ArticleACS omega2022
Protein Model Quality Estimation Using Molecular Dynamics Simulation.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Temperature-Dependent Thumb Domain Dynamics of Xylanase TsaGH11: Insights from Molecular Dynamics Simulations.International journal of molecular sciences · 2026Article
- MC-350013 and Corylin Emerge as Promising Neuroprotective Agents through Dual Targeting of xCT and GLT-1.Journal of molecular neuroscience : MN · 2026Article
- Immunoinformatics-driven multi-epitope vaccine design targeting PSMA, STEAP1, and B7H3 for prostate cancer.Frontiers in medicine · 2026Article
- Network pharmacology and molecular docking to reveal the pharmacological mechanisms ofIn silico pharmacology · 2025Article
- Disrupting the network of co-evolving amino terminal domain residues relieves mitochondrial calcium uptake inhibition by MCUb.Computational and structural biotechnology journal · 2025Article
- Phytoconstituents of Withania somnifera (L.) Dunal (Ashwagandha) unveiled potential cerebroside sulfotransferase inhibitors: insight through virtual screening, molecular dynamics, toxicity, and reverse pharmacophore analysis.Journal of biological engineering · 2024Article
- A rationally designed antigen elicits protective antibodies against multiple nosocomial Gram-positive pathogens.NPJ vaccines · 2024Article
- Article
- Exploration of phytoconstituents of Medhya Rasayana herbs to identify potential inhibitors forFrontiers in molecular biosciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The estimation of protein model quality remains a challenging task and is important for protein structural model utilization. In the last decade, existing methods that rely on machine learning to deep learning have been developed and shown progressive improvement. Despite utilizing more sophisticated techniques and introducing new features, none of these methods employ explicit protein structure stability information. Hypothetically, protein model quality might be indicated by its structural stability in an in silico system disclosed by the structural difference from its initial structure. One of the possible methods to exploit such information is by implementing molecular dynamics simulations that have shown successful applications in many research fields. We present a novel approach by introducing explicit protein structure stability information using molecular dynamics simulation. Despite using only simple features, small data with no training process required, and a short molecular dynamics simulation time, our method shows comparable performance to the state-of-the-art deep learning-based method.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.