Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation.

Protein aggregation is behind the genesis of incurable diseases and imposes constraints on drug discovery and the industrial production and formulation of proteins. Over the years, we have been advancing the Aggresscan3D (A3D) method, aiming to deepen our comprehension of protein aggregation and assist the engineering of protein solubility. Since its inception, A3D has become one of the most popular structure-based aggregation predictors because of its performance, modular functionalities, RESTful service for extensive screenings, and intuitive user interface. Building on this foundation, we introduce Aggrescan4D (A4D), significantly extending A3D's functionality. A4D is aimed at predicting the pH-dependent aggregation of protein structures, and features an evolutionary-informed automatic mutation protocol to engineer protein solubility without compromising structure and stability. It also integrates precalculated results for the nearly 500,000 jobs in the A3D Model Organisms Database and structure retrieval from the AlphaFold database. Globally, A4D constitutes a comprehensive tool for understanding, predicting, and designing solutions for specific protein aggregation challenges. The A4D web server and extensive documentation are available at https://biocomp.chem.uw.edu.pl/a4d/. This website is free and open to all users without a login requirement.

A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms.

Protein aggregation has been associated with aging and different pathologies and represents a bottleneck in the industrial production of biotherapeutics. Numerous past studies performed in Escherichia coli and other model organisms have allowed to dissect the biophysical principles underlying this process. This knowledge fuelled the development of computational tools, such as Aggrescan 3D (A3D) to forecast and re-design protein aggregation. Here, we present the A3D Model Organism Database (A3D-MODB) http://biocomp.chem.uw.edu.pl/A3D2/MODB, a comprehensive resource for the study of structural protein aggregation in the proteomes of 12 key model species spanning distant biological clades. In addition to A3D predictions, this resource incorporates information useful for contextualizing protein aggregation, including membrane protein topology and structural model confidence, as an indirect reporter of protein disorder. The database is openly accessible without any need for registration. We foresee A3D-MOBD evolving into a central hub for conducting comprehensive, multi-species analyses of protein aggregation, fostering the development of protein-based solutions for medical, biotechnological, agricultural and industrial applications.

imputomics: web server and R package for missing values imputation in metabolomics data.

MOTIVATION: Missing values are commonly observed in metabolomics data from mass spectrometry. Imputing them is crucial because it assures data completeness, increases the statistical power of analyses, prevents inaccurate results, and improves the quality of exploratory analysis, statistical modeling, and machine learning. Numerous Missing Value Imputation Algorithms (MVIAs) employ heuristics or statistical models to replace missing information with estimates. In the context of metabolomics data, we identified 52 MVIAs implemented across 70 R functions. Nevertheless, the usage of those 52 established methods poses challenges due to package dependency issues, lack of documentation, and their instability. RESULTS: Our R package, 'imputomics', provides a convenient wrapper around 41 (plus random imputation as a baseline model) out of 52 MVIAs in the form of a command-line tool and a web application. In addition, we propose a novel functionality for selecting MVIAs recommended for metabolomics data with the best performance or execution time. AVAILABILITY AND IMPLEMENTATION: 'imputomics' is freely available as an R package (github.com/BioGenies/imputomics) and a Shiny web application (biogenies.info/imputomics-ws). The documentation is available at biogenies.info/imputomics.

AmyloGraph: a comprehensive database of amyloid-amyloid interactions.

Information about the impact of interactions between amyloid proteins on their fibrillization propensity is scattered among many experimental articles and presented in unstructured form. We manually curated information located in almost 200 publications (selected out of 562 initially considered), obtaining details of 883 experimentally studied interactions between 46 amyloid proteins or peptides. We also proposed a novel standardized terminology for the description of amyloid-amyloid interactions, which is included in our database, covering all currently known types of such a cross-talk, including inhibition of fibrillization, cross-seeding and other phenomena. The new approach allows for more specific studies on amyloids and their interactions, by providing very well-defined data. AmyloGraph, an online database presenting information on amyloid-amyloid interactions, is available at (http://AmyloGraph.com/). Its functionalities are also accessible as the R package (https://github.com/KotulskaLab/AmyloGraph). AmyloGraph is the only publicly available repository for experimentally determined amyloid-amyloid interactions.

adhesiomeR: a tool for Escherichia coli adhesin classification and analysis.

