Molecular Recognition Features (MoRFs) are short, interaction-prone segments of protein disorder that undergo disorder-to-order transitions upon specific binding, representing a specific class of intrinsically disordered regions that exhibit molecular recognition and binding functions. MoRFs are common in various proteomes and occupy a unique structural and functional niche in which function is a direct consequence of intrinsic disorder. Example MoRFs collected from the Protein Data Bank (PDB) have been divided into three subtypes according to their structures in the bound state: α-MoRFs form α-helices, β-MoRFs form β-strands, and ι-MoRFs form structures without a regular pattern of backbone hydrogen bonds. These example MoRFs were indicated to be intrinsically disordered in the absence of their binding partners by several criteria. In this study we used several geometric and physiochemical criteria to examine the properties of 62 α-, 20 β- and 176 ι-MoRF complex structures. Interface residues were examined by calculating differences in accessible surface area between the complex and isolated monomers. The compositions and physiochemical properties of MoRF and MoRF partner interface residues were compared to the interface residues of homodimers, heterodimers, and antigen-antibody complexes. Our analysis indicates that there are significant differences in residue composition and several geometric and physicochemical properties that can be used to discriminate, with a high degree of accuracy, between various interfaces in protein interaction datasets. Implications of these findings for the development of MoRF-partner interaction predictors are discussed. In addition, structural changes upon MoRF-to-partner complex formation were examined for several illustrative examples.

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Characterization of Molecular Recognition Features, MoRFs, and Their Binding Partners

Motivation: Modelling protein–protein interactions (PPIs) is an increasingly important aspect of structural bioinformatics. However, predicting PPIs using in silico docking techniques is computationally very expensive. Developing very fast protein docking tools will be useful for studying large-scale PPI networks, and could contribute to the rational design of new drugs.

Results: The Hex spherical polar Fourier protein docking algorithm has been implemented on Nvidia graphics processor units (GPUs). On a GTX 285 GPU, an exhaustive and densely sampled 6D docking search can be calculated in just 15 s using multiple 1D fast Fourier transforms (FFTs). This represents a 45-fold speed-up over the corresponding calculation on a single CPU, being at least two orders of magnitude times faster than a similar CPU calculation using ZDOCK 3.0.1, and estimated to be at least three orders of magnitude faster than the GPU-accelerated version of PIPER on comparable hardware. Hence, for the first time, exhaustive FFT-based protein docking calculations may now be performed in a matter of seconds on a contemporary GPU. Three-dimensional Hex FFT correlations are also accelerated by the GPU, but the speed-up factor of only 2.5 is much less than that obtained with 1D FFTs. Thus, the Hex algorithm appears to be especially well suited to exploit GPUs compared to conventional 3D FFT docking approaches.

Availability: http://hex.loria.fr/ and http://hexserver.loria.fr/

Contact: dave.ritchie@loria.fr

Supplementary information:Supplementary data are available at Bioinformatics online.

/pdf/ultra-fast-fft-protein-docking-on-graphics-processors-rv98fwzax3.pdf

Ultra-fast FFT protein docking on graphics processors

The seven members of the human 14-3-3 protein family regulate a diverse range of cell signaling pathways by formation of protein–protein complexes with signaling proteins that contain phosphorylated Ser/Thr residues within specific sequence motifs. Previously, crystal structures of three 14-3-3 isoforms (zeta, sigma, and tau) have been reported, with structural data for two isoforms deposited in the Protein Data Bank (zeta and sigma). In this study, we provide structural detail for five 14-3-3 isoforms bound to ligands, providing structural coverage for all isoforms of a human protein family. A comparative structural analysis of the seven 14-3-3 proteins revealed specificity determinants for binding of phosphopeptides in a specific orientation, target domain interaction surfaces and flexible adaptation of 14-3-3 proteins through domain movements. Specifically, the structures of the beta isoform in its apo and peptide bound forms showed that its binding site can exhibit structural flexibility to facilitate binding of its protein and peptide partners. In addition, the complex of 14-3-3 beta with the exoenzyme S peptide displayed a secondary structural element in the 14-3-3 peptide binding groove. These results show that the 14-3-3 proteins are adaptable structures in which internal flexibility is likely to facilitate recognition and binding of their interaction partners.

https://www.pnas.org/content/pnas/103/46/17237.full.pdf

Structural Basis for Protein-Protein Interactions in the 14-3-3 Protein Family.

