疟原虫属的寄生虫物种引起疟疾,这仍然是一个主要的全球健康问题,由于寄生虫对现有的抗疟药物的耐药性和增加的治疗成本。因此,疟原虫蛋白质组中具有新靶标的新抗疟化合物的计算预测。是制药行业非常重要的目标。我们可以预期,临床前试验的成功取决于试验本身的条件,药物的化学结构,目标蛋白的结构,以及控制该蛋白质在蛋白质组中表达的因素,例如基因(脱氧核糖核酸,DNA)序列和/或染色体结构。然而,没有同时考虑所有这些因素的计算模型的报告。这种分析的一些困难是数据在不同数据集中的分散,数据的高度异质性,等。在这项工作中,我们分析了三个数据库ChEMBL(欧洲分子生物学实验室的化学数据库),UniProt(通用蛋白质资源),和NCBI-GDV(国家生物技术信息中心-基因组数据查看器)来实现这一目标。ChEMBL数据集包含潜在抗疟化合物的17,758种独特测定的结果,包括化合物结构的数字描述符(变量)以及有关测定条件的大量信息。NCBI-GDV和UniProt数据集包括基因序列,蛋白质,以及它们的功能。此外,我们还从ChEMBL数据集创建了分类变量的两个分区(cassayj=caj和cdataj=cdj)。这些分区包含编码关于临床前测定的实验条件(caj)或关于数据的性质和质量(cdj)的信息的变量。这些分类变量包括有关22个生物活性参数(ca0)的信息,28个靶蛋白(ca1),和9种测定生物(CA2),等。我们还创建了另一个分区(cprotj=cpj),包括具有有关靶蛋白生物学信息的分类变量,基因,和染色体。这些变量涵盖32个基因(cp0),10条染色体(cp1),基因取向(cp2),和31蛋白质功能(cp3)。我们使用摄动理论机器学习信息融合(IFPTML)算法将所有这些信息(来自三个数据库)映射到并训练预测模型。Shannon的熵度量Shk(数值变量)用于量化有关药物结构的信息,蛋白质序列,基因序列,和相同信息尺度的染色体。具有移动平均(MA)算子形式的微扰理论算子(PTO)已用于量化结构变量中相对于分类变量的不同子集(分区)的期望值的扰动(偏差)。我们使用一般判别分析(GDA)获得了三个IFPTML模型,具有单变量拆分的分类树(CTUS),和具有线性组合的分类树(CTLC)。IFPTML-CTLC表现出更好的性能,对于训练/验证集,灵敏度Sn(%)=83.6/85.1,特异性Sp(%)=89.8/89.7。分别。该模型可以成为一种有用的工具,用于优化新的抗疟药化合物的临床前测定疟原虫蛋白质组中的不同蛋白质。
The parasite species of genus Plasmodium causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds with novel targets in the proteome of Plasmodium sp. is a very important goal for the pharmaceutical industry. We can expect that the success of the pre-clinical assay depends on the conditions of assay per se, the chemical structure of the drug, the structure of the target protein to be targeted, as well as on factors governing the expression of this protein in the proteome such as genes (Deoxyribonucleic acid, DNA) sequence and/or chromosomes structure. However, there are no reports of computational models that consider all these factors simultaneously. Some of the difficulties for this kind of analysis are the dispersion of data in different datasets, the high heterogeneity of data, etc. In this work, we analyzed three databases ChEMBL (Chemical database of the European Molecular Biology Laboratory),
UniProt (Universal Protein Resource), and NCBI-GDV (National Center for Biotechnology Information-Genome Data Viewer) to achieve this goal. The ChEMBL dataset contains outcomes for 17,758 unique assays of potential Antimalarial compounds including numeric descriptors (variables) for the structure of compounds as well as a huge amount of information about the conditions of assays. The NCBI-GDV and
UniProt datasets include the sequence of genes, proteins, and their functions. In addition, we also created two partitions (cassayj = caj and cdataj = cdj) of categorical variables from theChEMBL dataset. These partitions contain variables that encode information about experimental conditions of preclinical assays (caj) or about the nature and quality of data (cdj). These categorical variables include information about 22 parameters of biological activity (ca0), 28 target proteins (ca1), and 9 organisms of assay (ca2), etc. We also created another partition of (cprotj = cpj) including categorical variables with biological information about the target proteins, genes, and chromosomes. These variables cover32 genes (cp0), 10 chromosomes (cp1), gene orientation (cp2), and 31 protein functions (cp3). We used a Perturbation-Theory Machine Learning Information Fusion (IFPTML) algorithm to map all this information (from three databases) into and train a predictive model. Shannon\'s entropy measure Shk (numerical variables) was used to quantify the information about the structure of drugs, protein sequences, gene sequences, and chromosomes in the same information scale. Perturbation Theory Operators (PTOs) with the form of Moving Average (MA) operators have been used to quantify perturbations (deviations) in the structural variables with respect to their expected values for different subsets (partitions) of categorical variables. We obtained three IFPTML models using General Discriminant Analysis (GDA), Classification Tree with Univariate Splits (CTUS), and Classification Tree with Linear Combinations (CTLC). The IFPTML-CTLC presented the better performance with Sensitivity Sn(%) = 83.6/85.1, and Specificity Sp(%) = 89.8/89.7 for training/validation sets, respectively. This model could become a useful tool for the optimization of preclinical assays of new Antimalarial compounds vs. different proteins in the proteome of Plasmodium.