Current Computer - Aided Drug Design - Volume 6, Issue 2, 2010

An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features.

Chemical compound libraries are the basic database for virtual (in silico) drug screening, and the number of entries has reached 20 million. Many drug-like compound libraries for virtual drug screening have been developed and released. In this review, the process of constructing a database for virtual screening is reviewed, and several popular databases are introduced. Several kinds of focused libraries have been developed. The author has developed databases for metalloproteases, and the details of the libraries are described. The library for metalloproteases was developed by improving the generation of the dominant-ion forms. For instance, the SH group is treated as S- in this library while all SH groups are protonated in the conventional libraries. In addition, metal complexes were examined as new candidates of drug-like compounds. Finally, a method for generating chemical space is introduced, and the diversity of compound libraries is discussed.

Multivariate statistical methods are commonly used in the analysis of quantitative structure-activity/property relationships (QSAR and QSPR, respectively). The partial least squares (PLS) method is of particular interest because it can analyze data containing numerous X variables with strongly collinear and noisy characteristics and can simultaneously model several response variables Y. Furthermore, it can provide us with several prediction regions and diagnostic plots as statistical measures. PLS has evolved or changed for coping with the severe demands associated with the complex data structures of X and Y variables. In this review article, we selected five advanced PLS techniques and outlined their algorithms with representative examples. In particular, we made efforts to describe how to disclose the inner relations embedded in data and how to use this information for molecular design.

Monoclonal antibodies capable of recognizing antigens with high affinity and specificity represent a wellestablished class of biological agents. Since the development of hybridoma technology in 1975, advances in recombinant DNA technologies and computational and biophysical methods have allowed us to develop a better understanding of the relationships between antibody sequence, structure, and function. These advances enable us to manipulate antibody sequences with the goal of improving upon, or creating new biological or biophysical properties. In this review we will focus on recent successes in using structure-guided computational methods to design antibodies and antibody-like molecules with optimized affinity and specificity to antigen and for enhancing protein stability.

Design of novel H5N1 inhibitors is currently a research topic of vital importance owing to both a recent pandemic threat by the worldwide spread of H5N1 avian influenza and the high resistance of H5N1 virus to the most widely used commercial drug, oseltamivir-OTV (Tamiflu). There has been much progress in this field recently. This review covers recent work on bioinformatics studies, structure based design, computer modeling and molecular dynamics simulations.