Introducing a 4th dimension to protein-protein docking

Computational mapping of anchoring spots on protein surfaces

Protein-protein and protein-peptide interactions are often controlled by few strong contacts that involve hot spot residues. Computational detection of such contacts, termed here anchoring spots, is important for understanding recognition processes and for predicting interactions; it is an essential step in designing interaction interfaces and therapeutic agents. We describe ANCHORSMAP, an algorithm for computational mapping of amino acid side chains on protein surfaces. The algorithm consists of two stages: A geometry based stage (LSMdet), in which sub-pockets adequate for binding single side chains are detected and amino acid probes are scattered near them, and an energy based stage in which optimal positions of the probes are determined through repeated energy minimization and clustering of nearby poses and their DeltaG are calculated. ANCHORSMAP employs a new function for DeltaG calculations, which is specifically designed for the context of protein-protein recognition by introducing a correction in the electrostatic energy term that compensates for the dielectric shielding exerted by a hypothetical protein bound to the probe. The algorithm successfully detects known anchoring sites and accurately positions the probes. The calculated DeltaG rank high the correct anchoring spots in maps produced for unbound proteins. We find that Arg, Trp, Glu and Tyr, which are favorite hot spot residues, are also more selective of their binding environment. The usefulness of anchoring spots mapping is demonstrated by detecting the binding surfaces in the protein-protein complex barnase/barstar and the protein-peptide complex kinase/PKI, and by identifying phenylalanine anchoring sites on the surface of the nuclear transporter NTF2, C-terminus anchors on PDZ domains and phenol anchors on thermolysin. Finally, we discuss the role of anchoring spots in molecular recognition processes.

Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking

Many available methods aimed at incorporating the receptor flexibility in ligand docking are computationally expensive, require a high level of user intervention, and were tested only on benchmarks of limited size and diversity. Here we describe the four-dimensional (4D) docking approach that allows seamless incorporation of receptor conformational ensembles in a single docking simulation and reduces the sampling time while preserving the accuracy of traditional ensemble docking. The approach was tested on a benchmark of 99 therapeutically relevant proteins and 300 diverse ligands (half of them experimental or marketed drugs). The conformational variability of the binding pockets was represented by the available crystallographic data, with the total of 1113 receptor structures. The 4D docking method reproduced the correct ligand binding geometry in 77.3% of the benchmark cases, matching the success rate of the traditional approach but employed on average only one-fourth of the time during the ligand sampling phase.

Optimised amino acid specific weighting factors for unbound protein docking

Background

One of the most challenging aspects of protein-protein docking is the inclusion of flexibility into the docking procedure. We developed a postfilter where the grid-representation of proteins for docking is extended by an optimised weighting factor for each amino acid.

Results

For up to 86% of the evaluated complexes a near-native structure was within the top 5% of the ranked prediction output. The weighting factors obtained by the optimisation procedure correlate to a certain extent with the flexibility of the amino acids, their hydrophobicity and with their propensity to be in the interface.

Conclusion

Use of the optimised amino acid specific parameters yields a strong increase of near-native structures on the first ranks of the prediction.

Molecular flexibility in ab initio drug docking to DNA: binding-site and binding-mode transitions in all-atom Monte Carlo simulations

The dynamics of biological processes depend on the structure and flexibility of the interacting molecules. In particular, the conformational diversity of DNA allows for large deformations upon binding. Drug–DNA interactions are of high pharmaceutical interest since the mode of action of anticancer, antiviral, antibacterial and other drugs is directly associated with their binding to DNA. A reliable prediction of drug–DNA binding at the atomic level by molecular docking methods provides the basis for the design of new drug compounds. Here, we propose a novel Monte Carlo (MC) algorithm for drug–DNA docking that accounts for the molecular flexibility of both constituents and samples the docking geometry without any prior binding-site selection. The binding of the antimalarial drug methylene blue at the DNA minor groove with a preference of binding to AT-rich over GC-rich base sequences is obtained in MC simulations in accordance with experimental data. In addition, the transition between two drug–DNA-binding modes, intercalation and minor-groove binding, has been achieved in dependence on the DNA base sequence. The reliable ab initio prediction of drug–DNA binding achieved by our new MC docking algorithm is an important step towards a realistic description of the structure and dynamics of molecular recognition in biological systems.