This article gives an overview of recent progress in protein-protein docking and it identifies several directions for future research. Recent results from the CAPRI blind docking experiments show that docking algorithms are steadily improving in both reliability and accuracy. Current docking algorithms employ a range of efficient search and scoring strategies, including e.g. fast Fourier transform correlations, geometric hashing, and Monte Carlo techniques. These approaches can often produce a relatively small list of up to a few thousand orientations, amongst which a near-native binding mode is often observed. However, despite the use of improved scoring functions which typically include models of desolvation, hydrophobicity, and electrostatics, current algorithms still have difficulty in identifying the correct solution from the list of false positives, or decoys. Nonetheless, significant progress is being made through better use of bioinformatics, biochemical, and biophysical information such as e.g. sequence conservation analysis, protein interaction databases, alanine scanning, and NMR residual dipolar coupling restraints to help identify key binding residues. Promising new approaches to incorporate models of protein flexibility during docking are being developed, including the use of molecular dynamics snapshots, rotameric and off-rotamer searches, internal coordinate mechanics, and principal component analysis based techniques. Some investigators now use explicit solvent models in their docking protocols. Many of these approaches can be computationally intensive, although new silicon chip technologies such as programmable graphics processor units are beginning to offer competitive alternatives to conventional high performance computer systems. As cryo-EM techniques improve apace, docking NMR and X-ray protein structures into low resolution EM density maps is helping to bridge the resolution gap between these complementary techniques. The use of symmetry and fragment assembly constraints are also helping to make possible docking-based predictions of large multimeric protein complexes. In the near future, the closer integration of docking algorithms with protein interface prediction software, structural databases, and sequence analysis techniques should help produce better predictions of protein interaction networks and more accurate structural models of the fundamental molecular interactions within the cell.

/pdf/recent-progress-and-future-directions-in-protein-protein-2j4hq7ng01.pdf

Recent progress and future directions in protein-protein docking.

http://www.jbc.org/content/early/2010/11/03/jbc.M110.191411.full.pdf

Recognition and specificity determinants of the human cbx chromodomains.

Understanding energetics and mechanism of protein-protein association remains one of the biggest theoretical problems in structural biology. It is assumed that desolvation must play an essential role during the association process, and indeed protein-protein interfaces in obligate complexes have been found to be highly hydrophobic. However, the identification of protein interaction sites from surface analysis of proteins involved in non-obligate protein-protein complexes is more challenging. Here we present Optimal Docking Area (ODA), a new fast and accurate method of analyzing a protein surface in search of areas with favorable energy change when buried upon protein-protein association. The method identifies continuous surface patches with optimal docking desolvation energy based on atomic solvation parameters adjusted for protein-protein docking. The procedure has been validated on the unbound structures of a total of 66 non-homologous proteins involved in non-obligate protein-protein hetero-complexes of known structure. Optimal docking areas with significant low-docking surface energy were found in around half of the proteins. The 'ODA hot spots' detected in X-ray unbound structures were correctly located in the known protein-protein binding sites in 80% of the cases. The role of these low-surface-energy areas during complex formation is discussed. Burial of these regions during protein-protein association may favor the complexed configurations with near-native interfaces but otherwise arbitrary orientations, thus driving the formation of an encounter complex. The patch prediction procedure is freely accessible at http://www.molsoft.com/oda and can be easily scaled up for predictions in structural proteomics.

Optimal docking area: a new method for predicting protein-protein interaction sites.

