Perrotta A, Malacarne D, Taningher M, Pesenti R, Paolucci M, Parodi S
Laboratorio di Oncologia Sperimentale, Istituto Nazionale per la Ricerca sul Cancro, Genova, Italy.
Environ Mol Mutagen. 1996;28(1):31-50. doi: 10.1002/(SICI)1098-2280(1996)28:1<31::AID-EM7>3.0.CO;2-H.
We have applied a new software program, based on graph theory and developed by our group, to predict mutagenicity in Salmonella. The software analyzes, as information in input, the structural formula and the biological activities of a relatively large database of chemicals to generate any possible molecular fragment with size ranging from two to ten nonhydrogen atoms, and detects (as predictors of biological activity) those fragments statistically associated with the biological property investigated. Our previous work used the program to predict carcinogenicity in small rodents. In the current work we applied a modified version of the program, which bases its predictions solely on the most important fragment present in a given molecule, considering as practically negligible the effects of additional less important fragments. For Salmonella mutagenicity we used a database of 551 compounds, and the program achieved a level of predictivity (73.9%) comparable to that obtained by other authors using the Computer Automated Structure Evaluation (CASE) program. We evaluated the relative contributions of biophores and biophobes to overall predictivity: biophores tended to be more important than biophobes, and chemicals containing both biophores and biophobes were more difficult to predict. Many of the molecular fragments identified by the program as being strongly associated with mutagenic activity were similar to the structural alerts identified by the human experts Ashby and Tennant. Our results tend to confirm that structural alerts useful to predict Salmonella mutagenicity are generally not very strong predictors of rodent carcinogenicity. Although the predictivity level achieved for oncogenic activity improved when the program was directly trained with carcinogenicity data, carcinogenicity as a biological endpoint was still more difficult to predict than Salmonella mutagenicity.
我们应用了一款基于图论且由我们团队开发的新软件程序，来预测沙门氏菌中的致突变性。该软件将一个相对较大的化学品数据库的结构式和生物活性作为输入信息进行分析，以生成大小从两个到十个非氢原子不等的任何可能的分子片段，并检测（作为生物活性的预测指标）那些与所研究的生物学特性有统计学关联的片段。我们之前的工作使用该程序来预测小型啮齿动物的致癌性。在当前工作中，我们应用了该程序的一个修改版本，它仅基于给定分子中存在的最重要片段进行预测，认为其他不太重要的片段的影响实际上可以忽略不计。对于沙门氏菌致突变性，我们使用了一个包含551种化合物的数据库，该程序实现的预测水平（73.9%）与其他作者使用计算机自动结构评估（CASE）程序所获得的水平相当。我们评估了亲生物基团和疏生物基团对总体预测性的相对贡献：亲生物基团往往比疏生物基团更重要，同时含有亲生物基团和疏生物基团的化学品更难预测。该程序鉴定出的许多与诱变活性密切相关的分子片段类似于人类专家阿什比和坦南特所确定的结构警示。我们的结果倾向于证实，对预测沙门氏菌致突变性有用的结构警示通常不是啮齿动物致癌性的很强的预测指标。尽管当该程序直接用致癌性数据进行训练时，致癌活性的预测水平有所提高，但作为生物学终点的致癌性仍然比沙门氏菌致突变性更难预测。
