ArticleJACS Au2021
Accurate Machine Learning Prediction of Protein Circular Dichroism Spectra with Embedded Density Descriptors.
Article in JACS Au, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 38 citations in OpenAlex.
- Coverage-Dependent Free-Energy Bottlenecks in CO Adsorption and Desorption on Ru(0001).JACS Au · 2026Article
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Stereochemistry-aware string-based molecular generation.PNAS nexus · 2025Article
- Spectra-descriptor-based machine learning for predicting protein-ligand interactions.Chemical science · 2025Article
- The Molecular Recognition of Lurasidone by Human Serum Albumin: A Combined Experimental and Computational Approach.Molecules (Basel, Switzerland) · 2025Article
- Optical chirality of all dielectric q-BIC metasurface with symmetry breaking.Nanophotonics (Berlin, Germany) · 2025Article
- KCD: A prediction web server of knowledge-based circular dichroism.Protein science : a publication of the Protein Society · 2024Article
- Universal machine learning for the response of atomistic systems to external fields.Nature communications · 2023Article
- Tidying up the conformational ensemble of a disordered peptide by computational prediction of spectroscopic fingerprints.Chemical science · 2023Article
- Machine Learning Descriptors for Data-Driven Catalysis Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
- Impact of conformation and intramolecular interactions on vibrational circular dichroism spectra identified with machine learning.Communications chemistry · 2023Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A data-driven approach to simulate circular dichroism (CD) spectra is appealing for fast protein secondary structure determination, yet the challenge of predicting electric and magnetic transition dipole moments poses a substantial barrier for the goal. To address this problem, we designed a new machine learning (ML) protocol in which ordinary pure geometry-based descriptors are replaced with alternative embedded density descriptors and electric and magnetic transition dipole moments are successfully predicted with an accuracy comparable to first-principle calculation. The ML model is able to not only simulate protein CD spectra nearly 4 orders of magnitude faster than conventional first-principle simulation but also obtain CD spectra in good agreement with experiments. Finally, we predicted a series of CD spectra of the Trp-cage protein associated with continuous changes of protein configuration along its folding path, showing the potential of our ML model for supporting real-time CD spectroscopy study of protein dynamics.
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.