From sequence to structure to networks

A report on the 7th European Conference on Computational Biology (ECCB), Cagliari, Italy, 22-26 September 2008

Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server

When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods to five complete proteomes, we found that 30–65% of all predicted signal peptides and 25–35% of all predicted transmembrane topologies overlap. This impairs predictions of 5–10% of the proteome, hence this is an important issue in protein annotation

Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides

Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.publishedVersio

ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data.

This report presents the results from the 2016 Association of Biomolecular Resource Facilities Proteome Informatics Research Group (iPRG) study on proteoform inference and false discovery rate (FDR) estimation from bottom-up proteomics data. For this study, 3 replicate Q Exactive Orbitrap liquid chromatography-tandom mass spectrometry datasets were generated from each of

Predicting transmembrane topology and signal peptides with hidden Markov models

Transmembrane proteins make up a large and important class of proteins. About 20% of all genes encode transmembrane proteins. They control both substances and information going in and out of a cell. Yet basic knowledge about membrane insertion and folding is sparse, and our ability to identify, over-express, purify, and crystallize transmembrane proteins lags far behind the field of water-soluble proteins. It is diffcult to determine the three dimensional structures of transmembrane proteins. erefore, researchers normally attempt to determine their topology, i.e. which parts of the protein are buried in the membrane, and on what side of the membrane are the other parts located. Proteins aimed for export have an N-terminal sequence known as a signal peptide that is inserted into the membrane and cleaved off. The same mechanism that inserts transmembrane proteins into their membranes also handles the export of protein with signal peptides. Transmembrane helices and signal peptides thus have many features in common. In silico methods for predicting transmembrane topology and methods for predicting signal peptides from amino acid sequence are a fast and relatively accurate alternative to biochemical experiments. A methodology called hidden Markov models (HMMs) has proved particularly useful for these and other prediction tasks. In this thesis, properties of transmembrane topology predictors and signal peptide predictors are investigated. It includes three novel HMM based prediction methods. i) A combined transmembrane topology and signal peptide predictor, Phobius. The paper shows that cross predictions, i.e. signal peptides predicted as transmembrane helices and vice versa, are a common problem. About 10% of the genes in E.coli have overlapping signal peptide and transmembrane helix predictions by conventional predictors. We were able to dramatically lower these false cross predictions. ii)Amethod for detecting remote G protein-coupled receptor (GPCR) families,GPCRHMM. GPCRs are a very large and divergent superfamily of transmembrane proteins. We designed a hidden Markov model based on the topological regions of the superfamily. We searched five genomes and predicted 120 previously not annotated sequences as possible GPCRs. e majority of these predictions (102) were in C. elegans, but 4 were found in human and 7 in mouse. We as well conclude that a family of odorant receptors in Drosophila are not GPCRs. iii)Amethod to improve predictions with HMMs of generic sequence features (such as transmembrane segments or signal peptides) by including homologs. We show that the performance of Phobius using this decoder was significantly better than with other decoders. We also assessed the difficulty of benchmark sets used in transmembrane topology prediction. By studying the level of agreement between different predictors applied to typical benchmark sets andwhole proteome sets,we concluded that the benchmark sets are far easier to predict than reality. In other words, the accuracies reported in benchmark studies are exaggerated. Thesis also includes a paper presenting a hypothesis of the transmembrane topology of presenilin, a protein involved in the development of Alzheimer's disease. By comparing the output of several transmembrane topology predictors with experimental results from previous studies, a novel nine-transmembrane topology with an extracellular C-terminus was elucidated

A simple null model for inferences from network enrichment analysis.

A prevailing technique to infer function from lists of identifications, from molecular biological high-throughput experiments, is over-representation analysis, where the identifications are compared to predefined sets of related genes often referred to as pathways. As at least some pathways are known to be incomplete in their annotation, algorithmic efforts have been made to complement them with information from functional association networks. While the terminology varies in the literature, we will here refer to such methods as Network Enrichment Analysis (NEA). Traditionally, the significance of inferences from NEA has been assigned using a null model constructed from randomizations of the network. Here we instead argue for a null model that more directly relates to the set of genes being studied, and have designed one dynamic programming algorithm that calculates the score distribution of NEA scores that makes it possible to assign unbiased mid p values to inferences. We also implemented a random sampling method, carrying out the same task. We demonstrate that our method obtains a superior statistical calibration as compared to the popular NEA inference engine, BinoX, while also providing statistics that are easier to interpret