Current Computer - Aided Drug Design - Volume 5, Issue 3, 2009

No biological process can be fully described by computational techniques unless water is taken into consideration. Unfortunately, accurate representation of the water environment in any biological process is normally too timeconsuming with present techniques. Commonly, in ligand-binding docking, all water molecules are removed from the experimental structure data before the system is prepared, in which case it is assumed that all water effects are included in the scoring function. However, many scientific studies include particular water molecules explicitly, following chemical intuition. The choice of the number of water molecules and their positions depends substantially on the problem to be tackled and the information available about the problem. However, not much has been published comparing the quality of docking results with and without water molecules. This paper reviews those docking studies in which the effect of the inclusion of water molecules is analyzed in a large set of compounds. Explicit water molecules can be added to the system in different ways. The most popular option consists of including some water molecules already observed in crystal structures. However, tightly bound water molecules in a protein-ligand complex may not be conserved in complexes with other ligands. This problem can be tackled by coupling the prediction of water molecules with the docking calculation. Programs in which this feature is included are reviewed.

Activation of voltage-dependent K+ (Kv) channels modulates cellular excitability in several physiological systems including neuronal and cardiac cells. Given recent progress in structure determination and in computation technology, molecular dynamics (MD) simulation analyses have become a valuable tool in studies of voltage-dependent gating of Kv channels. However, due to the complex voltage-sensing mechanisms and interplays between the voltage sensor and the ion-conduction pathway, and because of limitations in MD simulation technology, the technical details are crucial to performing meaningful simulations. This review briefly introduces the current understanding of Kv channel molecular physiology, and then considers, in depth, the progress in Kv channel simulations and the physiological insights on Kv channels obtained from such computational studies. Effectiveness of several techniques involving steered molecular dynamics, potential mean force analyses and coarse-grained simulation methods in the analyses of interactions among helices of the voltage sensor domain (VSD) and the coupling between VSD and the pore domain is stressed. The interactions among helices of the VSD, which determine the kinetics of the voltage-dependent gating, are strong, and therefore the potential usefulness of artificial restraints is discussed. Given information of an extensive set of mutants that effect gating kinetics, usefulness of in silico mutagenesis is stressed. Progress in??molecular dynamics simulation analyses of gating modifier toxins is also reviewed. Based on the two different binding modes observed in the MD simulation, a model is proposed for the mechanisms of these gating modifiers. This model gives us insight on some of the enigmas associated with the gating modifiers involving hanatoxin, VsTx1 and GsMTx4.

Reverse transcriptase (RT), an essential enzyme for HIV-1 (human immunodeficiency virus type-1) life cycle, is a key target in drug discovery efforts against HIV-1 infection. Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are very specific to HIV-1 RT and have relatively less toxicity than the nucleoside reverse transcriptase inhibitors (NRTIs). However, the rapid emergence of drug-resistant viral strains has limited the therapeutic efficacy of these inhibitors. In this review, recent advances in computer-aided drug design (CADD) for design of new compounds against HIV-1 RT based on ligand-based drug design (LBDD) using 2D-and 3D-QSAR approaches, structure-based drug design (SBDD) with the combination of molecular docking, virtual screening and de novo drug design, molecular simulations and particular interaction calculated from quantum chemical calculations are discussed. Their successful applications are also highlighted.

Quantitative structure-activity relationships (QSARs) techniques are routinely used in modern computer-aided drug design. In this review, the common generalization and computational procedures of QSAR methods (CoMFA, CoMSIA, and HQSA) are described in detail. The predictive ability of CoMFA and CoMSIA models depends directly on the quality of molecular alignment, the selection of probe, the difference of steps and type of charge. Moreover, it is worth noting that the active conformation plays a key role in molecular alignment, and it is a very difficult task to select the active conformations for drug molecules. The approaches to determine the active conformation are also reviewed. Furthermore, strategies including the selection of different fields and the chemometric methods for QSAR model to improve predictive capabilities between the structures of drugs and the biological activities are suggested in this review. The predictive ability of HQSAR models is directly dependent on the hologram length, fragment size, and distinction parameters. By using these techniques, our recent case studies of QSAR on two categories of drugs are presented. One is a series of the inhibitors of estrogen receptor (ER), 3-arylquinazolinethione derivatives, which is a key drug target for the treatment of osteoporosis and breast cancer. The other is a series of inhibitors of human eosinophil phosphodiesterase, 5,6- dihydro-(9H)-pyrazolo[3,4-c]-1,2,4- triazolo [4,3-a]pyridines, which is a drug target for the treatment of inflammation. Satisfactory models of QSAR (with high predictive ability) on two categories of drugs were obtained with optimized parameters. According to the models of QSAR obtained in our study, new drug molecules with higher activity were proposed.