Adhesins are crucial factors in the virulence of bacterial pathogens such as Escherichia coli. However, to date no resources have been dedicated to the detailed analysis of E. coli adhesins. Here, we provide adhesiomeR software that enables characterization of the complete adhesin repertoire, termed the adhesiome. AdhesiomeR incorporates the most comprehensive database of E. coli adhesins and facilitates an extensive analysis of adhesiome. We demonstrate that adhesiomeR achieves 98% accuracy when compared with experimental analyses. Based on analysis of 15,000 E. coli genomes, we define novel adhesiome profiles and clusters, providing a nomenclature for a unified comparison of E. coli adhesiomes.

Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data.

Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.

Testing Antimicrobial Properties of Selected Short Amyloids.

Amyloids and antimicrobial peptides (AMPs) have many similarities, e.g., both kill microorganisms by destroying their membranes, form aggregates, and modulate the innate immune system. Given these similarities and the fact that the antimicrobial properties of short amyloids have not yet been investigated, we chose a group of potentially antimicrobial short amyloids to verify their impact on bacterial and eukaryotic cells. We used AmpGram, a best-performing AMP classification model, and selected ten amyloids with the highest AMP probability for our experimental research. Our results indicate that four tested amyloids: VQIVCK, VCIVYK, KCWCFT, and GGYLLG, formed aggregates under the conditions routinely used to evaluate peptide antimicrobial properties, but none of the tested amyloids exhibited antimicrobial or cytotoxic properties. Accordingly, they should be included in the negative datasets to train the next-generation AMP prediction models, based on experimentally confirmed AMP and non-AMP sequences. In the article, we also emphasize the importance of reporting non-AMPs, given that only a handful of such sequences have been officially confirmed.

MegaGO: A Fast Yet Powerful Approach to Assess Functional Gene Ontology Similarity across Meta-Omics Data Sets.

The study of microbiomes has gained in importance over the past few years and has led to the emergence of the fields of metagenomics, metatranscriptomics, and metaproteomics. While initially focused on the study of biodiversity within these communities, the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute the functional similarity between multiple data sets. MegaGO is high performing: Each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license and is available at https://github.com/MEGA-GO/.

HaDeX: an R package and web-server for analysis of data from hydrogen-deuterium exchange mass spectrometry experiments.

MOTIVATION: Hydrogen-deuterium mass spectrometry (HDX-MS) is a rapidly developing technique for monitoring dynamics and interactions of proteins. The development of new devices has to be followed with new software suites addressing emerging standards in data analysis. RESULTS: We propose HaDeX, a novel tool for processing, analysis and visualization of HDX-MS experiments. HaDeX supports a reproducible analytical process, including data exploration, quality control and generation of publication-quality figures. AVAILABILITY AND IMPLEMENTATION: HaDeX is available primarily as a web-server (http://mslab-ibb.pl/shiny/HaDeX/), but its all functionalities are also accessible as the R package (https://CRAN.R-project.org/package=HaDeX) and standalone software (https://sourceforge.net/projects/HaDeX/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of Natural Mutations Responsible for Altered Infection Phenotypes of Salmonella enterica Clinical Isolates by Using Cell Line Infection Screens.

The initial steps of Salmonella pathogenesis involve adhesion to and invasion into host epithelial cells. While well-studied for Salmonella enterica serovar Typhimurium, the factors contributing to this process in other, host-adapted serovars remains unexplored. Here, we screened clinical isolates of serovars Gallinarum, Dublin, Choleraesuis, Typhimurium, and Enteritidis for adhesion to and invasion into intestinal epithelial cell lines of human, porcine, and chicken origins. Thirty isolates with altered infectivity were used for genomic analyses, and 14 genes and novel mutations associated with high or low infectivity were identified. The functions of candidate genes included virulence gene expression regulation and cell wall or membrane synthesis and components. The role of several of these genes in Salmonella adhesion to and invasion into cells has not previously been investigated. The genes dksA (encoding a stringent response regulator) and sanA (encoding a vancomycin high-temperature exclusion protein) were selected for further analyses, and we confirmed their roles in adhesion to and invasion into host cells. Furthermore, transcriptomic analyses were performed for S Enteritidis and S Typhimurium, with two highly infective and two marginally infective isolates for each serovar. Expression profiles for the isolates with altered infection phenotypes revealed the importance of type 3 secretion system expression levels in the determination of an isolate's infection phenotype. Taken together, these data indicate a new role in cell host infection for genes or gene variants previously not associated with adhesion to and invasion into the epithelial cells.IMPORTANCESalmonella is a foodborne pathogen affecting over 200 million people and resulting in over 200,000 fatal cases per year. Its adhesion to and invasion into intestinal epithelial cells represent one of the first and key steps in the pathogenesis of salmonellosis. Still, around 35 to 40% of bacterial genes have no experimentally validated function, and their contribution to bacterial virulence, including adhesion and invasion, remains largely unknown. Therefore, the significance of this study is in the identification of new genes or gene allelic variants previously not associated with adhesion and invasion. It is well established that blocking adhesion and/or invasion would stop or hamper bacterial infection; therefore, the new findings from this study could be used in future developments of anti-Salmonella therapy targeting genes involved in these key processes. Such treatment could be a valuable alternative, as the prevalence of antibiotic-resistant bacteria is increasing very rapidly.

