Adjusting estimative prediction limits

This note presents a direct adjustment of the estimative predictionlimit to reduce the coverage error from a target value to third-orderaccuracy. The adjustment is asymptotically equivalent to thoseof Barndorff-Nielsen & Cox (1994, 1996) and Vidoni (1998).It has a simpler form with a plug-in estimator of the coverageprobability of the estimative limit at the target value.     

Extending the accuracy limits of prediction for side-chain conformations

Current techniques for the prediction of side-chain conformations on a fixed backbone have an accuracy limit of about 1.0-1.5 A rmsd for core residues. We have carried out a detailed and systematic analysis of the factors that influence the prediction of side-chain conformation and, on this basis, have succeeded in extending the limits of side-chain prediction for core residues to about 0.7 A rmsd from native, and 94 % and 89 % of chi(1) and chi(1+2 ) dihedral angles correctly predicted to within 20 degrees of native, respectively. These results are obtained using a force-field that accounts for only van der Waals interactions and torsional potentials. Prediction accuracy is strongly dependent on the rotamer library used. That is, a complete and detailed rotamer library is essential. The greatest accuracy was obtained with an extensive rotamer library, containing over 7560 members, in which bond lengths and bond angles were taken from the database rather than simply assuming idealized values. Perhaps the most surprising finding is that the combinatorial problem normally associated with the prediction of the side-chain conformation does not appear to be important. This conclusion is based on the fact that the prediction of the conformation of a single side-chain with all others fixed in their native conformations is only slightly more accurate than the simultaneous prediction of all side-chain dihedral angles.     

Practical lessons from protein structure prediction

Despite recent efforts to develop automated protein structure determination protocols, structural genomics projects are slow in generating fold assignments for complete proteomes, and spatial structures remain unknown for many protein families. Alternative cheap and fast methods to assign folds using prediction algorithms continue to provide valuable structural information for many proteins. The development of high-quality prediction methods has been boosted in the last years by objective community-wide assessment experiments. This paper gives an overview of the currently available practical approaches to protein structure prediction capable of generating accurate fold assignment. Recent advances in assessment of the prediction quality are also discussed.     

Secondary structural wobble: the limits of protein prediction accuracy

At present, accuracies of secondary structural prediction scarcely go beyond 70-75%. Secondary structural comparison is carried out among sequence-identified proteins. The results show natural wobble between different secondary structural types is possible in homologous families, and the best prediction accuracy will rarely be 100%. Besides shortcoming of the prediction approaches, secondary structural wobble is found to be responsible for nearly all secondary structural prediction limits. Only average 73.2% of amino acid residue is conserved in secondary structural types. The wobble allows alpha-class/coil and beta-class/coil transitions but not direct alpha-class/beta-class transition. Propensity values representing the statistical occurrence of 20 amino acid residues in secondary structural wobbles are given.     

Hierarchical multi-label prediction of gene function

MOTIVATION: Assigning functions for unknown genes based on diverse large-scale data is a key task in functional genomics. Previous work on gene function prediction has addressed this problem using independent classifiers for each function. However, such an approach ignores the structure of functional class taxonomies, such as the Gene Ontology (GO). Over a hierarchy of functional classes, a group of independent classifiers where each one predicts gene membership to a particular class can produce a hierarchically inconsistent set of predictions, where for a given gene a specific class may be predicted positive while its inclusive parent class is predicted negative. Taking the hierarchical structure into account resolves such inconsistencies and provides an opportunity for leveraging all classifiers in the hierarchy to achieve higher specificity of predictions. RESULTS: We developed a Bayesian framework for combining multiple classifiers based on the functional taxonomy constraints. Using a hierarchy of support vector machine (SVM) classifiers trained on multiple data types, we combined predictions in our Bayesian framework to obtain the most probable consistent set of predictions. Experiments show that over a 105-node subhierarchy of the GO, our Bayesian framework improves predictions for 93 nodes. As an additional benefit, our method also provides implicit calibration of SVM margin outputs to probabilities. Using this method, we make function predictions for multiple proteins, and experimentally confirm predictions for proteins involved in mitosis. SUPPLEMENTARY INFORMATION: Results for the 105 selected GO classes and predictions for 1059 unknown genes are available at: http://function.princeton.edu/genesite/ CONTACT: ogt@cs.princeton.edu.     

Empirical limits for template-based protein structure prediction: the CASP5 example

Most protein structure prediction methods use templates to assist in the construction of protein models. In this paper, we analyse the current state of template-based modelling approaches and reach an estimate of the empirical limits of these methods. Our analysis show that current prediction methods are already reaching these empirical accuracy limits in the easier cases, where finding a close homologue to the native target structure is not a problem. However, we find that even in the absence of alignment errors and using optimal templates, template-based methods have intrinsic limitations, suggesting that other methodologies, such as ab initio procedures, must be used if accuracy is ultimately to be improved.     

Future directions in protein function prediction

New directions in computational methods for the prediction of protein function are discussed. THEMATICS, a method for the location and characterization of the active sites of enzymes, is featured. THEMATICS, for Theoretical Microscopic Titration Curves, is based on well-established finite-difference Poisson-Boltzmann methods for computing the electric field function of a protein. THEMATICS requires only the structure of the subject protein and thus may be applied to proteins that bear no similarity in structure or sequence to any previously characterized protein. The unique features of catalytic sites in proteins are discussed. Discussion of the chemical basis for the predictive powers of THEMATICS is featured in this paper. Some results are given for three illustrative examples: HIV-1 protease, human apurinic/apyrimidinic endonuclease, and human adenosine kinase.     

Practical limits on the maximal speed of solution exchange for patch clamp experiments.

