraviolet – VUV) data. However, the existing algorithms used to calculate protein secondary structures from CD data have not been designed to take optimal advantage of the additional information in these low wavelength data.     

Analysis of proteins with the 'hot dog' fold: Prediction of function and identification of catalytic residues of hypothetical proteins

Background  

The hot dog fold has been found in more than sixty proteins since the first report of its existence about a decade ago. The fold appears to have a strong association with fatty acid biosynthesis, its regulation and metabolism, as the proteins with this fold are predominantly coenzyme A-binding enzymes with a variety of substrates located at their active sites.     

Revealing divergent evolution, identifying circular permutations and detecting active-sites by protein structure comparison

Background  

Protein structure comparison is one of the most important problems in computational biology and plays a key role in protein structure prediction, fold family classification, motif finding, phylogenetic tree reconstruction and protein docking.     

A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data

Background  

Cluster analysis has become a standard computational method for gene function discovery as well as for more general explanatory data analysis. A number of different approaches have been proposed for that purpose, out of which different mixture models provide a principled probabilistic framework. Cluster analysis is increasingly often supplemented with multiple data sources nowadays, and these heterogeneous information sources should be made as efficient use of as possible.     

TIM-Finder: A new method for identifying TIM-barrel proteins

Background  

The triosephosphate isomerase (TIM)-barrel fold occurs frequently in the proteomes of different organisms, and the known TIM-barrel proteins have been found to play diverse functional roles. To accelerate the exploration of the sequence-structure protein landscape in the TIM-barrel fold, a computational tool that allows sensitive detection of TIM-barrel proteins is required.     

Automatic generation of 3D motifs for classification of protein binding sites

Background  

Since many of the new protein structures delivered by high-throughput processes do not have any known function, there is a need for structure-based prediction of protein function. Protein 3D structures can be clustered according to their fold or secondary structures to produce classes of some functional significance. A recent alternative has been to detect specific 3D motifs which are often associated to active sites. Unfortunately, there are very few known 3D motifs, which are usually the result of a manual process, compared to the number of sequential motifs already known. In this paper, we report a method to automatically generate 3D motifs of protein structure binding sites based on consensus atom positions and evaluate it on a set of adenine based ligands.     

Inferring modules from human protein interactome classes

Background  

The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features.     

Genome wide prediction of protein function via a generic knowledge discovery approach based on evidence integration

Background  

The automation of many common molecular biology techniques has resulted in the accumulation of vast quantities of experimental data. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system (e.g. knowledge of genes and their products, and the biological roles of proteins, their molecular functions, localizations and interaction networks). We present a technique called Global Mapping of Unknown Proteins (GMUP) which uses the Gene Ontology Index to relate diverse sources of experimental data by creation of an abstraction layer of evidence data. This abstraction layer is used as input to a neural network which, once trained, can be used to predict function from the evidence data of unannotated proteins. The method allows us to include almost any experimental data set related to protein function, which incorporates the Gene Ontology, to our evidence data in order to seek relationships between the different sets.     

Supervised inference of gene-regulatory networks

Background  

Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks.