ABSTRACT
Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins. Protease-controlled proteolysis plays a key role in the degradation and recycling of proteins, which is essential for various physiological processes. Thus, solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases, as well as for therapeutic target identification and pharmaceutical applicability. Consequently, there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural information. In this study, we present Procleave, a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural information. Structural features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method, which turned out to be critical for the performance of Procleave. The optimal approximations of all structural parameter values were encoded in a conditional random field (CRF) computational framework, alongside sequence and chemical group-based features. Here, we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent tests. Procleave is capable of correctly identifying most cleavage sites in the case study. Importantly, when applied to the human structural proteome encompassing 17,628 protein structures, Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different proteases. Procleave is implemented as a webserver and is freely accessible at http://procleave.erc.monash.edu/.
ABSTRACT
In order to enhance the alkaline polygalacturonate lyase (PGL) productivity by Pichia pastoris, we developed a constant cell concentration culture strategy by methanol feeding (called as CCCM culture) used in the continuous cultures. We controlled reasonable cell concentrations in the bioprocess by different strategies of methanol feeding. Using this CCCM culture with DCW 75 g/L, we significantly enhanced the PGL productivity (Qv) and the average specific enzyme production rate (Qx) of PGL to 6.11 U/(mL x h) and 81.5 U/(g x h), increased by 42.1% and 191.2% than the fed-batch culture with high cell density, respectively. The final PGL activity was 441.9 U/mL. Moreover, the extracellular protease concentration is 1.9 mg/L and the cell viability is more than 94% after 120 hour induction. The results show that this new strategy is advantageous in reducing proteolytic degradation and enhancing cell viability.