Genome-wide high-throughput signal peptide screening via plasmid pUC256E improves protease secretion in Lactiplantibacillus plantarum and Pediococcus acidilactici

Background Proteases catalyze the hydrolysis of peptide bonds of proteins, thereby improving dietary protein digestibility, nutrient availability, as well as flavor and texture of fermented food and feed products. The lactobacilli Lactiplantibacillus plantarum (formerly Lactobacillus plantarum) and Pediococcus acidilactici are widely used in food and feed fermentations due to their broad metabolic capabilities and safe use. However, extracellular protease activity in these two species is low. Here, we optimized protease expression and secretion in L. plantarum and P. acidilactici via a genetic engineering strategy. Results To this end, we first developed a versatile and stable plasmid, pUC256E, which can propagate in both L. plantarum and P. acidilactici. We then confirmed expression and secretion of protease PepG1 as a functional enzyme in both strains with the aid of the previously described L. plantarum-derived signal peptide LP_0373. To further increase secretion of PepG1, we carried out a genome-wide experimental screening of signal peptide functionality. A total of 155 predicted signal peptides originating from L. plantarum and 110 predicted signal peptides from P. acidilactici were expressed and screened for extracellular proteolytic activity in the two different strains, respectively. We identified 12 L. plantarum signal peptides and eight P. acidilactici signal peptides that resulted in improved yield of secreted PepG1. No significant correlation was found between signal peptide sequence properties and its performance with PepG1. Conclusion The vector developed here provides a powerful tool for rapid experimental screening of signal peptides in both L. plantarum and P. acidilactici. Moreover, the set of novel signal peptides identified was widely distributed across strains of the same species and even across some closely related species. This indicates their potential applicability also for the secretion of other proteins of interest in other L. plantarum or P. acidilactici host strains. Our findings demonstrate that screening a library of homologous signal peptides is an attractive strategy to identify the optimal signal peptide for the target protein, resulting in improved protein export.

SignalP 6.0 predicts all five types of signal peptides using protein language models

Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data.

A Method for Computationally Constructing Eukaryotic Synthetic Signal Peptide Sequences

We developed a computational method for constructing synthetic signal peptides from a base set of signal peptides (SPs) and non-SP sequences. A large number of structured "building blocks", represented as m-step ordered pairs of amino acids, are extracted from the base. Using a straightforward procedure, the building blocks enable the construction of a diverse set of synthetic SPs that could be utilized for industrial and therapeutic purposes. We have validated the proposed methodology using existing sequence prediction platforms such as Signal-BLAST and MULocDeep. In one experiment, 9,555 protein sequences were generated from a large randomly selected set of "building blocks". Signal-BLAST identified 8,444 (88%) of the sequences as signal peptides. In addition, the Signal-BLAST tool predicted that the generated synthetic sequences belonged to 854 distinct eukaryotic organisms. Here, we provide detailed descriptions and results from various experiments illustrating the potential usefulness of the methodology in generating signal peptide protein sequences.

Precursors of select GPI-anchored proteins positively regulate GPI biosynthesis under the control of ERAD

We previously reported that glycosylphosphatidylinositol (GPI) biosynthesis is regulated by endoplasmic reticulum associated degradation (ERAD); however, the underlying mechanistic basis remains unclear. Based on a genome-wide CRISPR–Cas9 screen, we show that a widely expressed GPI-anchored protein CD55 precursor and ER-resident ARV1 together upregulate GPI biosynthesis under ERAD-deficient conditions. In cells defective in GPI transamidase, GPI-anchored protein precursors fail to obtain GPI, remaining the uncleaved GPI-attachment signal at the C-termini. We show that ERAD deficiency causes accumulation of the CD55 precursor, which in turn upregulates GPI biosynthesis, where the GPI-attachment signal peptide is the active element. Among the 32 GPI-anchored proteins tested, only the GPI-attachment signal peptides of CD55 and CD48 enhance GPI biosynthesis. ARV1 is essential for the GPI upregulation by CD55 precursor. Our data demonstrate an ARV1-dependent regulatory connection between GPI biosynthesis and precursors of select GPI-anchored proteins that are under the control of ERAD.

