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.

: a Python library for assessment of three-dimensional electron microscopy density fits.

Three-dimensional electron microscopy 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.

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.

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.