Psychoactive Plant Database - Neuroactive Phytochemical Collection

1. Biologia (Bratisl). 2022;77(10):2989-3000. doi: 10.1007/s11756-022-01159-8.
Epub  2022 Jul 4.

In vitro evaluation of bioactive properties of banana sap.

Gupta G(1), Saxena S(1), Baranwal M(1), Reddy MS(1).

Author information:
(1)Department of Biotechnology, Thapar Institute of Engineering and Technology, 
Patiala, Punjab 147004 India.

Banana sap is currently designated as a waste subsequent to utilization of 
pseudo stem in pulp and paper industry as well as other applications which is 
contributing to the environmental pollution. In the present study, banana sap 
and its crude extracts were evaluated for antimicrobial, antioxidant and 
anticancer properties. The role of oxidized and un-oxidized banana sap for its 
antimicrobial potential against a microbial test panel comprising gram positive 
as well as gram negative bacteria and Candida albicans using in vitro micro 
broth dilution assay. The un-oxidized banana sap exhibited a significantly 
higher antibacterial potential as evident by a lower minimal inhibitory 
concentration (MIC) ranging between 15.625 to 62.5 mg/mL. In vitro radical 
scavenging activity of dichloromethane (DCM) extract of banana sap by DPPH 
method exhibited 54.62 ± 1.09 (µg/mL) IC50 value at the concentration of 
1 mg/mL. Dichloromethane extract of banana sap showed maximum cytotoxic effect 
with human breast cancer (MCF-7) cell proliferation at the concentration of 
100 µg/mL which was 78.37 ± 0.05% and the cytotoxic effect significantly 
increased with increasing concentration of banana sap extract. Furthermore, LCMS 
studies revealed the presence of bioactive compounds in dichloromethane extract 
of banana sap, such as rescinnamine derivative, dihydrorescinnamine and epimedin 
A. The present study suggested that banana sap is a promising source of 
bioactive compounds with relevant antimicrobial, antioxidant and anticancer 
properties.

© The Author(s), under exclusive licence to Plant Science and Biodiversity 
Centre, Slovak Academy of Sciences (SAS), Institute of Zoology, Slovak Academy 
of Sciences (SAS), Institute of Molecular Biology, Slovak Academy of Sciences 
(SAS) 2022.

DOI: 10.1007/s11756-022-01159-8
PMCID: PMC9251593
PMID: 35814925

Conflict of interest statement: Conflict of interest:The authors declare that 
they have no conflict of interest.


2. Comput Struct Biotechnol J. 2021;19:3133-3148. doi:
10.1016/j.csbj.2021.05.037.  Epub 2021 May 24.

Prediction of repurposed drugs for Coronaviruses using artificial intelligence 
and machine learning.

Rajput A(1), Thakur A(1)(2), Mukhopadhyay A(1)(2), Kamboj S(1)(2), Rastogi 
A(1)(2), Gautam S(1)(2), Jassal H(1), Kumar M(1)(2).

Author information:
(1)Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, 
Council of Scientific and Industrial Research (CSIR), Sector 39-A, Chandigarh 
160036, India.
(2)Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, 
India.

The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory 
Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the 
Coronaviridae family were responsible for causing epidemics earlier. To tackle 
these viruses, there is a lack of approved antiviral drugs. Therefore, we have 
developed robust computational methods to predict the repurposed drugs using 
machine learning techniques namely Support Vector Machine, Random Forest, 
k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the 
experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) 
from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), 
MERS (123), and overall Coronaviruses (414) were subdivided into the 
training/testing and independent validation datasets, followed by the extraction 
of chemical/structural descriptors and fingerprints (17968). The highly relevant 
features were filtered using the recursive feature selection algorithm. The 
selected chemical descriptors were used to develop prediction models with 
Pearson's correlation coefficients ranging from 0.60 to 0.90 on 
training/testing. The robustness of the predictive models was further ensured 
using external independent validation datasets, decoy datasets, applicability 
domain, and chemical analyses. The developed models were used to predict 
promising repurposed drug candidates against coronaviruses after scanning the 
DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by 
molecular docking against the spike protein complex with ACE receptor. We found 
potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, 
Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. 
These 'anticorona' computational models would assist in antiviral drug discovery 
against SARS-CoV-2 and other Coronaviruses.

© 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of 
Computational and Structural Biotechnology.

DOI: 10.1016/j.csbj.2021.05.037
PMCID: PMC8141697
PMID: 34055238

Conflict of interest statement: The authors declare that they have no known 
competing financial interests or personal relationships that could have appeared 
to influence the work reported in this paper.


3. J Nat Prod. 2021 Apr 23;84(4):1283-1293. doi: 10.1021/acs.jnatprod.0c01381.
Epub  2021 Apr 9.

Decomposition Profile Data Analysis for Deep Understanding of Multiple Effects 
of Natural Products.

Nemoto S(1), Morita K(1), Mizuno T(1), Kusuhara H(1).

Author information:
(1)Graduate School of Pharmaceutical Sciences, The University of Tokyo, Bunkyo, 
Tokyo 113-8654, Japan.

It is difficult to understand the entire effect of a natural product because 
such products generally have multiple effects. We propose a strategy to 
understand these effects effectively by decomposing them with a profile data 
analysis method we developed. A transcriptome profile data set was obtained from 
a public database and analyzed. Considering their high similarity in structure 
and transcriptome profile, we focused on rescinnamine and syrosingopine. 
Decomposed effects predicted clear differences between the compounds. Two of the 
decomposed effects, SREBF1 activation and HDAC inhibition, were investigated 
experimentally because the relationship between these effects and the compounds 
had not yet been reported. Analyses in vitro validated these effects, and their 
strength was consistent with predicted scores. Moreover, the number of outliers 
in decomposed effects per compound was higher in natural products than in drugs 
in the data set, which is consistent with the nature of the effects of natural 
products.

DOI: 10.1021/acs.jnatprod.0c01381
PMID: 33836128 [Indexed for MEDLINE]


4. J Biomol Struct Dyn. 2022 Apr;40(6):2516-2529. doi: 
10.1080/07391102.2020.1840442. Epub 2020 Nov 2.

Unravelling the drugability of MSI2 RNA recognition motif (RRM) protein and the 
prediction of their effective antileukemia inhibitors from traditional herb 
concoctions.

Adeniyi JN(1), Adeniyi AA(2)(3), Moodley R(4), Nlooto M(5), Ngcobo M(1), Gomo 
E(1), Conradie J(2).

Author information:
(1)Traditional Medicine Laboratory, School of Nursing and Public Health, College 
of Health Sciences, University of KwaZulu-Natal, Durban, South Africa.
(2)Department of Chemistry, University of the Free State, Bloemfontein, South 
Africa.
(3)Department of Industrial Chemistry, Federal University Oye Ekiti, Ekiti, 
Nigeria.
(4)School of Chemistry and Physics, College of Agriculture, Engineering and 
Science, University of KwaZulu-Natal, Durban, South Africa.
(5)Department of Pharmacy, School of Health Care Sciences, University of 
Limpopo, Sovenga, South Africa.

MSI2 is a homolog 2 of the Musashi RNA binding proteins (MSI) and is known to 
contribute to acute myeloid leukaemia (AML) and expressed up to 70% in AML 
patients. High expression of MSI2 has been found to lead to the lower overall 
survival of patients with AML. This study proposed the potential antagonists of 
MSI2 RNA-recognition motifs (MSI2 RRM1) derived from the LC-MS analysis of three 
traditional herbal samples. The LC-MS analysis of the three traditional herbs 
concoctions yields a total of 271 unique molecules of which 262 were screened 
against MSI2 RRM1 protein. After the dynamic study of the selected 8 top 
molecules from the virtual screening, the five most promising ligands emerged as 
potential MSI2 antagonists compare to the reference experimental molecule. The 
results show that the dynamic of MSI2 RRM1 protein is accompanied by a rare even 
of protein chain dissociation and re-association as evident in both the bound 
and unbound state of the protein. The unbound protein experience earlier chain 
dissociation compare to ligand-bound protein indicating that ligand binding to 
the protein slows down the dissociation time but thereafter increases the 
frequency of alternation between the protein chain association and dissociation 
after the first experience. Interestingly, the re-association of the protein 
chain is also accompanied by full restoration of the ligands to the binding 
site. The drug candidate Methotrexate (M3) and rescinnamine (M9) are listed 
among the promising antagonist of MSI2 with unique properties compared to a less 
promising molecule Ergotamine (M6).Communicated by Ramaswamy H. Sarma.

