Patterson Scott
Celera, Rockville, USA.
Bioinformatics. 2002;18 Suppl 2:S181. doi: 10.1093/bioinformatics/18.suppl_2.s181.
The identification of potential targets for therapeutic intervention can be accomplished on a systematic basis by a variety of techniques that include quantitative analysis of gene-specific mRNA levels and expressed proteins in normal and diseased cells. Differences in the expression levels of nucleic acid and protein gene products could suggest protein drug targets that are directly causative of disease, or reveal biochemical pathways that could be modulated by therapeutic molecules. Any effort based on mRNA or protein expression level comparisons could be confounded by a number of factors: level in steady-state may not be correlated with actual encoded protein levels; differentially expressed protein levels might be a result of disease process, and not causative of the process, and therapeutic intervention based on such a difference will be unproductive and the differential expression of mRNA or protein may be the result of biological variation unrelated to the disease process under study. In order to address these possibly confounding factors, it is necessary to validate potential targets by establishing their firm association with disease, and their minimal distribution in non-diseased tissues of any type. This requirement suggests that emphasis on true and reproducible quantitation of protein expression levels in a variety of samples will be an effective and highly efficient method of generating drug targets with a high degree of utility. To achieve this aim, we have established an industrial-scale proteomics-based discovery platform consisting of cell biology, protein chemistry, and mass spectrometry technical groups together with bioinformatics groups. The analytical method used for quantitation employs isotope labeling for differential analysis (ICATTM, Applied Biosystems, Inc.). With this technique, tryptic peptides are generated from labeled proteins that have been specifically captured from various subcellular locations or protein families. The resulting peptides are identified and quantified by mass spectrometry. To evaluate this approach on a large-scale, we have applied it to a study of continuous cell lines derived from human pancreatic adenocarcinomas. We have been able to establish processes for target discovery for small molecule drug targets as well as therapeutic antibody target identification for cell surface proteins. In addition, we have developed a process for identification of serum markers of this disease based upon standardized fractionation procedures. The results of these analyses will be presented together with the some of the issues from both the wet and dry (computational) lab that need to be addressed in such an undertaking.
通过多种技术可以系统地确定治疗干预的潜在靶点,这些技术包括对正常细胞和患病细胞中基因特异性mRNA水平和表达蛋白进行定量分析。核酸和蛋白质基因产物表达水平的差异可能提示直接导致疾病的蛋白质药物靶点,或者揭示可被治疗分子调节的生化途径。基于mRNA或蛋白质表达水平比较的任何努力都可能受到多种因素的干扰:稳态水平可能与实际编码的蛋白质水平不相关;差异表达的蛋白质水平可能是疾病过程的结果,而非该过程的病因,基于这种差异的治疗干预将是无效的,并且mRNA或蛋白质的差异表达可能是与所研究的疾病过程无关的生物学变异的结果。为了解决这些可能的混杂因素,有必要通过建立潜在靶点与疾病的牢固关联以及它们在任何类型的非患病组织中的最小分布来验证这些靶点。这一要求表明,强调对各种样品中蛋白质表达水平进行真实且可重复的定量分析,将是生成具有高度实用性的药物靶点的一种有效且高效的方法。为实现这一目标,我们建立了一个基于蛋白质组学的工业规模发现平台,该平台由细胞生物学、蛋白质化学、质谱技术团队以及生物信息学团队组成。用于定量分析的方法采用同位素标记进行差异分析(ICATTM,应用生物系统公司)。利用这种技术,从已从各种亚细胞位置或蛋白质家族中特异性捕获的标记蛋白质中生成胰蛋白酶肽段。所得肽段通过质谱进行鉴定和定量。为了大规模评估这种方法,我们将其应用于对源自人胰腺腺癌的连续细胞系的研究。我们已经能够建立小分子药物靶点的靶点发现过程以及细胞表面蛋白的治疗性抗体靶点鉴定过程。此外,我们还基于标准化分离程序开发了一种鉴定该疾病血清标志物的方法。这些分析结果将与在此类工作中需要解决的一些来自湿实验室和干实验室(计算)的问题一起呈现。