Optimized Bacteria are Environmental Prediction Engines

Experimentalists have observed phenotypic variability in isogenic bacteria populations. We explore the hypothesis that in fluctuating environments this variability is tuned to maximize a bacterium's expected log growth rate, potentially aided by epigenetic markers that store information about past environments. We show that, in a complex, memoryful environment, the maximal expected log growth rate is linear in the instantaneous predictive information---the mutual information between a bacterium's epigenetic markers and future environmental states. Hence, under resource constraints, optimal epigenetic markers are causal states---the minimal sufficient statistics for prediction. This is the minimal amount of information about the past needed to predict the future as well as possible. We suggest new theoretical investigations into and new experiments on bacteria phenotypic bet-hedging in fluctuating complex environments.Comment: 7 pages, 1 figure; http://csc.ucdavis.edu/~cmg/compmech/pubs/obepe.ht

Predicting receptor-ligand pairs through kernel learning

<p>Abstract</p> <p>Background</p> <p>Regulation of cellular events is, often, initiated via extracellular signaling. Extracellular signaling occurs when a circulating ligand interacts with one or more membrane-bound receptors. Identification of receptor-ligand pairs is thus an important and specific form of PPI prediction.</p> <p>Results</p> <p>Given a set of disparate data sources (expression data, domain content, and phylogenetic profile) we seek to predict new receptor-ligand pairs. We create a combined kernel classifier and assess its performance with respect to the Database of Ligand-Receptor Partners (DLRP) 'golden standard' as well as the method proposed by Gertz <it>et al. </it>Among our findings, we discover that our predictions for the tgfβ family accurately reconstruct over 76% of the supported edges (0.76 recall and 0.67 precision) of the receptor-ligand bipartite graph defined by the DLRP "golden standard". In addition, for the tgfβ family, the combined kernel classifier is able to relatively improve upon the Gertz <it>et al. </it>work by a factor of approximately 1.5 when considering that our method has an <it>F</it>-measure of 0.71 while that of Gertz <it>et al. </it>has a value of 0.48.</p> <p>Conclusions</p> <p>The prediction of receptor-ligand pairings is a difficult and complex task. We have demonstrated that using kernel learning on multiple data sources provides a stronger alternative to the existing method in solving this task.</p

Structure-based Prediction of Protein-protein Interaction Networks across Proteomes

Protein-protein interactions (PPIs) orchestrate virtually all cellular processes, therefore, their exhaustive exploration is essential for the comprehensive understanding of cellular networks. Significant efforts have been devoted to expand the coverage of the proteome-wide interaction space at molecular level. A number of experimental techniques have been developed to discover PPIs, however these approaches have some limitations such as the high costs and long times of experiments, noisy data sets, and often high false positive rate and inter-study discrepancies. Given experimental limitations, computational methods are increasingly becoming important for detection and structural characterization of PPIs. In that regard, we have developed a novel pipeline for high-throughput PPI prediction based on all-to-all rigid body docking of protein structures. We focus on two questions, ‘how do proteins interact?’ and ‘which proteins interact?’. The method combines molecular modeling, structural bioinformatics, machine learning, and functional annotation data to answer these questions and it can be used for genome-wide molecular reconstruction of protein-protein interaction networks. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Further, we validated our method against a few human pathways. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques

Predicting protein interface residues using easily accessible on-line resources

© The Author 2015. Published by Oxford University Press. It has beenmore than a decade since the completion of the Human Genome Project that provided us with a complete list of human proteins. The next obvious task is to figure out how various parts interact with each other. On that account, we re- view 10methods for protein interface prediction, which are freely available as web servers. In addition, we comparatively evaluate their performance on a common data set comprising different quality target structures. We find that using experi- mental structures and high-quality homology models, structure-basedmethods outperformthose using only protein se- quences, with global template-based approaches providing the best performance. Formoderate-qualitymodels, sequence- basedmethods often performbetter than those structure-based techniques that rely on fine atomic details. We note that post-processing protocols implemented in severalmethods quantitatively improve the results only for experimental struc- tures, suggesting that these procedures should be tuned up for computer-generatedmodels. Finally, we anticipate that advancedmeta-prediction protocols are likely to enhance interface residue prediction. Notwithstanding further improve- ments, easily accessible web servers already provide the scientific community with convenient resources for the identifica- tion of protein-protein interaction sites

Physicochemical property distributions for accurate and rapid pairwise protein homology detection

<p>Abstract</p> <p>Background</p> <p>The challenge of remote homology detection is that many evolutionarily related sequences have very little similarity at the amino acid level. Kernel-based discriminative methods, such as support vector machines (SVMs), that use vector representations of sequences derived from sequence properties have been shown to have superior accuracy when compared to traditional approaches for the task of remote homology detection.</p> <p>Results</p> <p>We introduce a new method for feature vector representation based on the physicochemical properties of the primary protein sequence. A distribution of physicochemical property scores are assembled from 4-mers of the sequence and normalized based on the null distribution of the property over all possible 4-mers. With this approach there is little computational cost associated with the transformation of the protein into feature space, and overall performance in terms of remote homology detection is comparable with current state-of-the-art methods. We demonstrate that the features can be used for the task of pairwise remote homology detection with improved accuracy versus sequence-based methods such as BLAST and other feature-based methods of similar computational cost.</p> <p>Conclusions</p> <p>A protein feature method based on physicochemical properties is a viable approach for extracting features in a computationally inexpensive manner while retaining the sensitivity of SVM protein homology detection. Furthermore, identifying features that can be used for generic pairwise homology detection in lieu of family-based homology detection is important for applications such as large database searches and comparative genomics.</p

Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines

The chemical modification of histones at specific DNA regulatory elements is linked to the activation, inactivation and poising of genes. A number of tools exist to predict enhancers from chromatin modification maps, but their practical application is limited because they either (i) consider a smaller number of marks than those necessary to define the various enhancer classes or (ii) work with an excessive number of marks, which is experimentally unviable. We have developed a method for chromatin state detection using support vector machines in combination with genetic algorithm optimization, called ChromaGenSVM. ChromaGenSVM selects optimum combinations of specific histone epigenetic marks to predict enhancers. In an independent test, ChromaGenSVM recovered 88% of the experimentally supported enhancers in the pilot ENCODE region of interferon gamma-treated HeLa cells. Furthermore, ChromaGenSVM successfully combined the profiles of only five distinct methylation and acetylation marks from ChIP-seq libraries done in human CD4+ T cells to predict ∼21 000 experimentally supported enhancers within 1.0 kb regions and with a precision of ∼90%, thereby improving previous predictions on the same dataset by 21%. The combined results indicate that ChromaGenSVM comfortably outperforms previously published methods and that enhancers are best predicted by specific combinations of histone methylation and acetylation marks