Özlem Tastan Bishop

Deep mutational scanning (DMS) provides comprehensive maps of protein variant effects but remains experimentally intensive. Machine learning (ML) approache

Associated data and scripts for the research article, titled "Machine Learning Framework for Cost Effective Deep Mutational Scanning through Targeted Substitution Profiling", in press in the BMC Bioinformatics journal.

Associated data and scripts for the research article, titled "Machine Learning Framework for Cost Effective Deep Mutational Scanning through Targeted Substitution Profiling", in press in the BMC Bioinformatics journal.

Understanding the effects of single-nucleotide variations on the three-dimensional structures of drug-metabolizing enzymes at the atomic level is not a well-studied field and deserves attention as it could provide insights into tackling both drug resistance and drug efficacy/toxicity issues. Here, we examined molecular dynamics simulations of the isoniazid-metabolizing enzyme arylamine N-acetyltransferase 2 (NAT2) and established a computational approach to decipher how NAT2 transitions from being a rapid acetylator to a slow acetylator in the presence of residue variations. After revealing slight differences between two rapid acetylators (the NAT2 reference allele and the legacy reference allele), we compared them to five variants with slow clinical phenotypes. In all slow acetylators, we observed a significant reduction in hydrogen bonding and dynamic residue network metric values in functional residues. For example, in the R64Q+K268R variant, the R64Q substitution causes a substantial reduction of hydrogen bonds, resulting in increased root-mean-square fluctuation and changes in residue networks. This is evidenced by the significantly decreased betweenness centrality values of catalytic D122 and putative isoniazid-binding residues S125 and F217, as well as the significantly decreased eigencentrality values of N72, D122, and the active site loop residue G124. Also, in this variant, the average distance between the putative isoniazid-binding residue F217 and catalytic residues H107 and D122 is significantly reduced. This structural shift may affect ligand binding affinity and catalysis. Overall, our findings offer new insights into how allosteric variations alter the NAT2 mechanism, leading to changes in drug exposure.

Targeting Plasmodium falciparum (Pf) aminoacyl tRNA synthetases is a viable strategy to overcome malaria parasite multi-drug resistance. Here, we focused on Pf Isoleucyl tRNA synthetase (PfIleRS) to identify potential allosteric inhibitors from 1019 South African Natural Compounds (SANC). Eleven potential hits, which passed ADMET and PAINS, were selected based on their docking binding affinity which was higher for PfIleRS than for human IleRS. Molecular dynamics simulations revealed that the compounds, particularly SANC456, commonly induced considerable changes in the global conformation and dynamics of PfIleRS, suggesting potential allosteric modulatory effects. Importantly, all 11 SANC hits reduced the binding affinity of the nucleotide AMP molecule by at least 25%. Some SANC ligand-bound systems (SANC456, SANC1095, and SANC1104) significantly increased the distance between the AMP and Ile ligands. Possible explanations for these changes were explored using three dynamic residue network centrality metrics. Betweenness centrality identified a major allosteric pathway in holo PfIleRS spanning the entire protein length. In contrast, SANC382, SANC456, SANC522, SANC806 and SANC1095 ligand-bound systems exhibited delta BC pathways (SANC-protein minus holo-protein), induced by the ligands, extending from their respective pockets into the active site. Additionally, eigenvector centrality revealed two important residue clusters either side of the holo active site which became altered in the ligand-bound systems, indicating possible allosteric activity. Lastly, many SANC systems showed decreased closeness centrality of zinc finger and active site residues, including the HYGH and KMSKR motifs. We believe that the compounds identified in this study as potential allosteric inhibitors have strong translational potential and warrant further investigation through in vitro and in vivo experiments. Overall, they hold promise as starting points for the development of new and effective antimalarial therapies, particularly against multidrug-resistant Plasmodium parasites.

Pyrimidine nucleotides are essential for a wide variety of cellular processes and are synthesized either via a salvage pathway or through de novo biosynthesis. The latter is particularly important in proliferating cells, such as infectious diseases and cancer cells. Aspartate transcarbamoylase (ATCase) catalyzes the first committed and rate‐limiting step in the de novo pyrimidine biosynthesis pathway, making it an attractive therapeutic target for various diseases. This review summarizes the development of a series of allosteric ATCase inhibitors, advancing them as potential candidates for malarial, tuberculosis and cancer therapies. Furthermore, it explores the potential for these compounds to be expanded into drugs targeting neglected tropical diseases, antimicrobial‐resistant infections caused by the ESKAPE pathogens, and their possible application as herbicides. We identify the likely equivalent allosteric pocket in these systems and perform a structure and sequence‐based analysis of the residues comprising it, providing a rationale for continued exploration of this compound series as both specific and broad‐range inhibitors. The review concludes by emphasizing the importance of continued research into ATCase inhibitors, given their potential broad applicability in treating diverse diseases to enhance both human health and agricultural practices.

