Nucleotide degradation and production of hypoxanthine in some Indian marine and freshwater fishes

Changes in nucleotides and production of hypoxanthine in rohu (Labeo rohita), mrigal (Cihhrina mrigala) and common carp (Cyprinus carpio) during storage at 2-4°C were examined. Differences were observed between common carp and others. Production of hypoxanthine in pomfret (Stromateus argenteus), cat fish (Arius macronotacanthus), shark (Scoliodon spp.), seer fish (Scomberomorus guttatus), ray fish (Dasyatis imbricata) and prawns (Parapenaeopsis stylifera) was examined during storage at 2-4°C and -28°C. At 2-4°C hypoxanthine increased regularly but at -28°C there was no increase during storage over 28 weeks

Opening of DNA double strands by helicases. Active versus passive opening

Helicase opening of double-stranded nucleic acids may be "active" (the helicase directly destabilizes the dsNA to promote opening) or "passive" (the helicase binds ssNA available due to a thermal fluctuation which opens part of the dsNA). We describe helicase opening of dsNA, based on helicases which bind single NA strands and move towards the double-stranded region, using a discrete ``hopping'' model. The interaction between the helicase and the junction where the double strand opens is characterized by an interaction potential. The form of the potential determines whether the opening is active or passive. We calculate the rate of passive opening for the helicase PcrA, and show that the rate increases when the opening is active. Finally, we examine how to choose the interaction potential to optimize the rate of strand separation. One important result is our finding that active opening can increase the unwinding rate by 7 fold compared to passive opening.Comment: 13 pages, 3 figure

Monomeric PcrA helicase processively unwinds plasmid lengths of DNA in the presence of the initiator protein RepD

The helicase PcrA unwinds DNA during asymmetric replication of plasmids, acting with an initiator protein, in our case RepD. Detailed kinetics of PcrA activity were measured using bulk solution and a single-molecule imaging technique to investigate the oligomeric state of the active helicase complex, its processivity and the mechanism of unwinding. By tethering either DNA or PcrA to a microscope coverslip surface, unwinding of both linear and natural circular plasmid DNA by PcrA/RepD was followed in real-time using total internal reflection fluorescence microscopy. Visualization was achieved using a fluorescent single-stranded DNA-binding protein. The single-molecule data show that PcrA, in combination with RepD, can unwind plasmid lengths of DNA in a single run, and that PcrA is active as a monomer. Although the average rate of unwinding was similar in single-molecule and bulk solution assays, the single-molecule experiments revealed a wide distribution of unwinding speeds by different molecules. The average rate of unwinding was several-fold slower than the PcrA translocation rate on single-stranded DNA, suggesting that DNA unwinding may proceed via a partially passive mechanism. However, the fastest dsDNA unwinding rates measured in the single-molecule unwinding assays approached the PcrA translocation speed measured on ssDNA

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction

Swelling-Induced Delamination Causes Folding of Surface-Tethered Polymer Gels

ABSTRACT: When a polymer film that is weakly attached to a rigid substrate is exposed to solvent, swelling-induced compres-sive stress nucleates buckle delamination of the film from the substrate. Surprisingly, the buckles do not have a sinusoidal profile, instead, the film near the delamination buckles slides toward the buckles causing growth of sharp folds of high aspect ratio. These folds do not result from a wrinkle-to-fold transition; instead, the film goes directly from a flat state to a folded state. The folds persist even after the solvent evaporates. We propose that patterned delamination and folding may be exploited to realize high-aspect ratio topological features on surfaces through control of a set of boundary constraints arising from the interrelation of film-surface adhesion, film thickness and degree of swellabilty

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

The conserved C-terminus of the PcrA/UvrD helicase interacts directly with RNA polymerase

Copyright: © 2013 Gwynn et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by a Wellcome Trust project grant to MD (Reference: 077368), an ERC starting grant to MD (Acronym: SM-DNA-REPAIR) and a BBSRC project grant to PM, NS and MD (Reference: BB/I003142/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

HMMER web server: interactive sequence similarity searching

HMMER is a software suite for protein sequence similarity searches using probabilistic methods. Previously, HMMER has mainly been available only as a computationally intensive UNIX command-line tool, restricting its use. Recent advances in the software, HMMER3, have resulted in a 100-fold speed gain relative to previous versions. It is now feasible to make efficient profile hidden Markov model (profile HMM) searches via the web. A HMMER web server (http://hmmer.janelia.org) has been designed and implemented such that most protein database searches return within a few seconds. Methods are available for searching either a single protein sequence, multiple protein sequence alignment or profile HMM against a target sequence database, and for searching a protein sequence against Pfam. The web server is designed to cater to a range of different user expertise and accepts batch uploading of multiple queries at once. All search methods are also available as RESTful web services, thereby allowing them to be readily integrated as remotely executed tasks in locally scripted workflows. We have focused on minimizing search times and the ability to rapidly display tabular results, regardless of the number of matches found, developing graphical summaries of the search results to provide quick, intuitive appraisement of them

E-MSD: an integrated data resource for bioinformatics

The Macromolecular Structure Database (MSD) group (http://www.ebi.ac.uk/msd/) continues to enhance the quality and consistency of macromolecular structure data in the worldwide Protein Data Bank (wwPDB) and to work towards the integration of various bioinformatics data resources. One of the major obstacles to the improved integration of structural databases such as MSD and sequence databases like UniProt is the absence of up to date and well-maintained mapping between corresponding entries. We have worked closely with the UniProt group at the EBI to clean up the taxonomy and sequence cross-reference information in the MSD and UniProt databases. This information is vital for the reliable integration of the sequence family databases such as Pfam and Interpro with the structure-oriented databases of SCOP and CATH. This information has been made available to the eFamily group (http://www.efamily.org.uk/) and now forms the basis of the regular interchange of information between the member databases (MSD, UniProt, Pfam, Interpro, SCOP and CATH). This exchange of annotation information has enriched the structural information in the MSD database with annotation from wider sequence-oriented resources. This work was carried out under the ‘Structure Integration with Function, Taxonomy and Sequences (SIFTS)’ initiative (http://www.ebi.ac.uk/msd-srv/docs/sifts) in the MSD group