CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.htmlComment: 11 pages, 13 figure

Crystal structure of the N domain of Lon protease from Mycobacterium avium complex.

Lon protease is evolutionarily conserved in prokaryotes and eukaryotic organelles. The primary function of Lon is to selectively degrade abnormal and certain regulatory proteins to maintain the homeostasis in vivo. Lon mainly consists of three functional domains and the N-terminal domain is required for the substrate selection and recognition. However, the precise contribution of the N-terminal domain remains elusive. Here, we determined the crystal structure of the N-terminal 192-residue construct of Lon protease from Mycobacterium avium complex at 2.4 å resolution，and measured NMR-relaxation parameters of backbones. This structure consists of two subdomains, the β-strand rich N-terminal subdomain and the five-helix bundle of C-terminal subdomain, connected by a flexible linker，and is similar to the overall structure of the N domain of Escherichia coli Lon even though their sequence identity is only 26%. The obtained NMR-relaxation parameters reveal two stabilized loops involved in the structural packing of the compact N domain and a turn structure formation. The performed homology comparison suggests that structural and sequence variations in the N domain may be closely related to the substrate selectivity of Lon variants. Our results provide the structure and dynamics characterization of a new Lon N domain, and will help to define the precise contribution of the Lon N-terminal domain to the substrate recognition

XCloud-VIP: Virtual Peak Enables Highly Accelerated NMR Spectroscopy and Faithful Quantitative Measures

Background: Nuclear Magnetic Resonance (NMR) spectroscopy is an important bio-engineering tool to determine the metabolic concentrations, molecule structures and so on. The data acquisition time, however, is very long in multi-dimensional NMR. To accelerate data acquisition, non-uniformly sampling is an effective way but may encounter severe spectral distortions and unfaithful quantitative measures when the acceleration factor is high. Objective: To reconstruct high fidelity spectra from highly accelerated NMR and achieve much better quantitative measures. Methods: A virtual peak (VIP) approach is proposed to self-learn the prior spectral information, such as the central frequency and peak lineshape, and then feed these information into the reconstruction. The proposed method is further implemented with cloud computing to facilitate online, open, and easy access. Results: Results on synthetic and experimental data demonstrate that, compared with the state-of-the-art method, the new approach provides much better reconstruction of low-intensity peaks and significantly improves the quantitative measures, including the regression of peak intensity, the distances between nuclear pairs, and concentrations of metabolics in mixtures. Conclusion: Self-learning prior peak information can improve the reconstruction and quantitative measures of spectra. Significance: This approach enables highly accelerated NMR and may promote time-consuming applications such as quantitative and time-resolved NMR experiments

Crysalis:An integrated server for computational analysis and design of protein crystallization

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/

Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity

X-ray crystallography is the main tool for structural determination of proteins. Yet, the underlying crystallization process is costly, has a high attrition rate and involves a series of trial-and-error attempts to obtain diffraction-quality crystals. The Structural Genomics Consortium aims to systematically solve representative structures of major protein-fold classes using primarily high-throughput X-ray crystallography. The attrition rate of these efforts can be improved by selection of proteins that are potentially easier to be crystallized. In this context, bioinformatics approaches have been developed to predict crystallization propensities based on protein sequences. These approaches are used to facilitate prioritization of the most promising target proteins, search for alternative structural orthologues of the target proteins and suggest designs of constructs capable of potentially enhancing the likelihood of successful crystallization. We reviewed and compared nine predictors of protein crystallization propensity. Moreover, we demonstrated that integrating selected outputs from multiple predictors as candidate input features to build the predictive model results in a significantly higher predictive performance when compared to using these predictors individually. Furthermore, we also introduced a new and accurate predictor of protein crystallization propensity, Crysf, which uses functional features extracted from UniProt as inputs. This comprehensive review will assist structural biologists in selecting the most appropriate predictor, and is also beneficial for bioinformaticians to develop a new generation of predictive algorithms

Spatial dispersion state of carbon nanotubes in a freeze-drying method prepared carbon fiber based preform and its effect on electrical conductivity of carbon fiber/epoxy composite

This paper first studied the spatial dispersion state of carbon nanotubes (CNTs) in different positions of a freeze-drying method prepared carbon fiber/CNTs preform. The results demonstrated that continuous CNT networks could be assembled within each fiber bundle, while between fiber bundles and in interlaminar space, only large CNT sheets with certain thickness were observed. Despite these, the electrical conductivity along different directions of the freeze-drying method prepared composite was significantly improved, which was associated with the optimization of spatial dispersion of CNTs in the whole preform and composite. Relationship between electrical performances of composite and spatial dispersion state of CNTs was also discussed. ? 2014 Elsevier B.V

Crystal structure of the polyketide cyclase from Mycobacterium tuberculosis

About 40% of proteins are classified as conserved hypothetical proteins in Mycobacterium tuberculosis (TB). Identification and characterization of these proteins are beneficial to understand the pathogenesis of TB and exploiting novel drugs for TB treatments. The polyketide cyclase, a protein from M. tuberculosis (MtPC) has been annotated as a hypothetical protein in Uniprot database. Sequence analysis shows that the MtPC belongs to the NTF2-like superfamily proteins with diverse functions. Here, we determined the crystal structure of MtPC at a resolution of 2.4 Å and measured backbone relaxation parameters for the MtPC protein. MtPC exists as a dimer in solution, and each subunit contains a six-stranded mixed β-sheet and three α helixes which are arranged in the order α1-α2-β1-β2-α3-β3-β4-β5-β6. The NMR dynamics analysis showed that the overall structure of MtPC is highly rigid on ps-ns time scales. Furthermore, we predicted the potential function of MtPC based on the crystal structure. Our results lay the basis for further exploiting and mechanistically understanding the biological functions of MtPC.<?A3B2 tlsb?

Effect of frozen conditions on dispersion morphologies of carbon nanotubes and electrical conductivity of carbon fiber/epoxy composites

Carbon nanotubes (CNTs) were introduced into three-dimensional carbon fiber fabric/epoxy composites with assistance of freeze-drying technique. Different frozen conditions, including slow-freezing, quick-freezing and unidirectional freezing, were utilized to affect CNT dispersion morphologies in fiber/CNTs preforms and composites. Electrical conductivity of composites was measured. The results demonstrated that: (1) frozen conditions had an obvious effect on dispersion morphologies of CNTs in preforms; (2) quick-freezing and unidirectional freezing were beneficial for CNTs to disperse in preforms and improve electrical conductivity of composites, compared to slow-freezing; (3) application of unidirectional freezing made the arrangement of CNTs possess certain orientation and, as consequence, electrical conductivity along freezing direction of composites was enhanced significantly. ? 2014 Elsevier B.V

Spatial dispersion state of carbon nanotubes in a freeze-drying method prepared carbon fiber based preform and its effect on electrical conductivity of carbon fiber/epoxy composite

This paper first studied the spatial dispersion state of carbon nanotubes (CNTs) in different positions of a freeze-drying method prepared carbon fiber/CNTs preform. The results demonstrated that continuous CNT networks could be assembled within each fiber bundle, while between fiber bundles and in interlaminar space, only large CNT sheets with certain thickness were observed. Despite these, the electrical conductivity along different directions of the freeze-drying method prepared composite was significantly improved, which was associated with the optimization of spatial dispersion of CNTs in the whole preform and composite. Relationship between electrical performances of composite and spatial dispersion state of CNTs was also discussed. ? 2014 Elsevier B.V