A protein-protein docking benchmark

We have developed a nonredundant benchmark for testing protein-protein docking algorithms. Currently it contains 59 test cases: 22 enzyme-inhibitor complexes, 19 antibody-antigen complexes, 11 other complexes, and 7 difficult test cases. Thirty-one of the test cases, for which the unbound structures of both the receptor and ligand are available, are classified as follows: 16 enzyme-inhibitor, 5 antibody-antigen, 5 others, and 5 difficult. Such a centralized resource should benefit the docking community not only as a large curated test set but also as a common ground for comparing different algorithms. The benchmark is available at (http://zlab.bu.edu/~rong/dock/benchmark.shtml).     

On docking, scoring and assessing protein-DNA complexes in a rigid-body framework

We consider the identification of interacting protein-nucleic acid partners using the rigid body docking method FTdock, which is systematic and exhaustive in the exploration of docking conformations. The accuracy of rigid body docking methods is tested using known protein-DNA complexes for which the docked and undocked structures are both available. Additional tests with large decoy sets probe the efficacy of two published statistically derived scoring functions that contain a huge number of parameters. In contrast, we demonstrate that state-of-the-art machine learning techniques can enormously reduce the number of parameters required, thereby identifying the relevant docking features using a miniscule fraction of the number of parameters in the prior works. The present machine learning study considers a 300 dimensional vector (dependent on only 15 parameters), termed the Chemical Context Profile (CCP), where each dimension reflects a specific type of protein amino acid-nucleic acid base interaction. The CCP is designed to capture the chemical complementarities of the interface and is well suited for machine learning techniques. Our objective function is the Chemical Context Discrepancy (CCD), which is defined as the angle between the native system's CCP vector and the decoy's vector and which serves as a substitute for the more commonly used root mean squared deviation (RMSD). We demonstrate that the CCP provides a useful scoring function when certain dimensions are properly weighted. Finally, we explore how the amino acids on a protein's surface can help guide DNA binding, first through long-range interactions, followed by direct contacts, according to specific preferences for either the major or minor grooves of the DNA.     

Information-driven protein-DNA docking using HADDOCK: it is a matter of flexibility

Intrinsic flexibility of DNA has hampered the development of efficient protein−DNA docking methods. In this study we extend HADDOCK (High Ambiguity Driven DOCKing) [C. Dominguez, R. Boelens and A. M. J. J. Bonvin (2003) J. Am. Chem. Soc. 125, 1731–1737] to explicitly deal with DNA flexibility. HADDOCK uses non-structural experimental data to drive the docking during a rigid-body energy minimization, and semi-flexible and water refinement stages. The latter allow for flexibility of all DNA nucleotides and the residues of the protein at the predicted interface. We evaluated our approach on the monomeric repressor−DNA complexes formed by bacteriophage 434 Cro, the Escherichia coli Lac headpiece and bacteriophage P22 Arc. Starting from unbound proteins and canonical B-DNA we correctly predict the correct spatial disposition of the complexes and the specific conformation of the DNA in the published complexes. This information is subsequently used to generate a library of pre-bent and twisted DNA structures that served as input for a second docking round. The resulting top ranking solutions exhibit high similarity to the published complexes in terms of root mean square deviations, intermolecular contacts and DNA conformation. Our two-stage docking method is thus able to successfully predict protein−DNA complexes from unbound constituents using non-structural experimental data to drive the docking.     

An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants

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A DNA-centric look at protein-DNA complexes

The availability of many DNA-protein structures makes their classification timely and important. In this issue of Structure, the method of Akinori Sarai and his collaborators (Prabakaran et al., 2006) utilizes aspects of the binding interactions and DNA properties to identify seven clusters of structures with a classification scheme that differs significantly from previous approaches.     

A model for protein-DNA interaction dynamics

We map a simplified version of the protein-DNA interaction problem into an Ising-model in a random magnetic field. The model includes a "head" which moves along the chain while interacting with the underlying spins. The head moves by using the statistical fluctuations of base openings. A Monte Carlo (MC) simulation of this model reveals the possibility of biased diffusion in one direction, followed by sequence identification and binding. The model provides some insight into the mechanisms used by some repressor proteins to diffuse and bind to specific DNA-binding sites.     

Tramtrack protein-DNA interactions. A cross-linking study

Interaction of the Tramtrack protein from Drosophila melanogaster with DNA was analyzed by a cross-linking method. Tramtrack residues cross-linkable to the partially depurinated DNA were identified by direct sequencing. The N-terminal alpha-amino group of the protein DNA-binding domain was found to be the major product of cross-linking. The location of the N terminus on the DNA was determined by identification of the DNA bases that were cross-linked to the protein alpha-amino group. We conclude that accessory N-terminal peptide preceding the first zinc finger of Tramtrack directly interacts with DNA, both in specific and nonspecific DNA-protein complexes. Our finding explains the role in the protein binding of the DNA bases outside of the direct interaction with the zinc fingers.     

A global benchmark study using affinity-based biosensors

To explore the variability in biosensor studies, 150 participants from 20 countries were given the same protein samples and asked to determine kinetic rate constants for the interaction. We chose a protein system that was amenable to analysis using different biosensor platforms as well as by users of different expertise levels. The two proteins (a 50-kDa Fab and a 60-kDa glutathione S-transferase [GST] antigen) form a relatively high-affinity complex, so participants needed to optimize several experimental parameters, including ligand immobilization and regeneration conditions as well as analyte concentrations and injection/dissociation times. Although most participants collected binding responses that could be fit to yield kinetic parameters, the quality of a few data sets could have been improved by optimizing the assay design. Once these outliers were removed, the average reported affinity across the remaining panel of participants was 620 pM with a standard deviation of 980 pM. These results demonstrate that when this biosensor assay was designed and executed appropriately, the reported rate constants were consistent, and independent of which protein was immobilized and which biosensor was used.     

