Molecular Mechanism of Happiness and Self Purpose As Anti-Aging: in Silico Studies for Phytochemical Replacement Therapy

1. Overview

Scientific Rationale & Importance: This research proposal promotes novel approach in Longevity Research that may called as Psychomimetic Nutrients. We hypothesized that very complex anti-aging effect is promoted by Happiness and Self Purpose that have specific signature in molecular mechanism which we also promote phytochemicals from Indonesia biodiversity for the replacement therapy. Using Pharmacology data and AI Driven Reverse Docking, we identified natural chemicals that have Dual-Action activities: 1) Modulating positive neuroendocrine pathway (such as Oxytocin receptor); 2) inhibit cellular aging factors (such as mTOR and LINE-1 Retrotransposon). This leads into initial steps toward Computational Systematic Mechanism for Longevity.

Confirmation of Funding/Facilities: This study will be conducted as In-Silico (Molecular Docking or Computational). All of the hardware (High-Performance Computing/GPU Clusters) will be rented from Cloud Providers (AWS/Google Cloud) with the proposed funding. Accessing to Chemical Databases (HerbalDB Indonesia, Knapsack) was confirmed.

Estimated Timeline: 6 months (Month 1-2: Data Mining & AI Screening; Month 3-4: Massive Pharmacology Data Analysis; Month 5-6: Molecular Docking Simulation.

Data Sharing Commitment: We commit to Open Science principles. List of 100 candidates of bioactive chemicals, 3D structures of docking results, and analysis draft will be published openly in ResearchHub and GitHub repository.

2. Introduction

Theoretical Background: Aging process is not merely an accumulation of physical disruptions, but also from more fundamental factors such as Psychobiology Factors. Recent Study by Cole et al. (2007) showed several individuals with Eudaimonic Well-being (self-purpose) had expressed low Conserved Transcriptional Response to Adversity (CTRA) gene which correlated with the lowering of chronic inflammatory and increasing anti-viral responses of the immune system. Otherwise, chronic stress is well known for shortening of telomer and LINE-1.

Key Literature & Gap: Our current literatures nowadays (such as Blue Zones) confirmed lifestyle-longetivy relationship. However, there is any systematic research yet for mapping of "Happiness and Self-Purpose" induced molecules and searching of its similar compound from natural resources. Recent pharmacology intervention (such as Metformin/Rapamycin) often be studied as single target therapy underestimating of complex body mechanism and mind relationship.

Research Questions:

To what extent do endogenous neuro-endocrine ligands associated with Eudaimonic well-being (e.g., Oxytocin, Dopamine) exhibit structural binding affinity towards key aging-regulating proteins (e.g., mTOR, SIRT1, LINE-1) in an in-silico environment?
Which specific phytochemicals derived from the Indonesian Biodiversity Database possess pharmacophoric properties capable of structurally mimicking these endogenous "happiness ligands"?
Do these identified "Psychomimetic" candidates demonstrate a dual-action mechanism functioning simultaneously as neuromodulators and potent gero-protectors within a constructed replacement therapy?

Hypotheses

H1 (The Molecular Bridge): Endogenous ligands associated with Eudaimonia (e.g., Oxytocin) possess inherent, high-affinity binding interfaces with key gero-protective targets (e.g., SIRT1, mTOR), thereby establishing a quantifiable "molecular bridge" between positive psychological states and cellular longevity.
H2 (The Mimetic Discovery): Specific phytochemicals within the Indonesian Biodiversity Database contain pharmacophoric features that are structurally homologous to these endogenous ligands, allowing them to function as "Happy-Mimetics."
H3 (The Dual-Action Mechanism): These identified mimetic candidates will exhibit a distinct poly-pharmacological profile within the biological network, characterized by a dual-modulatory capacity: simultaneous agonism of neuro-endocrine receptors and inhibition of senescence-driving pathways (e.g., LINE-1/NF-kB).

3. Methods

Participants/Sample

In-Silico Library (Primary Sample): The primary dataset consists of a curated database containing approximately ~3,000 3D structures of bioactive compounds (secondary metabolites) sourced from the Indonesian Biodiversity Library, encompassing both medicinal plants and marine biota.

