SUMMARY: We present NMRtist, an online platform that combines deep learning, large-scale optimization and cloud computing to automate protein NMR spectra analysis. Our website provides virtual storage for NMR spectra deposition together with a set of applications designed for automated peak picking, chemical shift assignment and protein structure determination. The system can be used by non-experts and allows protein assignments and structures to be determined within hours after the measurements, strictly without any human intervention. AVAILABILITY AND IMPLEMENTATION: NMRtist is freely available to non-commercial users at https://nmrtist.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: A better understanding of oligosaccharides and their wide-ranging functions in almost every aspect of biology and medicine promises to uncover hidden layers of biology and will support the development of better therapies. Elucidating the chemical structure of an unknown oligosaccharide remains a challenge. Efficient tools are required for non-targeted glycomics. Chemical shifts are a rich source of information about the topology and configuration of biomolecules, whose potential is however not fully explored for oligosaccharides. We hypothesize that the chemical shifts of each monosaccharide are unique for each saccharide type with a certain linkage pattern, so that correlated data measured by NMR spectroscopy can be used to identify the chemical nature of a carbohydrate. Results: We present here an efficient search algorithm, GlycoNMRSearch, which matches either a subset or the entire set of chemical shifts of an unidentified monosaccharide spin system to all spin systems in an NMR database. The search output is much more precise than earlier search functions and highly similar matches suggest the chemical structure of the spin system within the oligosaccharide. Thus, searching for connected chemical shift correlations within all electronically available NMR data of oligosaccharides is a very efficient way of identifying the chemical structure of unknown oligosaccharides. With an improved database in the future, GlycoNMRSearch will be even more efficient deducing chemical structures of oligosaccharides and there is a high chance that it becomes an indispensable technique for glycomics. Availability and implementation: The search algorithm presented here, together with a graphical user interface, is available at http://glyconmrsearch.nmrhub.eu. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Automated selection of signals in protein NMR spectra, known as peak picking, has been studied for over 20 years, nevertheless existing peak picking methods are still largely deficient. Accurate and precise automated peak picking would accelerate the structure calculation, and analysis of dynamics and interactions of macromolecules. Recent advancement in handling big data, together with an outburst of machine learning techniques, offer an opportunity to tackle the peak picking problem substantially faster than manual picking and on par with human accuracy. In particular, deep learning has proven to systematically achieve human-level performance in various recognition tasks, and thus emerges as an ideal tool to address automated identification of NMR signals. Results: We have applied a convolutional neural network for visual analysis of multidimensional NMR spectra. A comprehensive test on 31 manually annotated spectra has demonstrated top-tier average precision (AP) of 0.9596, 0.9058 and 0.8271 for backbone, side-chain and NOESY spectra, respectively. Furthermore, a combination of extracted peak lists with automated assignment routine, FLYA, outperformed other methods, including the manual one, and led to correct resonance assignment at the levels of 90.40%, 89.90% and 90.20% for three benchmark proteins. Availability and implementation: The proposed model is a part of a Dumpling software (platform for protein NMR data analysis), and is available at https://dumpling.bio/. Supplementary information: Supplementary data are available at Bioinformatics online.

In drug discovery field, one of the major used techniques is Nuclear Magnetic Resonance spectroscopy (NMR). To date, most of the steps in NMR analysis have been automated. The remaining step, peak picking, is still performed manually, what is extremely time-consuming and constitutes limiting factor in NMR-based drug discovery. Peak picking is a process of recognition of 2D/3D patterns in NMR image. To help automation of peak picking we have prepared a considerable benchmark, which enables testing various approaches. We also propose a baseline framework based on object detection scheme, which is well studied in the field of computer vision, and addresses automation of peak picking. Finally, we present the results of empirical studies, where a few different methods were compared, and show that peak pickers based on our framework significantly outperform the remaining methods for complex spectra and behave comparably for average ones.

MOTIVATION: A detailed analysis of multidimensional NMR spectra of macromolecules requires the identification of individual resonances (peaks). This task can be tedious and time-consuming and often requires support by experienced users. Automated peak picking algorithms were introduced more than 25 years ago, but there are still major deficiencies/flaws that often prevent complete and error free peak picking of biological macromolecule spectra. The major challenges of automated peak picking algorithms is both the distinction of artifacts from real peaks particularly from those with irregular shapes and also picking peaks in spectral regions with overlapping resonances which are very hard to resolve by existing computer algorithms. In both of these cases a visual inspection approach could be more effective than a 'blind' algorithm. RESULTS: We present a novel approach using computer vision (CV) methodology which could be better adapted to the problem of peak recognition. After suitable 'training' we successfully applied the CV algorithm to spectra of medium-sized soluble proteins up to molecular weights of 26 kDa and to a 130 kDa complex of a tetrameric membrane protein in detergent micelles. Our CV approach outperforms commonly used programs. With suitable training datasets the application of the presented method can be extended to automated peak picking in multidimensional spectra of nucleic acids or carbohydrates and adapted to solid-state NMR spectra. AVAILABILITY AND IMPLEMENTATION: CV-Peak Picker is available upon request from the authors. CONTACT: [email protected]; [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Wearable smart systems that measure and process vital sings from human body have become important tool in eHealth, rehabilitation and recreation. We developed a system that aims to support recreational physical activity by delivering communication and computational services. The system architecture allows to implement advanced data processing methods in the form of computational services and easily include additional services according to the user needs.