Variability of Amyloid Propensity in Imperfect Repeats of CsgA Protein of Salmonella enterica and Escherichia coli.

CsgA is an aggregating protein from bacterial biofilms, representing a class of functional amyloids. Its amyloid propensity is defined by five fragments (R1-R5) of the sequence, representing non-perfect repeats. Gate-keeper amino acid residues, specific to each fragment, define the fragment's propensity for self-aggregation and aggregating characteristics of the whole protein. We study the self-aggregation and secondary structures of the repeat fragments of Salmonella enterica and Escherichia coli and comparatively analyze their potential effects on these proteins in a bacterial biofilm. Using bioinformatics predictors, ATR-FTIR and FT-Raman spectroscopy techniques, circular dichroism, and transmission electron microscopy, we confirmed self-aggregation of R1, R3, R5 fragments, as previously reported for Escherichia coli, however, with different temporal characteristics for each species. We also observed aggregation propensities of R4 fragment of Salmonella enterica that is different than that of Escherichia coli. Our studies showed that amyloid structures of CsgA repeats are more easily formed and more durable in Salmonella enterica than those in Escherichia coli.

Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram.

Antimicrobial peptides (AMPs) are molecules widespread in all branches of the tree of life that participate in host defense and/or microbial competition. Due to their positive charge, hydrophobicity and amphipathicity, they preferentially disrupt negatively charged bacterial membranes. AMPs are considered an important alternative to traditional antibiotics, especially at the time when multidrug-resistant bacteria being on the rise. Therefore, to reduce the costs of experimental research, robust computational tools for AMP prediction and identification of the best AMP candidates are essential. AmpGram is our novel tool for AMP prediction; it outperforms top-ranking AMP classifiers, including AMPScanner, CAMPR3R and iAMPpred. It is the first AMP prediction tool created for longer AMPs and for high-throughput proteomic screening. AmpGram prediction reliability was confirmed on the example of lactoferrin and thrombin. The former is a well known antimicrobial protein and the latter a cryptic one. Both proteins produce (after protease treatment) functional AMPs that have been experimentally validated at molecular level. The lactoferrin and thrombin AMPs were located in the antimicrobial regions clearly detected by AmpGram. Moreover, AmpGram also provides a list of shot 10 amino acid fragments in the antimicrobial regions, along with their probability predictions; these can be used for further studies and the rational design of new AMPs. AmpGram is available as a web-server, and an easy-to-use R package for proteomic analysis at CRAN repository.

Simultaneous detection and quantification of DNA and protein biomarkers in spectrum of cardiovascular diseases in a microfluidic microbead chip.

The rapid and simultaneous detection of DNA and protein biomarkers is necessary to detect the outbreak of a disease or to monitor a disease. For example, cardiovascular diseases are a major cause of adult mortality worldwide. We have developed a rapidly adaptable platform to assess biomarkers using a microfluidic technology. Our model mimics autoantibodies against three proteins, C-reactive protein (CRP), brain natriuretic peptide (BNP), and low-density lipoprotein (LDL). Cell-free mitochondrial DNA (cfmDNA) and DNA controls are detected via fluorescence probes. The biomarkers are covalently bound on the surface of size- (11-15 µm) and dual-color encoded microbeads and immobilized as planar layer in a microfluidic chip flow cell. Binding events of target molecules were analyzed by fluorescence measurements with a fully automatized fluorescence microscope (end-point and real-time) developed in house. The model system was optimized for buffers and immobilization strategies of the microbeads to enable the simultaneous detection of protein and DNA biomarkers. All prime target molecules (anti-CRP, anti-BNP, anti-LDL, cfmDNA) and the controls were successfully detected both in independent reactions and simultaneously. In addition, the biomarkers could also be detected in spiked human serum in a similar way as in the optimized buffer system. The detection limit specified by the manufacturer is reduced by at least a factor of five for each biomarker as a result of the antibody detection and kinetic experiments indicate that nearly 50 % of the fluorescence intensity is achieved within 7 min. For rapid data inspection, we have developed the open source software digilogger, which can be applied for data evaluation and visualization. Graphical abstract.