The common approach to electronic publishing three-dimensional biological data in peer-reviewed journals or for disseminating such data within the scientific community has historically required the writing of a two dimensional flat and non-interactive document. The frequent limit on the number of two dimensional color figures permitted in journals further reduces the opportunities for the provision of visualizations. Often the targeted readers of these documents are not expert in the gathering and manipulation of the fundamental data from which the document was provided. For example, three-dimensional protein structures and complexes require the downloading of coordinates from the PDB [1] and then the knowledge and skill to operate a standalone program to simply reproduce the view corresponding to the figure in question and to further interpret it. Further data, such as protein sequences, parts of the electron density [2], etc., may also need to be obtained from remote databases and manipulated to interpret the results and conclusions of the underlying publication. This approach is clearly sub-optimal for scientists who have no experience in protein structure and its visualization.

A breakthrough in the ability to collate and integrate pertinent data types and annotations relating to such data into one file that is compact and platform-independent was the iSee platform [3]. In this approach, arbitrary numbers of three-dimensional structural visualizations are possible and the renditions are embedded via hyperlinks in a textual annotation akin to a standard scientific publication. Previously these so-called iSee ‘datapacks’ could be read and viewed using freely-available browser software. A number of other approaches to visualize three-dimensional structures within the context of text have been made since iSee was developed (e.g. [4]–[6]). However, neither these nor iSee have been able to provide full integration within the requirements of peer-reviewed publications and to support both the web delivery of an electronic publication and the local file delivery in a consistent manner. We have recognized that there is a clear requirement to improve the integration and delivery for publication and general dissemination of this type of work.

In this overview we provide details of our activeICM approach for the peer-reviewed publishing and sharing of integrated and interactive iSee datapacks, or 3D documents, which is the core technology of the PLoS ONE Collection of which this overview paper is part of.

http://ablab.ucsd.edu/pdf/09_3D_Raush_PLoSOne.pdf

A New Method for Publishing Three-Dimensional Content

In recent years, protein–protein interactions are becoming the object of increasing attention in many different fields, such as structural biology, molecular biology, systems biology, and drug discovery. From a structural biology perspective, it would be desirable to integrate current efforts into the structural proteomics programs. Given that experimental determination of many protein–protein complex structures is highly challenging, and in the context of current high-performance computational capabilities, different computer tools are being developed to help in this task. Among them, computational docking aims to predict the structure of a protein–protein complex starting from the atomic coordinates of its individual components, and in recent years, a growing number of docking approaches are being reported with increased predictive capabilities. The improvement of speed and accuracy of these docking methods, together with the modeling of the interaction networks that regulate the most critical processes in a living organism, will be essential for computational proteomics. The ultimate goal is the rational design of drugs capable of specifically inhibiting or modifying protein–protein interactions of therapeutic significance. While rational design of protein–protein interaction inhibitors is at its very early stage, the first results are promising.

Computer applications for prediction of protein-protein interactions and rational drug design

Background: Two recent technological advances dramatically reducing the rate of false-negatives in activity prediction by docking flexible 3D models of compounds include multi-conformational docking (mPockDock) and the docking of candidates to atomic property fields derived by co-crystallized ligands (mApfDock). Results: The mApfDock and mPockDock provide the AUC of 90.4 and 83.8%, respectively. The mApfDock gave better performance when compounds required large induced-fit pocket changes unseen in crystallography, whereas the mPockDock is superior when the co-crystallized ligands do not represent sufficient chemical and binding location diversity. Conclusion: Both approaches proved to be efficient for scaffold hopping; they are complementary when the coverage of the co-crystallized complexes is poor but become convergent when the complexes are diverse enough.

Max Totrov

Papers

Optimal docking area: a new method for predicting protein-protein interaction sites.

A New Method for Publishing Three-Dimensional Content

Computer applications for prediction of protein-protein interactions and rational drug design

Docking to multiple pockets or ligand fields for screening, activity prediction and scaffold hopping