For target identification using affinity chromatography, conventional method requires multiple steps as follows; SDS-PAGE, CBB staining, excision of gel, destaining, reduction, trypsinization, and application to LC-MS/MS system (7 steps); these steps can be cumbersome, time-consuming and require expensive installation. Furthermore, CBB staining used in conventional method can detect proteins over nanogram order. In contrast, our proposed protocol for predicting target protein allows us to use western blotting to detect proteins in picogram order. Indeed, we found two incednine-binding proteins by this prediction. Additionally, we can enhance the precision of COPICAT by feeding back the experimental results to the system.
In this work, PIK3CG, PARP1, and ACACA were revealed to bind to incednine by applying our protocol to identify potential target proteins of chemical compounds. These proteins are potential targets of incednine because it has been reported that these proteins are related to cancer survival and drug resistance, as follows.
PI3KCG encodes p110 catalytic subunit isoform p110γ and heterodimerizes with regulatory subunit p101, composing class IB PI3K in the PI3K family [21, 22]. Although PIK3CG and PIK3C2B are distant homologous with 20% sequence identity, incednine selectively binds to PIK3CG but not PIK3C2B (Figure 2). In contrast to class IA, class IB PI3K acts downstream of G-protein coupled receptors (GPCR). It has been reported that p110γ was upregulated and activated by the chimeric oncogene Bcr-Abl expression to contribute to cell proliferation and drug resistance in chronic myelogenous leukemia , and was found to be highly and specifically expressed among the PI3K family in human pancreatic cancer , suggesting that class IB PI3K might relate to cell survival and drug resistance. Product of enzymatic activation of class IB PI3K as class IA, phosphatidylinositol-3,4,5-trisphosphate, makes BAD dissociate from Bcl-xL and promotes cell survival via Akt activation . Therefore class IB PI3K might contribute cell survival in Bcl-xL-overexpressing cells.
PARP1 is a member of the PARP protein superfamily that catalyzes the polymerization of ADP-ribose moieties onto target proteins, using NAD+ as a substrate and releasing nicotine amide in the process . PARP1 activity is important for the regulation of homeostasis and the maintenance of genomic stability, participating in DNA repair, the regulation of transcription, DNA replication, cell differentiation, proliferation and cell death [26–28]. Many in vitro and in vivo experiments demonstrated that inhibition of PARP1 potentiates the cytotoxicity of anti-cancer drugs and ionizing radiation [29–32]. Therefore, incednine could bind to PARP1 and could function as antagonist of anti-apoptotic PARP1 protein. Alternatively, PARP1 is emerging as an important activator of caspase-independent cell death. It has been previously reported that PARP1 mediates the release of apoptosis-inducing factor (AIF), one of the initiators of caspase-independent cell death, possibly due to enzymatic over-activation [33–35]. We also observed that co-treatment of Bcl-xL-overexpressing Ms-1 cells with incednine and ant-tumor drugs induced AIF release and subsequent caspase-independent cell death (unpublished data); therefore, we can not exclude the possibility that incednine binds to PARP1 and functions as PARP1 agonist by accerelating AIF release.
However, the most likely candidate of an incednine target protein is ACACA (acetyl-CoA carboxylase-α), which was classified in cluster 9. ACACA is the rate-limiting enzyme for long-chain fatty acid synthesis that catalyzes the ATP-dependent carboxylation of acetyl-CoA to malonyl-CoA, playing a critical role in cellular energy storage and lipid synthesis . There is strong evidence that cancer cell proliferation and survival are dependent on de novo fatty acid synthesis [37–40]. Additionally, ACACA is upregulated in multiple types of human cancers [41, 42]; therefore, ACACA may also contribute to cell survival in Bcl-xL-overexpressing tumor cells. Indeed, our preliminary experiments suggested that chemical inhibition of ACACA using TOFA (5-tetradecyloxy-2-furoic acid, ACACA antagonist) or small interfering RNA-mediated ACACA silencing results in the induction of apoptosis in Bcl-xL-overexpressing human small cell lung carcinoma Ms-1 cells when combined with anti-tumor drugs as does incednine (unpublished observation), suggesting that ACACA might be a molecular target of incednine. The possibility that incednine targets ACACA is being actively investigated.
While our experimental verification implied the relatively low precision value 28.6% (2/7), new detections of two incednine-binding proteins in addition to previously identified 53 proteins are significant. On the other hand, while we selected 7 candidates by clustering 182 predicted proteins for experimental verification, more comprehensive verification experiments for the 182 predicted proteins are needed.
The application of our method to incednine resulted in 28.6% (2/7) precision according to in vitro pull-down assay. However, this relatively low precision value does not represent the true statistical significance of the method and is not comparable to the benchmark performances (including 98.4% precision) by 10-fold cross-validation for COPICAT system.
This 28.6% precision can be evaluated by using the following P
Here, N is the number of human proteins, M is the number of proteins potentially binding to the incednine, t is the number of tested proteins, and p is the number of true positives. With N =24,245, which is the number of human proteins in the KEGG repository, and M = N × 1%≒243, which is based on the overestimated assumption that 1% of all proteins could be regarded as potential binding proteins for the incednine. This P-value defines the probability that the prediction precision can be obtained by random selection of proteins. Then, P-value of 0.002 was obtained for the prediction precision 28.6%. This small P-value means that 28.6% (2/7) precision can be obtained with very small chance by random selection, and therefore, this small P-value proves the validity of our method.