deficiency modulates the stromal environment in the pretumorigenic rat mammary gland.

Background: Neurofibromin, coded by the tumor suppressor gene, is the main negative regulator of the RAS pathway and is frequently mutated in various cancers. Women with Neurofibromatosis Type I (NF1)-a tumor predisposition syndrome caused by a germline mutation-have an increased risk of developing aggressive breast cancer with poorer prognosis. The mechanism by which mutations lead to breast cancer tumorigenesis is not well understood.

Mining literature and pathway data to explore the relations of ketamine with neurotransmitters and gut microbiota using a knowledge-graph.

Motivation: Up-to-date pathway knowledge is usually presented in scientific publications for human reading, making it difficult to utilize these resources for semantic integration and computational analysis of biological pathways. We here present an approach to mining knowledge graphs by combining manual curation with automated named entity recognition and automated relation extraction. This approach allows us to study pathway-related questions in detail, which we here show using the ketamine pathway, aiming to help improve understanding of the role of gut microbiota in the antidepressant effects of ketamine.

Ten quick tips for sequence-based prediction of protein properties using machine learning.

The ubiquitous availability of genome sequencing data explains the popularity of machine learning-based methods for the prediction of protein properties from their amino acid sequences. Over the years, while revising our own work, reading submitted manuscripts as well as published papers, we have noticed several recurring issues, which make some reported findings hard to understand and replicate. We suspect this may be due to biologists being unfamiliar with machine learning methodology, or conversely, machine learning experts may miss some of the knowledge needed to correctly apply their methods to proteins.

Towards a knowledge graph for pre-/probiotics and microbiota-gut-brain axis diseases.

Scientific publications present biological relationships but are structured for human reading, making it difficult to use this resource for semantic integration and querying. Existing databases, on the other hand, are well structured for automated analysis, but do not contain comprehensive biological knowledge. We devised an approach for constructing comprehensive knowledge graphs from these two types of resources and applied it to investigate relationships between pre-/probiotics and microbiota-gut-brain axis diseases.

ProteinGLUE multi-task benchmark suite for self-supervised protein modeling.

Self-supervised language modeling is a rapidly developing approach for the analysis of protein sequence data. However, work in this area is heterogeneous and diverse, making comparison of models and methods difficult. Moreover, models are often evaluated only on one or two downstream tasks, making it unclear whether the models capture generally useful properties.

Multi-task learning to leverage partially annotated data for PPI interface prediction.

Protein protein interactions (PPI) are crucial for protein functioning, nevertheless predicting residues in PPI interfaces from the protein sequence remains a challenging problem. In addition, structure-based functional annotations, such as the PPI interface annotations, are scarce: only for about one-third of all protein structures residue-based PPI interface annotations are available. If we want to use a deep learning strategy, we have to overcome the problem of limited data availability.

PIPENN: protein interface prediction from sequence with an ensemble of neural nets.

Motivation: The interactions between proteins and other molecules are essential to many biological and cellular processes. Experimental identification of interface residues is a time-consuming, costly and challenging task, while protein sequence data are ubiquitous. Consequently, many computational and machine learning approaches have been developed over the years to predict such interface residues from sequence.

SeRenDIP-CE: sequence-based interface prediction for conformational epitopes.

Motivation: Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen's epitope region, as a special type of protein-protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments toward the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody.

Online biophysical predictions for SARS-CoV-2 proteins.

Background: The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures.

Impact of Preanalytical Factors on the Measurement of Tumor Tissue Biomarkers Using Immunohistochemistry.

Analysis of formalin-fixed paraffin-embedded (FFPE) tissue by immunohistochemistry (IHC) is commonplace in clinical and research laboratories. However, reports suggest that IHC results can be compromised by biospecimen preanalytical factors. The National Cancer Institute's Biospecimen Preanalytical Variables Program conducted a systematic study to examine the potential effects of delay to fixation (DTF) and time in fixative (TIF) on IHC using 24 cancer biomarkers.

Stability of Ketoprofen Methylester in Plasma of Different Species.

Background: Pharmacokinetic and pharmacodynamic assessment of ester-containing drugs can be impacted by hydrolysis of the drugs in plasma samples post blood collection. The impact is different in the plasma of different species.

Objective: This study evaluated the stability of a prodrug, ketoprofen methylester (KME), in commercially purchased and freshly collected plasma of mouse, rat, dog, cat, pig, sheep, cattle and horse.

Predicting the relationships between gut microbiota and mental disorders with knowledge graphs.

Gut microbiota produce and modulate the production of neurotransmitters which have been implicated in mental disorders. Neurotransmitters may act as 'matchmaker' between gut microbiota imbalance and mental disorders. Most of the relevant research effort goes into the relationship between gut microbiota and neurotransmitters and the other between neurotransmitters and mental disorders, while few studies collect and analyze the dispersed research results in systematic ways.