Enabling reproducible real-time quantitative PCR research: the RDML package.

MOTIVATION: Reproducibility, a cornerstone of research, requires defined data formats, which include the setup and output of experiments. The real-time PCR data markup language (RDML) is a recommended standard of the minimum information for publication of quantitative real-time PCR experiments guidelines. Despite the popularity of the RDML format for analysis of quantitative PCR data, handling of RDML files is not yet widely supported in all PCR curve analysis softwares. RESULTS: This study describes the open-source RDML package for the statistical computing language R. RDML is compatible with RDML versions ≤ 1.2 and provides functionality to (i) import RDML data; (ii) extract sample information (e.g. targets and concentration); (iii) transform data to various formats of the R environment; (iv) generate human-readable run summaries; and (v) to create RDML files from user data. In addition, RDML offers a graphical user interface to read, edit and create RDML files. AVAILABILITY AND IMPLEMENTATION: https://cran.r-project.org/package=RDML. rdmlEdit server http://shtest.evrogen.net/rdmlEdit/. Documentation: http://kablag.github.io/RDML/. CONTACT: k.blag@yandex.ru. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of Signal Peptides in Proteins from Malaria Parasites.

Signal peptides are N-terminal presequences responsible for targeting proteins to the endomembrane system, and subsequent subcellular or extracellular compartments, and consequently condition their proper function. The significance of signal peptides stimulates development of new computational methods for their detection. These methods employ learning systems trained on datasets comprising signal peptides from different types of proteins and taxonomic groups. As a result, the accuracy of predictions are high in the case of signal peptides that are well-represented in databases, but might be low in other, atypical cases. Such atypical signal peptides are present in proteins found in apicomplexan parasites, causative agents of malaria and toxoplasmosis. Apicomplexan proteins have a unique amino acid composition due to their AT-biased genomes. Therefore, we designed a new, more flexible and universal probabilistic model for recognition of atypical eukaryotic signal peptides. Our approach called signalHsmm includes knowledge about the structure of signal peptides and physicochemical properties of amino acids. It is able to recognize signal peptides from the malaria parasites and related species more accurately than popular programs. Moreover, it is still universal enough to provide prediction of other signal peptides on par with the best preforming predictors.

Genotypic and Phenotypic Characteristics Associated with Biofilm Formation by Human Clinical Escherichia coli Isolates of Different Pathotypes.

Bacterial biofilm formation is a widespread phenomenon and a complex process requiring a set of genes facilitating the initial adhesion, maturation, and production of the extracellular polymeric matrix and subsequent dispersal of bacteria. Most studies on Escherichia coli biofilm formation have investigated nonpathogenic E. coli K-12 strains. Due to the extensive focus on laboratory strains in most studies, there is poor information regarding biofilm formation by pathogenic E. coli isolates. In this study, we genotypically and phenotypically characterized 187 human clinical E. coli isolates representing various pathotypes (e.g., uropathogenic, enteropathogenic, and enteroaggregative E. coli). We investigated the presence of biofilm-associated genes ("genotype") and phenotypically analyzed the isolates for motility and curli and cellulose production ("phenotype"). We developed a new screening method to examine the in vitro biofilm formation ability. In summary, we found a high prevalence of biofilm-associated genes. However, we could not detect a biofilm-associated gene or specific phenotype correlating with the biofilm formation ability. In contrast, we did identify an association of increased biofilm formation with a specific E. coli pathotype. Enteroaggregative E. coli (EAEC) was found to exhibit the highest capacity for biofilm formation. Using our image-based technology for the screening of biofilm formation, we demonstrated the characteristic biofilm formation pattern of EAEC, consisting of thick bacterial aggregates. In summary, our results highlight the fact that biofilm-promoting factors shown to be critical for biofilm formation in nonpathogenic strains do not reflect their impact in clinical isolates and that the ability of biofilm formation is a defined characteristic of EAEC.IMPORTANCE Bacterial biofilms are ubiquitous and consist of sessile bacterial cells surrounded by a self-produced extracellular polymeric matrix. They cause chronic and device-related infections due to their high resistance to antibiotics and the host immune system. In nonpathogenic Escherichia coli, cell surface components playing a pivotal role in biofilm formation are well known. In contrast, there is poor information for their role in biofilm formation of pathogenic isolates. Our study provides insights into the correlation of biofilm-associated genes or specific phenotypes with the biofilm formation ability of commensal and pathogenic E. coli Additionally, we describe a newly developed method enabling qualitative biofilm analysis by automated image analysis, which is beneficial for high-throughput screenings. Our results help to establish a better understanding of E. coli biofilm formation.

