An iterative self-refining and self-evaluating approach for protein model quality estimation.

Evaluating or predicting the quality of protein models (i.e., predicted protein tertiary structures) without knowing their native structures is important for selecting and appropriately using protein models.

A conformation ensemble approach to protein residue-residue contact.

Background: Protein residue-residue contact prediction is important for protein model generation and model evaluation. Here we develop a conformation ensemble approach to improve residue-residue contact prediction. We collect a number of structural models stemming from a variety of methods and implementations.

A comprehensive overview of computational protein disorder prediction methods.

Over the past decade there has been a growing acknowledgement that a large proportion of proteins within most proteomes contain disordered regions. Disordered regions are segments of the protein chain which do not adopt a stable structure. Recognition of disordered regions in a protein is of great importance for protein structure prediction, protein structure determination and function annotation as these regions have a close relationship with protein expression and functionality.

APOLLO: a quality assessment service for single and multiple protein models.

Summary: We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.

A protein domain co-occurrence network approach for predicting protein function and inferring species phylogeny.

Protein Domain Co-occurrence Network (DCN) is a biological network that has not been fully-studied. We analyzed the properties of the DCNs of H. sapiens, S.

Enzymatic activity of the soybean ecto-apyrase GS52 is essential for stimulation of nodulation.

Nitrogen is an essential nutrient for plant growth. In the Rhizobium-legume symbiosis, root nodules are the sites of bacterial nitrogen fixation, in which atmospheric nitrogen is converted into a form that plants can utilize. While recent studies suggested an important role for the soybean (Glycine max) ecto-apyrase GS52 in rhizobial root hair infection and root nodule formation, precisely how this protein impacts the nodulation process remains undetermined.

DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning.

Background: Accurate identification of protein domain boundaries is useful for protein structure determination and prediction. However, predicting protein domain boundaries from a sequence is still very challenging and largely unsolved.

Results: We developed a new method to integrate the classification power of machine learning with evolutionary signals embedded in protein families in order to improve protein domain boundary prediction.

A scalable and integrative system for pathway bioinformatics and systems biology.

Motivation: Progress in systems biology depends on developing scalable informatics tools to predictively model, visualize, and flexibly store information about complex biological systems. Scalability of these tools, as well as their ability to integrate within larger frameworks of evolving tools, is critical to address the multi-scale and size complexity of biological systems.

Results: Using current software technology, such as self-generation of database and object code from UML schemas, facilitates rapid updating of a scalable expert assistance system for modeling biological pathways.

Root hair systems biology.

Plant functional genomic studies have largely measured the response of whole plants, organs and tissues, resulting in the dilution of the signal from individual cells. Methods are needed where the full repertoire of functional genomic tools can be applied to a single plant cell. Root hair cells are an attractive model to study the biology of a single, differentiated cell type because of their ease of isolation, polar growth, and role in water and nutrient uptake, as well as being the site of infection by nitrogen-fixing bacteria.

SeqRate: sequence-based protein folding type classification and rates prediction.

Background: Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input.

MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8.

Motivation: Protein structure prediction is one of the most important problems in structural bioinformatics. Here we describe MULTICOM, a multi-level combination approach to improve the various steps in protein structure prediction. In contrast to those methods which look for the best templates, alignments and models, our approach tries to combine complementary and alternative templates, alignments and models to achieve on average better accuracy.

SoyDB: a knowledge database of soybean transcription factors.

Background: Transcription factors play the crucial rule of regulating gene expression and influence almost all biological processes. Systematically identifying and annotating transcription factors can greatly aid further understanding their functions and mechanisms. In this article, we present SoyDB, a user friendly database containing comprehensive knowledge of soybean transcription factors.

PreDisorder: ab initio sequence-based prediction of protein disordered regions.

Background: Disordered regions are segments of the protein chain which do not adopt stable structures. Such segments are often of interest because they have a close relationship with protein expression and functionality. As such, protein disorder prediction is important for protein structure prediction, structure determination and function annotation.

Prediction of global and local quality of CASP8 models by MULTICOM series.

