Artificial Genetically Encoded Peptides and Proteins as Next-Generation Therapeutics: Selection of \nligands for Mycobacterium tuberculosis UDP-Galactopyranose Mutase as Potential Inhibitors.
Bibliographic record
Abstract
Peptides and small proteins provide a dynamic platform for drug discovery and therapeutics. They \nhave a wide range of applications including inhibition of protein-protein interaction, inhibition of transporter \nand enzyme activity, imaging, and as co-crystallisation ligand for structural studies. In this study, we employed \nin vitro selection of ligands, using mRNA display, from an artificial genetically encoded library of \npeptides/proteins to identify candidates that bind the enzyme Mycobacterium tuberculosis UDPGalactopyranose mutase (MtUGM). The enzyme catalyzes the reversible conversion of UDP-galactopyranose \n(UDP-Galp) to UDP-galactofuranose (UDP-Galf), which is then assembled as the galactofuran layer in \nMycobacterium tuberculosis (Mtb) cell wall. Cell wall biosynthesis is essential for Mtb survival and \npathogenicity, deletion in genes involved in this process have proven lethal. We successfully identified \nmacrocyclic peptides (MCPs) and affibodies specific for MtUGM from a library with a diversity >1012 clones \nthrough random non-standard peptide integrated discovery (RaPID) system and mRNA display, respectively. \nEnrichment of positive binders was observed for both selection processes suggesting binding specificity. \nPrevious studies reveal that natural product-like MCPs can inhibit enzyme activity; their small size, \nconformational stability and high affinity for the target makes them attractive ligands. Thus, we hypothesize \nthat identified MCPs may show inhibition of MtUGM thereby halting cell wall biosynthesis. Discovered \naffibodies will be used for structural analysis of MtUGM. The World Health Organisation (WHO) reported \ntuberculosis (TB) caused by Mtb to be one of the leading causes of death worldwide, with increasing incidences \nof drug resistant strains necessitating discovery of novel therapeutics. Through our study, we present a novel \napproach for discovering peptide/protein-based ligands for MtUGM and hope to develop an assay for \nscreening MCPs for potential inhibitors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".