chipPCR: an R package to pre-process raw data of amplification curves.

MOTIVATION: Both the quantitative real-time polymerase chain reaction (qPCR) and quantitative isothermal amplification (qIA) are standard methods for nucleic acid quantification. Numerous real-time read-out technologies have been developed. Despite the continuous interest in amplification-based techniques, there are only few tools for pre-processing of amplification data. However, a transparent tool for precise control of raw data is indispensable in several scenarios, for example, during the development of new instruments. RESULTS: chipPCR is an R: package for the pre-processing and quality analysis of raw data of amplification curves. The package takes advantage of R: 's S4 object model and offers an extensible environment. chipPCR contains tools for raw data exploration: normalization, baselining, imputation of missing values, a powerful wrapper for amplification curve smoothing and a function to detect the start and end of an amplification curve. The capabilities of the software are enhanced by the implementation of algorithms unavailable in R: , such as a 5-point stencil for derivative interpolation. Simulation tools, statistical tests, plots for data quality management, amplification efficiency/quantification cycle calculation, and datasets from qPCR and qIA experiments are part of the package. Core functionalities are integrated in GUIs (web-based and standalone shiny applications), thus streamlining analysis and report generation. AVAILABILITY AND IMPLEMENTATION: http://cran.r-project.org/web/packages/chipPCR. Source code: https://github.com/michbur/chipPCR. CONTACT: stefan.roediger@b-tu.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Impact of smoothing on parameter estimation in quantitative DNA amplification experiments.

BACKGROUND: Quantification cycle (Cq) and amplification efficiency (AE) are parameters mathematically extracted from raw data to characterize quantitative PCR (qPCR) reactions and quantify the copy number in a sample. Little attention has been paid to the effects of preprocessing and the use of smoothing or filtering approaches to compensate for noisy data. Existing algorithms largely are taken for granted, and it is unclear which of the various methods is most informative. We investigated the effect of smoothing and filtering algorithms on amplification curve data. METHODS: We obtained published high-replicate qPCR data sets from standard block thermocyclers and other cycler platforms and statistically evaluated the impact of smoothing on Cq and AE. RESULTS: Our results indicate that selected smoothing algorithms affect estimates of Cq and AE considerably. The commonly used moving average filter performed worst in all qPCR scenarios. The Savitzky-Golay smoother, cubic splines, and Whittaker smoother resulted overall in the least bias in our setting and exhibited low sensitivity to differences in qPCR AE, whereas other smoothers, such as running mean, introduced an AE-dependent bias. CONCLUSIONS: The selection of a smoothing algorithm is an important step in developing data analysis pipelines for real-time PCR experiments. We offer guidelines for selection of an appropriate smoothing algorithm in diagnostic qPCR applications. The findings of our study were implemented in the R packages chipPCR and qpcR as a basis for the implementation of an analytical strategy.

Challenges and opportunities in processing NanoString nCounter data.

NanoString nCounter is a medium-throughput technology used in mRNA and miRNA differential expression studies. It offers several advantages, including the absence of an amplification step and the ability to analyze low-grade samples. Despite its considerable strengths, the popularity of the nCounter platform in experimental research stabilized in 2022 and 2023, and this trend may continue in the upcoming years. Such stagnation could potentially be attributed to the absence of a standardized analytical pipeline or the indication of optimal processing methods for nCounter data analysis. To standardize the description of the nCounter data analysis workflow, we divided it into five distinct steps: data pre-processing, quality control, background correction, normalization and differential expression analysis. Next, we evaluated eleven R packages dedicated to nCounter data processing to point out functionalities belonging to these steps and provide comments on their applications in studies of mRNA and miRNA samples.

Structural information in therapeutic peptides: Emerging applications in biomedicine.

Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.