Co-Expression Network Models Suggest that Stress Increases Tolerance to Mutations

Network models are a well established tool for studying the robustness of complex systems, including modelling the effect of loss of function mutations in protein interaction networks. Past work has concentrated on average damage caused by random node removal, with little attention to the shape of the damage distribution. In this work, we use fission yeast co-expression networks before and after exposure to stress to model the effect of stress on mutational robustness. We find that exposure to stress decreases the average damage from node removal, suggesting stress induces greater tolerance to loss of function mutations. The shape of the damage distribution is also changed upon stress, with a greater incidence of extreme damage after exposure to stress. We demonstrate that the change in shape of the damage distribution can have considerable functional consequences, highlighting the need to consider the damage distribution in addition to average behaviour

The history of the CATH structural classification of protein domains

This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review the expansion of the CATH and SCOP resources to capture predicted domain structures in the genome sequence data and to provide information on the likely functions of proteins mediated by their constituent domains. The establishment of comprehensive function annotation resources has also meant that domain families can be functionally annotated allowing insights into functional divergence and evolution within protein families

Gene Function Prediction from Functional Association Networks Using Kernel Partial Least Squares Regression

With the growing availability of large-scale biological datasets, automated methods of extracting functionally meaningful information from this data are becoming increasingly important. Data relating to functional association between genes or proteins, such as co-expression or functional association, is often represented in terms of gene or protein networks. Several methods of predicting gene function from these networks have been proposed. However, evaluating the relative performance of these algorithms may not be trivial: concerns have been raised over biases in different benchmarking methods and datasets, particularly relating to non-independence of functional association data and test data. In this paper we propose a new network-based gene function prediction algorithm using a commute-time kernel and partial least squares regression (Compass). We compare Compass to GeneMANIA, a leading network-based prediction algorithm, using a number of different benchmarks, and find that Compass outperforms GeneMANIA on these benchmarks. We also explicitly explore problems associated with the non-independence of functional association data and test data. We find that a benchmark based on the Gene Ontology database, which, directly or indirectly, incorporates information from other databases, may considerably overestimate the performance of algorithms exploiting functional association data for prediction

MACiE: a database of enzyme reaction mechanisms.

SUMMARY: MACiE (mechanism, annotation and classification in enzymes) is a publicly available web-based database, held in CMLReact (an XML application), that aims to help our understanding of the evolution of enzyme catalytic mechanisms and also to create a classification system which reflects the actual chemical mechanism (catalytic steps) of an enzyme reaction, not only the overall reaction. AVAILABILITY: http://www-mitchell.ch.cam.ac.uk/macie/.EPSRC (G.L.H. and J.B.O.M.), the BBSRC (G.J.B. and J.M.T.—CASE studentship in association with Roche Products Ltd; N.M.O.B. and J.B.O.M.—grant BB/C51320X/1), the Chilean Government’s Ministerio de Planificacio´n y Cooperacio´n and Cambridge Overseas Trust (D.E.A.) for funding and Unilever for supporting the Centre for Molecular Science Informatics.application note restricted to 2 printed pages web site: http://www-mitchell.ch.cam.ac.uk/macie

Structural and energetic analyses of SARS-CoV-2 N-terminal domain characterise sugar binding pockets and suggest putative impacts of variants on COVID-19 transmission

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 is an ongoing pandemic that causes significant health/socioeconomic burden. Variants of concern (VOCs) have emerged affecting transmissibility, disease severity and re-infection risk. Studies suggest that the - N-terminal domain (NTD) of the spike protein may have a role in facilitating virus entry via sialic-acid receptor binding. Furthermore, most VOCs include novel NTD variants. Despite global sequence and structure similarity, most sialic-acid binding pockets in NTD vary across coronaviruses. Our work suggests ongoing evolutionary tuning of the sugar-binding pockets and recent analyses have shown that NTD insertions in VOCs tend to lie close to loops. We extended the structural characterisation of these sugar-binding pockets and explored whether variants could enhance sialic acid-binding. We found that recent NTD insertions in VOCs (i.e., Gamma, Delta and Omicron variants) and emerging variants of interest (VOIs) (i.e., Iota, Lambda and Theta variants) frequently lie close to sugar-binding pockets. For some variants, including the recent Omicron VOC, we find increases in predicted sialic acid-binding energy, compared to the original SARS-CoV-2, which may contribute to increased transmission. These binding observations are supported by molecular dynamics simulations (MD). We examined the similarity of NTD across Betacoronaviruses to determine whether the sugar-binding pockets are sufficiently similar to be exploited in drug design. Whilst most pockets are too structurally variable, we detected a previously unknown highly structurally conserved pocket which can be investigated in pursuit of a generic pan-Betacoronavirus drug. Our structure-based analyses help rationalise the effects of VOCs and provide hypotheses for experiments. Our findings suggest a strong need for experimental monitoring of changes in NTD of VOCs

Multiple and diverse structural changes affect the breakpoint regions of polymorphic inversions across the Drosophila genus

Chromosomal polymorphism is widespread in the Drosophila genus, with extensive evidence supporting its adaptive character in diverse species. Moreover, inversions are the major contributors to the genus chromosomal evolution. The molecular characterization of a reduced number of polymorphic inversion breakpoints in Drosophila melanogaster and Drosophila subobscura supports that their inversions would have mostly originated through a mechanism that generates duplications staggered double-strand breaks and has thus the potential to contribute to their adaptive character. There is also evidence for inversion breakpoint reuse at different time scales. Here, we have characterized the breakpoints of two inversions of D. subobscura O4 and O8 involved in complex arrangements that are frequent in the warm parts of the species distribution area. The duplications detected at their breakpoints are consistent with their origin through the staggered-break mechanism, which further supports it as the prevalent mechanism in D. subobscura. The comparative analysis of inversions breakpoint regions across the Drosophila genus has revealed several genes affected by multiple disruptions due not only to inversions but also to single-gene transpositions and duplications

The molecular characterization of fixed inversions breakpoints unveils the ancestral character of the Drosophila guanche chromosomal arrangements

Cytological studies revealed that the number of chromosomes and their organization varies across species. The increasing availability of whole genome sequences of multiple species across specific phylogenies has confirmed and greatly extended these cytological observations. In the Drosophila genus, the ancestral karyotype consists of five rod-like acrocentric chromosomes (Muller elements A to E) and one dot-like chromosome (element F), each exhibiting a generally conserved gene content. Chromosomal fusions and paracentric inversions are thus the major contributors, respectively, to chromosome number variation among species and to gene order variation within chromosomal element. The subobscura cluster of Drosophila consists in three species that retain the genus ancestral karyotype and differ by a reduced number of fixed inversions. Here, we have used cytological information and the D. guanche genome sequence to identify and molecularly characterize the breakpoints of inversions that became fixed since the D. guanche-D. subobscura split. Our results have led us to propose a modified version of the D. guanche cytological map of its X chromosome, and to establish that (i) most inversions became fixed in the D. subobscura lineage and (ii) the order in which the four X chromosome overlapping inversions occurred and became fixed

A new spontaneous chromosomal inversion in a classical laboratory strain of <em>Drosophila subobscura</em>

Drosophila subobscura stands out for its rich chromosomal polymorphism in natural populations. Krimbas (1992) reviewed up to 66 spontaneous chromosomal inversions that combined into 79 arrangements. Some of these inversions are common in the whole range of the species distribution, but others are only present either at low frequencies across the species distribution area or in a restricted geographical area