USPNet: unbiased organism-agnostic signal peptide predictor with deep protein language model

Signal peptide is a short peptide located in the N-terminus of proteins. It plays an important role in targeting and transferring transmembrane proteins and secreted proteins to correct positions. Compared with traditional experimental methods to identify and discover signal peptides,the computational methods are faster and more efficient, which are more practical for the analysis of thousands or even millions of protein sequences in reality, especially for the metagenomic data. Therefore, computational tools are recently proposed to classify signal peptides and predict cleavage site positions, but most of them disregard the extreme data imbalance problem in these tasks. In addition, almost all these methods rely on additional group information of proteins to boost their performances, which, however, may not always be available. To deal with these issues, in this paper, we present Unbiased Organism-agnostic Signal Peptide Network(USPNet), a signal peptide prediction and cleavage site prediction model based on deep protein language model. We propose to use label distribution-aware margin (LDAM) loss and evolutionary scale modeling (ESM) embedding to handle data imbalance and object-dependence problems. Extensive experimental results demonstrate that the proposed method significantly outperforms all the previous methods on the classification performance. Additional study on the simulated metagenomic data further indicates that our model is a more universal and robust tool without dependency on additional group information of proteins, with the Matthews correlation coefficient improved by up to 17.5‰. The proposed method will be potentially useful to discover new signal peptides from the abundant metagenomic data.

Molecular Modeling of Signal Peptide Recognition by Eukaryotic Sec Complexes

Here, we review recent molecular modelling and simulation studies of the Sec translocon, the primary component/channel of protein translocation into the endoplasmic reticulum (ER) and bacterial periplasm, respectively. Our focus is placed on the eukaryotic Sec61, but we also mention modelling studies on prokaryotic SecY since both systems operate in related ways. Cryo-EM structures are now available for different conformational states of the Sec61 complex, ranging from the idle or closed state over an inhibited state with the inhibitor mycolactone bound near the lateral gate, up to a translocating state with bound substrate peptide in the translocation pore. For all these states, computational studies have addressed the conformational dynamics of the translocon with respect to the pore ring, the plug region, and the lateral gate. Also, molecular simulations are addressing mechanistic issues of insertion into the ER membrane vs. translocation into the ER, how signal-peptides are recognised at all in the translocation pore, and how accessory proteins affect the Sec61 conformation in the co- and post-translational pathways.

Nanopore sequencing and de novo assembly of a misidentified Camelpox vaccine reveals putative epigenetic modifications and alternate protein signal peptides

DNA viruses can exploit host cellular epigenetic processes to their advantage; however, the epigenome status of most DNA viruses remains undetermined. Third generation sequencing technologies allow for the identification of modified nucleotides from sequencing experiments without specialized sample preparation, permitting the detection of non-canonical epigenetic modifications that may distinguish viral nucleic acid from that of their host, thus identifying attractive targets for advanced therapeutics and diagnostics. We present a novel nanopore de novo assembly pipeline used to assemble a misidentified Camelpox vaccine. Two confirmed deletions of this vaccine strain in comparison to the closely related Vaccinia virus strain modified vaccinia Ankara make it one of the smallest non-vector derived orthopoxvirus genomes to be reported. Annotation of the assembly revealed a previously unreported signal peptide at the start of protein A38 and several predicted signal peptides that were found to differ from those previously described. Putative epigenetic modifications around various motifs have been identified and the assembly confirmed previous work showing the vaccine genome to most closely resemble that of Vaccinia virus strain Modified Vaccinia Ankara. The pipeline may be used for other DNA viruses, increasing the understanding of DNA virus evolution, virulence, host preference, and epigenomics.

Bacterial signal peptides: structure, optimization, and applications

Bacterial signal peptides are N-terminal tags that direct proteins for export through one of various transport pathways. These signal peptides are highly important as they are the key determinants of transport, ensuring that the correct protein ends up at the correct pathway. While these peptides consist of three domains with well conserved biochemical properties, there still remains a large amount of diversity between the signal sequences for different proteins, transport pathways, and bacterial species. Recent advancements have allowed us to predict signal sequences and manipulate them in an attempt to optimize export efficiency. This knowledge can then be exploited in the field of recombinant protein production wherein bacterial species can be used to produce and secrete proteins of interest. By fusing the protein with an optimized signal peptide, the yield or rate of export can be improved. This review focuses on signal peptides for two primary transport pathways (Sec and Tat) in E. coli specifically, with an emphasis on applications and the production of recombinant proteins.