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Fully Automated Screening of a Combinatorial Library to Avoid False Positives: Application to Tetanus Toxoid Ligand Identification.

ABSTRACT: Peptide ligands are widely used in protein purification by affinity chromatography. Here, we applied a fully automated two-stage library screening method that avoids false positive peptidyl-bead selection and applied it to tetanus toxoid purification. The first library screening was performed using only sulforhodamine (a fluorescent dye), and fluorescent beads were isolated automatically by flow cytometry and discarded. A second screening was then performed with the rest of the library, using the target protein (tetanus toxoid)-rhodamine conjugate. This time, fluorescent beads were isolated, and peptide sequences were identified by matrix-assisted laser desorption/ionization tandem mass spectrometry. Those appearing with greater frequency were synthesized and immobilized on agarose to evaluate a range of chromatographic purification conditions. The affinity matrix PTx1-agarose (Ac-Leu-Arg-Val-Tyr-His-Gly-Gly-Ala-Gly-Lys-agarose) showed the best performance when 20 mM sodium phosphate, 0.05% Tween 20, pH 5.9 as adsorption buffer and 100 mM Tris-HCl, 100 mM NaCl, pH 8.0 as elution buffer were used. A pure tetanus toxoid (Ttx) was loaded on a chromatographic column filled with the PTx1 matrix, and 96% adsorption was achieved, with a K _d of 9.18 ± 0.07 nmol/L and a q _m of 1.31 ± 0.029 μmol Ttx/mL matrix. Next, a Clostridium tetani culture supernatant treated with formaldehyde (to obtain the toxoid) was applied as a sample. The sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis showed a band, identified by electrospray ionization mass spectrometry as the Ttx, that appeared only in the elution fraction, where an S-layer protein was also detected.

SUBMITTER: Martinez Ceron MC

PROVIDER: S-EPMC8319927 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundA large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation.ResultsFor all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results.ConclusionsOf the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

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Fully Automated Screening of a Combinatorial Library to Avoid False Positives: Application to Tetanus Toxoid Ligand Identification.

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