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A Stochastic Landscape Approach for Protein Folding State Classification
Faran Michael, Ray Dhiman, Nag Shubhadeep, Raucci Umberto, Parrinello Michele, Bisker Gili. Journal of chemical theory and computation, 2024-06-26
Topic: Brain proteins, microglia and lab models
Protein folding is a critical process that determines the functional state of proteins. Proper folding is essential for proteins to acquire their functional three-dimensional structures and execute their biological role, whereas misfolded proteins can lead to various diseases, including neurodegenerative disorders like Alzheimer's and Parkinson's. Therefore, a deeper understanding of protein folding is vital for understanding disease mechanisms and developing therapeutic strategies. This study introduces the Stochastic Landscape Classification (SLC), an innovative, automated, nonlearning algorithm that quantitatively analyzes protein folding dynamics. Focusing on collective variables (CVs) - low-dimensional representations of complex dynamical systems like molecular dynamics (MD) of macromolecules - the SLC approach segments the CVs into distinct macrostates, revealing the protein folding pathway explored by MD simulations. The segmentation is achieved by analyzing changes in CV trends and clustering these segments using a standard density-based spatial clustering of applications with noise (DBSCAN) scheme. Applied to the MD-based CV trajectories of Chignolin and Trp-Cage proteins, the SLC demonstrates apposite accuracy, validated by comparing standard classification metrics against ground-truth data. These metrics affirm the efficacy of the SLC in capturing intricate protein dynamics and offer a method to evaluate and select the most informative CVs. The practical application of this technique lies in its ability to provide a detailed, quantitative description of protein folding processes, with significant implications for understanding and manipulating protein behavior in industrial and pharmaceutical contexts.
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APA 7th
Michael, F., Dhiman, R., Shubhadeep, N., Umberto, R., Michele, P., & Gili, B. (2024). A Stochastic Landscape Approach for Protein Folding State Classification. Journal of chemical theory and computation. https://doi.org/10.1021/acs.jctc.4c00464
Vancouver
Michael F, Dhiman R, Shubhadeep N, Umberto R, Michele P, Gili B. A Stochastic Landscape Approach for Protein Folding State Classification. Journal of chemical theory and computation. 2024. doi:10.1021/acs.jctc.4c00464. PMID: 38924770.
BibTeX
@article{michael2024-38924770,
author = {Michael, Faran and Dhiman, Ray and Shubhadeep, Nag and Umberto, Raucci and Michele, Parrinello and Gili, Bisker},
title = {{A Stochastic Landscape Approach for Protein Folding State Classification}},
journal = {Journal of chemical theory and computation},
year = {2024},
doi = {10.1021/acs.jctc.4c00464},
pmid = {38924770},
url = {https://doi.org/10.1021/acs.jctc.4c00464}
}
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Key findings
- The practical application of this technique lies in its ability to provide a detailed, quantitative description of protein folding processes, with significant implications for understanding and manipulating protein behavior in industrial and pharmaceutical contexts.
Why it matters
This study introduces a method for classifying protein folding states, which the authors say could help understand disease mechanisms and develop therapeutic strategies. It is a computational method tested on two proteins and reports no findings about treating or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 40/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of chemical theory and computation) |
| Journal's citation record | 3 / 4 | Journal of Chemical Theory and Computation: journal h-index 275 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 3 / 7 | Cited 16 times (OpenAlex, 2026-10-17) |
| Citations per year | 2 / 5 | 4.0 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 2 / 6 | Cited 1.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 16 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Gili Bisker: h-index 34 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Tel Aviv University: institution h-index 1,021 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38924770) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 40 / 100 | — |
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