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PreprintCited by 8Computational Drug Discovery MethodsProtein Degradation and Inhibitorsvaccines and immunoinformatics approaches

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A chronopharmacology-friendly multi-target therapeutics based on AI: the example of therapeutic hypothermia

Open access
Liu, Fei, Jiang, Xiangkang, Zhang, Mao·2022·Briefings in Bioinformatics
DOI 10.1101/2022.03.07.22271997PMC PMC9487598

Preprint — published as 10.1093/bib/bbac365 · Paper page

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Abstract

Nowadays, the complexity of disease mechanisms and inadequacy of single-target therapies in restoring the biological system has inevitably instigated the strategy of multi-target therapeutics with the application of hybrid and chimeric drugs. However, the related method is still unable to solve the conflicts between targets or between drugs. With the release of high-precision protein structure prediction artificial intelligence (AI), large-scale high-precision protein structure prediction and docking become possible. In this article, we propose a multi-target drug discovery method. Then we take an example of therapeutic hypothermia (TH). We performed protein structure prediction for all targets of each group by AlphaFold2 and RoseTTAFold. QuickVina 2 is then used for molecular docking of the proteins and drugs. After docking, we use PageRank to get the rank of drugs and drug combinations of each group. Given the differences in the scoring of different proteins, the method can effectively avoid inhibiting beneficial proteins. So it’s friendly to chronopharmacology. This method also have potential in precision medicine for its high compatibility with bioinformatics.

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Figures

Figure 1 Representative workflow for bioinfo-pharmacology drug discovery.
Figure 1 Representative workflow for bioinfo-pharmacology drug discovery.CC BY-NC · PubMed Central
Figure 2 Circular visualization of expression patterns and clustering of hypothermia treatment.
Figure 2 Circular visualization of expression patterns and clustering of hypothermia treatment.CC BY-NC · PubMed Central
Figure 3 The 3D structures of target proteins. (A) ARMCX5, (B) CCDC, (C) CDSN, (D) CEACAM1, (E) CHAC1, (F) CIART, (G) CIRP, (H) NQO1, (I) NR1D1, (J) NUDT22 and (K) RAMP3.
Figure 3 The 3D structures of target proteins. (A) ARMCX5, (B) CCDC, (C) CDSN, (D) CEACAM1, (E) CHAC1, (F) CIART, (G) CIRP, (H) NQO1, (I) NR1D1, (J) NUDT22 and (K) RAMP3.CC BY-NC · PubMed Central
Figure 4 The ConSurf analysis and predicted active sites of target proteins. The upper 11 pictures are ConSurf analysis results. The darker the color, the more conserved the amino acids and the higher the probability of active sites. The last 11 pictures are predicted active sites. The colored areas indicate potential binding pockets of targets. (A and L) ARMCX5, (B and M) CCDC, (C and N) CDSN, (D and O) CEACAM1, (E and P) CHAC1, (F and Q) CIART, (G & R) CIRP, (H and S) NQO1, (I and T) NR1D1, (J and U) NUDT22 and (K and V) RAMP3.
Figure 4 The ConSurf analysis and predicted active sites of target proteins. The upper 11 pictures are ConSurf analysis results. The darker the color, the more conserved the amino acids and the higher the probability of active sites. The last 11 pictures are predicted active sites. The colored areas indicate potential binding pockets of targets. (A and L) ARMCX5, (B and M) CCDC, (C and N) CDSN, (D and O) CEACAM1, (E and P) CHAC1, (F and Q) CIART, (G & R) CIRP, (H and S) NQO1, (I and T) NR1D1, (J and U) NUDT22 and (K and V) RAMP3.CC BY-NC · PubMed Central
Figure 5 The best docking molecular for each protein. (A) ARMCX5, (B) CCDC, (C) CDSN, (D) CEACAM1, (E) CHAC1, (F) CIART, (G) CIRP, (H) NQO1, (I) NR1D1, (J) NUDT22 and (K) RAMP3.
Figure 5 The best docking molecular for each protein. (A) ARMCX5, (B) CCDC, (C) CDSN, (D) CEACAM1, (E) CHAC1, (F) CIART, (G) CIRP, (H) NQO1, (I) NR1D1, (J) NUDT22 and (K) RAMP3.CC BY-NC · PubMed Central
Figure 6 The RMSD and RMSF of MD simulation. (A) The RMSD of proteins. (B) The RMSF of proteins. (C) The RMSD of each molecular of proteins.
Figure 6 The RMSD and RMSF of MD simulation. (A) The RMSD of proteins. (B) The RMSF of proteins. (C) The RMSD of each molecular of proteins.CC BY-NC · PubMed Central

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Citations

Cites

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Metadata

Authors
Liu, F., Jiang, X., & Zhang, M.
Affiliations
Second Affiliated Hospital of Zhejiang University (CN)
Corresponding author
Zhang, Mao
ORCID
Venue
Briefings in Bioinformatics
Publisher
openRxiv
Published
2022-03-10
Volume
23
Issue
5
ISSN
1467-5463 (ISSN-L)
DOI
10.1101/2022.03.07.22271997
PMC
PMC9487598
OpenAlex
W4296394147
Cited by
8
References
1
Citations by year
2026: 2 · 2025: 2 · 2024: 1 · 2023: 3
FWCI
0.81
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Briefings in Bioinformatics (publishedVersion, cc-by-nc)
  • medRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
Universität Hamburg
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
multi-target drug discovery, therapeutic hypothermia, chronotherapy, protein structure prediction, AlphaFold2, RoseTTAFold, molecular docking, PageRank, side effect prediction, precision medicine, pharmacogenomics
MeSH terms
Artificial Intelligence, Hypothermia, Induced, Drug Discovery, Drug Chronotherapy, Molecular Docking Simulation

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