Comparative Analysis of Protein Structure Alignments

Background: Several methods are currently available for the comparison of protein structures. These methods have been analysed regarding the performance in the identification of structurally/evolutionary related proteins, but so far there has been less focus on the objective comparison between the alignments produced by different methods. Results: We analysed and compared the structural alignments obtained by different methods using three sets of pairs of structurally related proteins. The first set corresponds to 355 pairs of remote homologous proteins according to the SCOP database (ASTRAL40 set). The second set was derived from the SISYPHUS database and includes 69 protein pairs (SISY set). The third set consists of 40 pairs that are challenging to align (RIPC set). The alignment of pairs of this set requires indels of considerable number and size and some of the proteins are related by circular permutations, show extensive conformational variability or include repetitions. Two standard methods (CE and DALI) were applied to align the proteins in the ASTRAL40 set. The extent of structural similarity identified by both methods is highly correlated and the alignments from the two methods agree on average in more than half of the aligned positions. CE, DALI, as well as four additional methods (FATCAT, MATRAS, C -match and SHEBA) were then compared using the SISY and RIPC sets. The accuracy of the alignments was assessed by comparison to reference alignments. The alignments generated by the different methods on average match more than half of the reference alignments in the SISY set. The alignments obtained in the more challenging RIPC set tend to differ considerably and match reference alignments less successfully than the SISY set alignments. Conclusion: The alignments produced by different methods tend to agree to a considerable extent, but the agreement is lower for the more challenging pairs. The results for the comparison to reference alignments are encouraging, but also indicate that there is still room for improvement.(VLID)221254

NOXclass: prediction of protein-protein interaction types

BACKGROUND: Structural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available. RESULTS: Six interface properties have been investigated on a dataset of 243 protein interactions. The six properties have been combined using a support vector machine algorithm, resulting in NOXclass, a classifier for distinguishing obligate, non-obligate and crystal packing interactions. We achieve an accuracy of 91.8% for the classification of these three types of interactions using a leave-one-out cross-validation procedure. CONCLUSION: NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available. We expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists. A web server based on the method and the datasets used in this study are available at

Plasmids Increase the Competitive Ability of Plasmid-Bearing Cells Even When Transconjugants Are Poor Donors, as Shown by Computer Simulations

Bacterial cells often suffer a fitness cost after conjugative plasmids’ entry because these cells replicate slower than plasmid-free cells. Compensatory mutations may appear after tens of or a few hundred generations, reducing or eliminating this cost. A previous work based on a mathematical model and computer simulations has shown that plasmid-bearing cells already adapted to the plasmid may gain a fitness advantage when plasmids transfer into neighboring plasmid-free cells because these cells are still unadapted to the plasmid. These slow-growing transconjugants use fewer resources, which can benefit donor cells. However, opportunities for compensatory mutations in transconjugants increase if these cells become numerous (through replication or conjugation). Moreover, transconjugants also gain an advantage when transferring the plasmid, but the original donors may be too distant from conjugation events to gain an advantage. To understand which consequence prevails, we performed further computer simulations allowing versus banning transfer from transconjugants. The advantage to donors is higher if transconjugants do not transfer plasmids, mainly when donors are rare and when the plasmid transfer rate (from donors) is high. These results show that conjugative plasmids are efficient biological weapons even if the transconjugant cells are poor plasmid donors. After some time, conjugative plasmids gain other host-benefit genes, such as virulence and drug-resistance.info:eu-repo/semantics/publishedVersio

GOTax: investigating biological processes and biochemical activities along the taxonomic tree

GOTax, a novel web-based platform that integrates protein annotation with protein family classification and taxonomy, allows for an extensive assessment of functional similarity between proteins and for comparing and analyzing the distribution of protein families and protein functions over different taxonomic groups

Engineered Ashbya gossypii for single-cell oil production from non-detoxified Eucalyptus bark hydrolysate

[Excerpt] Ashbya gossypii is a filamentous fungus industrially used for riboflavin production, a bioprocess in which downstream product recovery is facilitated by the ability of this fungus to undergo autolysis during the late stationary phase of growth or at low temperature [1]. In addition to riboflavin, engineered A. gossypii strains are capable of producing other compounds of interest for the food and feed industry, among which Single-Cell Oils (SCOs) from media containing mixed formulations of detoxified corn-cob hydrolysate, sugarcane molasses or crude glycerol [2]. [...]This work was supported by Compete 2020, Portugal 2020 and Lisboa 2020 through MoveToLowC (POCI-01-0247-FEDER-046117) and by FCT through the strategic funding of UIDB/04469/2020 and project ESSEntial (PTDC/BII-BTI/1858/2021).info:eu-repo/semantics/publishedVersio

