Deep embeddings to comprehend and visualize microbiome protein space.

Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the possibility of predicting their function through conventional homology-based approaches is limited, which indicates the need for further research on alignment-free methods. Here, we leverage a deep-learning-based representation of proteins to assess its utility in alignment-free analysis of microbial proteins. We trained a language model on the Unified Human Gastrointestinal Protein catalogue and validated the resulting protein representation on the bacterial part of the SwissProt database. Finally, we present a use case on proteins involved in SCFA metabolism. Results indicate that the deep learning model manages to accurately represent features related to protein structure and function, allowing for alignment-free protein analyses. Technologies that contextualize metagenomic data are a promising direction to deeply understand the microbiome.

The standardisation of the approach to metagenomic human gut analysis: from sample collection to microbiome profiling.

In recent years, the number of metagenomic studies increased significantly. Wide range of factors, including the tremendous community complexity and variability, is contributing to the challenge in reliable microbiome community profiling. Many approaches have been proposed to overcome these problems making hardly possible to compare results of different studies. The significant differences between procedures used in metagenomic research are reflected in a variation of the obtained results. This calls for the need for standardisation of the procedure, to reduce the confounding factors originating from DNA isolation, sequencing and bioinformatics analyses in order to ensure that the differences in microbiome composition are of a true biological origin. Although the best practices for metagenomics studies have been the topic of several publications and the main aim of the International Human Microbiome Standard (IHMS) project, standardisation of the procedure for generating and analysing metagenomic data is still far from being achieved. To highlight the difficulties in the standardisation of metagenomics methods, we thoroughly examined each step of the analysis of the human gut microbiome. We tested the DNA isolation procedure, preparation of NGS libraries for next-generation sequencing, and bioinformatics analysis, aimed at identifying microbial taxa. We showed that the homogenisation time is the leading factor impacting sample diversity, with the recommendation for a shorter homogenisation time (10 min). Ten minutes of homogenisation allows for better reflection of the bacteria gram-positive/gram-negative ratio, and the obtained results are the least heterogenous in terms of beta-diversity of samples microbial composition. Besides increasing the homogenisation time, we observed further potential impact of the library preparation kit on the gut microbiome profiling. Moreover, our analysis revealed that the choice of the library preparation kit influences the reproducibility of the results, which is an important factor that has to be taken into account in every experiment. In this study, a tagmentation-based kit allowed for obtaining the most reproducible results. We also considered the choice of the computational tool for determining the composition of intestinal microbiota, with Kraken2/Bracken pipeline outperforming MetaPhlAn2 in our in silico experiments. The design of an experiment and a detailed establishment of an experimental protocol may have a serious impact on determining the taxonomic profile of the intestinal microbiome community. Results of our experiment can be helpful for a wide range of studies that aim to better understand the role of the gut microbiome, as well as for clinical purposes.

The importance of atomic partial charges in the reproduction of intermolecular interactions for the triacetin - a model of glycerol backbone.

Lipids play a central role within the cell. They not only encompass it but are also engaged in many processes such as cellular transport and energy production. Despite ongoing advances in experimental studies, computer simulations are a viable method to trace their behavior at the atomic level and on an elusive time scale. In molecular modeling studies, the quality of the obtained results is associated with the considered force field and its parameters. In the present work, the authors have investigated the procedure of partial charges fitting on the example of a triacetin molecule, containing chemical moieties present in the glycerol backbone. The goal of the study was to validate assigned partial charges based on the quality of the torsion profiles using optimally assigned torsional coefficients and reproduction of the condensed phase properties of triacetin. We applied various approaches and noticed a significant improvement in the parameterization of triacetin compared to the original one. The results showed that it is important to take into account the intermolecular interactions in the partial charges fitting procedure to obtain good quality validation results.

Condensed phase properties of n-pentadecane emerging from application of biomolecular force fields.

For over 20 years, the OPLS-All Atom (OPLS-AA) force field has been efficiently used in molecular modelling studies of proteins, carbohydrates and nucleic acids. OPLS-AA is successfully applied in computer modelling of many organic compounds, including decane and shorter alkanes, but it fails when employed for longer linear alkanes, whose chemical structure corresponds to hydrocarbon tails in phospholipids constituting cellular membranes. There have been several attempts to address this problem. In this work, we compare the ability to reproduce various condensed phase properties by six distinct sets of force field parameters which can be assigned to phospholipid hydrocarbon chains. In this comparison, we include three alternative sets of the OPLS-AA force field, as well as the commonly used CHARMM C36, Slipids, and Berger lipids' parameters.

Identification of Differentiating Metabolic Pathways between Infant Gut Microbiome Populations Reveals Depletion of Function-Level Adaptation to Human Milk in the Finnish Population.

A variety of autoimmune and allergy events are becoming increasingly common, especially in Western countries. Some pieces of research link such conditions with the composition of microbiota during infancy. In this period, the predominant form of nutrition for gut microbiota is oligosaccharides from human milk (HMO). A number of gut-colonizing strains, such as Bifidobacterium and Bacteroides, are able to utilize HMO, but only some Bifidobacterium strains have evolved to digest the specific composition of human oligosaccharides. Differences in the proportions of the two genera that are able to utilize HMO have already been associated with the frequency of allergies and autoimmune diseases in the Finnish and the Russian populations. Our results show that differences in terms of the taxonomic annotation do not explain the reason for the differences in the Bifidobacterium/Bacteroides ratio between the Finnish and the Russian populations. In this paper, we present the results of function-level analysis. Unlike the typical workflow for gene abundance analysis, BiomeScout technology explains the differences in the Bifidobacterium/Bacteroides ratio. Our research shows the differences in the abundances of the two enzymes that are crucial for the utilization of short type 1 oligosaccharides.IMPORTANCE Knowing the limitations of taxonomy-based research, there is an emerging need for the development of higher-resolution techniques. The significance of this research is demonstrated by the novel method used for the analysis of function-level metagenomes. BiomeScout-the presented technology-utilizes proprietary algorithms for the detection of differences between functionalities present in metagenomic samples.