TEMPy2: a Python library with improved 3D electron microscopy density-fitting and validation workflows

Structural determination of molecular complexes by cryo-EM requires large, often complex processing of the image data that are initially obtained. Here, TEMPy2, an update of the TEMPy package to process, optimize and assess cryo-EM maps and the structures fitted to them, is described. New optimization routines, comprehensive automated checks and workflows to perform these tasks are described

Amphitrite: a program for processing travelling wave ion mobility mass spectrometry data

Since the introduction of travelling wave (T-Wave)-based ion mobility in 2007 a large number of research laboratories have embraced the technique, particularly those working in the field of structural biology. The development of software to process the data generated from this technique, however, has been limited. We present a novel software package that enables the processing of T-Wave ion mobility data. The program can deconvolute components in a mass spectrum and uses this information to extract corresponding arrival time distributions (ATDs) with minimal user intervention. It can also be used to automatically create a collision cross section (CCS) calibration and apply this to subsequent files of interest. A number of applications of the software, and how it enhances the information content extracted from the raw data, are illustrated using model proteins

TEMPy: a Python library for assessment of 3D electron microscopy density fits

Three-dimensional electron microscopy (3D EM) is currently one of the most promising techniques used to study macromolecular assemblies. Rigid and flexible fitting of atomic models into density maps is often essential to gain further insights into the assemblies they represent. Currently, tools that facilitate the assessment of fitted atomic models and maps are needed. TEMPy – Template and EM comparison using Python – is a toolkit designed for this purpose. The library includes a set of methods to assess density fits in intermediate-to-low resolution maps, both globally and locally. It also provides procedures for single fit assessment, ensemble generation of fits, clustering, multiple and consensus scoring, as well as plots and output files for visualisation purposes to help the user in analysing rigid and flexible fits. The modular nature of TEMPy helps the integration of scoring and assessment of fits into large pipelines, making it a tool for both novice and expert structural biologists

Improving reverse vaccinology with a machine learning approach

Reverse vaccinology aims to accelerate subunit vaccine design by rapidly predicting which proteins in a pathogenic bacterial proteome are putative protective antigens.Support vector machine classification is a machine learning approach that has been applied to solve numerous classification problems in biological sciences but has not previously been incorporated into a reverse vaccinology approach. A training data set of 136 bacterial protective antigens paired with 136 non-antigens was constructed and bioinformatic tools were used to annotate this data for predicted protein features, many of which are associated with antigenicity (i.e. extracellular localization, signal peptides and B-cell epitopes). Annotation was used to train support vector machine classifiers that exhibited a maximum accuracy of 92% for discriminating protective antigens from non-antigens as assessed by a leave-tenth-out cross validation approach. These accuracies were superior to those achieved when annotating training data with auto and cross covariance transformations of z-descriptors for hydrophobicity, molecular size and polarity, or when classification was performed using regression methods. To further validate support vector machine classifiers,they were used to rank all the proteins in six bacterial proteomes for their antigenicity. Protective antigens from the training data were significantly recalled (enriched) in the top 75 ranked proteins for all six proteomes as assessed by a Fisher’s exact test (p < 0.05). This paper describes a superior workflow for performing reverse vaccinology studies and provides a benchmark training data set that can be used to evaluate future methodological improvements