SUMMARY: DockIT is a tool that has a unique set of physical and graphical features for interactive molecular docking. It enables the user to bring a ligand and a receptor into a docking pose by controlling relative position and orientation, either with a mouse and keyboard, or with a haptic device. Atomic interactions are modelled using molecular dynamics-based force-fields with the force on the ligand being felt on a haptic device. Real-time calculation and display of intermolecular hydrogen bonds and multipoint collision detection either using maximum force or maximum atomic overlap, mean that together with the ability to monitor selected intermolecular atomic distances, the user can find physically feasible docking poses that satisfy distance constraints derived from experimental methods. With these features and the ability to output and reload docked structures it can be used to accurately build up large multi-component molecular systems in preparation for molecular dynamics simulation. AVAILABILITY AND IMPLEMENTATION: DockIT is available free of charge for non-commercial use at http://www.haptimol.co.uk/downloads.htm. It requires a windows computer with GPU that supports OpenCL 1.2 and OpenGL 4.0. It may be used with a mouse and keyboard, or a haptic device from 3DSystems.

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.

MOTIVATION: A popular method for classification of protein domain movements apportions them into two main types: those with a 'hinge' mechanism and those with a 'shear' mechanism. The intuitive assignment of domain movements to these classes has limited the number of domain movements that can be classified in this way. Furthermore, whether intended or not, the term 'shear' is often interpreted to mean a relative translation of the domains. RESULTS: Numbers of occurrences of four different types of residue contact changes between domains were optimally combined by logistic regression using the training set of domain movements intuitively classified as hinge and shear to produce a predictor for hinge and shear. This predictor was applied to give a 10-fold increase in the number of examples over the number previously available with a high degree of precision. It is shown that overall a relative translation of domains is rare, and that there is no difference between hinge and shear mechanisms in this respect. However, the shear set contains significantly more examples of domains having a relative twisting movement than the hinge set. The angle of rotation is also shown to be a good discriminator between the two mechanisms. AVAILABILITY AND IMPLEMENTATION: Results are free to browse at http://www.cmp.uea.ac.uk/dyndom/interface/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper presents some initial results of an investigation into the use of machine learning methods to detect natively disordered regions in proteins from sequence information. A committee of Relevance Vector Machines is used to select the optimal window size for residue-by-residue prediction of disordered regions, based on local amino-acid composition. The minimal error rate of ≈ 15% is achieved using very long (205 residue) window lengths, with the classifier making little use of more local sequence information. This suggests that disorder arises principally due to large scale diffuse changes in mean hydropathy and to a lesser extent mean charge. We also demonstrate that the proportion of proteins having long disordered regions in operational conditions cannot be reliably estimated using a classifier trained on a balanced dataset.

MOTIVATION: The biological function of proteins is associated with a variety of motions, ranging from global domain motion to local motion of side chain. We propose a method, dihedral transition analysis (DTA), to identify significant dihedral angle changes between two distinct protein conformations and for characterization of the effect of these transitions on both local and global conformation. RESULTS: Applying DTA to a comprehensive and non-redundant dataset of 459 high-resolution pairs of protein structures, we found that a dihedral transition occurs in 82% of proteins. Multiple dihedral transitions are shown to occur cooperatively along the sequence, which allows us to separate a polypeptide chain into fragments with and without transitions, namely transition fragments (TFs) and stable fragments (SFs), respectively. By characterizing the magnitude of TF conformational change and the effect of the transition on the neighboring fragments, flap and hinge motions are identified as typical motions. DTA is also useful to detect protein motions, subtle in RMSD but significant in terms of dihedral angle changes, such as the peptide-plane flip, the side-chain flip and path-preserving motions. We conclude that DTA is a useful tool to extract potential functional motions, some of which might have been missed using conventional methods for protein motion analysis. AVAILABILITY: http://dynamics.iam.u-tokyo.ac.jp/DTA/

MOTIVATION: Structural features at protein-protein interfaces can be studied to understand protein-protein interactions. It was noticed that in a dataset of 45 multimeric proteins the interface could either be described as flat against flat or protruding/interwound. In the latter, residues within one chain were surrounded by those in other chains, whereas in the former they were not. RESULTS: A simple method was developed that could distinguish between these two types with results that matched those made by a human annotator. Applying this automatic method to a large dataset of 888 structures, chains at interfaces were categorized as non-surrounded or surrounded. It was found that the surrounded set had a significantly lower folding tendency using a sequence based measure, than the non-surrounded set. This suggests that before complexation, surrounded chains are relatively unstable and may be involved in 'fly-casting'. This is supported by the finding that terminal regions are overrepresented in the surrounded set. AVAILABILITY: http://cib.cf.ocha.ac.jp/DACSIS/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The current DynDom database of protein domain motions is a user-created database that suffers from selectivity and redundancy. The aim of the analysis presented here was to overcome both these limitations and to produce both a comprehensive and a non-redundant description of domain movements from structures stored in the current protein data bank. RESULTS: A multi-step procedure is applied that starts with grouping proteins in the structural databank into families based on sequence similarity. Multiple sequence alignment, conformational clustering and a dimensional clustering method based on the Gram-Schmidt algorithm are applied to members of each family to remove dynamic redundancy in their domain movements. Representative domain movements are described in terms of domains, hinge axes and hinge-bending residues using the DynDom program. The results show that within an average family of 11.5 members, there are on average only 1.31 different domain movements indicating a high redundancy in the movements these structures represent. This verifies earlier findings that domain movements are usually highly controlled. Despite the removal of this considerable redundancy, the process has resulted in double the number of domain movements stored in the user-created database. The data are organized in a relational database with a web-interface. AVAILABILITY: The database can be browsed and searched at http://www.cmp.uea.ac.uk/dyndom CONTACT: [email protected].

UNLABELLED: A relational database has been developed based on the results from the application of the DynDom program to a number of proteins for which multiple X-ray conformers are available. The database is populated via a web-based tool that allows visitors to the website to run the DynDom program server-side by selecting pairs of X-ray conformers by Protein Data Bank code and chain identifier. AVAILABILITY: The website can be found at: http://www.sys.uea.ac.uk/dyndom.