Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device.

Studies of the proteome would benefit greatly from methods to directly sequence and digitally quantify proteins and detect posttranslational modifications with single-molecule sensitivity. Here, we demonstrate single-molecule protein sequencing using a dynamic approach in which single peptides are probed in real time by a mixture of dye-labeled N-terminal amino acid recognizers and simultaneously cleaved by aminopeptidases. We annotate amino acids and identify the peptide sequence by measuring fluorescence intensity, lifetime, and binding kinetics on an integrated semiconductor chip. Our results demonstrate the kinetic principles that allow recognizers to identify multiple amino acids in an information-rich manner that enables discrimination of single amino acid substitutions and posttranslational modifications. With further development, we anticipate that this approach will offer a sensitive, scalable, and accessible platform for single-molecule proteomic studies and applications.

Host-microbiome protein-protein interactions capture disease-relevant pathways.

BACKGROUND: Host-microbe interactions are crucial for normal physiological and immune system development and are implicated in a variety of diseases, including inflammatory bowel disease (IBD), colorectal cancer (CRC), obesity, and type 2 diabetes (T2D). Despite large-scale case-control studies aimed at identifying microbial taxa or genes involved in pathogeneses, the mechanisms linking them to disease have thus far remained elusive. RESULTS: To identify potential pathways through which human-associated bacteria impact host health, we leverage publicly-available interspecies protein-protein interaction (PPI) data to find clusters of microbiome-derived proteins with high sequence identity to known human-protein interactors. We observe differential targeting of putative human-interacting bacterial genes in nine independent metagenomic studies, finding evidence that the microbiome broadly targets human proteins involved in immune, oncogenic, apoptotic, and endocrine signaling pathways in relation to IBD, CRC, obesity, and T2D diagnoses. CONCLUSIONS: This host-centric analysis provides a mechanistic hypothesis-generating platform and extensively adds human functional annotation to commensal bacterial proteins.

Functions predict horizontal gene transfer and the emergence of antibiotic resistance.

Phylogenetic distance, shared ecology, and genomic constraints are often cited as key drivers governing horizontal gene transfer (HGT), although their relative contributions are unclear. Here, we apply machine learning algorithms to a curated set of diverse bacterial genomes to tease apart the importance of specific functional traits on recent HGT events. We find that functional content accurately predicts the HGT network [area under the receiver operating characteristic curve (AUROC) = 0.983], and performance improves further (AUROC = 0.990) for transfers involving antibiotic resistance genes (ARGs), highlighting the importance of HGT machinery, niche-specific, and metabolic functions. We find that high-probability not-yet detected ARG transfer events are almost exclusive to human-associated bacteria. Our approach is robust at predicting the HGT networks of pathogens, including Acinetobacter baumannii and Escherichia coli, as well as within localized environments, such as an individual's gut microbiome.

Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer's Disease Neuroimaging (ADNI) database.

The Alzheimer's Disease Neuroimaging (ADNI) database is an expansive undertaking by government, academia, and industry to pool resources and data on subjects at various stage of symptomatic severity due to Alzheimer's disease. As expected, magnetic resonance imaging is a major component of the project. Full brain images are obtained at every 6-month visit. A range of cognitive tests studying executive function and memory are employed less frequently. Two blood draws (baseline, 6 months) provide samples to measure concentrations of approximately 145 plasma biomarkers. In addition, other diagnostic measurements are performed including PET imaging, cerebral spinal fluid measurements of amyloid-beta and tau peptides, as well as genetic tests, demographics, and vital signs. ADNI data is available upon review of an application. There have been numerous reports of how various processes evolve during AD progression, including alterations in metabolic and neuroendocrine activity, cell survival, and cognitive behavior. Lacking an analytic model at the onset, we leveraged recent advances in machine learning, which allow us to deal with large, non-linear systems with many variables. Of particular note was examining how well binary predictions of future disease states could be learned from simple, non-invasive measurements like those dependent on blood samples. Such measurements make relatively little demands on the time and effort of medical staff or patient. We report findings with recall/precision/area under the receiver operator curve after application of CART, Random Forest, Gradient Boosting, and Support Vector Machines, Our results show (i) Random Forests and Gradient Boosting work very well with such data, (ii) Prediction quality when applied to relatively easily obtained measurements (Cognitive scores, Genetic Risk and plasma biomarkers) achieve results that are competitive with magnetic resonance techniques. This is by no means an exhaustive study, but instead an exploration of the plausibility of defining a series of relatively inexpensive, broad population based tests.

Interactome INSIDER: a structural interactome browser for genomic studies.

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome.

A new algorithm and Web server, mutation3D (http://mutation3d.org), proposes driver genes in cancer by identifying clusters of amino acid substitutions within tertiary protein structures. We demonstrate the feasibility of using a 3D clustering approach to implicate proteins in cancer based on explorations of single proteins using the mutation3D Web interface. On a large scale, we show that clustering with mutation3D is able to separate functional from nonfunctional mutations by analyzing a combination of 8,869 known inherited disease mutations and 2,004 SNPs overlaid together upon the same sets of crystal structures and homology models. Further, we present a systematic analysis of whole-genome and whole-exome cancer datasets to demonstrate that mutation3D identifies many known cancer genes as well as previously underexplored target genes. The mutation3D Web interface allows users to analyze their own mutation data in a variety of popular formats and provides seamless access to explore mutation clusters derived from over 975,000 somatic mutations reported by 6,811 cancer sequencing studies. The mutation3D Web interface is freely available with all major browsers supported.