OpenMDlr: parallel, open-source tools for general protein structure modeling and refinement from pairwise distances.

SUMMARY: Easy-to-use, open-source, general-purpose programs for modeling a protein structure from inter-atomic distances are needed for modeling from experimental data and refinement of predicted protein structures. OpenMDlr is an open-source Python package for modeling protein structures from pairwise distances between any atoms, and optionally, dihedral angles. We provide a user-friendly input format for harnessing modern biomolecular force fields in an easy-to-install package that can efficiently make use of multiple compute cores. AVAILABILITY AND IMPLEMENTATION: OpenMDlr is available at https://github.com/BSDExabio/OpenMDlr-amber. The package is written in Python (versions 3.x). All dependencies are open-source and can be installed with the Conda package management system. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function.

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.

The elemental composition of virus particles: implications for marine biogeochemical cycles.

In marine environments, virus-mediated lysis of host cells leads to the release of cellular carbon and nutrients and is hypothesized to be a major driver of carbon recycling on a global scale. However, efforts to characterize the effects of viruses on nutrient cycles have overlooked the geochemical potential of the virus particles themselves, particularly with respect to their phosphorus content. In this Analysis article, we use a biophysical scaling model of intact virus particles that has been validated using sequence and structural information to quantify differences in the elemental stoichiometry of marine viruses compared with their microbial hosts. By extrapolating particle-scale estimates to the ecosystem scale, we propose that, under certain circumstances, marine virus populations could make an important contribution to the reservoir and cycling of oceanic phosphorus.

De-MetaST-BLAST: a tool for the validation of degenerate primer sets and data mining of publicly available metagenomes.

Development and use of primer sets to amplify nucleic acid sequences of interest is fundamental to studies spanning many life science disciplines. As such, the validation of primer sets is essential. Several computer programs have been created to aid in the initial selection of primer sequences that may or may not require multiple nucleotide combinations (i.e., degeneracies). Conversely, validation of primer specificity has remained largely unchanged for several decades, and there are currently few available programs that allows for an evaluation of primers containing degenerate nucleotide bases. To alleviate this gap, we developed the program De-MetaST that performs an in silico amplification using user defined nucleotide sequence dataset(s) and primer sequences that may contain degenerate bases. The program returns an output file that contains the in silico amplicons. When De-MetaST is paired with NCBI's BLAST (De-MetaST-BLAST), the program also returns the top 10 nr NCBI database hits for each recovered in silico amplicon. While the original motivation for development of this search tool was degenerate primer validation using the wealth of nucleotide sequences available in environmental metagenome and metatranscriptome databases, this search tool has potential utility in many data mining applications.

Comparative metagenomics of toxic freshwater cyanobacteria bloom communities on two continents.

Toxic cyanobacterial blooms have persisted in freshwater systems around the world for centuries and appear to be globally increasing in frequency and severity. Toxins produced by bloom-associated cyanobacteria can have drastic impacts on the ecosystem and surrounding communities, and bloom biomass can disrupt aquatic food webs and act as a driver for hypoxia. Little is currently known regarding the genomic content of the Microcystis strains that form blooms or the companion heterotrophic community associated with bloom events. To address these issues, we examined the bloom-associated microbial communities in single samples from Lake Erie (North America), Lake Tai (Taihu, China), and Grand Lakes St. Marys (OH, USA) using comparative metagenomics. Together the Cyanobacteria and Proteobacteria comprised >90% of each bloom bacterial community sample, although the dominant phylum varied between systems. Relative to the existing Microcystis aeruginosa NIES 843 genome, sequences from Lake Erie and Taihu revealed a number of metagenomic islands that were absent in the environmental samples. Moreover, despite variation in the phylogenetic assignments of bloom-associated organisms, the functional potential of bloom members remained relatively constant between systems. This pattern was particularly noticeable in the genomic contribution of nitrogen assimilation genes. In Taihu, the genetic elements associated with the assimilation and metabolism of nitrogen were predominantly associated with Proteobacteria, while these functions in the North American lakes were primarily contributed to by the Cyanobacteria. Our observations build on an emerging body of metagenomic surveys describing the functional potential of microbial communities as more highly conserved than that of their phylogenetic makeup within natural systems.