Dataset Information

Bosc2021 - MAIP: a web service for predicting blood‐stage malaria inhibitors

ABSTRACT:

Prediction of the antimalarial potential of small molecules. This model is an ensemble of smaller QSAR models trained on proprietary data from various sources, up to a total of >7M compounds. The training sets belong to Evotec, Johns Hopkins, MRCT, MMV - St. Jude, AZ, GSK, and St. Jude Vendor Library. The code and training data are not released, using this model posts predictions to the MAIP online server. The Ersilia Model Hub also offers MAIP-surrogate as a downloadable package for IP-sensitive queries.

Model Type: Predictive machine learning model.
Model Relevance: Predicts antimalarial activity.
Model Encoded by: Amna Ali (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam

Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos4zfy

ORGANISM(S): Homo sapiens

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2405210002 | biostudies-other |

SECONDARY ACCESSION(S): 33618772

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

MAIP: a web service for predicting blood-stage malaria inhibitors.

Bosc Nicolas N Felix Eloy E Arcila Ricardo R Mendez David D Saunders Martin R MR Green Darren V S DVS Ochoada Jason J Shelat Anang A AA Martin Eric J EJ Iyer Preeti P Engkvist Ola O Verras Andreas A Duffy James J Burrows Jeremy J Gardner J Mark F JMF Leach Andrew R AR

Journal of cheminformatics 20210222 1

Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify n ...[more]

PMID: 33618772

Similar Datasets

Project description:Spiroindolone and pyrazoleamide antimalarial compounds target Plasmodium falciparum P-type ATPase (PfATP4) and induce disruption of intra-cellular Na+ homeostasis. Recently, a PfATP4 mutation was discovered that confers resistance to a pyrazoleamide while increasing sensitivity to a spiroindolone. To understand the different cellular accommodation to PfATP4 disruptions, we examined biochemical and metabolic adaptations that underlie this seemingly contradictory response of P. falciparum to sublethal concentrations of each compound. We used a genetically engineered P. falciparum Dd2 strain (Dd2A211V) carrying an Ala211Val (A211V) mutation in PfATP4 to identify metabolic adaptations associated with the mutation that results in decreased sensitivity to PA21A092 (a pyrazoleamide) and increased sensitivity of KAE609 (a spiroindolone). We first identified sublethal doses of PA21A092 and KAE609 causing substantial reduction (30-70%) in Dd2A211V parasite replication. At this sublethal dose of PA21A092 (or KAE609), we collected metabolomic and transcriptomic data during the first intraerythrocytic developmental cycle (IDC). Finally, we integrated the time-resolved data with a whole-genome metabolic network model of P. falciparum to characterize antimalarial-induced physiological adaptations. We found that sublethal treatment with PA21A092 caused significant (p < 0.001) alterations in the abundances of 91 Plasmodium gene transcripts whereas only 21 transcripts were significantly altered due to sublethal treatment with KAE609. In the metabolomic data, we found a substantial alteration (fourfold) in the abundances of carbohydrate metabolites in the presence of either compounds. The estimated rates of macromolecule syntheses between the two antimalarial-treated conditions were also comparable, except for the rate of lipid synthesis. A closer examination of parasite metabolism in the presence of either compound indicated statistically significant differences in enzymatic activities associated with synthesis of phosphatidylcholine, phosphatidylserine, and phosphatidylinositol. Our results suggest that malaria parasites activate protein kinases via phospholipid-dependent signaling in response to the ionic perturbation induced by the Na+ homeostasis disruptor PA21A092. Therefore, we hypothesize that targeted disruption of phospholipid signaling in PA21A092-resistant parasites could be a means to block the emergence of resistance to PA21A092.

Dataset Information

Bosc2021 - MAIP: a web service for predicting blood‐stage malaria inhibitors

Publications

MAIP: a web service for predicting blood-stage malaria inhibitors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets