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

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A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia

Open access
Liu, Fei, Jiang, Xiangkang, Yang, Jingyuan, Tao, Jiawei, Zhang, Mao·2022·Briefings in Bioinformatics
DOI 10.1093/bib/bbac365PMC PMC9487598

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Abstract

Nowadays, the complexity of disease mechanisms and the inadequacy of single-target therapies in restoring the biological system have inevitably instigated the strategy of multi-target therapeutics with the analysis of each target individually. However, it is not suitable for dealing with the conflicts between targets or between drugs. With the release of high-precision protein structure prediction artificial intelligence, large-scale high-precision protein structure prediction and docking have become possible. In this article, we propose a multi-target drug discovery method by the example of therapeutic hypothermia (TH). First, we performed protein structure prediction for all protein targets of each group by AlphaFold2 and RoseTTAFold. Then, QuickVina 2 is used for molecular docking between the proteins and drugs. After docking, we use PageRank to rank single drugs and drug combinations of each group. The ePharmaLib was used for predicting the side effect targets. Given the differences in the weights of different targets, the method can effectively avoid inhibiting beneficial proteins while inhibiting harmful proteins. So it could minimize the conflicts between different doses and be friendly to chronotherapeutics. Besides, this method also has potential in precision medicine for its high compatibility with bioinformatics and promotes the development of pharmacogenomics and bioinfo-pharmacology.

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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., Yang, J., Tao, J., & Zhang, M.
Affiliations
Second Affiliated Hospital of Zhejiang University (CN)
Corresponding author
Zhang, Mao
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2022-09
Volume
23
Issue
5
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
DOI
10.1093/bib/bbac365
PMC
PMC9487598
OpenAlex
W4296394147
Cited by
8
References
3
Citations by year
2026: 2 · 2025: 2 · 2024: 1 · 2023: 3
FWCI
0.81
Type
journal article
Language
en
Open access
Licence
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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