Computer-aided Drug Design of Novel PLA(2) Inhibitor Candidates for Treatment of Snakebite

Phospholipases A(2) (PLA(2)) are enzymes commonly found in snake venoms from Viperidae and Elaphidae families, which are major components thereof. Many plants are used in traditional medicine its active agents against various effects induced by snakebite. This article presents the PLA(2) BthTX-I structure prediction based on homology modeling. In addition, we have performed virtual screening in a large database yielding a set of potential bioactive inhibitors. A flexible docking program was used to investigate the interactions between the receptor and the new ligands. We have performed molecular interaction fields (MIFs) calculations with the phospholipase model. Results confirm the important role of Lys49 for binding ligands and suggest three additional residues as well. We have proposed a theoretically nontoxic, drug-like, and potential novel BthTX-I inhibitor. These calculations have been used to guide the design of novel phospholipase inhibitors as potential lead compounds that may be optimized for future treatment of snakebite victims as well as other human diseases in which PLA(2) enzymes are involved.

Generation and Analysis of Interaction Energy Maps of p-Substituted Benzoic Acid [(5-Nitro-thiophen-2-yl)-methylene]-Hydrazides Active against MRSA

In this preliminary study eighteen p-substituted benzoic acid [(5-nitro-thiophen-2-yl)-methylene]-hydrazides with antimicrobial activity were evaluated against multidrug-resistant Staphylococcus aureus, correlating the three-dimensional characteristics of the ligands with their respective bioactivities. The computer programs Sybyl and CORINA were used, respectively, for the design and three-dimensional conversion of the ligands. Molecular interaction fields were calculated using GRID program. Calculations using Volsurf resulted in a statistically consistent model with 48 structural descriptors showing that hydrophobicity is a fundamental property in the analyzed biological response.

How language can help discrimination in the Neural Modelling Fields framework

The relationship between thought and language and, in particular, the issue of whether and how language influences thought is still a matter of fierce debate. Here we consider a discrimination task scenario to study language acquisition in which an agent receives linguistic input from an external teacher, in addition to sensory stimuli from the objects that exemplify the overlapping categories that make up the environment. Sensory and linguistic input signals are fused using the Neural Modelling Fields (NMF) categorization algorithm. We find that the agent with language is capable of differentiating object features that it could not distinguish without language. In this sense, the linguistic stimuli prompt the agent to redefine and refine the discrimination capacity of its sensory channels. (C) 2007 Elsevier Ltd. All rights reserved.

Three-Dimensional Quantitative Structure-Activity Relationship Study of Antitumor 2-Formylpyridine Thiosemicarbazones Derivatives as Inhibitors of Ribonucleotide Reductase

In order to extend previous SAR and QSAR studies, 3D-QSAR analysis has been performed using CoMFA and CoMSIA approaches applied to a set of 39 alpha-(N)-heterocyclic carboxaldehydes thiosemicarbazones with their inhibitory activity values (IC(50)) evaluated against ribonucleotide reductase (RNR) of H.Ep.-2 cells (human epidermoid carcinoma), taken from selected literature. Both rigid and field alignment methods, taking the unsubstituted 2-formylpyridine thiosemicarbazone in its syn conformation as template, have been used to generate multiple predictive CoMFA and CoMSIA models derived from training sets and validated with the corresponding test sets. Acceptable predictive correlation coefficients (Q(cv)(2) from 0.360 to 0.609 for CoMFA and Q(cv)(2) from 0.394 to 0.580 for CoMSIA models) with high fitted correlation coefficients (r` from 0.881 to 0.981 for CoMFA and r(2) from 0.938 to 0.993 for CoMSIA models) and low standard errors (s from 0.135 to 0.383 for CoMFA and s from 0.098 to 0.240 for CoMSIA models) were obtained. More precise CoMFA and CoMSIA models have been derived considering the subset of thiosemicarbazones (TSC) substituted only at 5-position of the pyridine ring (n=22). Reasonable predictive correlation coefficients (Q(cv)(2) from 0.486 to 0.683 for CoMFA and Q(cv)(2) from 0.565 to 0.791 for CoMSIA models) with high fitted correlation coefficients (r(2) from 0.896 to 0.997 for CoMFA and r(2) from 0.991 to 0.998 for CoMSIA models) and very low standard errors (s from 0.040 to 0.179 for CoMFA and s from 0.029 to 0.068 for CoMSIA models) were obtained. The stability of each CoMFA and CoMSIA models was further assessed by performing bootstrapping analysis. For the two sets the generated CoMSIA models showed, in general, better statistics than the corresponding CoMFA models. The analysis of CoMFA and CoMSIA contour maps suggest that a hydrogen bond acceptor near the nitrogen of the pyridine ring can enhance inhibitory activity values. This observation agrees with literature data, which suggests that the nitrogen pyridine lone pairs can complex with the iron ion leading to species that inhibits RNR. The derived CoMFA and CoMSIA models contribute to understand the structural features of this class of TSC as antitumor agents in terms of steric, electrostatic, hydrophobic and hydrogen bond donor and hydrogen bond acceptor fields as well as to the rational design of this key enzyme inhibitors.