Evidence map›Paper›PMID 38617634›Full record

ArticleACS omega2024

Theoretical Investigation of the Green-Synthesized Carbon-Based Nanomaterial Potential as Inhibitors of ACE2 for Blocking SARS-CoV-2 Binding.

Harsiwi Candra Sari, Haliza Hasnia Putri, Pinantun Wiguna Kusuma Paksi, Gabriel Hidayat, Silmi Rahma Amelia, Citra Deliana Dewi Sundari, Heni Rachmawati, Atthar Luqman Ivansyah, Fahdzi Muttaqien, Ferry Iskandar

Open access · goldAbstract read
In one paragraph

Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 93% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Harsiwi Candra SariMaster Program in Computational Science, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung, West Java 40132, Indonesia.
Haliza Hasnia PutriMaster Program in Computational Science, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung, West Java 40132, Indonesia.
Pinantun Wiguna Kusuma PaksiMaster Program in Computational Science, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung, West Java 40132, Indonesia.
Gabriel HidayatMaster Program in Computational Science, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung, West Java 40132, Indonesia.
Silmi Rahma AmeliaMaster Program in Computational Science, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung, West Java 40132, Indonesia.
Citra Deliana Dewi SundariDepartment of Chemistry, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung, West Java 40132, Indonesia.ORCID https://orcid.org/0000-0002-0106-276X
Heni RachmawatiSchool of Pharmacy, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung, West Java 40132, Indonesia.
Atthar Luqman IvansyahMaster Program in Computational Science, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung, West Java 40132, Indonesia.ORCID https://orcid.org/0000-0001-9716-8982
Fahdzi MuttaqienMaster Program in Computational Science, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung, West Java 40132, Indonesia.ORCID https://orcid.org/0000-0001-8970-444X
Ferry IskandarResearch Center for Nanosciences and Nanotechnology, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung, West Java 40132, Indonesia.ORCID https://orcid.org/0000-0002-0464-0035
Bandung Institute of Technology · ID

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the emergence of SARS-CoV-2 in 2020, the world has faced a global pandemic, emphasizing the urgent need for effective treatments to combat COVID-19. This study explores the use of green-synthesized carbon-based nanomaterials as potential inhibitors of ACE2, a critical receptor for SARS-CoV-2 entry into host cells. Specifically, the study examines four carbon-based nanomaterials, namely, CD1, CD2, CD3, and CD4 in amino, graphitic, pyridinic, and pyrrolic forms, respectively, synthesized from curcumin, to investigate their binding affinity with ACE2. Molecular docking studies revealed that CD3 (pyridinic form) exhibited the highest binding affinity with ACE2, surpassing that of the control compound, curcumin. Notably, CD3 formed hydrophobic interactions and hydrogen bonds with key ACE2 residues, suggesting its potential to block the binding of SARS-CoV-2 to human cells. Moreover, molecular dynamics simulations demonstrated the stability of these ligand-ACE2 complexes, further supporting the promise of CD3 as an inhibitor. Quantum chemical analyses, including frontier molecular orbitals, natural bond orbital analysis, and the quantum theory of atoms in molecules, unveiled valuable insights into the reactivity and interaction strengths of these ligands. CD3 exhibited desirable chemical properties, signifying its suitability for therapeutic development. The study's findings suggest that green-synthesized carbon-based nanomaterials, particularly CD3, have the potential to serve as effective inhibitors of ACE2, offering a promising avenue for the development of treatments against COVID-19. Further experimental validation is warranted to advance these findings and establish new therapies for the ongoing global pandemic.

Identifiers

PMID38617634
PMCPMC11007854
OpenAlexW4393216827

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.