Severe acute respiratory symptom coronavirus 2 (SARS-CoV-2) infection occurs via the attachment of the spike (S) protein’s receptor binding domain (RBD) to human ACE2 (hACE2). Natural polymorphisms in hACE2, particularly at the interface, may alter RBD–hACE2 interactions, potentially affecting viral infectivity across populations. This study identified the effects of six naturally occurring hACE2 polymorphisms with high allele frequency in the African population (S19P, K26R, M82I, K341R, N546D and D597Q) on the interaction with the S protein RBD of the BA.4/5 Omicron sub-lineage through post-molecular dynamics (MD), inter-protein interaction and dynamic residue network (DRN) analyses. Inter-protein interaction analysis suggested that the K26R variation, with the highest interactions, aligns with reports of enhanced RBD binding and increased SARS-CoV-2 susceptibility. Conversely, S19P, showing the fewest interactions and largest inter-protein distances, agrees with studies indicating it hinders RBD binding. The hACE2 M82I substitution destabilized RBD–hACE2 interactions, reducing contact frequency from 92 (WT) to 27. The K341R hACE2 variant, located distally, had allosteric effects that increased RBD–hACE2 contacts compared to WThACE2. This polymorphism has been linked to enhanced affinity for Alpha, Beta and Delta lineages. DRN analyses revealed that hACE2 polymorphisms may alter the interaction networks, especially in key residues involved in enzyme activity and RBD binding. Notably, S19P may weaken hACE2–RBD interactions, while M82I showed reduced centrality of zinc and chloride-coordinating residues, hinting at impaired communication pathways. Overall, our findings show that hACE2 polymorphisms affect S BA.4/5 RBD stability and modulate spike RBD–hACE2 interactions, potentially influencing SARS-CoV-2 infectivity—key insights for vaccine and therapeutic development.

The use of microsporidia as a disease-transmission-blocking tool has garnered significant attention. Microsporidia sp. MB, known for its ability to block malaria development in mosquitoes, is an optimal candidate for supplementing malaria vector control methods. This symbiont, found in Anopheles mosquitoes, can be transmitted both vertically and horizontally with minimal effects on its mosquito host. Its genome, recently sequenced from An. arabiensis, comprises a compact 5.9 Mbp. Here, we analyze the Microsporidia sp. MB genome, highlighting its major genomic features, gene content, and protein function. The genome contains 2247 genes, predominantly encoding enzymes. Unlike other members of the Enterocytozoonida group, Microsporidia sp. MB has retained most of the genes in the glycolytic pathway. Genes involved in RNA interference (RNAi) were also identified, suggesting a mechanism for host immune suppression. Importantly, meiosis-related genes (MRG) were detected, indicating potential for sexual reproduction in this organism. Comparative analyses revealed similarities with its closest relative, Vittaforma corneae, despite key differences in host interactions. This study provides an in-depth analysis of the newly sequenced Microsporidia sp. MB genome, uncovering its unique adaptations for intracellular parasitism, including retention of essential metabolic pathways and RNAi machinery. The identification of MRGs suggests the possibility of sexual reproduction, offering insights into the symbiont’s evolutionary strategies. Establishing a reference genome for Microsporidia sp. MB sets the foundation for future studies on its role in malaria transmission dynamics and host-parasite interactions.

Deciphering the effect of evolutionary mutations of viruses and predicting future mutations is crucial for designing long-lasting and effective drugs. While understanding the impact of current mutations on protein drug targets is feasible, predicting future mutations due to natural evolution of viruses and environmental pressures remains challenging. Here, we leveraged existing mutation data during the evolution of the SARS-CoV-2 protein drug target main protease (M ^pro ) to test the predictive power of dynamic residue network (DRN) analysis in identifying mutation cold and hot spots. We conducted molecular dynamics simulations on the M ^pro of SARS-CoV-2 (Wuhan strain) and calculated eight DRN metrics ( averaged BC , CC , DC , EC , ECC , KC, L , PR ), each of which identifies a unique network feature within the protein. The sets of residues with the highest and lowest values for each metric, comprising potential cold and hot spots, were compared to published biochemical analyses and per residue mutation frequencies observed across five SARS-CoV-2 lineages, encompassing a total of 191,878 sequences. Individual DRN metrics displayed only modest power to predict the mutation frequency of individual residues. However, integrating the eight DRN metrics with additional structural and sequence-derived metrics allowed us to develop machine learning models which significantly improved the prediction of residue mutation frequency. While further refinements should enhance accuracy, we demonstrated a robust method to understand pathogen evolution. This approach can also guide the development of long-lasting drugs by targeting functional residues located in and near active site, and allosteric sites, that are less prone to mutations.