A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community).

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives.

A framework for exhaustive modelling of genetic interaction patterns using Petri nets.

Motivation: Genetic interaction (GI) patterns are characterized by the phenotypes of interacting single and double mutated gene pairs. Uncovering the regulatory mechanisms of GIs would provide a better understanding of their role in biological processes, diseases and drug response. Computational analyses can provide insights into the underpinning mechanisms of GIs.

Assessment of Inhibition of Bovine Hepatic Cytochrome P450 by 43 Commercial Bovine Medicines Using a Combination of Assays and Pharmacokinetic Data from the Literature.

Background: There has been a lack of information about the inhibition of bovine medicines on bovine hepatic CYP450 at their commercial doses and dosing routes.

Objective: The aim of this work was to assess the inhibition of 43 bovine medicines on bovine hepatic CYP450 using a combination of in vitro assay and Cmax values from pharmacokinetic studies with their commercial doses and dosing routes in the literature.

Methods: Those drugs were first evaluated through a single point inhibitory assay at 3 μM in bovine liver microsomes for six specific CYP450 metabolisms, phenacetin o-deethylation, coumarin 7- hydroxylation, tolbutamide 4-hydroxylation, bufuralol 1-hydroxylation, chlorzoxazone 6-hydroxylation and midazolam 1'-hydroxylation.

The potential use of big data in oncology.

In this era of information technology, big data analysis is entering biomedical sciences. But what is big data, where do they come from and what can we do with it? In this commentary, the main sources of big data are explained, especially in (head and neck) oncology. It also touches upon the need to integrate various sources of clinical, pathological and quality-of-life data.

Tailor-made multiple sequence alignments using the PRALINE 2 alignment toolkit.

Summary: PRALINE 2 is a toolkit for custom multiple sequence alignment workflows. It can be used to incorporate sequence annotations, such as secondary structure or (DNA) motifs, into the alignment scoring, as well as to customize many other aspects of a progressive multiple alignment workflow.

Availability And Implementation: PRALINE 2 is implemented in Python and available as open source software on GitHub: https://github.

SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions.

Motivation: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions.

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern.

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types.

Pharmacokinetics and brain distribution of amitraz and its metabolites in rats.

Amitraz is an acaricide and insecticide widely used in agriculture and veterinary medicine. Although central nervous system (CNS) toxicity is one of major toxicities following oral ingestion of amitraz, the understanding of the cause of the toxicity is limited. This study evaluated the systemic and brain exposure of amitraz and its major metabolites, BTS27271, 2',4'-formoxylidide, and 2,4-dimethylaniline following administration of amitraz in male Sprague-Dawley rats.

Motif-Aware PRALINE: Improving the alignment of motif regions.

Protein or DNA motifs are sequence regions which possess biological importance. These regions are often highly conserved among homologous sequences. The generation of multiple sequence alignments (MSAs) with a correct alignment of the conserved sequence motifs is still difficult to achieve, due to the fact that the contribution of these typically short fragments is overshadowed by the rest of the sequence.

Deep sequencing identifies hepatitis B virus core protein signatures in chronic hepatitis B patients.

Background: We aimed to identify HBc amino acid differences between subgroups of chronic hepatitis B (CHB) patients.

Methods: Deep sequencing of HBc was performed in samples of 89 CHB patients (42 HBeAg positive, 47 HBeAg negative). Amino acid types were compared using Sequence Harmony to identify subgroup specific sites between HBeAg-positive and -negative patients, and between patients with combined response and non-response to peginterferon/adefovir combination therapy.

Aurora kinase A (AURKA) interaction with Wnt and Ras-MAPK signalling pathways in colorectal cancer.

Hyperactivation of Wnt and Ras-MAPK signalling are common events in development of colorectal adenomas. Further progression from adenoma-to-carcinoma is frequently associated with 20q gain and overexpression of Aurora kinase A (AURKA). Interestingly, AURKA has been shown to further enhance Wnt and Ras-MAPK signalling.

Seeing the trees through the forest: sequence-based homo- and heteromeric protein-protein interaction sites prediction using random forest.

Motivation: Genome sequencing is producing an ever-increasing amount of associated protein sequences. Few of these sequences have experimentally validated annotations, however, and computational predictions are becoming increasingly successful in producing such annotations. One key challenge remains the prediction of the amino acids in a given protein sequence that are involved in protein-protein interactions.

BioASF: a framework for automatically generating executable pathway models specified in BioPAX.

Motivation: Biological pathways play a key role in most cellular functions. To better understand these functions, diverse computational and cell biology researchers use biological pathway data for various analysis and modeling purposes. For specifying these biological pathways, a community of researchers has defined BioPAX and provided various tools for creating, validating and visualizing BioPAX models.