High-throughput screening of interacting proteins and their modulators in living cells
Bibliographic record
Abstract
1539 One of the main limitations with genome-proteome based anti-cancer drug development is the lack of understanding of the complicated interaction networks between various proteins and the target. Accordingly, it is very difficult to predict the potential adverse effects of the drug under design. High throughput screening is required to confirm protein interactions as well as to identify molecules that can either promote or inhibit these interactions as early as at the pre-clinical phases of drug development. Unfortunately, the current methods used for the screening of protein interactions both in vitro (e.g. protein array) and in vivo (e.g. yeast two-hybrid) have many limitations. These include multiple costly and time-consuming complicated procedures, non-specific interactions, along with high rate of false positive/negative findings. We have developed a high throughput screening approach using a high efficiency recombinase-based expression plasmid vector library, each plasmid expressing two tagged-ORFs of interest. Consequently, high throughput screening and verification of novel protein interactions can be easily performed based on FRET (fluorescence resonance energy transfer), PFC (protein fragment complementation) or BiFC (bimolecular fluorescence complementation). This novel method overcomes the aforementioned limitations and presents a unique route for the investigation of protein functions of intact proteins in their native state in vivo. Using this method, we achieved increased transformation efficiency, higher stringency detection, decreased false positive results and high-throughput formats. Our lab is currently applying the technology in screening and confirmation of Novel protein interactions in living prokaryotic and eukaryotic cells. Setting up high throughput protocols for the screening and verification of protein interactions. Identification of potential drug candidates (small molecular chemicals, peptides etc.) that can modulate protein interactions towards the development of new anti-cancer agents. For more information, contact: Sunita Asrani, Industrial Liaison Officer, Health Sciences, McMaster University Tel: (905) 525-9140 Ext. 28641 Fax: (905) 546-1372; Email: asranis@mcmaster.ca; JianPing Lu, Jehonathan Pinthus, Fax 001-905-575-6330 E-mail: jianping.lu@hrcc.on.ca; jehonathan.pinthus@hrcc.on.ca
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".