Stuart A. MacGowan

New and improved drugs are required for the treatment and ultimate eradication of malaria. The efficacy of front-line therapies is now threatened by emerging drug resistance; thus, new tools to support the development of drugs with a lower propensity for resistance are needed. Here, we describe the development of a RESistance Mapping And Profiling (ResMAP) platform for the identification of resistance-conferring mutations in Plasmodium drug targets. Proof-of-concept studies focused on interrogating the antimalarial drug target, Plasmodium falciparum lysyl tRNA synthetase (PfKRS). Saturation mutagenesis was used to construct a plasmid library encoding all conceivable mutations within a 20-residue span at the base of the PfKRS ATP-binding pocket. The superior transfection efficiency of Plasmodium knowlesi was exploited to generate a high coverage parasite library expressing PfKRS bearing all possible amino acid changes within this region of the enzyme. The selection of the library with PfKRS inhibitors, cladosporin and DDD01510706, successfully identified multiple resistance-conferring substitutions. Genetic validation of a subset of these mutations confirmed their direct role in resistance, with computational modeling used to dissect the structural basis of resistance. The application of ResMAP to inform the development of resistance-resilient antimalarials of the future is discussed. IMPORTANCE: An increase in treatment failures for malaria highlights an urgent need for new tools to understand and minimize the spread of drug resistance. We describe the development of a RESistance Mapping And Profiling (ResMAP) platform for the identification of resistance-conferring mutations in Plasmodium spp, the causative agent of malaria. Saturation mutagenesis was used to generate a mutation library containing all conceivable mutations for a region of the antimalarial-binding site of a promising drug target, Plasmodium falciparum lysyl tRNA synthetase (PfKRS). Screening of this high-coverage library with characterized PfKRS inhibitors revealed multiple resistance-conferring substitutions including several clinically relevant mutations. Genetic validation of these mutations confirmed resistance of up to 100-fold and computational modeling dissected their role in drug resistance. We discuss potential applications of this data including the potential to design compounds that can bypass the most serious resistance mutations and future resistance surveillance.

Protein evolution is constrained by structure and function, creating patterns in residue conservation that are routinely exploited to predict structure and other features. Similar constraints should affect variation across individuals, but it is only with the growth of human population sequencing that this has been tested at scale. Now, human population constraint has established applications in pathogenicity prediction, but it has not yet been explored for structural inference. Here, we map 2.4 million population variants to 5885 protein families and quantify residue-level constraint with a new Missense Enrichment Score (MES). Analysis of 61,214 structures from the PDB spanning 3661 families shows that missense depleted sites are enriched in buried residues or those involved in small-molecule or protein binding. MES is complementary to evolutionary conservation and a combined analysis allows a new classification of residues according to a conservation plane. This approach finds functional residues that are evolutionarily diverse, which can be related to specificity, as well as family-wide conserved sites that are critical for folding or function. We also find a possible contrast between lethal and non-lethal pathogenic sites, and a surprising clinical variant hot spot at a subset of missense enriched positions.

Fragment screening is used to identify binding sites and leads in drug discovery, but it is often unclear which binding sites are functionally important. Here, data from 37 experiments, and 1309 protein structures binding to 1601 ligands were analysed. A method to group ligands by binding sites is introduced and sites clustered according to profiles of relative solvent accessibility. This identified 293 unique ligand binding sites, grouped into four clusters (C1-4). C1 includes larger, buried, conserved, and population missense-depleted sites, enriched in known functional sites. C4 comprises smaller, accessible, divergent, missense-enriched sites, depleted in functional sites. A site in C1 is 28 times more likely to be functional than one in C4. Seventeen sites, which to the best of our knowledge are novel, in 13 proteins are identified as likely to be functionally important with examples from human tenascin and 5-aminolevulinate synthase highlighted. A multi-layer perceptron, and K-nearest neighbours model are presented to predict cluster labels for ligand binding sites with an accuracy of 96% and 100%, respectively, so allowing functional classification of sites for proteins not in this set. Our findings will be of interest to those studying protein-ligand interactions and developing new drugs or function modulators.

