Computational Investigation of Protein Chemistry: “S-Nitrosohemoglobin”
Bibliographic record
Abstract
Reliable computational studies of large biological systems have only recently become possible due to the availability of high-performance computing resources. In this contribution, we present large-scale quantum mechanics / molecular mechanics (QM/MM) studies of hemoglobin (Hb) and its derivatives. In recent years, Hb has been reported to play an important role in blood-flow regulation via its reactions with the vasodilator, nitric oxide (NO). "S-nitrosohemoglobin" (SNO-Hb) has then emerged as a key player. However, NO-derivatization of the conserved cysteine residue (Cysbeta93) of Hb has been proposed to yield either an S-nitrosothiol (RSNO), an S-hydroxyamino radical (RSN*OH) or a thionitroxide radical (RSNHO*). The relative stabilities of the different proposed chemical forms of "SNO-Hb" are being examined using large-scale QM/MM simulations, and the critical role of the protein environment in the relative stabilities of the "SNO-Hb" forms is demonstrated. Furthermore, it has been proposed that NO is first attached to the heme iron of the beta subunit of hemoglobin, and it is then displaced from the iron by the presence of molecular oxygen and transported to the Cysbeta93 residue. We also investigate the possible transport channels of NO from the heme to this cysteine residue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".