HLA-Modeler: Automated Homology Modeling of Human Leukocyte Antigens.

The three-dimensional (3D) structures of human leukocyte antigen (HLA) molecules are indispensable for the studies on the functions at molecular level. We have developed a homology modeling system named HLA-modeler specialized in the HLA molecules. Segment matching algorithm is employed for modeling and the optimization of the model is carried out by use of the PFROSST force field considering the implicit solvent model. In order to efficiently construct the homology models, HLA-modeler uses a local database of the 3D structures of HLA molecules. The structure of the antigenic peptide-binding site is important for the function and the 3D structure is highly conserved between various alleles. HLA-modeler optimizes the use of this structural motif. The leave-one-out cross-validation using the crystal structures of class I and class II HLA molecules has demonstrated that the rmsds of nonhydrogen atoms of the sites between homology models and crystal structures are less than 1.0 Å in most cases. The results have indicated that the 3D structures of the antigenic peptide-binding sites can be reproduced by HLA-modeler at the level almost corresponding to the crystal structures.

Fragment molecular orbital calculations on red fluorescent proteins (DsRed and mFruits).

We have performed a series of fragment molecular orbital (FMO) calculations for a family of red fluorescent proteins, DsRed and mFruits. The electronic transition energies were evaluated by the method of configuration interaction singles with perturbative doubles [CIS(D)] including higher-order corrections. The calculated values were in good agreement with the corresponding experimental peak values of spectra. Additionally, the chromophore environment was systematically analyzed in terms of the interaction energies between the pigment moiety and neighboring residues. It was theoretically revealed that the electrostatic interactions play a dominant role in the DsRed chromophore, whereas the color tunings in mFruits are controlled in a more delicate fashion.

VISCANA: visualized cluster analysis of protein-ligand interaction based on the ab initio fragment molecular orbital method for virtual ligand screening.

We have developed a visualized cluster analysis of protein-ligand interaction (VISCANA) that analyzes the pattern of the interaction of the receptor and ligand on the basis of quantum theory for virtual ligand screening. Kitaura et al. (Chem. Phys. Lett. 1999, 312, 319-324.) have proposed an ab initio fragment molecular orbital (FMO) method by which large molecules such as proteins can be easily treated with chemical accuracy. In the FMO method, a total energy of the molecule is evaluated by summation of fragment energies and interfragment interaction energies (IFIEs). In this paper, we have proposed a cluster analysis using the dissimilarity that is defined as the squared Euclidean distance between IFIEs of two ligands. Although the result of an ordered table by clustering is still a massive collection of numbers, we combine a clustering method with a graphical representation of the IFIEs by representing each data point with colors that quantitatively and qualitatively reflect the IFIEs. We applied VISCANA to a docking study of pharmacophores of the human estrogen receptor alpha ligand-binding domain (57 amino acid residues). By using VISCANA, we could classify even structurally different ligands into functionally similar clusters according to the interaction pattern of a ligand and amino acid residues of the receptor protein. In addition, VISCANA could estimate the correct docking conformation by analyzing patterns of the receptor-ligand interactions of some conformations through the docking calculation.

Development of KiBank, a database supporting structure-based drug design.

KiBank is a database of inhibition constant (Ki) values with 3D structures of target proteins and chemicals. Ki values were accumulated from peer-reviewed literature searched via PubMed. The 3D structure files of target proteins were originally from Protein Data Bank (PDB), while the 2D structure files of the chemicals were collected together with the Ki values and then converted into 3D ones. In KiBank, the chemical and protein 3D structures with hydrogen atoms were optimized by energy minimization and stored in MDL MOL and PDB format, respectively. KiBank is designed to support structure-based drug design. It provides structure files of proteins and chemicals ready for use in virtual screening through automated docking methods, while the Ki values can be applied for tests of docking/scoring combinations, program parameter settings, and calibration of empirical scoring functions. Additionally, the chemical structures and corresponding Ki values in KiBank are useful for lead optimization based on quantitative structure-activity relationship (QSAR) techniques. KiBank is updated on a daily basis and is freely available at . As of August 2004, KiBank contains 8000 Ki values, over 6000 chemicals and 166 proteins covering the subtypes of receptors and enzymes.

[KiBank: a database for computer-aided drug design based on protein-chemical interaction analysis].

KiBank is a database for computer-aided drug design and consists of binding affinities and chemical and target protein structures. Each chemical or protein structure with hydrogen atoms added was optimized by energy minimization and stored in PDB or MDL MOL file format, so that the structural data can be directly used for in silico binding studies. To describe the extent of inhibition, the inhibition constant (K(i)) value is used to simplify comparisons of strengths among chemical-protein bindings. As of April 2004, KiBank contained 142 proteins, over 5000 chemicals, and over 6000 binding affinity values that were published in peer-reviewed journals. The binding affinity values are currently mostly for membrane and nuclear receptors but are soon being expanded to other drug targets. KiBank is updated daily and can be accessed on the Web at http://kibank.iis.u-tokyo.ac.jp/at no charge.

HIV protease inhibitor nelfinavir inhibits replication of SARS-associated coronavirus.

A novel coronavirus has been identified as an etiological agent of severe acute respiratory syndrome (SARS). To rapidly identify anti-SARS drugs available for clinical use, we screened a set of compounds that included antiviral drugs already in wide use. Here we report that the HIV-1 protease inhibitor, nelfinavir, strongly inhibited replication of the SARS coronavirus (SARS-CoV). Nelfinavir inhibited the cytopathic effect induced by SARS-CoV infection. Expression of viral antigens was much lower in infected cells treated with nelfinavir than in untreated infected cells. Quantitative RT-PCR analysis showed that nelfinavir could decrease the production of virions from Vero cells. Experiments with various timings of drug addition revealed that nelfinavir exerted its effect not at the entry step, but at the post-entry step of SARS-CoV infection. Our results suggest that nelfinavir should be examined clinically for the treatment of SARS and has potential as a good lead compound for designing anti-SARS drugs.