2D-Qsar for 450 types of amino acid induction peptides with a novel substructure pair descriptor having wider scope

<p>Abstract</p> <p>Background</p> <p>Quantitative structure-activity relationships (QSAR) analysis of peptides is helpful for designing various types of drugs such as kinase inhibitor or antigen. Capturing various properties of peptides is essential for analyzing two-dimensional QSAR. A descriptor of peptides is an important element for capturing properties. The atom pair holographic (APH) code is designed for the description of peptides and it represents peptides as the combination of thirty-six types of key atoms and their intermediate binding between two key atoms.</p> <p>Results</p> <p>The substructure pair descriptor (SPAD) represents peptides as the combination of forty-nine types of key substructures and the sequence of amino acid residues between two substructures. The size of the key substructures is larger and the length of the sequence is longer than traditional descriptors. Similarity searches on C5a inhibitor data set and kinase inhibitor data set showed that order of inhibitors become three times higher by representing peptides with SPAD, respectively. Comparing scope of each descriptor shows that SPAD captures different properties from APH.</p> <p>Conclusion</p> <p>QSAR/QSPR for peptides is helpful for designing various types of drugs such as kinase inhibitor and antigen. SPAD is a novel and powerful descriptor for various types of peptides. Accuracy of QSAR/QSPR becomes higher by describing peptides with SPAD.</p

Discovery of a Non-Peptidic Inhibitor of West Nile Virus NS3 Protease by High-Throughput Docking

An estimated 2.5 billion people are at risk of diseases caused by dengue and West Nile virus. As of today, there are neither vaccines to prevent nor drugs to cure the severe infections caused by these viruses. The NS3 protease is one of the most promising targets for drug development against West Nile virus because it is an essential enzyme for viral replication and because success has been demonstrated with the closely related hepatitis C virus protease. We have discovered a small molecule that inhibits the NS3 protease of West Nile virus by computer-aided high-throughput docking, and validated it using three experimental techniques. The inhibitor has potential to be developed to a drug candidate to combat West Nile virus infections

Computational Fragment-Based Binding Site Identification by Ligand Competitive Saturation

Fragment-based drug discovery using NMR and x-ray crystallographic methods has proven utility but also non-trivial time, materials, and labor costs. Current computational fragment-based approaches circumvent these issues but suffer from limited representations of protein flexibility and solvation effects, leading to difficulties with rigorous ranking of fragment affinities. To overcome these limitations we describe an explicit solvent all-atom molecular dynamics methodology (SILCS: Site Identification by Ligand Competitive Saturation) that uses small aliphatic and aromatic molecules plus water molecules to map the affinity pattern of a protein for hydrophobic groups, aromatic groups, hydrogen bond donors, and hydrogen bond acceptors. By simultaneously incorporating ligands representative of all these functionalities, the method is an in silico free energy-based competition assay that generates three-dimensional probability maps of fragment binding (FragMaps) indicating favorable fragment∶protein interactions. Applied to the two-fold symmetric oncoprotein BCL-6, the SILCS method yields two-fold symmetric FragMaps that recapitulate the crystallographic binding modes of the SMRT and BCOR peptides. These FragMaps account both for important sequence and structure differences in the C-terminal halves of the two peptides and also the high mobility of the BCL-6 His116 sidechain in the peptide-binding groove. Such SILCS FragMaps can be used to qualitatively inform the design of small-molecule inhibitors or as scoring grids for high-throughput in silico docking that incorporate both an atomic-level description of solvation and protein flexibility

Structure-based optimization of potent and selective inhibitors of the tyrosine kinase erythropoietin producing human hepatocellular carcinoma receptor B4 (EphB4)

The tyrosine kinase EphB4 is an attractive target for drug design because of its recognized role in cancer-related angiogenesis. Recently, a series of commercially available xanthine derivatives were identified as micromolar inhibitors of EphB4 by high-throughput fragment-based docking into the ATP-binding site of the kinase domain. Here, we have exploited the binding mode obtained by automatic docking for the optimization of these EphB4 inhibitors by chemical synthesis. Addition of only two heavy atoms, methyl and hydroxyl groups, to compound 4 has yielded the single-digit nanomolar inhibitor 66, with a remarkable improvement of the ligand efficiency from 0.26 to 0.37 kcal/(mol per non-hydrogen atom). Compound 66 shows very high affinity for a few other tyrosine kinases with threonine as gatekeeper residue (Abl, Lck, and Src). On the other hand, it is selective against kinases with a larger gatekeeper. A 45 ns molecular dynamics (MD) simulation of the complex of EphB4 and compound 66 provides further validation of the binding mode obtained by fragment-based docking

Fragment-based de novo ligand design by multiobjective evolutionary optimization

GANDI (Genetic Algorithm-based de Novo Design of Inhibitors) is a computational tool for automatic fragment-based design of molecules within a protein binding site of known structure. A genetic algorithm and a tabu search act in concert to join predocked fragments with a user-supplied list of fragments. A novel feature of GANDI is the simultaneous optimization of force field energy and a term enforcing 2D-similarity to known inhibitor(s) or 3D-overlap to known binding mode(s). Scaffold hopping can be promoted by tuning the relative weights of these terms. The performance of GANDI is tested on cyclin-dependent kinase 2 (CDK2) using a library of about 14 000 fragments and the binding mode of a known oxindole inhibitor to bias the design. Top ranking GANDI molecules are involved in one to three hydrogen bonds with the backbone polar groups in the hinge region of CDK2, an interaction pattern observed in potent kinase inhibitors. Notably, a GANDI molecule with very favorable predicted binding affinity shares a 2-N-phenyl-1,3-thiazole-2,4-diamine moiety with a known nanomolar inhibitor of CDK2. Importantly, molecules with a favorable GANDI score are synthetic accessible. In fact, eight of the 1809 molecules designed by GANDI for CDK2 are found in the ZINC database of commercially available compounds which also contains about 600 compounds with identical scaffolds as those in the top ranking GANDI molecules

Structure-guided fragment-based in silico drug design of dengue protease inhibitors

10.1007/s10822-011-9418-0Journal of Computer-Aided Molecular Design253263-274JCAD