Understanding ligand properties is essential for computational high-throughput screening of transition metal complexes. However, ligand properties such as net charge and other information such as thei Show more
Understanding ligand properties is essential for computational high-throughput screening of transition metal complexes. However, ligand properties such as net charge and other information such as their application area are often absent or inconsistently recorded in crystallographic datasets. Here, we construct a ligand dataset from 126,985 mononuclear transition metal complexes curated from the Cambridge Structural Database. Using an iterative charge-balancing workflow that combines complex charges, metal oxidation states, and consensus across crystallographic observations, we confidently assign net charges to 66,810 ligands among 94,581 identified unique ligand structures to curate the Boston Open-Shell Ligand (BOS-Lig) dataset. The workflow assigns ligand charges in homoleptic complexes first and then iteratively propagates these assignments across heteroleptic environments, allowing charges to be inferred even when direct charge information is unavailable. We analyze cases where simple heuristics such as the octet rule would have failed and introduce a purity metric to identify when our charge assignments may be incorrect. Each ligand is also classified in terms of its metal coordinating atoms and whether there are multiple variants (i.e., hemilability). We then link complexes to their associated journal abstracts and apply a topic-modeling workflow to link 25,146 ligands with functional application areas spanning reactivity, redox chemistry, biological chemistry, and photophysical chemistry. Together, we provide an experimentally grounded dataset of ligand chemical space that connects charge and functional application as a foundation for computational screening and data-driven ligand design. Show less
Ran-binding domain-containing protein 2 (ZRANB2) is a zinc finger (ZF) protein that plays a key role in alternative splicing. ZRANB2 is composed of two ZF domains that contain four invariant cysteine Show more
Ran-binding domain-containing protein 2 (ZRANB2) is a zinc finger (ZF) protein that plays a key role in alternative splicing. ZRANB2 is composed of two ZF domains that contain four invariant cysteine residues per domain. ZRANB2 binds RNA targets that contain AGGUAA sequence motifs. Three constructs of ZRANB2, ZRANB2-ZF1 (first ZF domain), ZRANB2-ZF2 (second ZF domain), and ZRANB2-2D (both ZF domains), were isolated in the apo form and shown to bind Zn(II) via UV-visible-monitored competitive titrations with Co(II) as a spectroscopic probe. Zn binding to each construct led to the adoption of a limited secondary structure of each domain, as measured by circular dichroism (CD). Hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) of the two-domain construct, ZRANB2-2D, revealed that both ZF domains adopt a more rigid structure upon Zn binding. Zn binding to the first ZF domain resulted in a greater decrease in the conformational dynamics than Zn binding to the second ZF domain. RNA binding to TRA2B pre-mRNA, a physiological splicing target, was measured by fluorescence anisotropy (FA), and high-affinity RNA binding was found to require Zn coordination to both domains. HDX-MS of ZRANB2-2D with TRA2B RNA as well as two optimized RNA sequences that contain a single and double AGGUAA hexamer revealed additional protection from H/D exchange for ZRANB2 in the presence of RNA. Here, greater protection was observed for the second ZF of ZRANB2-2D, suggesting a larger effect on conformational dynamics. A model for zinc-mediated RNA binding of ZRANB2 is proposed. Show less
The success of cancer immunotherapies is predicated on the targeting of highly expressed neoepitopes, which preferentially favours malignancies with high mutational burden. Here we show that early res Show more
The success of cancer immunotherapies is predicated on the targeting of highly expressed neoepitopes, which preferentially favours malignancies with high mutational burden. Here we show that early responses by type-I interferons mediate the success of immune checkpoint inhibitors as well as epitope spreading in poorly immunogenic tumours and that these interferon responses can be enhanced via systemic administration of lipid particles loaded with RNA coding for tumour-unspecific antigens. In mice, the immune responses of tumours sensitive to checkpoint inhibitors were transferable to resistant tumours and resulted in heightened immunity with antigenic spreading that protected the animals from tumour rechallenge. Our findings show that the resistance of tumours to immunotherapy is dictated by the absence of a damage response, which can be restored by boosting early type-I interferon responses to enable epitope spreading and self-amplifying responses in treatment-refractory tumours. Show less
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potenti Show more
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation – and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration. Show less
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation Show more
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/ . Show less
Tumor-tissue and plasma concentrations of platinum were studied prospectively in two groups of eight patients who were suffering from advanced non-small-cell lung cancer. Treatments including two diff Show more
Tumor-tissue and plasma concentrations of platinum were studied prospectively in two groups of eight patients who were suffering from advanced non-small-cell lung cancer. Treatments including two different schedules of cisplatin administration (25 vs 100 mg/m2 on day 1) were compared. At 30 min after the beginning of the cisplatin infusion, blood samples and bronchoscopically obtained biopsy specimens were taken for determinations of platinum concentrations by means of flameless atomic absorption spectrophotometry. The procedure did not induce any complication. Total plasma platinum concentrations at 30 min were significantly lower (P<0.01) in patients receiving 25 mg/m2 (0.49±0.23 μg Pt/ml) than in those receiving 100 mg/m2 (1.44±0.62 μg Pt/ml), whereas no significant difference was observed in tumor-tissue platinum concentrations (22.49±53.89 ng Pt/mg in patients receiving 25 mg/m2 vs 51.13±65.52 ng Pt/mg in those receiving 100 mg/m2). There was a weak correlation between simultaneous plasma and tumor-tissue platinum concentrations at 30 min. Tumor-tissue platinum concentrations seem to be poorly influenced by the cisplatin dose. This finding suggests a great interindividual variability of platinum tumor-diffusion properties in non-small-cell lung cancer. Show less