Bibliographic record
Abstract
Solid phase peptide synthesis (SPPS) is an alternative to DNA expression for generating proteins, such as chemokines ( 1 – 1 ). DNA databases and cDNA cloning has resulted in an explosion in the number of new chemokines from 1995-1998. However, for studies of the protein, knowing the DNA sequence is only the first step. The chemokine must be generated and in its correctly processed and folded form, and then purified to homogeneity. Expression of the cDNA is the popular route to the protein; however, de novo chemical synthesis has some significant advantages (e.g., efficient SPPS can provide 10-100 mg of pure chemokine in only a few days). Chemically synthesized chemokines have the same three-dimensional structures as ribosome-assembled chemokines made by recombinant DNA expression ( 3 – 5 , 7 , 9 , 10 ). Functionally, they are indistinguishable. The cumulative results of our chemokine studies have shown that chemical synthesis is a straightforward route to chemokines and their analogs. In this chapter, I describe the principles and procedures that colleagues and I have developed for synthesis of chemokines ( 1 – 11 ). Despite many advantages ( see Subheading 1.1. , step 1 – 10 ), peptide synthesis has not been widely applied to proteins. One reason is that most researchers approach proteins from a biological, rather than a chemical, standpoint. Another is that in the early days of SPPS, the methods were limited to short peptides. However, gradual optimization of the chemistry eventually led to the synthesis of small proteins ( 12 – 14 ). Although the individual steps involved are straightforward, the methods require hands-on experience and a firm understanding of the principles involved. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.022 |
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".