Comparative analysis of anticoagulant influence on PMI estimation based on porcine blood metabolomics profile measured using GC-MS.

Introduction: Accurate post-mortem interval (PMI) estimation is essential in forensic investigations. Although various methods for PMI determination have been developed, only an approximate estimation is still achievable, and an accurate PMI indication is still challenging. Therefore, in this study, we employed gas chromatography-mass spectrometry (GC-MS)-based metabolomics to assess post-mortem changes in porcine blood samples collected with and without the addition of anticoagulant (EDTA).

Aggregating amyloid resources: A comprehensive review of databases on amyloid-like aggregation.

Protein aggregation is responsible for several degenerative conditions in humans, and it is also a bottleneck in industrial protein production and storage of biotherapeutics. Bioinformatics tools have been developed to predict and redesign protein solubility more efficiently by understanding the underlying principles behind aggregation. As more experimental data become available, dedicated resources for storing, indexing, classifying and consolidating experimental results have emerged.

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.

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.

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.

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.

Structural effects of charge destabilization and amino acid substitutions in amyloid fragments of CsgA.

The most studied functional amyloid is the CsgA, major curli subunit protein, which is produced by numerous strains of Enterobacteriaceae. Although CsgA sequences are highly conserved, they exhibit species diversity, which reflects the specific evolutionary and functional adaptability of the major curli subunit. Herein, we performed bioinformatics analyses to uncover the differences in the amyloidogenic properties of the R4 fragments in Escherichia coli and Salmonella enterica and proposed four mutants for more detailed studies: M1, M2, M3, and M4.

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.

aSynPEP-DB: a database of biogenic peptides for inhibiting α-synuclein aggregation.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, yet effective treatments able to stop or delay disease progression remain elusive. The aggregation of a presynaptic protein, α-synuclein (aSyn), is the primary neurological hallmark of PD and, thus, a promising target for therapeutic intervention. However, the lack of consensus on the molecular properties required to specifically bind the toxic species formed during aSyn aggregation has hindered the development of therapeutic molecules.

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.

Prediction of protein subplastid localization and origin with PlastoGram.

Due to their complex history, plastids possess proteins encoded in the nuclear and plastid genome. Moreover, these proteins localize to various subplastid compartments. Since protein localization is associated with its function, prediction of subplastid localization is one of the most important steps in plastid protein annotation, providing insight into their potential function.

Genomic characterization of enterohaemolysin-encoding haemolytic of animal and human origin.

Enterohaemolysin (Ehx) and alpha-haemolysin are virulence-associated factors (VAFs) causing the haemolytic phenotype in . It has been shown that chromosomally and plasmid-encoded alpha-haemolysin are characteristic of specific pathotypes, virulence-associated factors and hosts. However, the prevalence of alpha- and enterohaemolysin does not overlap in the majority of pathotypes.

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.

The dynamic landscape of peptide activity prediction.

Peptides are known to possess a plethora of beneficial properties and activities: antimicrobial, anticancer, anti-inflammatory or the ability to cross the blood-brain barrier are only a few examples of their functional diversity. For this reason, bioinformaticians are constantly developing and upgrading models to predict their activity , generating a steadily increasing number of available tools. Although these efforts have provided fruitful outcomes in the field, the vast and diverse amount of resources for peptide prediction can turn a simple prediction into an overwhelming searching process to find the optimal tool.

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.

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.

Intracellular Protein S-Nitrosylation-A Cells Response to Extracellular S100B and RAGE Receptor.

Human S100B is a small, multifunctional protein. Its activity, inside and outside cells, contributes to the biology of the brain, muscle, skin, and adipocyte tissues. Overexpression of S100B occurs in Down Syndrome, Alzheimer's disease, Creutzfeldt-Jakob disease, schizophrenia, multiple sclerosis, brain tumors, epilepsy, melanoma, myocardial infarction, muscle disorders, and sarcopenia.

Genome placement of alpha-haemolysin cluster is associated with alpha-haemolysin sequence variation, adhesin and iron acquisition factor profile of .

Since the discovery of haemolysis many studies focused on a deeper understanding of this phenotype in and its association with other virulence genes, diseases and pathogenic attributes/functions in the host. Our virulence-associated factor profiling and genome-wide association analysis of genomes of haemolytic and nonhaemolytic unveiled high prevalence of adhesins, iron acquisition genes and toxins in haemolytic bacteria. In the case of fimbriae with high prevalence, we analysed sequence variation of FimH, EcpD and CsgA, and showed that different adhesin variants were present in the analysed groups, indicating altered adhesive capabilities of haemolytic and nonhaemolytic .

Identification of amyloidogenic proteins in the microbiomes of a rat Parkinson's disease model and wild-type rats.

Cross seeding between amyloidogenic proteins in the gut is receiving increasing attention as a possible mechanism for initiation or acceleration of amyloid formation by aggregation-prone proteins such as αSN, which is central in the development of Parkinson's disease (PD). This is particularly pertinent in view of the growing number of functional (i.e.

Variability of Amyloid Propensity in Imperfect Repeats of CsgA Protein of and .

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.

countfitteR: efficient selection of count distributions to assess DNA damage.

Background: DNA double-strand breaks can be counted as discrete foci by imaging techniques. In personalized medicine and pharmacology, the analysis of counting data is relevant for numerous applications, e.g.

Bioinformatics methods for identification of amyloidogenic peptides show robustness to misannotated training data.

Several disorders are related to amyloid aggregation of proteins, for example Alzheimer's or Parkinson's diseases. Amyloid proteins form fibrils of aggregated beta structures. This is preceded by formation of oligomers-the most cytotoxic species.

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.

MIRRAGGE - Minimum Information Required for Reproducible AGGregation Experiments.

Reports on phase separation and amyloid formation for multiple proteins and aggregation-prone peptides are recurrently used to explore the molecular mechanisms associated with several human diseases. The information conveyed by these reports can be used directly in translational investigation, e.g.

CancerGram: An Effective Classifier for Differentiating Anticancer from Antimicrobial Peptides.

Antimicrobial peptides (AMPs) constitute a diverse group of bioactive molecules that provide multicellular organisms with protection against microorganisms, and microorganisms with weaponry for competition. Some AMPs can target cancer cells; thus, they are called anticancer peptides (ACPs). Due to their small size, positive charge, hydrophobicity and amphipathicity, AMPs and ACPs interact with negatively charged components of biological membranes.