Investigation of sequence features of hinge-bending regions in proteins with domain movements using kernel logistic regression.

BACKGROUND: Hinge-bending movements in proteins comprising two or more domains form a large class of functional movements. Hinge-bending regions demarcate protein domains and collectively control the domain movement. Consequently, the ability to recognise sequence features of hinge-bending regions and to be able to predict them from sequence alone would benefit various areas of protein research. For example, an understanding of how the sequence features of these regions relate to dynamic properties in multi-domain proteins would aid in the rational design of linkers in therapeutic fusion proteins. RESULTS: The DynDom database of protein domain movements comprises sequences annotated to indicate whether the amino acid residue is located within a hinge-bending region or within an intradomain region. Using statistical methods and Kernel Logistic Regression (KLR) models, this data was used to determine sequence features that favour or disfavour hinge-bending regions. This is a difficult classification problem as the number of negative cases (intradomain residues) is much larger than the number of positive cases (hinge residues). The statistical methods and the KLR models both show that cysteine has the lowest propensity for hinge-bending regions and proline has the highest, even though it is the most rigid amino acid. As hinge-bending regions have been previously shown to occur frequently at the terminal regions of the secondary structures, the propensity for proline at these regions is likely due to its tendency to break secondary structures. The KLR models also indicate that isoleucine may act as a domain-capping residue. We have found that a quadratic KLR model outperforms a linear KLR model and that improvement in performance occurs up to very long window lengths (eighty residues) indicating long-range correlations. CONCLUSION: In contrast to the only other approach that focused solely on interdomain hinge-bending regions, the method provides a modest and statistically significant improvement over a random classifier. An explanation of the KLR results is that in the prediction of hinge-bending regions a long-range correlation is at play between a small number amino acids that either favour or disfavour hinge-bending regions. The resulting sequence-based prediction tool, HingeSeek, is available to run through a webserver at hingeseek.cmp.uea.ac.uk.

Methodological improvements for the analysis of domain movements in large biomolecular complexes.

Domain movements play a prominent role in the function of many biomolecules such as the ribosome and F0F1-ATP synthase. As more structures of large biomolecules in different functional states become available as experimental techniques for structure determination advance, there is a need to develop methods to understand the conformational changes that occur. DynDom and DynDom3D were developed to analyse two structures of a biomolecule for domain movements. They both used an original method for domain recognition based on clustering of "rotation vectors". Here we introduce significant improvements in both the methodology and implementation of a tool for the analysis of domain movements in large multimeric biomolecules. The main improvement is in the recognition of domains by using all six degrees of freedom required to describe the movement of a rigid body. This is achieved by way of Chasles' theorem in which a rigid-body movement can be described as a screw movement about a unique axis. Thus clustering now includes, in addition to rotation vector data, screw-axis location data and axial climb data. This improves both the sensitivity of domain recognition and performance. A further improvement is the recognition and annotation of interdomain bending regions, something not done for multimeric biomolecules in DynDom3D. This is significant as it is these regions that collectively control the domain movement. The new stand-alone, platform-independent implementation, DynDom6D, can analyse biomolecules comprising protein, DNA and RNA, and employs an alignment method to automatically achieve the required equivalence of atoms in the two structures.

Morphing and docking visualisation of biomolecular structures using Multi-Dimensional Scaling.

Protein structures are often solved at atomic resolution in two states defining a functional movement but intervening conformations are usually unknown. Morphing methods generate intervening conformations between two known structures. When viewed as an animation using molecular graphics, a smooth, direct morph enables the eye to track changes in structure that might be otherwise missed. We present a morphing method that aims to linearly interpolate interatomic distances and which uses SMACOF (Scaling by MAjorisation of COmplicated Function) and multigrid techniques with a cut-off distance based weighting that optimizes the MolProbity score of intervening structures. The all-atom morphs are smooth, move directly between the two structures, and are shown, in general, to pass closer to a set of known intermediates than those generated using other methods. The techniques are also used for docking by putting the unbound structures in a "near-approach pose" and then morphing to the bound complex. The resulting GPU-accelerated tools are available on a webserver, Morphit_Pro, at http://morphit-pro.cmp.uea.ac.uk/ and more than 5000 domains movements available at the DynDom website can now be viewed as morphs http://morphit-pro.cmp.uea.ac.uk/dyndom/.