Set Of Protein-ligand Complexes Research Articles

Towards good correlation between fragment molecular orbital interaction energies and experimental IC50 for ligand binding: A case study of p38 MAP kinase

We describe several procedures for the preprocessing of fragment molecular orbital (FMO) calculations on p38 mitogen-activated protein (MAP) kinase and discuss the influence of the procedures on the protein–ligand interaction energies represented by inter-fragment interaction energies (IFIEs). The correlation between the summation of IFIEs for a ligand and amino acid residues of protein (IFIE-sum) and experimental affinity values (IC50) was poor when considered for the whole set of protein–ligand complexes. To improve the correlation for prediction of ligand binding affinity, we carefully classified data set by the ligand charge, the DFG-loop state (DFG-in/out loop), which is characteristic of kinase, and the scaffold of ligand. The correlation between IFIE-sums and the activity values was examined using the classified data set. As a result, it was confirmed that there was a selected data set that showed good correlation between IFIE-sum and activity value by appropriate classification. In addition, we found that the differences in protonation and hydrogen orientation caused by subtle differences in preprocessing led to a relatively large difference in IFIE values. Further, we also examined the effect of structure optimization with different force fields. It was confirmed that the difference in the force field had no significant effect on IFIE-sum. From the viewpoint of drug design using FMO calculations, various investigations on IFIE-sum in this research, such as those regarding several classifications of data set and the different procedures of structural preparation, would be expected to provide useful knowledge for improvement of prediction ability about the ligand binding affinity.

Index-Based Searching of Interaction Patterns in Large Collections of Protein-Ligand Interfaces.

Comparison of three-dimensional interaction patterns in large collections of protein-ligand interfaces is a key element for understanding protein-ligand interactions and supports various steps in the structure-based drug design process. Different methods exist that provide query systems to search for geometrical patterns in protein-ligand complexes. However, these tools do not meet all of the requirements, which are high query variability, an adjustable search set, and high retrieval speed. Here we present a new tool named PELIKAN that is able to search for a variety of geometrical queries in large protein structure collections in a reasonably short time. The data are stored in an SQLite database that can easily be constructed from any set of protein-ligand complexes. We present different test queries demonstrating the performance of the PELIKAN approach. Furthermore, two application scenarios show the usefulness of PELIKAN in structure-based design endeavors.

GalaxyDock: Protein–Ligand Docking with Flexible Protein Side-chains

An important issue in developing protein-ligand docking methods is how to incorporate receptor flexibility. Consideration of receptor flexibility using an ensemble of precompiled receptor conformations or by employing an effectively enlarged binding pocket has been reported to be useful. However, direct consideration of receptor flexibility during energy optimization of the docked conformation has been less popular because of the large increase in computational complexity. In this paper, we present a new docking program called GalaxyDock that accounts for the flexibility of preselected receptor side-chains by global optimization of an AutoDock-based energy function trained for flexible side-chain docking. This method was tested on 3 sets of protein-ligand complexes (HIV-PR, LXRβ, cAPK) and a diverse set of 16 proteins that involve side-chain conformational changes upon ligand binding. The cross-docking tests show that the performance of GalaxyDock is higher or comparable to previous flexible docking methods tested on the same sets, increasing the binding conformation prediction accuracy by 10%-60% compared to rigid-receptor docking. This encouraging result suggests that this powerful global energy optimization method may be further extended to incorporate larger magnitudes of receptor flexibility in the future. The program is available at http://galaxy.seoklab.org/softwares/galaxydock.html .

Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

GALAXYDock: Docking Ligands to Receptor Proteins with Selected Flexible Side-Chains

Protein-ligand docking techniques are one of the essential tools for structure-based drug design. Although conformational changes of protein receptor frequently occur in the binding process, a large fraction of contemporary docking programs ignore receptor flexibility. Consideration of protein flexibility efficiently and accurately in docking studies is challenging due to difficulties in finding accurate scoring function and in sampling conformations efficiently. Previously we developed a docking program called LigDockCSA which incorporates LigDock scoring function that combines AutoDock3 and PLP scoring function and conformational space annealing (CSA) as a sampling method. However, the program treats protein rigid. Here, we present GALAXYDock, an extension of LigDockCSA that accounts flexibility of pre-selected side-chains of receptor protein in the binding site. To test the performance of the new docking method, 3 sets of protein-ligand complexes that share similar backbone structures but have different side chain conformations were selected as test sets: HIV-PR, LXRβ and cAPK. The cross-docking results show that the accuracy of binding pose prediction is increased about 20% when compared with our previous rigid-receptor results.

QM methods in structure based design: Utility in probing protein–ligand interactions

Small changes in ligand structure can lead to large unexpected changes in activity yet it is often not possible to rationalize these effects using empirical modeling techniques, suggesting more effective methods are required. In this study we investigate the use of high level QM methods to study the interactions found within protein–ligand complexes as improved understanding of these could help in the design of new, more active molecules. We study aspects of ligand binding in a set of protein ligand complexes containing ligand efficient, fragment-like inhibitors as these structures are often challenging to determine experimentally. To assess the reliability of our theoretical models we compare the MP2/6-31+G** QM results to the original X-ray coordinates and to QM/MM B3LYP/6-31G*//UFF results which we have previously reported. We also contrast these results with data obtained from an analysis of the distribution of comparable interactions found in (a) high resolution kinase complexes (≤1.8 Å) from the PDB and (b) more generic, small molecule crystal structures from the CSD.