In silico screening of potent natural inhibitor compounds against Human DOPA Decarboxylase for management of Parkinson’s Disease
Bibliographic record
Abstract
Loss of dopaminergic neurons of the substantia nigra of the mid brain is a well studied pathophysiology of Parkinson's disease (PD), is the second most common neurodegenerative disorder. To compensate dopamine levels at the Central Nervous System (CNS) exogenous L-Dopa is generally administered. But the major part of the L-Dopa is metabolized by Dopa decarboxylase (DDC, E.C. 4.1.1.28), a pyridoxal 5 ' -phosphate (PLP) enzyme, which is abundant in CNS and hence, only 1-5% of L-Dopa reaches to dopaminergic neurons. In this context, co-administration of peripheral DDC inhibitors (carbidopa or benserazide) has been successfully used for the symptomatic treatment of PD patients. But, due to use of synthetic drugs many adverse effects have been reported during treatment. Therefore, the current study is planned to discover some plant based potent natural inhibitors against human DDC as an alternative way for the management of PD. This study was conducted through virtual screening and molecular docking of DDC enzyme with phytochemicals like withania somnifera (ashwagandha), glycine max (soybean), vicia faba (broad bean), and marsilea quadrifolia (sunsunia) etc to evaluate their inhibition properties. In silico study results shown a good binding affinity and predicted some of the phytochemicals as potent natural inhibitors against human DDC. This work could be validated further through experimental procedures.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".