Predicting the amyloid fold and the propensity of peptide segments to adopt amyloid-like structures remain a challenge. However, recent progress has facilitated structure-based prediction of steric zipper propensity and the use of machine learning to accelerate the calculation of predictive models across many scientific areas. Leveraging these advances, we have developed a new approach for rapid proteome-wide assessment of zipper profiles that is informed by four million steric zipper predictions collected over ten years. This collection is used to build a machine learning model capable of rapidly predicting steric zipper propensity, and allowing for the assessment of zippers at both the protein and proteome level. Our predictions show enrichment for zipper forming segments in proteins involved in cell wall reorganization in yeast, highlighting a potential category of interest for experimental characterization. Overall, our predictive model allows for the exploration of amyloid formation across the tree of life and provides a tool for assessment of both novel and designed sequences for zipper density.

MOTIVATION: The de novo prediction of 3D protein structure is enjoying a period of dramatic improvements. Often, a remaining difficulty is to select the model closest to the true structure from a group of low-energy candidates. To what extent can inter-residue contact predictions from multiple sequence alignments, information which is orthogonal to that used in most structure prediction algorithms, be used to identify those models most similar to the native protein structure? RESULTS: We present a Bayesian inference procedure to identify residue pairs that are spatially proximal in a protein structure. The method takes as input a multiple sequence alignment, and outputs an accurate posterior probability of proximity for each residue pair. We exploit a recent metagenomic sequencing project to create large, diverse and informative multiple sequence alignments for a test set of 1656 known protein structures. The method infers spatially proximal residue pairs in this test set with good accuracy: top-ranked predictions achieve an average accuracy of 38% (for an average 21-fold improvement over random predictions) in cross-validation tests. Notably, the accuracy of predicted 3D models generated by a range of structure prediction algorithms strongly correlates with how well the models satisfy probable residue contacts inferred via our method. This correlation allows for confident rejection of incorrect structural models. AVAILABILITY: An implementation of the method is freely available at http://www.doe-mbi.ucla.edu/services.

We consider the problem of how to detect cognate pairs of proteins that bind when each belongs to a large family of paralogs. To illustrate the problem, we have undertaken a genomewide analysis of interactions of members of the PE and PPE protein families of Mycobacterium tuberculosis. Our computational method uses structural information, operon organization, and protein coevolution to infer the interaction of PE and PPE proteins. Some 289 PE/PPE complexes were predicted out of a possible 5,590 PE/PPE pairs genomewide. Thirty-five of these predicted complexes were also found to have correlated mRNA expression, providing additional evidence for these interactions. We show that our method is applicable to other protein families, by analyzing interactions of the Esx family of proteins. Our resulting set of predictions is a starting point for genomewide experimental interaction screens of the PE and PPE families, and our method may be generally useful for detecting interactions of proteins within families having many paralogs.

UNLABELLED: The MiSink Plugin converts Cytoscape, an open-source bioinformatics platform for network visualization, to a graphical interface for the database of interacting proteins (DIP: http://dip.doe-mbi.ucla.edu). Seamless integration is possible by providing bi-directional communication between Cytoscape and any Web site supplying data in XML or tab-delimited format. AVAILABILITY: MiSink is freely available for download at http://dip.doe-mbi.ucla.edu/Software.cgi.

Abstract Motivation: A central problem in bioinformatics is how to capture information from the vast current scientific literature in a form suitable for analysis by computer. We address the special case of information on protein–protein interactions, and show that the frequencies of words in Medline abstracts can be used to determine whether or not a given paper discusses protein–protein interactions. For those papers determined to discuss this topic, the relevant information can be captured for the Database of Interacting Proteins. Furthermore, suitable gene annotations can also be captured. Results: Our Bayesian approach scores Medline abstracts for probability of discussing the topic of interest according to the frequencies of discriminating words found in the abstract. More than 80 discriminating words (e.g. complex, interaction, two-hybrid) were determined from a training set of 260 Medline abstracts corresponding to previously validated entries in the Database of Interacting Proteins. Using these words and a log likelihood scoring function, \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \({\sim}\) \end{document}2000 Medline abstracts were identified as describing interactions between yeast proteins. This approach now forms the basis for the rapid expansion of the Database of Interacting Proteins. Contact: [email protected]; [email protected]; [email protected] * These authors contributed equally to this work.

Profile analysis measures the similarity between a target sequence and a group of aligned sequences (the probe). The probe sequences are used to produce a position-specific scoring table (the profile) that can be aligned with any sequence (the target) using standard dynamic programming methods. We are developing a library of profiles, each describing a different structural motif. This allows any target sequence to be rapidly scanned for the presence of structural motifs. Levels of significance for the comparison of target sequences with the profile are determined in advance, permitting an objective decision to be made as to whether a protein is likely to possess a structural motif.