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The impact of whole genome sequence data on drug discovery--a malaria case study.

ABSTRACT: BACKGROUND: Identification and validation of a drug discovery target is a prominent step in drug development. In the post-genomic era it is possible to reevaluate the association of a gene with a specific biological function to see if a homologous gene can subsume this role. This concept has special relevance to drug discovery in human infectious diseases, like malaria. A trophozoite cysteine protease (falcipain-1) from the papain family, thought to be responsible for the degradation of erythrocyte hemoglobin, has been considered a promising target for drug discovery efforts owing to the antimalarial activity of peptide based covalent cysteine protease inhibitors. This led to the development of non-peptidic non-covalent inhibitors of falcipain-1 and their characterization as antimalarials. It is now clear from sequencing efforts that the malaria genome contains more than one cysteine protease and that falcipain-1 is not the most important contributor to hemoglobin degradation. Rather, falcipain-2 and falcipain-3 appear to account for the majority of cysteine hemoglobinase activity in the plasmodium trophozoite. MATERIALS AND METHODS: We have modeled the falcipain-2 cysteine protease from one of the major human malaria species, Plasmodium falciparum and compared it to our original work on falcipain-1. As with falcipain-1, computa-tional screening of the falcipain-2 active site was conducted using DOCK. Using structural superpositions within the protease family and evolutionary analysis of substrate specificity sites, we focused on the commonalities and the protein specific features to direct our drug discovery effort. RESULTS: Since 1993, the size of the Available Chemicals Directory had increased from 55313 to 195419 unique chemical structures. For falcipain-2, eight inhibitors were identified with IC50's against the enzyme between 1 and 7 microM. Application of three of these inhibitors to infected erythrocytes cured malaria in culture, but parasite death did not correlate with food vacuole abnormalities associated with the activity of mechanistic inhibitors of cysteine proteases like the epoxide E64. CONCLUSIONS: Using plasmodial falcipain proteases, we show how a protein family perspective can influence target discovery and inhibitor design. We suspect that parallel drug discovery programs where a family of targets is considered, rather than serial programs built on a single therapeutic focus, will become the dominant industrial paradigm. Economies of scale in assay development and in compound synthesis are expected owing to the functional and structural features of individual family members. One of the remaining challenges in post-genomic drug discovery is that inhibitors of one target are likely to show some activity against other family members. This lack of specificity may lead to difficulties in functional assignments and target validation as well as a complex side effect profile.

SUBMITTER: Joachimiak MP

PROVIDER: S-EPMC1949995 | biostudies-other | 2001 Oct

REPOSITORIES: biostudies-other

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The impact of whole genome sequence data on drug discovery--a malaria case study.

Joachimiak M P MP Chang C C Rosenthal P J PJ Cohen F E FE

Molecular medicine (Cambridge, Mass.) 20011001 10

<h4>Background</h4>Identification and validation of a drug discovery target is a prominent step in drug development. In the post-genomic era it is possible to reevaluate the association of a gene with a specific biological function to see if a homologous gene can subsume this role. This concept has special relevance to drug discovery in human infectious diseases, like malaria. A trophozoite cysteine protease (falcipain-1) from the papain family, thought to be responsible for the degradation of e ...[more]

PMID: 11713369

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Project description:Comparative genomics based on whole genome sequencing (WGS) is increasingly being applied to investigate questions within evolutionary and molecular biology, as well as questions concerning public health (e.g., pathogen outbreaks). Given the impact that conclusions derived from such analyses may have, we have evaluated the robustness of clustering individuals based on WGS data to three key factors: (1) next-generation sequencing (NGS) platform (HiSeq, MiSeq, IonTorrent, 454, and SOLiD), (2) algorithms used to construct a SNP (single nucleotide polymorphism) matrix (reference-based and reference-free), and (3) phylogenetic inference method (FastTreeMP, GARLI, and RAxML). We carried out these analyses on 194 whole genome sequences representing 107 unique Salmonella enterica subsp. enterica ser. Montevideo strains. Reference-based approaches for identifying SNPs produced trees that were significantly more similar to one another than those produced under the reference-free approach. Topologies inferred using a core matrix (i.e., no missing data) were significantly more discordant than those inferred using a non-core matrix that allows for some missing data. However, allowing for too much missing data likely results in a high false discovery rate of SNPs. When analyzing the same SNP matrix, we observed that the more thorough inference methods implemented in GARLI and RAxML produced more similar topologies than FastTreeMP. Our results also confirm that reproducibility varies among NGS platforms where the MiSeq had the lowest number of pairwise differences among replicate runs. Our investigation into the robustness of clustering patterns illustrates the importance of carefully considering how data from different platforms are combined and analyzed. We found clear differences in the topologies inferred, and certain methods performed significantly better than others for discriminating between the highly clonal organisms investigated here. The methods supported by our results represent a preliminary set of guidelines and a step towards developing validated standards for clustering based on whole genome sequence data.

Project description:The determination of the relationship between a pair of individuals is a fundamental application of genetics. Previously, we and others have demonstrated that identity-by-descent (IBD) information generated from high-density single-nucleotide polymorphism (SNP) data can greatly improve the power and accuracy of genetic relationship detection. Whole-genome sequencing (WGS) marks the final step in increasing genetic marker density by assaying all single-nucleotide variants (SNVs), and thus has the potential to further improve relationship detection by enabling more accurate detection of IBD segments and more precise resolution of IBD segment boundaries. However, WGS introduces new complexities that must be addressed in order to achieve these improvements in relationship detection. To evaluate these complexities, we estimated genetic relationships from WGS data for 1490 known pairwise relationships among 258 individuals in 30 families along with 46 population samples as controls. We identified several genomic regions with excess pairwise IBD in both the pedigree and control datasets using three established IBD methods: GERMLINE, fastIBD, and ISCA. These spurious IBD segments produced a 10-fold increase in the rate of detected false-positive relationships among controls compared to high-density microarray datasets. To address this issue, we developed a new method to identify and mask genomic regions with excess IBD. This method, implemented in ERSA 2.0, fully resolved the inflated cryptic relationship detection rates while improving relationship estimation accuracy. ERSA 2.0 detected all 1(st) through 6(th) degree relationships, and 55% of 9(th) through 11(th) degree relationships in the 30 families. We estimate that WGS data provides a 5% to 15% increase in relationship detection power relative to high-density microarray data for distant relationships. Our results identify regions of the genome that are highly problematic for IBD mapping and introduce new software to accurately detect 1(st) through 9(th) degree relationships from whole-genome sequence data.

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The impact of whole genome sequence data on drug discovery--a malaria case study.

Publications

The impact of whole genome sequence data on drug discovery--a malaria case study.

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