Damaged Proteins Bearing L-Isoaspartyl Residues and Aging: A Dynamic Equilibrium Between Generation of Isomerized Forms and Repair by PIMT
Bibliographic record
Abstract
Proteins are susceptible to numerous non-enzymatic post-translational modifications which occur both during normal aging and in neurodegenerative states. For instance, formation of abnormal L-isoaspartyl residues arising from both the deamidation of L-asparaginyl residues and the isomerization of L-aspartyl residues is a frequent chemical modification that affects proteins. The formation of L- isoaspartyl residues in proteins alters their three-dimensional structure leading usually to a loss of function. Notably, accumulation of isomerized proteins could contribute to metabolic dysfunctions in neuronal cells during aging reducing cognitive functions in elderly patients and would eventually promote the development of neurodegenerative diseases. The protein L-isoaspartyl (D-aspartyl) methyltransferase (PIMT) is an enzyme that recognizes and repairs the abnormal L-isoaspartyl residues in proteins. Its expression appears to decline during aging which could partially explain the build up of damaged proteins in old age. In this review, we summarize recent findings, based mostly on proteomic data, regarding the formation and accumulation of proteins bearing atypical L-isoaspartyl residues as well as PIMT functions during normal aging and in some neurodegenerative diseases. The emphasis is on possible molecular mechanisms controlling PIMT expression and the ability of PIMT to repair isomerized substrates during aging. Investigation of processes regulating age-related accumulation of isomerized proteins is a promising avenue to a better understanding of aging at the protein level. Keywords: Aging, Damaged proteins, L-isoaspartyl residues, PIMT, deamidation, L-asparaginyl residues, L-aspartyl residues, neurodegenerative diseases, lipid peroxidation, oxidation, glycation, isomerization, racemization, A-crystallin, protein kinase A (PKA), fibronectin, v3-integrin binding, monomeric enzyme, alanine, proteomic analysis, immunobloting, brain cytosol, hemodialysis fluid, cerebrospinal fluid, amniotic fluid, methylation, cortex, hippocampus, hypothalamus, striatum, immunochemical analysis, thalamus, amygdala, methamphetamine, epilepsy, cerebellum, schizophrenia, Huntington's disease, methyltransferase, synaptosomes, synuclein, Parkinson's disease, synuclein fibrils, Lewy bodies, immunohistochemistry, immunoreactivity, lymphocytes, methyl acceptor proteins, tubulin, insulin-like growth factor-I (IGF-I), estradiol, valproic acid, lysosomes, proteasomal activity, Alzheimer's disease, dementia, intraneuronal neurofibrillary tangles, amyloid (A) peptide, autopsy, cortical neuritic plaques, amyloid plaques
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".