The goal of this proposed effort is to develop predictive models of future Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) dihydrofolate reductase (DHFR) protein evolution that will facilitate hypothesis generation for likely future mutations in the wild, leading to discovery of novel anti-malarial therapeutics againt drug resistant strains in advance of these mutations. Through this SC3 research, we will perform a comprehensive structure-based analysis of DHFR protein evolution in order to generate site-specific predictive models of likely amino acid replacements and identify locations where compensating amino acid replacements may be occurring in response to selection pressures. Research will commence with generation of a comprehensive phylogenetic tree using DHFR protein sequences obtained from public domain databases. Ancestral sequences will be predicted for key clades in DHFR evolution. 3D homology models will then be generated for each of these ancestral sequences that will be added sequentially to an already existing structure-based sequence alignment generated from a superposition of experimentally determined x-ray crystal structures of wild-type (wt) DHFR from 22 species. Predictive models of site-specific amino acid replacements will be generated using tools and techniques taken from the field of computational intelligence and machine learning that include HMMs and ANNs. These models will be tested and validated by using the first 70% of the phylogeny in order to predict the remaining 30%. Using the insights gained from these predictive models, an analysis will be performed with mutant DHFR sequences of P. falciparum and P. vivax to study the intraspecies differentiation that gave rise to drug resistance. Hypothetical DHFR sequences and homology models representing next steps in Plasmodium DHFR evolution will be generated. In silico docking experiments will be performed with existing anti-malarial drugs as well as known inhibitors of DHFR. Examination of the predicted protein-ligand interactions from these studies will provide additional insights into the acquisition of drug resistance. Through this proposed effort, an innovative technology for studying and modeling DHFR protein evolution is realized. Predictive models of potential next steps in P. falciparum and P. vivax DHFR evolution will be generated as a proof of concept of this approach. This technology has far-reaching benefits including the generation of hypotheses for intraspecies differentiation and origins of drug resistance in P. falciparum and P. vivax as well as the ability to generate predictive models of future DHFR protein evolution providing the unique opportunity of getting a """"""""head start"""""""" on drug discovery before drug resistance develops in the wild.
Approximately forty-one percent of the world's population lives in areas where malaria is transmitted and each year and it is estimated that 350-500 million cases of malaria occur worldwide. Two of the most prevalent malaria strains, Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) have developed clinical resistance to antifolate compounds that target the enzyme dihydrofolate reductase (DHFR) such as pyrimethamine and cycloguanil. Consequently, there remains a serious and immediate need for the development of novel antimalarial therapeutics that target drug-resistant strains. Using the proposed approach, we will develop predictive models of future Pf-DHFR and Pv-DHFR protein evolution that will facilitate hypothesis generation for likely future mutations in the wild. Afterthe completion of this SC3 research, we plan to integrate these predictive models into a comprehensive computational intelligence- based drug discovery platform thus providing the unique opportunity of getting a head start on drug discovery resulting in timely development of novel anti-malarial therapeutics to meet future needs. This approach will be tested on DHFR for novel antimalarial drug discovery;however, the methods developed can be applied broadly in early stage drug discovery and development.
|Hecht, David; Fogel, Gary B (2012) Modeling the evolution of drug resistance in malaria. J Comput Aided Mol Des 26:1343-53|