Mapping of discontinuous conformational epitopes by amide hydrogen/deuterium exchange mass spectrometry and computational docking

Understanding antigen-antibody interactions at the sub-molecular level is of particular interest for scientific, regulatory, and intellectual property reasons, especially with increasing demand for monoclonal antibody therapeutic agents. Although various techniques are available for the determination of an epitope, there is no widely applicable, high-resolution, and reliable method available. Here, a combination approach using amide hydrogen/deuterium exchange coupled with proteolysis and mass spectrometry (HDX-MS) and computational docking was applied to investigate antigen-antibody interactions. HDX-MS is a widely applicable, medium-resolution, medium-throughput technology that can be applied to epitope identification. First, the epitopes of cytochrome c-E8, IL-13-CNTO607, and IL-17A-CAT-2200 interactions identified using the HDX-MS method were compared with those identified by X-ray co-crystal structures. The identified epitopes are in good agreement with those identified using high-resolution X-ray crystallography. Second, the HDX-MS data were used as constraints for computational docking. More specifically, the non-epitope residues of an antigen identified using HDX-MS were designated as binding ineligible during computational docking. This approach, termed HDX-DOCK, gave more tightly clustered docking poses than stand-alone docking for all antigen-antibody interactions examined and improved docking results significantly for the cytochrome c-E8 interaction.     

Identifying and quantifying metabolites by scoring peaks of GC-MS data

Background

Metabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. For this, gas chromatography-mass spectrometry (GC-MS) has been largely applied and many computational tools have been developed to support the analysis of metabolomics data. Among them, AMDIS is perhaps the most used tool for identifying and quantifying metabolites. However, AMDIS generates a high number of false-positives and does not have an interface amenable for high-throughput data analysis. Although additional computational tools have been developed for processing AMDIS results and to perform normalisations and statistical analysis of metabolomics data, there is not yet a single free software or package able to reliably identify and quantify metabolites analysed by GC-MS.

Results

Here we introduce a new algorithm, PScore, able to score peaks according to their likelihood of representing metabolites defined in a mass spectral library. We implemented PScore in a R package called MetaBox and evaluated the applicability and potential of MetaBox by comparing its performance against AMDIS results when analysing volatile organic compounds (VOC) from standard mixtures of metabolites and from female and male mice faecal samples. MetaBox reported lower percentages of false positives and false negatives, and was able to report a higher number of potential biomarkers associated to the metabolism of female and male mice.

Conclusions

Identification and quantification of metabolites is among the most critical and time-consuming steps in GC-MS metabolome analysis. Here we present an algorithm implemented in a R package, which allows users to construct flexible pipelines and analyse metabolomics data in a high-throughput manner.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0374-2) contains supplementary material, which is available to authorized users.     

A novel tool for individual haplotype inference using mixed data

Background

In many studies, researchers may recruit samples consisting of independent trios and unrelated individuals. However, most of the currently available haplotype inference methods do not cope well with these kinds of mixed data sets.

Methods

We propose a general and simple methodology using a mixture of weighted multinomial (MIXMUL) approach that combines separate haplotype information from unrelated individuals and independent trios for haplotype inference to the individual level.

Results

The new MIXMUL procedure improves over existing methods in that it can accurately estimate haplotype frequencies from mixed data sets and output probable haplotype pairs in optimized reconstruction outcomes for all subjects that have contributed to estimation. Simulation results showed that this new MIXMUL procedure competes well with the EM-based method, i.e. FAMHAP, under a few assumed scenarios.

Conclusion

The results showed that MIXMUL can provide accurate estimates similar to those haplotype frequencies obtained from FAMHAP and output the probable haplotype pairs in the most optimal reconstruction outcome for all subjects that have contributed to estimation. If available data consist of combinations of unrelated individuals and independent trios, the MIXMUL procedure can be used to estimate the haplotype frequencies accurately and output the most likely reconstructed haplotype pairs of each subject in the estimation.     

Ecological cues, gestation length, and birth timing in African buffalo (Syncerus caffer)

We examined annual variation in the timing of conception andparturition in the African buffalo (Syncerus caffer) and thesynchrony of birth timing with resource cues, using 8 yearsof monthly birth, rainfall, and vegetation data, measured asNormalized Difference Vegetation Index (NDVI). Monthly birthshad the strongest significant correlations with NDVI and rainfalllevels 12 and 13 months in the past, respectively. In addition,the synchrony of current year births corresponds most stronglyto the synchrony of the previous year's NDVI distribution. Becausethe gestation period of buffalo has been estimated to be around11 months, these findings suggest that improved protein levels,occurring approximately a month after the first green flushof the wet season, are either a trigger for conception or conceptionhas evolved to be synchronous with correlated environmentalcues that ensure females enter a period of peak body conditionaround the time of conception and/or parturition. With a gestationperiod of approximately 340 days, parturition occurs to takeadvantage of the period when forage has its highest proteincontent. A comparative analysis of gestation periods withinthe subfamily Bovinae indicates that African buffalo have aprotracted gestation for their body size, which we suggest isan adaptation to their seasonal environment. We also found thatinterannual variation in the birth distribution suggests a degreeof plasticity in the date of conception, and variation in thenumber of calves born each year suggest further synchrony ata timescale longer than a single year.