Reduction, alignment and visualisation of large diverse sequence families

Protein multiple sequence alignment benchmarking through secondary structure prediction

Motivation

Multiple sequence alignment (MSA) is commonly used to analyze sets of homologous protein or DNA sequences. This has lead to the development of many methods and packages for MSA over the past 30 years. Being able to compare different methods has been problematic and has relied on gold standard benchmark datasets of ‘true’ alignments or on MSA simulations. A number of protein benchmark datasets have been produced which rely on a combination of manual alignment and/or automated superposition of protein structures. These are either restricted to very small MSAs with few sequences or require manual alignment which can be subjective. In both cases, it remains very difficult to properly test MSAs of more than a few dozen sequences. PREFAB and HomFam both rely on using a small subset of sequences of known structure and do not fairly test the quality of a full MSA.

Results

In this paper we describe QuanTest, a fully automated and highly scalable test system for protein MSAs which is based on using secondary structure prediction accuracy (SSPA) to measure alignment quality. This is based on the assumption that better MSAs will give more accurate secondary structure predictions when we include sequences of known structure. SSPA measures the quality of an entire alignment however, not just the accuracy on a handful of selected sequences. It can be scaled to alignments of any size but here we demonstrate its use on alignments of either 200 or 1000 sequences. This allows the testing of slow accurate programs as well as faster, less accurate ones. We show that the scores from QuanTest are highly correlated with existing benchmark scores. We also validate the method by comparing a wide range of MSA alignment options and by including different levels of mis-alignment into MSA, and examining the effects on the scores.

Availability and Implementation

QuanTest is available from http://www.bioinf.ucd.ie/download/QuanTest.tgz

Supplementary information

Supplementary data are available at Bioinformatics online.

Exploring RNA conformational space under sparse distance restraints

We show that the application of a small number of restraints predicted by coevolution analysis can provide a powerful restriction on the conformational freedom of an RNA molecule. The greatest degree of restriction occurs when a contact is predicted between the distal ends of a pair of adjacent stemloops but even with this location additional flexibilities in the molecule can mask the contribution. Multiple cross-links, especially those including a pseudoknot provided the strongest restraint on conformational freedom with the effect being most apparent in topologically simple folds and less so if the fold is more topologically entwined. Little was expected for large structures (over 300 bases) and although a few strong localised restrictions were observed, they contributed little to the restraint of the overall fold. Although contacts predicted using a correlated mutation analysis can provide some powerful restrictions on the conformational freedom of RNA molecules, they are too erratic in their occurrence and distribution to provide a general approach to the problem of RNA 3D structure prediction from sequence.

Molecular Models for the Core Components of the Flagellar Type-III Secretion Complex

We show that by using a combination of computational methods, consistent three-dimensional molecular models can be proposed for the core proteins of the type-III secretion system. We employed a variety of approaches to reconcile disparate, and sometimes inconsistent, data sources into a coherent picture that for most of the proteins indicated a unique solution to the constraints. The range of difficulty spanned from the trivial (FliQ) to the difficult (FlhA and FliP). The uncertainties encountered with FlhA were largely the result of the greater number of helix packing possibilities allowed in a large protein, however, for FliP, there remains an uncertainty in how to reconcile the large displacement predicted between its two main helical hairpins and their ability to sit together happily across the bacterial membrane. As there is still no high resolution structural information on any of these proteins, we hope our predicted models may be of some use in aiding the interpretation of electron microscope images and in rationalising mutation data and experiments.