An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

Investigation of protein‐ligand interactions obtained from experiments has a crucial part in the design of newly discovered and effective drugs. Analyzing the data extracted from known interactions could help scientists to predict the binding affinities of promising ligands before conducting experiments. The objective of this study is to advance the CIFAP (compressed images for affinity prediction) method, which is relevant to a protein‐ligand model, identifying 2D electrostatic potential images by separating the binding site of protein‐ligand complexes and using the images for predicting the computational affinity information represented by pIC₅₀ values. The CIFAP method has 2 phases, namely, data modeling and prediction. In data modeling phase, the separated 3D structure of the binding pocket with the ligand inside is fitted into an electrostatic potential grid box, which is then compressed through 3 orthogonal directions into three 2D images for each protein‐ligand complex. Sequential floating forward selection technique is performed for acquiring prediction patterns from the images. In the prediction phase, support vector regression (SVR) and partial least squares regression are used for testing the quality of the CIFAP method for predicting the binding affinity of 45 CHK1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide. The results show that the CIFAP method using both support vector regression and partial least squares regression is very effective for predicting the binding affinities of CHK1‐ligand complexes with low‐error values and high correlation. As a future work, the results could be improved by working on the pose of the ligands inside the grid.     

In situ assembly of enzyme inhibitors using extended tethering

Cysteine aspartyl protease-3 (caspase-3) is a mediator of apoptosis and a therapeutic target for a wide range of diseases. Using a dynamic combinatorial technology, 'extended tethering', we identified unique nonpeptidic inhibitors for this enzyme. Extended tethering allowed the identification of ligands that bind to discrete regions of caspase-3 and also helped direct the assembly of these ligands into small-molecule inhibitors. We first designed a small-molecule 'extender' that irreversibly alkylates the cysteine residue of caspase-3 and also contains a thiol group. The modified protein was then screened against a library of disulfide-containing small-molecule fragments. Mass-spectrometry was used to identify ligands that bind noncovalently to the protein and that also form a disulfide linkage with the extender. Linking the selected fragments with binding elements from the extenders generates reversible, tight-binding molecules that are druglike and distinct from known inhibitors. One molecule derived from this approach inhibited apoptosis in cells.     

Fragmentation-tree density representation for crystallographic modelling of bound ligands

The identification and modelling of ligands into macromolecular models is important for understanding molecule's function and for designing inhibitors to modulate its activities. We describe new algorithms for the automated building of ligands into electron density maps in crystal structure determination. Location of the ligand-binding site is achieved by matching numerical shape features describing the ligand to those of density clusters using a "fragmentation-tree" density representation. The ligand molecule is built using two distinct algorithms exploiting free atoms with inter-atomic connectivity and Metropolis-based optimisation of the conformational state of the ligand, producing an ensemble of structures from which the final model is derived. The method was validated on several thousand entries from the Protein Data Bank. In the majority of cases, the ligand-binding site could be correctly located and the ligand model built with a coordinate accuracy of better than 1 ?. We anticipate that the method will be of routine use to anyone modelling ligands, lead compounds or even compound fragments as part of protein functional analyses or drug design efforts.     

A novel computational tool for automated structure-based drug design

The computer program LUDI for automated structure-based drug design is described. The program constructs possible new ligands for a given protein of known three-dimensional structure. This novel approach is based upon rules about energetically favourable non-bonded contact geometries between functional groups of the protein and the ligand which are derived from a statistical analysis of crystal packings of organic molecules. In a first step small fragments are docked into the protein binding site in such a way that hydrogen bonds and ionic interactions can be formed with the protein and hydrophobic pockets are filled with lipophilic groups of the ligands. The program can then append further fragments onto a previously positioned fragments or onto an already existing ligand (e.g., a lead structure that one seeks to improve). It is also possible to link several fragments together by bridge fragments to form a complete molecule. All putative ligands retrieved or constructed by LUDI are scored. We use a simple scoring function that was fitted to experimentally determined binding constants of protein–ligand complexes. LUCI is a very fast program with typical execution times of 1–5 min on a work station and is therefore suitable for interactive usage.     

Bivalent inhibitors of the tyrosine kinases ABL and SRC: determinants of potency and selectivity

We recently reported a chemical genetic method for generating bivalent inhibitors of protein kinases. This method relies on the use of the DNA repair enzyme O(6)-alkylguanine-DNA alkyltransferase (AGT) to display an ATP-competitive inhibitor and a ligand that targets a secondary binding domain. With this method potent and selective inhibitors of the tyrosine kinases SRC and ABL were identified. Here, we dissect the molecular determinants of the potency and selectivity of these bivalent ligands. Systematic analysis of ATP-competitive inhibitors with varying linker lengths revealed that SRC and ABL have differential sensitivities to ligand presentation. Generation of bivalent constructs that contain ligands with differential affinities for the ATP-binding sites and SH3 domains of SRC and ABL demonstrated the modular nature of inhibitors based on the AGT scaffold. Furthermore, these studies revealed that the interaction between the SH3 domain ligand and the kinase SH3 domain is the major selectivity determinant amongst closely-related tyrosine kinases. Finally, the potency of bivalent inhibitors against distinct phospho-isoforms of SRC was determined. Overall, these results provide insight into how individual ligands can be modified to provide more potent and selective bivalent inhibitors of protein kinases.     

Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein–ligand interactions based on a study on caspase 3 inhibitors

Abstract

The aim of this study is to propose an improved computational methodology, which is called Compressed Images for Affinity Prediction-2 (CIFAP-2) to predict binding affinities of structurally related protein–ligand complexes. CIFAP-2 method is established based on a protein–ligand model from which computational affinity information is obtained by utilizing 2D electrostatic potential images determined for the binding site of protein–ligand complexes. The quality of the prediction of the CIFAP-2 algorithm was tested using partial least squares regression (PLSR) as well as support vector regression (SVR) and adaptive neuro-fuzzy ?nference system (ANFIS), which are highly promising prediction methods in drug design. CIFAP-2 was applied on a protein–ligand complex system involving Caspase 3 (CASP3) and its 35 inhibitors possessing a common isatin sulfonamide pharmacophore. As a result, PLSR affinity prediction for the CASP3–ligand complexes gave rise to the most consistent information with reported empirical binding affinities (pIC₅₀) of the CASP3 inhibitors.     

Characterization of receptors with a new negative image: Use in molecular docking and lead optimization

The characterization of receptor binding sites is an important aspect of molecular docking, molecular recognition, and the structure-based design process. This characterization can take several forms: the receptor surface itself can be delineated or described, the space adjacent to the surface can be chemically mapped, or a negative image of the protein binding region can be generated. In this report, we describe a new method of constructing a negative image through generation of a set of spheres. These spheres lie along the receptor surface, and their centers represent possible ligand atom positions. By the method in which they are constructed, these spheres carry a limited amount of energetic and chemical information in addition to their primary geometric information. We test the accuracy of the image by comparing sphere positions to the positions of bound ligand atoms and propose a figure of merit for such tests. Then, we use the spheres to orient ligands in enzyme active sites and show how they can be used to generate low scoring configurations more efficiently than other approaches that search orientation space. In addition, two novel applications of these spheres are described: they are used to help identify structural differences among families of enzymes and to suggest points for ligand modification in analog design. Proteins 30:321–336, 1998. © 1998 Wiley-Liss, Inc.     

Biologically enhanced sampling geometric docking and backbone flexibility treatment with multiconformational superposition

An efficient biologically enhanced sampling geometric docking method is presented based on the FTDock algorithm to predict the protein-protein binding modes. The active site data from different sources, such as biochemical and biophysical experiments or theoretical analyses of sequence data, can be incorporated in the rotation-translation scan. When discretizing a protein onto a 3-dimensional (3D) grid, a zero value is given to grid points outside a sphere centered on the geometric center of specified residues. In this way, docking solutions are biased toward modes where the interface region is inside the sphere. We also adopt a multiconformational superposition scheme to represent backbone flexibility in the proteins. When these procedures were applied to the targets of CAPRI, a larger number of hits and smaller ligand root-mean-square deviations (RMSDs) were obtained at the conformational search stage in all cases, and especially Target 19. With Target 18, only 1 near-native structure was retained by the biologically enhanced sampling geometric docking method, but this number increased to 53 and the least ligand RMSD decreased from 8.1 A to 2.9 A after performing multiconformational superposition. These results were obtained after the CAPRI prediction deadlines.     

EXAFS studies of the zinc sites of UDP-(3-O-acyl)-N-acetylglucosamine deacetylase (LpxC)

Extended X-ray absorption fine structure (EXAFS) spectroscopy has been used to determine the structure of the Zn(II) sites in UDP-(3-O-acyl)-N-acetylglucosamine deacetylase (LpxC) from Aquifex aeolicus and Pseudomonas aeruginosa. The active site Zn(II) is four coordinate, with exclusively low-Z (nitrogen and oxygen) ligation in both enzymes. The amplitude of the outer-shell scattering from the histidine ligands is best fit using two histidine ligands, suggesting a ZnO(2)(His)(2) site, where O most likely represents a conserved aspartate and a solvent molecule. The same structure was found for Co(II)-substituted A. aeolicus LpxC, although in this case it is possible that the coordination sphere may expand to include a fifth low-Z ligand. EXAFS data were also measured for the Escherichia coli LpxC enzyme. When a single Co(II) is substituted for Zn(II) in the active site of E. coli LpxC, EXAFS data show the same ligand environment as is found for the P. aeruginosa and A. aeolicus enzymes. However, the EXAFS data for E. coli LpxC with two zinc ions bound per protein, with the second Zn(II) acting as an inhibitory metal, demonstrates that the inhibitory metal is bound to at least two high-Z (sulfur, presumably thiolate, or chlorine) ligands. Results of the outer-shell scattering analysis, combined with previous studies of the LpxC enzyme, indicate a novel zinc binding motif not found in any previously studied zinc metalloproteins.