Cleaved Fragments Prediction Algorithm (CFPA) application to calpain and caspase in apoptosis and necrotic cell death

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 Algorithm (CFPA) and achieves high homology with experimental results.