Reversible and Irreversible Interactions of Poly(3-hexylthiophene) with Oxygen Studied by Spin-Sensitive Methods

Understanding of degradation mechanisms in polymer:fullerene bulk-heterojunctions on the microscopic level aimed at improving their intrinsic stability is crucial for the breakthrough of organic photovoltaics. These materials are vulnerable to exposure to light and/or oxygen, hence they involve electronic excitations. To unambiguously probe the excited states of various multiplicities and their reactions with oxygen, we applied combined magneto-optical methods based on multifrequency (9 and 275 GHz) electron paramagnetic resonance (EPR), photoluminescence (PL), and PL-detected magnetic resonance (PLDMR) to the conjugated polymer poly(3-hexylthiophene) (P3HT) and polymer:fullerene bulk heterojunctions (P3HT:PCBM; PCBM = [6,6]-phenyl-C61-butyric acid methyl ester). We identified two distinct photochemical reaction routes, one being fully reversible and related to the formation of polymer:oxygen charge transfer complexes, the other one, irreversible, being related to the formation of singlet oxygen under participation of bound triplet excitons on the polymer chain. With respect to the blends, we discuss the protective effect of the methanofullerenes on the conjugated polymer bypassing the triplet exciton generation

SARS-CoV-2 structural coverage map reveals viral protein assembly, mimicry, and hijacking mechanisms

We modeled 3D structures of all SARS-CoV-2 proteins, generating 2,060 models that span 69% of the viral proteome and provide details not available elsewhere. We found that ˜6% of the proteome mimicked human proteins, while ˜7% was implicated in hijacking mechanisms that reverse post-translational modifications, block host translation, and disable host defenses; a further ˜29% self-assembled into heteromeric states that provided insight into how the viral replication and translation complex forms. To make these 3D models more accessible, we devised a structural coverage map, a novel visualization method to show what is-and is not-known about the 3D structure of the viral proteome. We integrated the coverage map into an accompanying online resource (https://aquaria.ws/covid) that can be used to find and explore models corresponding to the 79 structural states identified in this work. The resulting Aquaria-COVID resource helps scientists use emerging structural data to understand the mechanisms underlying coronavirus infection and draws attention to the 31% of the viral proteome that remains structurally unknown or dark

The role of fullerenes in the environmental stability of polymer:fullerene solar cells

Environmental stability is a common challenge for the commercialisation of low cost, encapsulation-free organic opto-electronic devices. Understanding the role of materials degradation is the key to address this challenge, but most such studies have been limited to conjugated polymers. Here we quantitatively study the role of the common fullerene derivative PCBM in limiting the stability of benchmark organic solar cells, showing that a minor fraction (<1%) of photo-oxidised PCBM, induced by short exposure to either solar or ambient laboratory lighting conditions in air, consistent with typical processing and operating conditions, is sufficient to compromise device performance severely. We identify the effects of photo-oxidation of PCBM on its chemical structure, and connect this to specific changes in its electronic structure, which significantly alter the electron transport and recombination kinetics. The effect of photo-oxidation on device current–voltage characteristics, electron mobility and density of states could all be explained with the same model of photoinduced defects acting as trap states. Our results demonstrate that the photochemical instability of PCBM and chemically similar fullerenes remains a barrier for the commercialisation of organic opto-electronic devices

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

Discovery of numerous novel small genes in the intergenic regions of the <em>Escherichia coli</em> O157:H7 Sakai genome.

In the past, short protein-coding genes were often disregarded by genome annotation pipelines. Transcriptome sequencing (RNAseq) signals outside of annotated genes have usually been interpreted to indicate either ncRNA or pervasive transcription. Therefore, in addition to the transcriptome, the translatome (RIBOseq) of the enteric pathogen Escherichia coli O157:H7 strain Sakai was determined at two optimal growth conditions and a severe stress condition combining low temperature and high osmotic pressure. All intergenic open reading frames potentially encoding a protein of &ge; 30 amino acids were investigated with regard to coverage by transcription and translation signals and their translatability expressed by the ribosomal coverage value. This led to discovery of 465 unique, putative novel genes not yet annotated in this E. coli strain, which are evenly distributed over both DNA strands of the genome. For 255 of the novel genes, annotated homologs in other bacteria were found, and a machine-learning algorithm, trained on small protein-coding E. coli genes, predicted that 89% of these translated open reading frames represent bona fide genes. The remaining 210 putative novel genes without annotated homologs were compared to the 255 novel genes with homologs and to 250 short annotated genes of this E. coli strain. All three groups turned out to be similar with respect to their translatability distribution, fractions of differentially regulated genes, secondary structure composition, and the distribution of evolutionary constraint, suggesting that both novel groups represent legitimate genes. However, the machine-learning algorithm only recognized a small fraction of the 210 genes without annotated homologs. It is possible that these genes represent a novel group of genes, which have unusual features dissimilar to the genes of the machine-learning algorithm training set

RELIMO - Rezeptormodellierung und De Novo Design kombinatorischer Bibliotheken zum Design neuer Wirkstoffe. Teilprojekt 1: Koordination, Modellentwicklung und Rezeptormodellierung Abschlussbericht

Available from TIB Hannover: F03B671 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEBundesministerium fuer Bildung und Forschung, Berlin (Germany)DEGerman

Diagnostic Imaging Of The Temporomandibular Joint

PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein-protein binding sites (ISIS2), protein-polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org