Applications of semantic similarity measures

There has been much interest in uncovering protein-protein interactions and their underlying domain-domain interactions. Many experimental techniques have been developed, for example yeast-two-hybrid screening and tandem affinity purification. Since it is time consuming and expensive to perform exhaustive experimental screens, in silico methods are used for predicting interactions. However, all experimental and computational methods have considerable false positive and false negative rates. Therefore, it is necessary to validate experimentally determined and predicted interactions. One possibility for the validation of interactions is the comparison of the functions of the proteins or domains. Gene Ontology (GO) is widely accepted as a standard vocabulary for functional terms, and is used for annotating proteins and protein families with biological processes and their molecular functions. This annotation can be used for a functional comparison of interacting proteins or domains using semantic similarity measures. Another application of semantic similarity measures is the prioritization of disease genes. It is know that functionally similar proteins are often involved in the same or similar diseases. Therefore, functional similarity is used for predicting disease associations of proteins. In the first part of my talk, I will introduce some semantic and functional similarity measures that can be used for comparison of GO terms and proteins or protein families. Then, I will show their application for determining a confidence threshold for domain-domain interaction predictions. Additionally, I will present FunSimMat (http://www.funsimmat.de/), a comprehensive resource of functional similarity values available on the web. In the last part, I will introduce the problem of comparing diseases, and a first attempt to apply functional similarity measures based on GO to this problem

Harmful behaviour through plasmid transfer: a successful evolutionary strategy of bacteria harbouring conjugative plasmids

Conjugative plasmids are extrachromosomal mobile genetic elements pervasive among bacteria. Plasmids' acquisition often lowers cells' growth rate, so their ubiquity has been a matter of debate. Chromosomes occasionally mutate, rendering plasmids cost-free. However, these compensatory mutations typically take hundreds of generations to appear after plasmid arrival. By then, it could be too late to compete with fast-growing plasmid-free cells successfully. Moreover, arriving plasmids would have to wait hundreds of generations for compensatory mutations to appear in the chromosome of their new host. We hypothesize that plasmid-donor cells may use the plasmid as a ‘weapon’ to compete with plasmid-free cells, particularly in structured environments. Cells already adapted to plasmids may increase their inclusive fitness through plasmid transfer to impose a cost to nearby plasmid-free cells and increase the replication opportunities of nearby relatives. A mathematical model suggests conditions under which the proposed hypothesis works, and computer simulations tested the long-term plasmid maintenance. Our hypothesis explains the maintenance of conjugative plasmids not coding for beneficial genes. This article is part of the theme issue ‘The secret lives of microbial mobile genetic elements’.info:eu-repo/semantics/publishedVersio

The Social Distancing Imposed To Contain COVID-19 Can Affect Our Microbiome: a Double-Edged Sword in Human Health

Hygienic measures imposed to control the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and contain COVID-19 have proven effective in controlling the pandemic. In this article, we argue that these measures could impact the human microbiome in two different and disparate ways, acting as a double-edged sword in human health. New lines of research have shown that the diversity of human intestinal and oropharyngeal microbiomes can shape pulmonary viral infection progression. Here, we suggest that the disruption in microbial sharing, as it is associated with dysbiosis (loss of bacterial diversity associated with an imbalance of the microbiota with deleterious consequences for the host), may worsen the prognosis of COVID-19 disease. In addition, social detachment can also decrease the rate of transmission of antibiotic-resistant bacteria. Therefore, it seems crucial to perform new studies combining the pandemic control of COVID-19 with the diversity of the human microbiome.info:eu-repo/semantics/publishedVersio

The power of dying slowly - persistence as unintentional dormancy

Persistence is a state of bacterial dormancy where cells with low metabolic activity and growth rates are phenotypically tolerant to antibiotics and other cytotoxic substances. Given its obvious advantage to bacteria, several researchers have been looking for the genetic mechanism behind persistence. However, other authors argue that there is no such mechanism and that persistence results from inadvertent cell errors. In this case, the persistent population should decay according to a power-law with a particular exponent of −2. Studying persisters’ decay is, therefore, a valuable way to understand persistence. Here we simulated the fate of susceptible cells in laboratory experiments in the context of indirect resistance. Eventually, under indirect resistance, detoxifying drug-resistant cells save the persister cells that leave the dormant state and resume growth. The simulations presented here show that, by assuming a power-law decline, the exponent is close to −2, which is the expected value if persistence results from unintentional errors. Whether persisters are cells in a moribund state or, on the contrary, result from a genetic program, should impact the research of anti-persistent drugs.info:eu-repo/semantics/publishedVersio

The Perfect Condition for the Rising of Superbugs: Person-to-Person Contact and Antibiotic Use Are the Key Factors Responsible for the Positive Correlation between Antibiotic Resistance Gene Diversity and Virulence Gene Diversity in Human Metagenomes

Human metagenomes with a high diversity of virulence genes tend to have a high diversity of antibiotic-resistance genes and vice-versa. To understand this positive correlation, we simulated the transfer of these genes and bacterial pathogens in a community of interacting people that take antibiotics when infected by pathogens. Simulations show that people with higher diversity of virulence and resistance genes took antibiotics long ago, not recently. On the other extreme, we find people with low diversity of both gene types because they took antibiotics recently—while antibiotics select specific resistance genes, they also decrease gene diversity by eliminating bacteria. In general, the diversity of virulence and resistance genes becomes positively correlated whenever the transmission probability between people is higher than the probability of losing resistance genes. The positive correlation holds even under changes of several variables, such as the relative or total diversity of virulence and resistance genes, the contamination probability between individuals, the loss rate of resistance genes, or the social network type. Because the loss rate of resistance genes may be shallow, we conclude that the transmission between people and antibiotic usage are the leading causes for the positive correlation between virulence and antibiotic-resistance genes.info:eu-repo/semantics/publishedVersio