Materials and Procedures

Phase 1: AI-Driven Reverse Docking (Months 1-2)

Materials: Target protein structures (e.g., Oxytocin Receptor/OXTR, Glucocorticoid Receptor/NR3C1) retrieved from the AlphaFold Database.
Procedure: Utilization of Reverse Docking algorithms (AutoDock Vina & PyRx) to screen the 3,000 Indonesian ligands. The objective is to identify compounds that structurally complement the active sites of "happiness receptors."
Output: A list of "Top 50 Hits"—compounds exhibiting the most favorable Binding Energy scores (<-8.0 kcal/mol).

Phase 2: Network Pharmacology Construction (Months 3-4)

Procedure: Interaction mapping of candidates from Phase 1 using STITCH and STRING databases. We will construct a "Target-Compound-Disease Network" using Cytoscape.
Analysis: Analysis of topological parameters (Degree, Betweenness Centrality) to identify "Hub" compounds capable of bridging Mood Regulation pathways and Aging Pathways (e.g., LINE-1 inhibition, mTOR modulation).

Phase 3: Molecular Dynamics Simulation (Month 5-6)

Equipment: High-Performance GPU Cluster (NVIDIA A100 via Cloud infrastructure).
Procedure: Execution of 100-nanosecond Molecular Dynamics simulations using GROMACS to validate Ligand-Protein complex stability within a physiological environment (solvated system). This step is critical for eliminating False Positives derived from static docking.

Planned Analyses

Analysis Pipeline:

Data Preprocessing & Filtering:
- The compound library (~3,000 phytochemicals) will be curated using RDKit to remove duplicate structures or those with aberrant molecular weights.
- Lipinski’s Rule of Five and ADMET Profiling (via SwissADME) will be applied as upstream filters.
- IF a compound violates >2 Lipinski rules or is predicted to be toxic (hepatotoxic/mutagenic), THEN it will be excluded from the docking stage.
Consensus Docking Scoring:
- Docking results from AutoDock Vina will be ranked based on binding affinity (kcal/mol).
- Prediction: Compounds with affinity scores < -8.5 kcal/mol on the primary receptor (OXTR) are predicted to have a 60% higher probability of binding to secondary targets (SIRT1/mTOR) due to hydrophobic binding pocket similarities.
Network Topology Analysis:
- Using Cytoscape 3.9.1, we will calculate Degree Centrality (number of connections) and Betweenness Centrality (bridging role).
- Selection Criteria: Compounds selected as "Top Candidates" must exhibit a degree > network average + 2 SD (Standard Deviations), indicating significant multi-target properties.
Molecular Dynamics (MD) Trajectory Analysis:
- Analysis of RMSD (Root Mean Square Deviation) and RMSF (Root Mean Square Fluctuation) to quantify complex stability over 100 ns.
- IF RMSD fluctuation exceeds > 2.5 Å post-equilibration, THEN the complex is deemed unstable and the compound is discarded.
- Binding Free Energy analysis will be calculated using the MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method for final validation.

Ethics and Data Management

Ethical Statement: The proposed research is In-Silico (Molecular Docking Simulation) and does not require animal ethics approval.

Data Management & Sharing:

Preregistration: The study protocol will be preregistered on the Open Science Framework (OSF) or ResearchHub prior to the commencement of analysis.
Open Access: All screening datasets (Compound lists, docking output PDB files, and network topologies) will be uploaded to public repositories (Zenodo/GitHub) under a CC-BY 4.0 license.
Reproducibility: Python/R scripts used for statistical analysis and figure generation will be included in the final publication.

4. Pilot Data (Optional)

Preliminary Virtual Screening on Centella asiatica: We performed a "dry-run" of our workflow on 10 major compounds from Centella asiatica (Pegagan), a neurotonic plant indigenous to Indonesia.

