Prediction of the <i>P. falciparum</i> Target Space Relevant to Malaria Drug Discovery

<div><p>Malaria is still one of the most devastating infectious diseases, affecting hundreds of millions of patients worldwide. Even though there are several established drugs in clinical use for malaria treatment, there is an urgent need for new drugs acting through novel mechanisms of action due to the rapid development of resistance. Resistance emerges when the parasite manages to mutate the sequence of the drug targets to the extent that the protein can still perform its function in the parasite but can no longer be inhibited by the drug, which then becomes almost ineffective. The design of a new generation of malaria drugs targeting multiple essential proteins would make it more difficult for the parasite to develop full resistance without lethally disrupting some of its vital functions. The challenge is then to identify which set of <i>Plasmodium falciparum</i> proteins, among the millions of possible combinations, can be targeted at the same time by a given chemotype. To do that, we predicted first the targets of the close to 20,000 antimalarial hits identified recently in three independent phenotypic screening campaigns. All targets predicted were then projected onto the genome of <i>P. falciparum</i> using orthologous relationships. A total of 226 <i>P. falciparum</i> proteins were predicted to be hit by at least one compound, of which 39 were found to be significantly enriched by the presence and degree of affinity of phenotypically active compounds. The analysis of the chemically compatible target combinations containing at least one of those 39 targets led to the identification of a priority set of 64 multi-target profiles that can set the ground for a new generation of more robust malaria drugs.</p></div

Chemoisosterism in the Proteome

The concept of chemoisosterism of protein environments is introduced as the complementary property to bioisosterism of chemical fragments. In the same way that two chemical fragments are considered bioisosteric if they can bind to the same protein environment, two protein environments will be considered chemoisosteric if they can interact with the same chemical fragment. The basis for the identification of chemoisosteric relationships among protein environments was the increasing amount of crystal structures available currently for protein–ligand complexes. It is shown that one can recover the right location and orientation of chemical fragments constituting the native ligand in a nuclear receptor structure by using only chemoisosteric environments present in enzyme structures. Examples of the potential applicability of chemoisosterism in fragment-based drug discovery are provided

Drug-target network of 1,908 active compounds predicted for 147 <i>P. falciparum</i> proteins.

<p>Node colors encode target families (red: kinase; orange: protease; yellow: other enzyme; green: ribosomal protein; magenta: transporter/channel; blue: other protein; grey: unknown function), node shapes encode prioritization (hexagon: high priority; triangle: high affinity target; diamond: enriched target; square: other predictions). Targets from the same orthologous group are merged into one common node. Compounds are shown as white circles and grouped according to their target profiles. Edge width corresponds to the number of compounds in the respective group. Clinically used drugs are highlighted in light blue. The numbering corresponds to chloroquine, mefloquine, and artemether (1), quinine (2), pyrimethamine (3), proguanil (4), sulfadoxin (5), and atovaquone (6). Capital letters are used to identify the target hubs of Hsp90 (A), plasmepsin I, II, IV, and VI (B), bifunctional dihydrofolate reductase-thymidylate synthase (C), acyl-CoA synthetase (D), serine/threonine protein kinase ARK2 (E), and falcipain 2a, 2b, and 3 (F).</p

Selection of diverse multi-target profiles.

<p>The structures shown correspond to (A) the core scaffold of a set of 19 2,4-diaminoquinazolines, (B) TCMDC-132054, and (C) GNF-Pf-2272. The corresponding distributions of predicted affinities over multiple targets are provided on the right-hand side. For the set of 2,4-diaminoquinazolines (A), average predicted affinities are shown, with error bars giving the standard deviation over all compounds. Asterisks within the bars indicate the priority class of the respective target (‘****’ = high priority, ‘***’ = high affinity, ‘**’ = enriched, ‘*’ predicted target). Targets of the same orthologous group are joined to a single bar.</p

Data work-flow and library sizes during pre-processing of virtual target profiling.

<p>Data work-flow and library sizes during pre-processing of virtual target profiling.</p

High priority targets versus total number of targets within a predicted profile.

<p>The size of a circle relates to the number of compounds that address a profile of the given characteristics.</p

Protein classes of predicted targets.

<p>White bars represent the predicted relevant target space, whereas the different prioritized subsets are depicted by shaded bars.</p

Contributions of the individual data sets to the predicted target space.

<p>(A) Target space of active antimalarials only; (B) target space including both active and inactive compounds.</p

Distant Polypharmacology among MLP Chemical Probes

Small molecules are essential tool compounds to probe the role of proteins in biology and advance toward more efficient therapeutics. However, they are used without a complete knowledge of their selectivity across the entire proteome, at risk of confounding their effects due to unknown off-target interactions. Current state-of-the-art computational approaches to predicting the affinity profile of small molecules offer a means to anticipate potential nonobvious selectivity liabilities of chemical probes. The application of <i>in silico</i> target profiling on the full set of chemical probes from the NIH Molecular Libraries Program (MLP) resulted in the identification of biologically relevant <i>in vitro</i> affinities for proteins distantly related to the primary targets of ML006, ML123, ML141, and ML204, helping to lower the risk of their further use in chemical biology

Identification of Pim Kinases as Novel Targets for PJ34 with Confounding Effects in PARP Biology

Small molecules are widely used in chemical biology without complete knowledge of their target profile, at risk of deriving conclusions that ignore potential confounding effects from unknown off-target interactions. The prediction and further experimental confirmation of novel affinities for PJ34 on Pim1 (IC₅₀ = 3.7 μM) and Pim2 (IC₅₀ = 16 μM) serine/threonine kinases, together with their involvement in many of the processes relevant to PARP biology, questions the appropriateness of using PJ34 as a chemical tool to probe the biological role of PARP1 and PARP2 at the high micromolar concentrations applied in most studies