Studying ligand-gated ion channels often requires the ability to change solutions quickly. Using finite element models, I have examined the practical limitations of how fast solutions can be exchanged on an outside-out patch using a dual stream switcher. The primary factors controlling the speed of response are the flow velocity, proximity of the patch to the exit ports, the width of the partition between the two streams, the velocity with which the streams can be moved across the patch, and the viscosity of the solutions. The practical limit seems to be a rise time of approximately 20 microseconds. The rate-limiting step is the velocity of the (usually piezo) motor that translates the streams across the patch. Increasing the perfusate viscosity improves speed by slowing dissipation of the concentration gradients. A flow switcher can also be used for bipolar temperature jumps with a rise time of approximately 100 microseconds.     

Consistent probabilistic outputs for protein function prediction

Background:

The wide availability of genome-scale data for several organisms has stimulated interest in computational approaches to gene function prediction. Diverse machine learning methods have been applied to unicellular organisms with some success, but few have been extensively tested on higher level, multicellular organisms. A recent mouse function prediction project (MouseFunc) brought together nine bioinformatics teams applying a diverse array of methodologies to mount the first large-scale effort to predict gene function in the laboratory mouse.

Results:

In this paper, we describe our contribution to this project, an ensemble framework based on the support vector machine that integrates diverse datasets in the context of the Gene Ontology hierarchy. We carry out a detailed analysis of the performance of our ensemble and provide insights into which methods work best under a variety of prediction scenarios. In addition, we applied our method to Saccharomyces cerevisiae and have experimentally confirmed functions for a novel mitochondrial protein.

Conclusion:

Our method consistently performs among the top methods in the MouseFunc evaluation. Furthermore, it exhibits good classification performance across a variety of cellular processes and functions in both a multicellular organism and a unicellular organism, indicating its ability to discover novel biology in diverse settings.

     

Protein molecular function prediction by Bayesian phylogenomics

We present a statistical graphical model to infer specific molecular function for unannotated protein sequences using homology. Based on phylogenomic principles, SIFTER (Statistical Inference of Function Through Evolutionary Relationships) accurately predicts molecular function for members of a protein family given a reconciled phylogeny and available function annotations, even when the data are sparse or noisy. Our method produced specific and consistent molecular function predictions across 100 Pfam families in comparison to the Gene Ontology annotation database, BLAST, GOtcha, and Orthostrapper. We performed a more detailed exploration of functional predictions on the adenosine-5′-monophosphate/adenosine deaminase family and the lactate/malate dehydrogenase family, in the former case comparing the predictions against a gold standard set of published functional characterizations. Given function annotations for 3% of the proteins in the deaminase family, SIFTER achieves 96% accuracy in predicting molecular function for experimentally characterized proteins as reported in the literature. The accuracy of SIFTER on this dataset is a significant improvement over other currently available methods such as BLAST (75%), GeneQuiz (64%), GOtcha (89%), and Orthostrapper (11%). We also experimentally characterized the adenosine deaminase from Plasmodium falciparum, confirming SIFTER's prediction. The results illustrate the predictive power of exploiting a statistical model of function evolution in phylogenomic problems. A software implementation of SIFTER is available from the authors.     

An overview of in silico protein function prediction

As the protein databases continue to expand at an exponential rate, fed by daily uploads from multiple large scale genomic and metagenomic projects, the problem of assigning a function to each new protein has become the focus of significant research interest in recent times. Herein, we review the most recent advances in the field of automated function prediction (AFP). We begin by defining what is meant by biological “function” and the means of describing such functions using standardised machine readable ontologies. We then focus on the various function-prediction programs available, both sequence and structure based, and outline their associated strengths and weaknesses. Finally, we conclude with a brief overview of the future challenges and outstanding questions in the field, which still remain unanswered.     

Towards computational prediction of microRNA function and activity

While it has been established that microRNAs (miRNAs) play key roles throughout development and are dysregulated in many human pathologies, the specific processes and pathways regulated by individual miRNAs are mostly unknown. Here, we use computational target predictions in order to automatically infer the processes affected by human miRNAs. Our approach improves upon standard statistical tools by addressing specific characteristics of miRNA regulation. Our analysis is based on a novel compendium of experimentally verified miRNA-pathway and miRNA-process associations that we constructed, which can be a useful resource by itself. Our method also predicts novel miRNA-regulated pathways, refines the annotation of miRNAs for which only crude functions are known, and assigns differential functions to miRNAs with closely related sequences. Applying our approach to groups of co-expressed genes allows us to identify miRNAs and genomic miRNA clusters with functional importance in specific stages of early human development. A full list of the predicted mRNA functions is available at http://acgt.cs.tau.ac.il/fame/.     

Activation-induced cell death limits effector function of CD4 tumor-specific T cells

A number of studies have documented a critical role for tumor-specific CD4(+) cells in the augmentation of immunotherapeutic effector mechanisms. However, in the context of an extensive tumor burden, chronic stimulation of such CD4(+) T cells often leads to the up-regulation of both Fas and Fas ligand, and coexpression of these molecules can potentially result in activation-induced cell death and the subsequent loss of effector activity. To evaluate the importance of T cell persistence in an experimental model of immunotherapy, we used DO11 Th1 cells from wild-type, Fas-deficient, and Fas ligand-deficient mice as effector populations specific for a model tumor Ag consisting of an OVA-derived transmembrane fusion protein. We found that the prolonged survival of Fas-deficient DO11 Th1 cells led to a more sustained tumor-specific response both in vitro and in vivo. Importantly, both Fas- and Fas ligand-deficient Th1 cells delayed tumor growth and cause regression of established tumors more effectively than wild-type Th1 cells, indicating that resistance to activation-induced cell death significantly enhances T cell effector activity.