Novel drug-target interactions via link prediction and network embedding

BACKGROUND: As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures. RESULTS: We report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-drug and protein–protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target embedding vectors as input features. DT2Vec was compared with three top-performing graph similarity-based algorithms on a standard benchmark dataset and achieved competitive results. In order to explore credible novel DTIs, the model was applied to data from the ChEMBL repository that contain experimentally validated positive and negative interactions which yield a strong predictive model. Then, the developed model was applied to all possible unknown DTIs to predict new interactions. The applicability of DT2Vec as an effective method for drug repurposing is discussed through case studies and evaluation of some novel DTI predictions is undertaken using molecular docking. CONCLUSIONS: The proposed method was able to integrate and map chemical and genomic space into low-dimensional dense vectors and showed promising results in predicting novel DTIs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04650-w

Auto-Antibodies to β-F1-ATPase and Vimentin in Malignant Mesothelioma

Patients with Malignant Mesothelioma (MM) develop unidentified auto-antibodies to MM tumour antigens. This study was conducted to identify the targets of MM patient auto-antibodies in order to try to understand more of the anti-tumour response and to determine if these antibodies might be helpful for diagnosis or prognostication. Using MM patient sera in a Western immunoblott screening strategy, no common immunoreactive proteins were identified. The sera from one long-term survivor recognised a protein band of 50–60 kDa present in cell lysates from four of five MM cell lines tested. The immunoreactive proteins in this band were identified by 2D electrophoretic separation of a MM cell line protein lysate, followed by analysis of excised immunoreactive proteins on a MALDI TOF mass spectrometer and peptide mass fingerprinting. The immunoreactive proteins identified were vimentin (accession gi55977767) and the ATP synthase (F1-ATPase) beta chain (accession gi114549 and gi47606749). ELISA assays were developed for antibodies to these proteins. Neither vimentin (median and 95% CI 0.346; 0.32–0.468 for MM patients, 0.327; 0.308–0.428 for controls) nor ß-F1-ATPase (0.257; 0.221–0.453 for MM patients, 0.263; 0.22–0.35 for controls) showed significant differences in autoantibody levels between a group of MM patients and controls. Using a dichotomized antibody level (high, low) for these targets we demonstrated that vimentin antibody levels were not associated with survival. In contrast, high ß-F1-ATPase antibody levels were significantly associated with increased median survival (18 months) compared to low ß F1 ATPase antibody levels (9 months; p = 0.049). Immunohistochemical analysis on a MM tissue microarray showed cytoplasmic staining in 28 of 33 samples for vimentin and strong cytoplasmic staining in14 and weak in 16 samples for ß-F1-ATPase. Therefore antibodies to neither vimentin nor ß-F1-ATPase are useful for differential diagnosis of MM, however high antibody levels to ß-F1-ATPase may be associated with increased survival and this warrants further investigation

A tool kit for rapid cloning and expression of recombinant antibodies

Over the last four decades, molecular cloning has evolved tremendously. Efficient products allowing assembly of multiple DNA fragments have become available. However, cost-effective tools for engineering antibodies of different specificities, isotypes and species are still needed for many research and clinical applications in academia. Here, we report a method for one-step assembly of antibody heavy- and light-chain DNAs into a single mammalian expression vector, starting from DNAs encoding the desired variable and constant regions, which allows antibodies of different isotypes and specificity to be rapidly generated. As a proof of principle we have cloned, expressed and characterized functional recombinant tumor-associated antigen-specific chimeric IgE/κ and IgG(1)/κ, as well as recombinant grass pollen allergen Phl p 7 specific fully human IgE/λ and IgG(4)/λ antibodies. This method utilizing the antibody expression vectors, available at Addgene, has many applications, including the potential to support simultaneous processing of antibody panels, to facilitate mechanistic studies of antigen-antibody interactions and to conduct early evaluations of antibody functions

Clustering and Sharing Incentives in BitTorrent Systems

Peer-to-peer protocols play an increasingly instrumental role in Internet content distribution. Consequently, it is important to gain a full understanding of how these protocols behave in practice and how their parameters impact overall performance. We present the first experimental investigation of the peer selection strategy of the popular BitTorrent protocol in an instrumented private torrent. By observing the decisions of more than 40 nodes, we validate three BitTorrent properties that, though widely believed to hold, have not been demonstrated experimentally. These include the clustering of similar-bandwidth peers, the effectiveness of BitTorrent's sharing incentives, and the peers' high average upload utilization. In addition, our results show that BitTorrent's new choking algorithm in seed state provides uniform service to all peers, and that an underprovisioned initial seed leads to the absence of peer clustering and less effective sharing incentives. Based on our observations, we provide guidelines for seed provisioning by content providers, and discuss a tracker protocol extension that addresses an identified limitation of the protocol

Gravitational wave mergers as tracers of large scale structures

Clustering measurements of Gravitational Wave (GW) mergers in Luminosity Distance Space can be used in the future as a powerful tool for Cosmology. We consider tomographic measurements of the Angular Power Spectrum of mergers both in an Einstein Telescope-like detector network and in some more advanced scenarios (more sources, better distance measurements, better sky localization). We produce Fisher forecasts for both cosmological (matter and dark energy) and merger bias parameters. Our fiducial model for the number distribution and bias of GW events is based on results from hydrodynamical simulations. The cosmological parameter forecasts with Einstein Telescope are less powerful than those achievable in the near future via galaxy clustering observations with, e.g., Euclid. However, in the more advanced scenarios we see significant improvements. Moreover, we show that bias can be detected at high statistical significance. Regardless of the specific constraining power of different experiments, many aspects make this type of analysis interesting anyway

An Atlas of Human Regulatory T Helper-like Cells Reveals Features of Th2-like Tregs that Support a Tumorigenic Environment

Regulatory T cells (Tregs) play a pivotal role in maintaining immunological tolerance, but they can also play a detrimental role by preventing antitumor responses. Here, we characterized T helper (Th)-like Treg subsets to further delineate their biological function and tissue distribution, focusing on their possible contribution to disease states. RNA sequencing and functional assays revealed that Th2-like Tregs displayed higher viability and autocrine interleukin-2 (IL-2)-mediated activation than other subsets. Th2-like Tregs were preferentially found in tissues rather than circulation and exhibited the highest migratory capacity toward chemokines enriched at tumor sites. These cellular responses led us to hypothesize that this subset could play a role in maintaining a tumorigenic environment. Concurrently, Th2-like Tregs were enriched specifically in malignant tissues from patients with melanoma and colorectal cancer compared to healthy tissue. Overall, our results suggest that Th2-like Tregs may contribute to a tumorigenic environment due to their increased cell survival, higher migratory capacity, and selective T-effector suppressive ability. Graphical Abstrac