Result: The compound Asiaticoside demonstrated strong binding affinity towards the SIRT1 receptor (-9.2 kcal/mol) and moderate affinity towards the Oxytocin Receptor (-7.8 kcal/mol).
Network Insight: Preliminary analysis indicates that Asiaticoside interacts with the BDNF (Brain-Derived Neurotrophic Factor) signaling pathway, supporting the hypothesis that this compound can bridge neural health (mood) and cellular regeneration (aging).
Conclusion: This preliminary data validates that our pipeline is capable of detecting dual-action compounds as per hypothesis H2. Full-scale simulation (3,000 compounds) is required to identify more potent candidates.

5. Budget

Personnel Costs ($4,500)
- Researcher Stipend (PI): $750/month x 6 months. (To support the researcher dedicated time for data analysis, manuscript writing, and research coordination, ensuring minimal interference from clinical practice).
Computational Resources ($3,500)
- Cloud GPU Rental (Google Cloud): Estimated 500 computing hours for intensive Molecular Dynamics simulations (GROMACS). This is the most critical component for TRL 3 validation.
- Storage & Database Access: Costs for High-Throughput data storage and subscription-based journal/database access if required.
Publication & Dissemination ($2,000)
- Article Processing Charges (APC): For Open Access publication in Q1 journals or the ResearchHub Journal.
- Conference Fees: Presentation costs (if applicable).

6. References

Cole SW, Hawkley LC, Arevalo JM, Sung CY, Rose RM, Cacioppo JT. Social regulation of gene expression in human leukocytes. Genome Biol. 2007;8(9):R189. doi: 10.1186/gb-2007-8-9-r189. PMID: 17854483; PMCID: PMC2375027.
Fredrickson BL, Grewen KM, Coffey KA, Algoe SB, Firestine AM, Arevalo JM, Ma J, Cole SW. A functional genomic perspective on human well-being. Proc Natl Acad Sci U S A. 2013 Aug 13;110(33):13684-9. doi: 10.1073/pnas.1305419110. Epub 2013 Jul 29. PMID: 23898182; PMCID: PMC3746929.
López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: An expanding universe. Cell. 2023 Jan 19;186(2):243-278. doi: 10.1016/j.cell.2022.11.001. Epub 2023 Jan 3. PMID: 36599349.
Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008 Nov;4(11):682-90. doi: 10.1038/nchembio.118. PMID: 18936753.
De Cecco M, Ito T, Petrashen AP, Elias AE, Skvir NJ, Criscione SW, Caligiana A, Brocculi G, Adney EM, Boeke JD, Le O, Beauséjour C, Ambati J, Ambati K, Simon M, Seluanov A, Gorbunova V, Slagboom PE, Helfand SL, Neretti N, Sedivy JM. L1 drives IFN in senescent cells and promotes age-associated inflammation. Nature. 2019 Feb;566(7742):73-78. doi: 10.1038/s41586-018-0784-9. Epub 2019 Feb 6. Erratum in: Nature. 2019 Aug;572(7767):E5. doi: 10.1038/s41586-019-1350-9. PMID: 30728521; PMCID: PMC6519963.
Bryant, P., Pozzati, G. & Elofsson, A. Improved prediction of protein-protein interactions using AlphaFold2. Nat Commun 13, 1265 (2022). https://doi.org/10.1038/s41467-022-28865-w
Buettner D, Skemp S. Blue Zones: Lessons From the World's Longest Lived. Am J Lifestyle Med. 2016 Jul 7;10(5):318-321. doi: 10.1177/1559827616637066. PMID: 30202288; PMCID: PMC6125071.
Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010 Jan 30;31(2):455-61. doi: 10.1002/jcc.21334. PMID: 19499576; PMCID: PMC3041641.

Molecular Mechanism of Happiness and Self Purpose As Anti-Aging: in Silico Studies for Phytochemical Replacement Therapy

Funding Progress

Nonprofit Recipient

Topics

DOI

Funding Progress

Nonprofit Recipient

Topics

DOI

Molecular Mechanism of Happiness and Self Purpose As Anti-Aging: in Silico Studies for Phytochemical Replacement Therapy

Funding Progress

Nonprofit Recipient

Topics

DOI