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首页> 外文会议>Institute of Electrical and Electronics Engineers International Conference on Electro/Information Technology >Cleaved Fragments Prediction Algorithm (CFPA) application to calpain and caspase in apoptosis and necrotic cell death
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Cleaved Fragments Prediction Algorithm (CFPA) application to calpain and caspase in apoptosis and necrotic cell death

机译：裂片预测算法（CFPA）在钙蛋白酶和半胱天冬酶的凋亡和坏死细胞死亡中的应用

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The activation of cysteine proteases, calpain and caspase-3, which orchestrate the two major types of cell death, necrosis and apoptosis in various neurological and neurodegenerative disorders, drive cleavage of susceptible cellular proteins whose Breakdown Products (BDPs) can be utilized as biochemical markers; these markers can distinguish the molecular root causes among different types of neural cell death. There is an immense need to make such distinction between calpain and caspase-dependant dominated types of cell injury which is crucial in order to identify the injury mechanisms; thus, creating opportunities for neurotherapy development. Calpain protease is activated in various necrotic and apoptotic conditions generating calpain-specific cleaved fragments, while caspase-3 is predominantly activated in neuronal apoptosis generating caspase-3-specific cleaved fragments. Yet, despite the difference in cleavage specificity between calpain and caspase, some cellular proteins are dually susceptible to both proteases in some neurotoxic challenges such as hypoxia-hypoglycemia and excitotoxin treatment. During their activation, it is difficult to identify the resulting fragments despite the advanced experimental proteomics techniques in the field of degradomics. Current approaches rely on experimental techniques involving western blotting technique coupled with protein sequencing to identify the sequence specific and fragmentation site of the specific BDP(s). The main purpose of this work is to establish a new efficient and accurate methodological tool based on dynamic programming to predict those BDPs computationally with an algorithm of space complexity O(mn) and time complexity O(NN'mn), where the comprised parameters correspond to number of protein sequences, number of consensus sequences, length of each protein sequence, and length of each consensus sequence, respectively. The current algorithm is based on a modification of the Cleaved Fragments Prediction Alg- rithm (CFPA) and achieves high homology with experimental results.
机译：半胱氨酸蛋白酶，钙蛋白酶和半胱天冬酶3的激活，在各种神经系统疾病和神经退行性疾病中协调细胞死亡，坏死和凋亡的两种主要类型，驱动易感细胞蛋白的裂解，其分解产物（BDPs）可用作生化标记;这些标记物可以区分不同类型的神经细胞死亡的分子根源。迫切需要在钙蛋白酶和依赖半胱天冬酶的细胞损伤的主要类型之间进行这种区分，这对于确定损伤机制至关重要。因此，为神经疗法的发展创造了机会。钙蛋白酶蛋白酶在多种坏死和凋亡条件下被激活，产生钙蛋白酶特异性裂解的片段，而胱天蛋白酶3主要在神经元凋亡中被激活，产生胱氨酸蛋白酶3特异性裂解的片段。然而，尽管钙蛋白酶和半胱天冬酶的切割特异性不同，但是在一些神经毒性挑战（例如低氧低血糖症和兴奋性毒素治疗）中，某些细胞蛋白对两种蛋白酶都具有双重敏感性。在它们的激活过程中，尽管在降解组学领域有先进的实验蛋白质组学技术，但仍很难鉴定出所产生的片段。当前的方法依赖于涉及蛋白质印迹技术的实验方法，该方法涉及蛋白质印迹法，以鉴定特异性BDP的序列特异性和片段化位点。这项工作的主要目的是建立一个基于动态编程的高效，准确的方法论工具，以空间复杂度O（mn）和时间复杂度O（NN'mn）的算法对那些BDP进行计算预测，其中包含的参数对应分别为蛋白质序列数，共有序列数，每个蛋白质序列的长度和每个共有序列的长度。当前的算法基于对切分片段预测算法（CFPA）的修改，并且与实验结果具有很高的同源性。

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