Biology subjects

Sommer, M. O. A.

Publications and source records attributed to Sommer, M. O. A..

3 recordsLinked to original sources

bioRxiv2018

NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning

The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unravelling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed.\n\nHere, we present an updated and extended version of the NetSurfP tool (http://www.cbs.dtu.dk/services/NetSurfP-2.0/), that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences.\n\nWe assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3-class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1,000 proteins in less than 2 hours, and complete proteomes in less than 1 day.

bioinformatics↗

bioRxiv2017

Genetic-metabolic coupling for targeted metabolic engineering

To produce chemicals, microbes typically employ potent biosynthetic enzymes that interact with native metabolism to affect cell fitness as well as chemical production. However, production optimization largely relies on data collected from wild type strains in the absence of metabolic perturbations, thus limiting their relevance to specific process scenarios. Here, we address this issue by coupling cell fitness to the production of thiamine diphosphate in Escherichia coli using a synthetic RNA biosensor.\n\nWe apply this system to interrogate a library of transposon mutants to elucidate the native gene network influencing both cell fitness and thiamine production. Specifically, we identify uncharacterized effectors of the OxyR-SoxR stress response that limit thiamine biosynthesis via alternative regulation of iron storage and Fe-S-cluster inclusion in enzymes.\n\nOur study represents a new generalizable approach for the reliable high-throughput identification of genetic targets of both known and unknown function that are directly relevant to a specific biosynthetic process.

bioengineering↗

bioRxiv2017

The Resistome Of Important Human Pathogens

Genes capable of conferring resistance to clinically used antibiotics have been found in many different natural environments. However, a concise overview of the resistance genes found in common human bacterial pathogens is lacking, which complicates risk ranking of environmental reservoirs. Here, we present an analysis of potential antibiotic resistance genes in the 17 most common bacterial pathogens isolated from humans. We analyzed more than 20,000 bacterial genomes and defined a clinical resistome as the set of resistance genes found across these genomes. Using this database, we uncovered the co-occurrence frequencies of the resistance gene clusters within each species enabling identification of co-dissemination and co-selection patterns. The resistance genes identified in this study represent the subset of the environmental resistome that is clinically relevant and the dataset and approach provides a baseline for further investigations into the abundance of clinically relevant resistance genes across different environments. To facilitate an easy overview the data is presented at the species level at www.resistome.biosustain.dtu.dk.

microbiology↗