We reasoned these functionalities should be integrated in order to assist not only in choosing appropriate expression systems, but also in optimising the expression and solubility levels of recombinant proteins. Moreover, these tools are offered through different independent services. These tools are either limited to predicting the venoms of certain organisms, such as spiders, or they are not designed to predict the signal peptides of toxins, rather to predict the toxicity of mature peptides. Only a very few tools can detect toxic proteins, for example, SpiderP, ClanTox and ToxinPred ( 57–59). Several web tools exist for predicting SPs ( 52–56). In addition, the presence of SPs should almost always be checked when planning the expression experiments for uncharacterised proteins.Įxisting web tools predict or optimise either protein expression or solubility alone ( 43–51). However, the Sec dependent pathway, which is common across all forms of life, has been widely used for recombinant protein expression because of higher protein production capacity and quality ( 41, 42). In addition, different pathways have different advantages, for example, the SRP dependent pathway can be used for rapidly folding proteins ( 41). Detection of SPs or fusion of a suitable SP at the N-terminus is useful for optimising protein production ( 37–40). Secretory proteins usually have a short peptide at the N-terminus called signal peptide (SP), which is responsible for the translocation of secretory proteins via the Sec, signal recognition particle (SRP) or twin arginine transport (Tat) pathways ( 33–36). Therefore, the translocation efficiency of these proteins plays an important role in the yield quantity and quality. The intracellular accumulation of heterologous secretory proteins may be toxic to the host cells. With these in mind, we have recently formulated the solubility-weighted index (SWI), which outperforms recent solubility prediction tools based on machine-learning algorithms ( 32).īesides, many recombinant proteins of interest are secretory. Nevertheless, accurate solubility prediction could save resources and aid in designing soluble proteins before the experiments. A number of methods have been suggested to improve protein solubility, for example, truncation, mutagenesis, and the use of solubility-enhancing tags ( 2, 29–31). However, almost half of the successfully expressed proteins are insoluble ( ), which makes the recombinant protein production process challenging. In addition to high protein expression level, high solubility is preferable for the purification and long-term storage of recombinant proteins. Accessibility is computed by considering all possible structures for a region, weighted by free energy, not just the single structure with the MFE ( 28). However, more recent work shows that the mRNA accessibility of translation initiation sites outperforms MFE in predicting relative protein levels from mRNA sequences ( 26, 27). Recent systematic studies suggest that MFE is the most important feature in protein expression ( 24, 25). This, in turn, hinders the development of accurate prediction/optimisation tools. Many of these features are not independent, making it challenging to distinguish the impacts of individual features ( 24). These features are mostly related to codon usage, such as the codon adaptation index and tRNA adaptation index ( 13–17), or measures of mRNA secondary structure, such as G+C content, minimum free energy (MFE) of RNA secondary structure, and mRNA:ncRNA interaction avoidance ( 18–23). Since mRNA abundance alone is insufficient to explain protein abundance ( 8–12), several features of mRNA sequence have been proposed to affect protein expression. However, low protein expression and aggregation are the two major bottlenecks of recombinant protein production ( 1–7). Recombinant protein production is a key process for life science research and the development of biotherapeutics.
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