Dataset Information

Sequence-based prediction of permissive stretches for internal protein tagging and knockdown.

ABSTRACT: BACKGROUND:Internal tagging of proteins by inserting small functional peptides into surface accessible permissive sites has proven to be an indispensable tool for basic and applied science. Permissive sites are typically identified by transposon mutagenesis on a case-by-case basis, limiting scalability and their exploitation as a system-wide protein engineering tool. METHODS:We developed an apporach for predicting permissive stretches (PSs) in proteins based on the identification of length-variable regions (regions containing indels) in homologous proteins. RESULTS:We verify that a protein's primary structure information alone is sufficient to identify PSs. Identified PSs are predicted to be predominantly surface accessible; hence, the position of inserted peptides is likely suitable for diverse applications. We demonstrate the viability of this approach by inserting a Tobacco etch virus protease recognition site (TEV-tag) into several PSs in a wide range of proteins, from small monomeric enzymes (adenylate kinase) to large multi-subunit molecular machines (ATP synthase) and verify their functionality after insertion. We apply this method to engineer conditional protein knockdowns directly in the Escherichia coli chromosome and generate a cell-free platform with enhanced nucleotide stability. CONCLUSIONS:Functional internally tagged proteins can be rationally designed and directly chromosomally implemented. Critical for the successful design of protein knockdowns was the incorporation of surface accessibility and secondary structure predictions, as well as the design of an improved TEV-tag that enables efficient hydrolysis when inserted into the middle of a protein. This versatile and portable approach can likely be adapted for other applications, and broadly adopted. We provide guidelines for the design of internally tagged proteins in order to empower scientists with little or no protein engineering expertise to internally tag their target proteins.

SUBMITTER: Oesterle S

PROVIDER: S-EPMC5661948 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sequence-based prediction of permissive stretches for internal protein tagging and knockdown.

Oesterle Sabine S Roberts Tania Michelle TM Widmer Lukas Andreas LA Mustafa Harun H Panke Sven S Billerbeck Sonja S

BMC biology 20171030 1

<h4>Background</h4>Internal tagging of proteins by inserting small functional peptides into surface accessible permissive sites has proven to be an indispensable tool for basic and applied science. Permissive sites are typically identified by transposon mutagenesis on a case-by-case basis, limiting scalability and their exploitation as a system-wide protein engineering tool.<h4>Methods</h4>We developed an apporach for predicting permissive stretches (PSs) in proteins based on the identification ...[more]

PMID: 29084520

Similar Datasets

Project description:BackgroundDue to increasing resistance development among bacteria, antimicrobial peptides (AMPs), are receiving increased attention. Ideally, AMP should display high bactericidal potency, but low toxicity against (human) eukaryotic cells. Additionally, short and proteolytically stable AMPs are desired to maximize bioavailability and therapeutic versatility.Methodology and principal findingsA facile approach is demonstrated for reaching high potency of ultra-short antimicrobal peptides through end-tagging with W and F stretches. Focusing on a peptide derived from kininogen, KNKGKKNGKH (KNK10) and truncations thereof, end-tagging resulted in enhanced bactericidal effect against Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus. Through end-tagging, potency and salt resistance could be maintained down to 4-7 amino acids in the hydrophilic template peptide. Although tagging resulted in increased eukaryotic cell permeabilization at low ionic strength, the latter was insignificant at physiological ionic strength and in the presence of serum. Quantitatively, the most potent peptides investigated displayed bactericidal effects comparable to, or in excess of, that of the benchmark antimicrobial peptide LL-37. The higher bactericidal potency of the tagged peptides correlated to a higher degree of binding to bacteria, and resulting bacterial wall rupture. Analogously, tagging enhanced peptide-induced rupture of liposomes, particularly anionic ones. Additionally, end-tagging facilitated binding to bacterial lipopolysaccharide, both effects probably contributing to the selectivity displayed by these peptides between bacteria and eukaryotic cells. Importantly, W-tagging resulted in peptides with maintained stability against proteolytic degradation by human leukocyte elastase, as well as staphylococcal aureolysin and V8 proteinase. The biological relevance of these findings was demonstrated ex vivo for pig skin infected by S. aureus and E. coli.Conclusions/significanceEnd-tagging by hydrophobic amino acid stretches may be employed to enhance bactericidal potency also of ultra-short AMPs at maintained limited toxicity. The approach is of general applicability, and facilitates straightforward synthesis of hydrophobically modified AMPs without the need for post-peptide synthesis modifications.

Project description:MotivationX-ray crystallography-based protein structure determination, which accounts for majority of solved structures, is characterized by relatively low success rates. One solution is to build tools which support selection of targets that are more likely to crystallize. Several in silico methods that predict propensity of diffraction-quality crystallization from protein chains were developed. We show that the quality of their predictions drops when applied to more recent crystallization trails, which calls for new solutions. We propose a novel approach that alleviates drawbacks of the existing methods by using a recent dataset and improved protocol to annotate progress along the crystallization process, by predicting the success of the entire process and steps which result in the failed attempts, and by utilizing a compact and comprehensive set of sequence-derived inputs to generate accurate predictions.ResultsThe proposed PPCpred (predictor of protein Production, Purification and Crystallization) predict propensity for production of diffraction-quality crystals, production of crystals, purification and production of the protein material. PPCpred utilizes comprehensive set of inputs based on energy and hydrophobicity indices, composition of certain amino acid types, predicted disorder, secondary structure and solvent accessibility, and content of certain buried and exposed residues. Our method significantly outperforms alignment-based predictions and several modern crystallization propensity predictors. Receiver operating characteristic (ROC) curves show that PPCpred is particularly useful for users who desire high true positive (TP) rates, i.e. low rate of mispredictions for solvable chains. Our model reveals several intuitive factors that influence the success of individual steps and the entire crystallization process, including the content of Cys, buried His and Ser, hydrophobic/hydrophilic segments and the number of predicted disordered segments.Availabilityhttp://biomine.ece.ualberta.ca/PPCpred/.Contactlkurgan@ece.ualberta.ca.

Dataset Information

Sequence-based prediction of permissive stretches for internal protein tagging and knockdown.

Publications

Sequence-based prediction of permissive stretches for internal protein tagging and knockdown.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets