Xun Li

The term cell necrobiology is introduced to comprise the life processes associated with morphological, biochemical, and molecular changes which predispose, precede, and accompany cell death, as well as the consequences and tissue response to cell death. Two alternative modes of cell death can be distinguished, apoptosis and accidental cell death, generally defined as necrosis. The wide interest in necrobiology in many disciplines stems from the realization that apoptosis, whether it occurs physiologically or as a manifestation of a pathological state, is an active mode of cell death and a subject of complex regulatory processes. A possibility exists, therefore, to interact with the regulatory machinery and thereby modulate the cell's propensity to die in response to intrinsic or exogenous signals. Flow cytometry appears to be the methodology of choice to study various aspects of necrobiology. It offers all the advantages of rapid, multiparameter analysis of large populations of individual cells to investigate the biological processes associated with cell death. Numerous methods have been developed to identify apoptotic and necrotic cells and are widely used in various disciplines, in particular in oncology and immunology. The methods based on changes in cell morphology, plasma membrane structure and transport function, function of cell organelles, DNA stability to denaturation, and endonucleolytic DNA degradation are reviewed and their applicability in the research laboratory and in the clinical setting is discussed. Improper use of flow cytometry in analysis of cell death and in data interpretation also is discussed. The most severe errors are due to i) misclassification of nuclear fragments and individual apoptotic bodies as single apoptotic cells, ii) assumption that the apoptotic index represents the rate of cell death, and iii) failure to confirm by microscopy that the cells classified by flow cytometry as apoptotic or necrotic do indeed show morphology consistent with this classification. It is expected that flow cytometry will be the dominant methodology for necrobiology.

We have developed a new method, i.e., XLOGP3, for logP computation. XLOGP3 predicts the logP value of a query compound by using the known logP value of a reference compound as a starting point. The difference in the logP values of the query compound and the reference compound is then estimated by an additive model. The additive model implemented in XLOGP3 uses a total of 87 atom/group types and two correction factors as descriptors. It is calibrated on a training set of 8199 organic compounds with reliable logP data through a multivariate linear regression analysis. For a given query compound, the compound showing the highest structural similarity in the training set will be selected as the reference compound. Structural similarity is quantified based on topological torsion descriptors. XLOGP3 has been tested along with its predecessor, i.e., XLOGP2, as well as several popular logP methods on two independent test sets: one contains 406 small-molecule drugs approved by the FDA and the other contains 219 oligopeptides. On both test sets, XLOGP3 produces more accurate predictions than most of the other methods with average unsigned errors of 0.24-0.51 units. Compared to conventional additive methods, XLOGP3 does not rely on an extensive classification of fragments and correction factors in order to improve accuracy. It is also able to utilize the ever-increasing experimentally measured logP data more effectively.

Scoring functions are widely applied to the evaluation of protein-ligand binding in structure-based drug design. We have conducted a comparative assessment of 16 popular scoring functions implemented in main-stream commercial software or released by academic research groups. A set of 195 diverse protein-ligand complexes with high-resolution crystal structures and reliable binding constants were selected through a systematic nonredundant sampling of the PDBbind database and used as the primary test set in our study. All scoring functions were evaluated in three aspects, that is, "docking power", "ranking power", and "scoring power", and all evaluations were independent from the context of molecular docking or virtual screening. As for "docking power", six scoring functions, including GOLD::ASP, DS::PLP1, DrugScore(PDB), GlideScore-SP, DS::LigScore, and GOLD::ChemScore, achieved success rates over 70% when the acceptance cutoff was root-mean-square deviation < 2.0 A. Combining these scoring functions into consensus scoring schemes improved the success rates to 80% or even higher. As for "ranking power" and "scoring power", the top four scoring functions on the primary test set were X-Score, DrugScore(CSD), DS::PLP, and SYBYL::ChemScore. They were able to correctly rank the protein-ligand complexes containing the same type of protein with success rates around 50%. Correlation coefficients between the experimental binding constants and the binding scores computed by these scoring functions ranged from 0.545 to 0.644. Besides the primary test set, each scoring function was also tested on four additional test sets, each consisting of a certain number of protein-ligand complexes containing one particular type of protein. Our study serves as an updated benchmark for evaluating the general performance of today's scoring functions. Our results indicate that no single scoring function consistently outperforms others in all three aspects. Thus, it is important in practice to choose the appropriate scoring functions for different purposes.