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Abstract: PIF1 is a conserved helicase and G4 DNA binding and unwinding enzyme, with roles in genome stability. Human PIF1 (hPIF1) is poorly understood, but its functions can become critical for tumour cell survival during oncogene-driven replication stress. Here we report the discovery, via an X-ray crystallographic fragment screen (XChem), of hPIF1 DNA binding and unwinding inhibitors. A structure was obtained with a 4-phenylthiazol-2-amine fragment bound in a pocket between helicase domains 2A and 2B, with additional contacts to Valine 258 from domain 1A. The compound makes specific interactions, notably through Leucine 548 and Alanine 551, that constrain conformational adjustments between domains 2A and 2B, previously linked to ATP hydrolysis and DNA unwinding. We next synthesized a range of related compounds and characterized their effects on hPIF1 DNA-binding and helicase activity in vitro, expanding the structure activity relationship (SAR) around the initial hit. A systematic analysis of clinical cancer databases is also presented here, supporting the notion that hPIF1 upregulation may represent a specific cancer cell vulnerability. The research demonstrates that hPIF1 is a tractable target through 4-phenylthiazol-2-amine derivatives as inhibitors of its helicase action, setting a foundation for creation of a novel class of anti-cancer therapeutics.

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Abstract: Microbial growth often occurs within multicellular communities called biofilms, where cells are enveloped by a protective extracellular matrix. Bacillus subtilis serves as a model organism for biofilm research and produces two crucial secreted proteins, BslA and TasA, vital for biofilm matrix formation. BslA exhibits surface-active properties, spontaneously self-assembling at hydrophobic/hydrophilic interfaces to form an elastic protein film, which renders B. subtilis biofilm surfaces water-repellent. TasA is traditionally considered a fiber-forming protein with multiple matrix-related functions. In our current study, we investigate whether TasA also possesses interfacial properties and whether it has any impact on BslA’s ability to form an interfacial protein film. Our research demonstrates that TasA indeed exhibits interfacial activity, partitioning to hydrophobic/hydrophilic interfaces, stabilizing emulsions, and forming an interfacial protein film. Interestingly, TasA undergoes interface-induced restructuring similar to BslA, showing an increase in β-strand secondary structure. Unlike BslA, TasA rapidly reaches the interface and forms nonelastic films that rapidly relax under pressure. Through mixed protein pendant drop experiments, we assess the influence of TasA on BslA film formation, revealing that TasA and other surface-active molecules can compete for interface space, potentially preventing BslA from forming a stable elastic film. This raises a critical question: how does BslA self-assemble to form the hydrophobic “raincoat” observed in biofilms in the presence of other potentially surface-active species? We propose a model wherein surface-active molecules, including TasA, initially compete with BslA for interface space. However, under lateral compression or pressure, BslA retains its position, expelling other molecules into the bulk. This resilience at the interface may result from structural rearrangements and lateral interactions between BslA subunits. This combined mechanism likely explains BslA’s role in forming a stable film integral to B. subtilis biofilm hydrophobicity.

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Abstract: Fragment-based lead discovery (FBLD) is a powerful application for developing ligands as modulators of disease targets. This approach strategy involves identification of interactions between low-molecular weight compounds (100–300 Da) and their putative targets, often with low affinity (KD ~0.1–1 mM) interactions. The focus of this screening methodology is to optimize and streamline identification of fragments with higher ligand efficiency (LE) than typical high-throughput screening. The focus of this review is on the last half decade of fragment-based drug discovery strategies that have been used for antimicrobial drug discovery.

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Abstract: The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by identifying interactions between drug molecules and target proteins based on computational methods have gained growing attention. Here, we propose the DeepLPI, a novel deep learning-based model that mainly consists of ResNet-based 1-dimensional convolutional neural network (1D CNN) and bi-directional long short term memory network (biLSTM), to establish an end-to-end framework for protein–ligand interaction prediction. We first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two ResNet-based 1D CNN modules to derive features, respectively. The extracted feature vectors are concatenated and further fed into the biLSTM network, followed by the MLP module to finally predict protein–ligand interaction. We downloaded the well-known BindingDB and Davis dataset for training and testing our DeepLPI model. We also applied DeepLPI on a COVID-19 dataset for externally evaluating the prediction ability of DeepLPI. To benchmark our model, we compared our DeepLPI with the baseline methods of DeepCDA and DeepDTA, and observed that our DeepLPI outperformed these methods, suggesting the high accuracy of the DeepLPI towards protein–ligand interaction prediction. The high prediction performance of DeepLPI on the different datasets displayed its high capability of protein–ligand interaction in generalization, demonstrating that the DeepLPI has the potential to pinpoint new drug-target interactions and to find better destinations for proven drugs.

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Abstract: The COVID-19 pandemic created an unprecedented global healthcare emergency prompting the exploration of new therapeutic avenues, including drug repurposing. A large number of ongoing studies revealed pervasive issues in clinical research, such as the lack of accessible and organised data. Moreover, current shortcomings in clinical studies highlighted the need for a multi-faceted approach to tackle this health crisis. Thus, we set out to explore and develop new strategies for drug repositioning by employing computational pharmacology, data mining, systems biology, and computational chemistry to advance shared efforts in identifying key targets, affected networks, and potential pharmaceutical intervention options. Our study revealed that formulating pharmacological strategies should rely on both therapeutic targets and their networks. We showed how data mining can reveal regulatory patterns, capture novel targets, alert about side-effects, and help identify new therapeutic avenues. We also highlighted the importance of the miRNA regulatory layer and how this information could be used to monitor disease progression or devise treatment strategies. Importantly, our work bridged the interactome with the chemical compound space to better understand the complex landscape of COVID-19 drugs. Machine and deep learning allowed us to showcase limitations in current chemical libraries for COVID-19 suggesting that both in silico and experimental analyses should be combined to retrieve therapeutically valuable compounds. Based on the gathered data, we strongly advocate for taking this opportunity to establish robust practices for treating today's and future infectious diseases by preparing solid analytical frameworks.