Evaluating the quality of protein structure models is important for selecting and using models. Here, we describe the MULTICOM series of model quality predictors which contains three predictors tested in the CASP8 experiments. We evaluated these predictors on 120 CASP8 targets.

NNcon: improved protein contact map prediction using 2D-recursive neural networks.

Protein contact map prediction is useful for protein folding rate prediction, model selection and 3D structure prediction. Here we describe NNcon, a fast and reliable contact map prediction server and software. NNcon was ranked among the most accurate residue contact predictors in the Eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8), 2008.

The polycystic kidney disease-related proteins Bicc1 and SamCystin interact.

Mutations in either the Bicaudal-C or the Anks6 gene which encode the Bicc1 and SamCystin proteins respectively cause formation of renal cysts in rodent models of polycystic kidney disease, however their role in the mammalian kidney is unknown. Immunolocalization studies demonstrated that, unlike many other PKD-related proteins, SamCystin and Bicc1 do not localize to the primary cilia of cultured kidney cells. Epitope-tagged recombinant SamCystin and Bicc1 proteins were transiently transfected into inner medullary collecting duct (IMCD) cells and co-immunoprecipitated.

Evaluating the absolute quality of a single protein model using structural features and support vector machines.

Knowing the quality of a protein structure model is important for its appropriate usage. We developed a model evaluation method to assess the absolute quality of a single protein model using only structural features with support vector machine regression. The method assigns an absolute quantitative score (i.

Protein disorder prediction at multiple levels of sensitivity and specificity.

Background: Many protein regions and some entire proteins have no definite tertiary structure, existing instead as dynamic, disorder ensembles under different physiochemical circumstances. Identification of these protein disorder regions is important for protein production, protein structure prediction and determination, and protein function annotation. A number of different disorder prediction software and web services have been developed since the first predictor was designed by Dunker's lab in 1997.

HMMEditor: a visual editing tool for profile hidden Markov model.

Background: Profile Hidden Markov Model (HMM) is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available.

A multi-template combination algorithm for protein comparative modeling.

Background: Multiple protein templates are commonly used in manual protein structure prediction. However, few automated algorithms of selecting and combining multiple templates are available.

Results: Here we develop an effective multi-template combination algorithm for protein comparative modeling.

Machine learning methods for protein structure prediction.

Machine learning methods are widely used in bioinformatics and computational and systems biology. Here, we review the development of machine learning methods for protein structure prediction, one of the most fundamental problems in structural biology and bioinformatics. Protein structure prediction is such a complex problem that it is often decomposed and attacked at four different levels: 1-D prediction of structural features along the primary sequence of amino acids; 2-D prediction of spatial relationships between amino acids; 3-D prediction of the tertiary structure of a protein; and 4-D prediction of the quaternary structure of a multiprotein complex.

TMBpro: secondary structure, beta-contact and tertiary structure prediction of transmembrane beta-barrel proteins.

Motivation: Transmembrane beta-barrel (TMB) proteins are embedded in the outer membranes of mitochondria, Gram-negative bacteria and chloroplasts. These proteins perform critical functions, including active ion-transport and passive nutrient intake. Therefore, there is a need for accurate prediction of secondary and tertiary structure of TMB proteins.

Assessment of predictions submitted for the CASP7 domain prediction category.

This paper details the assessment process and evaluation results for the Critical Assessment of Protein Structure Prediction (CASP7) domain prediction category. Domain predictions were assessed using the Normalized Domain Overlap score introduced in CASP6 and the accuracy of prediction of domain break points. The results of the analysis clearly demonstrate that the best methods are able to make consistently reliable predictions when the target has a structural template, although they are less good when the domain break occurs in a region not covered by a template.

DOMAC: an accurate, hybrid protein domain prediction server.

Protein domain prediction is important for protein structure prediction, structure determination, function annotation, mutagenesis analysis and protein engineering. Here we describe an accurate protein domain prediction server (DOMAC) combining both template-based and ab initio methods. The preliminary version of the server was ranked among the top domain prediction servers in the seventh edition of Critical Assessment of Techniques for Protein Structure Prediction (CASP7), 2006.