Abstract The molecular evolution of a protein is constrained by its structure and function. This is how patterns of evolutionary conservation in a multiple sequence alignment can be exploited by algorithms like AlphaFold to predict structure and other features. Human population sequencing offers the potential to show similar trends within a single species. However, although gene-level and domain-level aggregation of variants is routinely applied, data still preclude simple residue-level analysis per protein. Here, we aggregate population variants within protein domain families to define the missense enrichment score (MES) which captures population constraint at positions in the family. Contrasting MES with evolutionary conservation identifies positions that are key to protein-ligand and protein-protein interaction specificity as well as locations critical to folding, function and pathogenicity. This approach also highlights a new class of pathogenic variant hot spot at positions that are evolutionarily conserved yet enriched in missense variants. We found 5,086 positions in 766 domain families were missense depleted (covering 365,300 residues in the human proteome) and 13,490 positions in 3,591 families were enriched in missense variants (340,829 residues in human) and provide a map of residue-level population constraint in human. Comparison to 61,214 three dimensional structures from the Protein Data Bank, covering 40,394 sequences from 3,661 Pfam domains, and over 10,000 variants labelled pathogenic in ClinVar provides validation of these sites in context with structure and pathogenicity. Application to the GPCR family identifies 17 sites important for ligand binding specificity as well as sites key to function in all members of the GPCR family. For nuclear receptor ligand binding domains, of 10 likely specificity sites, six are in direct contact with ligands. In P450s the most conserved and missense depleted sites interact with the electron donor. This study will have broad applications to: (a) providing focus for functional studies of specific proteins by mutagenesis; (b) refining pathogenicity prediction models; (c) highlighting which residue interactions to target when refining the specificity of small-molecule drugs.

While treatment options for human African trypanosomiasis (HAT) have improved significantly, there is still a need for new drugs with eradication now a realistic possibility. Here, we report the development of 2,4-diaminothiazoles that demonstrate significant potency against Trypanosoma brucei, the causative agent of HAT. Using phenotypic screening to guide structure-activity relationships, potent drug-like inhibitors were developed. Proof of concept was established in an animal model of the hemolymphatic stage of HAT. To treat the meningoencephalitic stage of infection, compounds were optimized for pharmacokinetic properties, including blood-brain barrier penetration. However, in vivo efficacy was not achieved, in part due to compounds evolving from a cytocidal to a cytostatic mechanism of action. Subsequent studies identified a nonessential kinase involved in the inositol biosynthesis pathway as the molecular target of these cytostatic compounds. These studies highlight the need for cytocidal drugs for the treatment of HAT and the importance of static-cidal screening of analogues.

SARS-CoV-2 Spike (Spike) binds to human angiotensin-converting enzyme 2 (ACE2) and the strength of this interaction could influence parameters relating to virulence. To explore whether population variants in ACE2 influence Spike binding and hence infection, we selected 10 ACE2 variants based on affinity predictions and prevalence in gnomAD and measured their affinities and kinetics for Spike receptor binding domain through surface plasmon resonance (SPR) at 37°C. We discovered variants that reduce and enhance binding, including three ACE2 variants that strongly inhibited (p.Glu37Lys, ∆\DeltaG = -1.33 \pm 0.15 kcal mol-1 and p.Gly352Val, predicted ∆\DeltaG = -1.17 kcal mol-1) or abolished (p.Asp355Asn) binding. We also identified two variants with distinct population distributions that enhanced affinity for Spike. ACE2 p.Ser19Pro (∆\DeltaG = 0.59 \pm 0.08 kcal mol-1) is predominant in the gnomAD African cohort (AF = 0.003) whilst p.Lys26Arg (∆\DeltaG = 0.26 \pm 0.09 kcal mol-1) is predominant in the Ashkenazi Jewish (AF = 0.01) and European non-Finnish (AF = 0.006) cohorts. We compared ACE2 variant affinities to published SARS-CoV-2 pseudotype infectivity data and confirmed that ACE2 variants with reduced affinity for Spike can protect cells from infection. The effect of variants with enhanced Spike affinity remains unclear, but we propose a mechanism whereby these alleles could cause greater viral spreading across tissues and cell types, as is consistent with emerging understanding regarding the interplay between receptor affinity and cell-surface abundance. Finally, we compared mCSM-PPI2 ∆\DeltaG predictions against our SPR data to assess the utility of predictions in this system. We found that predictions of decreased binding were well-correlated with experiment and could be improved by calibration, but disappointingly, predictions of highly enhanced binding were unreliable. Recalibrated predictions for all possible ACE2 missense variants at the Spike interface were calculated and used to estimate the overall burden of ACE2 variants on Covid-19.

The Dundee Resource for Sequence Analysis and Structure Prediction (DRSASP; http://www.compbio.dundee.ac.uk/drsasp.html) is a collection of web services provided by the Barton Group at the University of Dundee. DRSASP’s flagship services are the JPred4 webserver for secondary structure and solvent accessibility prediction and the JABAWS 2.2 webserver for multiple sequence alignment, disorder prediction, amino acid conservation calculations, and specificity-determining site prediction. DRSASP resources are available through conventional web interfaces and APIs but are also integrated into the Jalview sequence analysis workbench, which enables the composition of multitool interactive workflows. Other existing Barton Group tools are being brought under the banner of DRSASP, including NoD (Nucleolar localization sequence detector) and 14-3-3-Pred. New resources are being developed that enable the analysis of population genetic data in evolutionary and 3D structural contexts. Existing resources are actively developed to exploit new technologies and maintain parity with evolving web standards. DRSASP provides substantial computational resources for public use, and since 2016 DRSASP services have completed over 1.5 million jobs.