Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence

Sequence-derived structural and physicochemical features have been extensively used for analyzing and predicting structural, functional, expression and interaction profiles of proteins and peptides. PROFEAT has been developed as a web server for computing commonly used features of proteins and peptides from amino acid sequence. To facilitate more extensive studies of protein and peptides, numerous improvements and updates have been made to PROFEAT. We added new functions for computing descriptors of protein–protein and protein–small molecule interactions, segment descriptors for local properties of protein sequences, topological descriptors for peptide sequences and small molecule structures. We also added new feature groups for proteins and peptides (pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, total amino acid properties and atomic-level topological descriptors) as well as for small molecules (atomic-level topological descriptors). Overall, PROFEAT computes 11 feature groups of descriptors for proteins and peptides, and a feature group of more than 400 descriptors for small molecules plus the derived features for protein–protein and protein–small molecule interactions. Our computational algorithms have been extensively tested and used in a number of published works for predicting proteins of specific structural or functional classes, protein–protein interactions, peptides of specific functions and quantitative structure activity relationships of small molecules. PROFEAT is accessible free of charge at http://bidd.cz3.nus.edu.sg/cgi-bin/prof/protein/profnew.cgi

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

BACKGROUND: Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics. RESULTS: In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model. CONCLUSIONS: Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques

The potential of Ion Mobility Mass Spectrometry for high-throughput and high-resolution lipidomics

Lipids are a large and highly diverse family of biomolecules, which play essential structural, storage and signalling roles in cells and tissues. Although traditional mass spectrometry (MS) approaches used in lipidomics are highly sensitive and selective, lipid analysis remains challenging due to the chemical diversity of lipid structures, multiple isobaric species and incomplete separation using many forms of chromatography. Ion mobility (IM) separates ions in the gas phase based on their physicochemical properties. Addition of IM to the traditional lipidomic workflow both enhances separation of complex lipid mixtures, beneficial for lipid identification, and improves isomer resolution. Herein, we discuss the recent developments in IM-MS for lipidomics.This work was supported by Agilent Technologies, Santa Clara, USA; and the Medical Research Council, UK (MC UP A90 1006 & MC PC 13030)

Effects of Traveling Wave Ion Mobility Separation on Data Independent Acquisition in Proteomics Studies

qTOF mass spectrometry and traveling wave ion mobility separation (TWIMS) hybrid instruments (q- TWIMS-TOF) have recently become commercially available. Ion mobility separation allows an additional dimension of precursor separation inside the instrument, without incurring an increase in instrument time. We comprehensively investigated the effects of TWIMS on data-independent acquisition on a Synapt G2 instrument. We observed that if fragmentation is performed post TWIMS, more accurate assignment of fragment ions to precursors is possible in data independent acquisition. This allows up to 60% higher proteome coverage and higher confidence of protein and peptide identifications. Moreover, the majority of peptides and proteins identified upon application of TWIMS span the lower intensity range of the proteome. It has also been demonstrated in several studies that employing IMS results in higher peak capacity of separation and consequently more accurate and precise quantitation of lower intensity precursor ions. We observe that employing TWIMS results in an attenuation of the detected ion current. We postulate that this effect is binary; sensitivity is reduced due to ion scattering during transfer into a high pressure “IMS zone”, sensitivity is reduced due to the saturation of detector digitizer as a result of the IMS concentration effect. This latter effect limits the useful linear range of quantitation, compromising quantitation accuracy of high intensity peptides. We demonstrate that the signal loss from detector saturation and transmission loss can be deconvoluted by investigation of the peptide isotopic envelope. We discuss the origin and extent of signal loss and suggest methods to minimize these effects on q-TWIMS-TOF instrument in the light of different experimental designs and other IMS/MS platforms described previously

Spectroscopic Determination of Aboveground Biomass in Grasslands Using Spectral Transformations, Support Vector Machine and Partial Least Squares Regression

[EN] Aboveground biomass (AGB) is one of the strategic biophysical variables of interest in vegetation studies. The main objective of this study was to evaluate the Support Vector Machine (SVM) and Partial Least Squares Regression (PLSR) for estimating the AGB of grasslands from field spectrometer data and to find out which data pre-processing approach was the most suitable. The most accurate model to predict the total AGB involved PLSR and the Maximum Band Depth index derived from the continuum removed reflectance in the absorption features between 916–1,120 nm and 1,079–1,297 nm (R2 = 0.939, RMSE = 7.120 g/m2). Regarding the green fraction of the AGB, the Area Over the Minimum index derived from the continuum removed spectra provided the most accurate model overall (R2 = 0.939, RMSE = 3.172 g/m2). Identifying the appropriate absorption features was proved to be crucial to improve the performance of PLSR to estimate the total and green aboveground biomass, by using the indices derived from those spectral regions. Ordinary Least Square Regression could be used as a surrogate for the PLSR approach with the Area Over the Minimum index as the independent variable, although the resulting model would not be as accurate.SIThis research has been partially funded by the Junta de Castilla y Leó

Modelling leaf area index in a tropical grassland using multi-temporal hyperspectral data.

Master of Science in Environmental science.Abstract available in PDF file

The use of recently developed mass spectrometry-based proteomic approaches for the study of methylocella silvestris BL2

The study of the protein complement, termed proteomics, has advanced over the last twenty years as a consequence of developments in mass spectrometry. Currently, improvements in mass spectrometry-based approaches are targeted towards achieving information on both the identity and abundance of proteins. Increased numbers of protein identifications are obtained by simplifying the analyte of interest. This can be achieved with the use of separation techniques, including two-dimensional liquid chromatography (2D-LC). Ion mobility coupled to mass spectrometry has recently been shown to be a useful post-ionisation separation tool for proteomic studies. The utility of these technologies for obtaining both qualitative and quantitative information is not extensively addressed in the current literature. The use of a recently developed 2D-LC system, together with a method of ion mobility separation and a label-free quantitative approach for proteomic studies has been evaluated here for characterising the proteome of the bacterium Methylocella silvestris. This bacterium is the first methane-utilising bacteria also discovered to grow on substrates containing carbon-carbon bonds, and has great biotechnological potential. The metabolism of this bacterium was studied by obtaining information on its soluble proteome when grown with methane, propane, succinate, acetate, methanol, methylamine or trimethylamine. The benefits and limitations of 2D-LC and ion mobility for profiling and labelfree quantitative studies were demonstrated for simple mixtures and complex bacterial extracts. The combination of both 2D-LC and ion mobility was also achieved, resulting in wider proteome coverage when compared to the respective stand-alone approaches. A cluster of expressed genes that were greatly up-regulated under trimethylamine growth and monomethylamine growth were proposed to be involved in the indirect pathway for trimethylamine metabolism. It was further verified that one of these genes expresses the previously unidentified trimethylamine monooxygenase. A propane assimilation route was proposed, based on information obtained on the levels of primary oxidation enzymes and downstream central metabolic pathways