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Aim: To analyze the polymorphism in exon 4 of the gamma-synuclein gene (SNCG) in patients with autoantibodies against the gamma-synuclein protein., Material and Methods: To identify autoantibodies against gamma-synuclein, the serum from patients with chronic cerebral ischemia and cervical osteochondrosis was used. All patients were women of the Slavic ethnic group, mean age 61±5 years. The isolated genomic DNA was used to determine the point mutation in exon 4 by the restriction endonuclease HphI and subsequent sequencing of the resulting fragments to confirm the results., Results and Conclusion: Antibodies against gamma-synuclein were identified in 2 patients with chronic cerebral ischemia and 3 with cervical osteochondrosis. All five patients had a T to A substitution at position 371, which was detected by the restriction endonuclease HphI resulting in a hydrolysis of the amplicon and the formation of two fragments. The subsequent sequencing of exon 4 of the SNCG revealed no other mutations and confirmed the T to A substitution. This single nucleotide polymorphism results in the amino acid substitution of glutamic acid to valine at position 110 (out of 127), changing its physicochemical properties and the ability to form aggregates as well as post-translational modifications. The obtained results provide grounds for further association studies of SNCG polymorphism in patients with various diseases of the nervous system.

In this study, we analyzed serum for the presence of antibodies to gamma-synuclein in patients with amyotrophic lateral sclerosis (ALS) compared to the control group of patients with other neurological diseases and healthy control donors. As a result, antibodies against gamma-synuclein are not an ALS-specific feature and have been identified in patients with ALS as well as in the control group patients. Patients with the impaired cerebral circulation showed increased incidence of autoantibodies to gamma-synuclein, yet the difference lacks statistical representativeness due to limited sample size.

We present here a greatly updated version of an earlier study on the conformational energies of protein-ligand complexes in the Protein Data Bank (PDB) [Nicklaus et al. Bioorg. Med. Chem. 1995, 3, 411-428], with the goal of improving on all possible aspects such as number and selection of ligand instances, energy calculations performed, and additional analyses conducted. Starting from about 357,000 ligand instances deposited in the 2008 version of the Ligand Expo database of the experimental 3D coordinates of all small-molecule instances in the PDB, we created a "high-quality" subset of ligand instances by various filtering steps including application of crystallographic quality criteria and structural unambiguousness. Submission of 640 Gaussian 03 jobs yielded a set of about 415 successfully concluded runs. We used a stepwise optimization of internal degrees of freedom at the DFT level of theory with the B3LYP/6-31G(d) basis set and a single-point energy calculation at B3LYP/6-311++G(3df,2p) after each round of (partial) optimization to separate energy changes due to bond length stretches vs bond angle changes vs torsion changes. Even for the most "conservative" choice of all the possible conformational energies-the energy difference between the conformation in which all internal degrees of freedom except torsions have been optimized and the fully optimized conformer-significant energy values were found. The range of 0 to ~25 kcal/mol was populated quite evenly and independently of the crystallographic resolution. A smaller number of "outliers" of yet higher energies were seen only at resolutions above 1.3 Å. The energies showed some correlation with molecular size and flexibility but not with crystallographic quality metrics such as the Cruickshank diffraction-component precision index (DPI) and R(free)-R, or with the ligand instance-specific metrics such as occupancy-weighted B-factor (OWAB), real-space R factor (RSR), and real-space correlation coefficient (RSCC). We repeated these calculations with the solvent model IEFPCM, which yielded energy differences that were generally somewhat lower than the corresponding vacuum results but did not produce a qualitatively different picture. Torsional sampling around the crystal conformation at the molecular mechanics level using the MMFF94s force field typically led to an increase in energy., (© 2012 American Chemical Society)

The size of conformational ensembles required for regular coverage of the conformational space of druglike molecules was examined. Using the conformer generation program Omega, the number of regularly distributed conformers (NRC) of flexible compounds was determined as a function of the root-mean-square deviation (RMSD) resolution of coverage. A regression equation was developed predicting the NRC of a molecule as a function of RMSD. The model yielded R(2) of 0.91 for both training and test sets, which consisted of 3414 and 3352 compounds, respectively. Utilizing 14 504 ligands from the Protein Data Bank with experimentally determined 3-D conformations, the regression equation was applied to the estimation of the NRC and the success rate of reproduction of experimental conformations from a theoretical conformation ensemble as a function of RMSD and flexibility was explored.

The application of the program PASS (Prediction of Activity Spectra for Substances) to about 250 000 compounds of the NCI Open Database and the incorporation of over 64 million PASS predictions in the Enhanced NCI Database Browser are described. A total of 565 different types of activity are included, encompassing general pharmacological effects, specific mechanisms of action, known toxicities, and others. Application of this Web-based service to prediction of activities of the kinds "Angiogenesis inhibitor," "Antiviral (HIV)", and a set of activities that can be associated with antineoplastic action are reported. For this latter data set, a very substantial enrichment over random selection was found in the PASS predictions. It is shown how the user can conduct complex searches by combining ranges of PASS-predicted probabilities of compounds to be active or to be inactive, respectively, with, e.g., value ranges of physicochemical parameters, presence or absence of particular substructural fragment, and other search criteria.

The computer system PASS provides simultaneous prediction of several hundreds of biological activity types for any drug-like compound. The prediction is based on the analysis of structure-activity relationships of the training set including more than 30000 known biologically active compounds. In this paper we investigate the influence on the accuracy of predicting the types of activity with PASS by (a) reduction of the number of structures in the training set and (b) reduction of the number of known activities in the training set. The compounds from the MDDR database are used to create heterogeneous training and evaluation sets. We demonstrate that predictions are robust despite the exclusion of up to 60% of information.