A blotting procedure which preserves specific protein-DNA interactions

A quick, quantitative, and nonselective electrophoretic transfer of proteins from acetic acid-urea gels onto nitrocellulose, which preserves their ability to interact specifically with DNA, is achieved when exposure to dodecyl sulfate ions is avoided and a special type of nitrocellulose is used which contains cellulose phosphate ester. Filter-adsorbed histone H1 and other nuclear proteins from an insect, Chironomus thummi, were tested for binding of an AT-rich DNA sequence from the heterochromatin of the same organism under competitive conditions. On the blots, histone H1 exhibited the dependency of DNA binding on NaCl concentration and the preference for AT-rich DNA or poly[d(A-T)] found in quantitative filter-binding studies. By stepwise alteration of the NaCl molarity and competing Escherichia coli DNA concentration, respectively, in the binding buffer, two minor protein fractions could be identified in the heterogeneous extracts, one of which bound preferentially to AT-rich DNA, and the other bound to this sequence at up to 500 mM NaCl. Exposure to dodecyl sulfate led to a disappearance of the ability of these proteins to interact specifically with DNA. While nondenaturing transfer by diffusion (L. Levinger and A. Varshavsky (1982) Proc. Natl. Acad. Sci. USA 79, 7152) is a procedure that requires about 2 days, the present technique of gentle protein transfer for DNA binding studies requires only 2 to 3 h.     

A structure-based benchmark for protein-protein binding affinity

We have assembled a nonredundant set of 144 protein-protein complexes that have high-resolution structures available for both the complexes and their unbound components, and for which dissociation constants have been measured by biophysical methods. The set is diverse in terms of the biological functions it represents, with complexes that involve G-proteins and receptor extracellular domains, as well as antigen/antibody, enzyme/inhibitor, and enzyme/substrate complexes. It is also diverse in terms of the partners' affinity for each other, with K(d) ranging between 10(-5) and 10(-14) M. Nine pairs of entries represent closely related complexes that have a similar structure, but a very different affinity, each pair comprising a cognate and a noncognate assembly. The unbound structures of the component proteins being available, conformation changes can be assessed. They are significant in most of the complexes, and large movements or disorder-to-order transitions are frequently observed. The set may be used to benchmark biophysical models aiming to relate affinity to structure in protein-protein interactions, taking into account the reactants and the conformation changes that accompany the association reaction, instead of just the final product.     

Analyzing protein-DNA recognition mechanisms

We present a computational algorithm that can be used to analyze the generic mechanisms involved in protein-DNA recognition. Our approach is based on energy calculations for the full set of base sequences that can be threaded onto the DNA within a protein-DNA complex. It is able to reproduce experimental consensus binding sequences for a variety of DNA binding proteins and also correlates well with the order of measured binding free energies. These results suggest that the crystal structure of a protein-DNA complex can be used to identify all potential binding sequences. By analyzing the energy contributions that lead to base sequence selectivity, it is possible to quantify the concept of direct versus indirect recognition and to identify a new concept describing whether the protein-DNA interaction and DNA deformation terms select optimal binding sites by acting in accord or in disaccord.     

A benchmark for how well neural nets generalize

This paper discusses the problem of extending the domain of learning sets and introduces HERBIE, a program which achieves this through graphical procedures rather than via neural networks. It is argued that for theoretical reasons HERBIE is well-suited to serving as a benchmark for measuring generalization efficacy, and therefore to serving as a means of testing claims of emergent distributed intelligence in neural nets. The successful results of tests of HERBIE as a pattern recognizer are presented, and HERBIE's behavior is favorably compared to neural nets for several real generalization problems. Finally, applications of HERBIE independent of its serving as a generalization benchmark, particularly in the area of cognitive science, are discussed.     

Ultrasensitive protein-DNA binding assays

Protein-DNA binding assays have been used in a variety of fields from fundamental studies regarding the binding process itself, to serving as probes for the detection, quantification and separation of target analytes. These assays have been used for the study of protein-DNA complex stoichiometry, the detection of DNA damage, and real-time separation of free and bound complexes by electrophoretic mobility. Synthetic DNA oligonucleotides, known as aptamers, have been increasingly used for affinity binding assays to proteins, as well as for separation studies and as biosensors. Recent advances have been made in protein-DNA binding assays using capillary electrophoresis, laser-induced fluorescence, fluorescence polarization, molecular beacons, and affinity chromatography.     

A holistic approach to protein docking

Docking of unbound protein structures into a complex has gained significant progress in recent years, but nonetheless still poses a great challenge. We have pursued a holistic approach to docking which brings together effective methods at different stages. First, protein-protein interaction sites are predicted or obtained from experimental studies in the literature. Interface prediction/experimental data are then used to guide the generation of docked poses or to rank docked poses generated from an unbiased search. Finally, selected models are refined by lengthy molecular dynamics (MD) simulations in explicit water. For CAPRI target T27, we used information on interaction sites as input to drive docking and as a filter to rank docked poses. Lead candidates were then clustered according to RMSD among them. From the clustering, 10 models were selected and subject to refinement by MD simulations. Our Model 7 is rated number one among all submissions according to L_rmsd. Six of our other submissions are rated acceptable. As scorer, eight of our submissions are rated acceptable.