The structure and function of macromolecules depend critically on the ionization (protonation) states of their acidic and basic groups. A number of existing practical methods predict protonation equilibrium pK constants of macromolecules based upon their atomic resolution Protein Data Bank (PDB) structures; the calculations are often performed within the framework of the continuum electrostatics model. Unfortunately, these methodologies are complex, involve multiple steps and require considerable investment of effort. Our web server provides access to a tool that automates this process, allowing both experts and novices to quickly obtain estimates of pKs as well as other related characteristics of biomolecules such as isoelectric points, titration curves and energies of protonation microstates. Protons are added to the input structure according to the calculated ionization states of its titratable groups at the user-specified pH; the output is in the PQR (PDB + charges + radii) format. In addition, corresponding coordinate and topology files are generated in the format supported by the molecular modeling package AMBER. The server is intended for a broad community of biochemists, molecular modelers, structural biologists and drug designers; it can also be used as an educational tool in biochemistry courses.

BackgroundGrowing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the “best hits” of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively.ResultsEvaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories.ConclusionsThe deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at http://bench.cs.vt.edu/deeparg.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0401-z) contains supplementary material, which is available to authorized users.

Reactive O(2) species (ROS) are produced in both unstressed and stressed cells. Plants have well-developed defence systems against ROS, involving both limiting the formation of ROS as well as instituting its removal. Under unstressed conditions, the formation and removal of O(2) are in balance. However, the defence system, when presented with increased ROS formation under stress conditions, can be overwhelmed. Within a cell, the superoxide dismutases (SODs) constitute the first line of defence against ROS. Specialization of function among the SODs may be due to a combination of the influence of subcellular location of the enzyme and upstream sequences in the genomic sequence. The commonality of elements in the upstream sequences of Fe, Mn and Cu/Zn SODs suggests a relatively recent origin for those regulatory regions. The differences in the upstream regions of the three FeSOD genes suggest differing regulatory control which is borne out in the research literature. The finding that the upstream sequences of Mn and peroxisomal Cu/Zn SODs have three common elements suggests a common regulatory pathway. The tools are available to dissect further the molecular basis for antioxidant defence responses in plant cells. SODs are clearly among the most important of those defences, when coupled with the necessary downstream events for full detoxification of ROS.

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Supplementary data are available at Bioinformatics online.