DOI: 10.1080/07391102.2020.1840442
PMID: 33131412 [Indexed for MEDLINE]


5. World J Biol Psychiatry. 2020 Dec;21(10):775-783. doi: 
10.1080/15622975.2018.1492734. Epub 2018 Aug 3.

The use of a gene expression signature and connectivity map to repurpose drugs 
for bipolar disorder.

Kidnapillai S(1), Bortolasci CC(1), Udawela M(2), Panizzutti B(3), Spolding 
B(1), Connor T(1), Sanigorski A(1), Dean OM(2)(4)(5), Crowley T(1)(6), Jamain 
S(7), Gray L(1)(2), Scarr E(2)(8), Leboyer M(7), Dean B(2)(9), Berk 
M(2)(4)(5)(10), Walder K(1).

Author information:
(1)Centre for Molecular and Medical Research, School of Medicine, Deakin 
University, Geelong, Australia.
(2)The Florey Institute of Neuroscience and Mental Health, Parkville, Australia.
(3)Laboratory of Molecular Psychiatry, Hospital de Clínicas de Porto Alegre 
(HCPA) and Programa de Pós-graduação em Psiquiatria e Ciências do Comportamento, 
Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.
(4)IMPACT Strategic Research Centre, School of Medicine, Barwon Health, Deakin 
University, Geelong, Australia.
(5)Department of Psychiatry, the University of Melbourne, Parkville, Australia.
(6)Bioinformatics Core Research Facility (BCRF), Deakin University, Geelong, 
Australia.
(7)INSERM U955, Psychiatrie Translationnelle, Université Paris Est, Créteil, 
France.
(8)Faculty of Veterinary and Agricultural Sciences, Melbourne Veterinary School, 
The University of Melbourne, Victoria, Australia.
(9)Faculty of Health Arts and Design, Centre for Mental Health, Swinburne 
University, Victoria, Australia.
(10)Orygen, the National, Centre of Excellence in Youth Mental Health, 
Parkville, Australia.

To create a gene expression signature (GES) to represent the biological effects 
of a combination of known drugs for bipolar disorder (BD) on cultured human 
neuronal cells (NT2-N) and rat brains, which also has evidence of differential 
expression in individuals with BD. To use the GES to identify new drugs for BD 
using Connectivity Map (CMap).Methods: NT2-N (n = 20) cells and rats (n = 8) 
were treated with a BD drug combination (lithium, valproate, quetiapine and 
lamotrigine) or vehicle for 24 and 6 h, respectively. Following next-generation 
sequencing, the differential expression of genes was assessed using edgeR in R. 
The derived GES was compared to differentially expressed genes in post-mortem 
brains of individuals with BD. The GES was then used in CMap analysis to 
identify similarly acting drugs.Results: A total of 88 genes showed evidence of 
differential expression in response to the drug combination in both models, and 
therefore comprised the GES. Six of these genes showed evidence of differential 
expression in post-mortem brains of individuals with BD. CMap analysis 
identified 10 compounds (camptothecin, chlorambucil, flupenthixol, valdecoxib, 
rescinnamine, GW-8510, cinnarizine, lomustine, mifepristone and nimesulide) 
acting similarly to the BD drug combination.Conclusions: This study shows that 
GES and CMap can be used as tools to repurpose drugs for BD.

DOI: 10.1080/15622975.2018.1492734
PMID: 29956574 [Indexed for MEDLINE]