Time-Prolonged Release of Tumor-Targeted Protein–MMAE Nanoconjugates from Implantable Hybrid Materials

The sustained release of small, tumor-targeted cytotoxic drugs is an unmet need in cancer therapies, which usually rely on punctual administration regimens of non-targeted drugs. Here, we have developed a novel concept of protein–drug nanoconjugates, which are packaged as slow-releasing chemically hybrid depots and sustain a prolonged secretion of the therapeutic agent. For this, we covalently attached hydrophobic molecules (including the antitumoral drug Monomethyl Auristatin E) to a protein targeting a tumoral cell surface marker abundant in several human neoplasias, namely the cytokine receptor CXCR4. By this, a controlled aggregation of the complex is achieved, resulting in mechanically stable protein–drug microparticles. These materials, which are mimetics of bacterial inclusion bodies and of mammalian secretory granules, allow the slow leakage of fully functional conjugates at the nanoscale, both in vitro and in vivo. Upon subcutaneous administration in a mouse model of human CXCR4+ lymphoma, the protein–drug depots release nanoconjugates for at least 10 days, which accumulate in the tumor with a potent antitumoral effect. The modification of scaffold cell-targeted proteins by hydrophobic drug conjugation is then shown as a novel transversal platform for the design of slow releasing protein–drug depots, with potential application in a broad spectrum of clinical settings.

GNN-SubNet: disease subnetwork detection with explainable Graph Neural Networks

The tremendous success of graphical neural networks (GNNs) has already had a major impact on systems biology research. For example, GNNs are currently used for drug target recognition in protein-drug interaction networks as well as cancer gene discovery and more. Important aspects whose practical relevance is often underestimated are comprehensibility, interpretability, and explainability. In this work, we present a graph-based deep learning framework for disease subnetwork detection via explainable GNNs. In our framework, each patient is represented by the topology of a protein-protein network (PPI), and the nodes are enriched by molecular multimodal data, such as gene expression and DNA methylation. Therefore, our novel modification of the GNNexplainer for model-wide explanations can detect potential disease subnetworks, which is of high practical relevance. The proposed methods are implemented in the GNN-SubNet Python program, which we have made freely available on our GitHub for the international research community (https://github.com/pievos101/GNN-SubNet).

Probing Protein Aggregation at Buried Interfaces: Distinguishing Adsorbed Protein Monomer, Dimer, and Monomer-Dimer Mixture in Situ

Protein adsorption on surfaces greatly impacts many applications such as biomedical materials, anti-biofouling coatings, bio-separation membranes, biosensors, and antibody protein drugs etc. For example, protein drug adsorption on widely used...

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even...

Network analysis of Dengue NS1 interacting core human proteins driving dengue pathogenesis

Aim: We aimed to identify critical human proteins involved in cathepsin L regulation Background: It has been shown that Dengue Virus (DENV) NS1 activates cathepsin L (CTSL). The CTSL activates heparanase, which cleaves heparan sulfate proteoglycans and causes dengue pathogenesis. NS1 directly interacts with PTBP1 and Gab proteins. Gab protein activates the Ras signaling pathway. Still, no known direct interaction partners are linking GAB1 to cathepsin L. Objective: Our objective includes three main points.1-Network analysis of NS1 interacting human proteins 2- Identification of protein-drug and protein-disease interactions 3- Identification of core proteins involved in cathepsin L regulation. Method: We collected NS1 interacting Human proteins from DenHunt, Int-Act Molecular Interaction Database, Virus Mentha, Virus Pathogen Database and Analysis Resource (ViPR), and Virus MINT. We employed Pesca, cytohubba, and centiscape as the significant plug-ins in Cytoscape for network analysis. To study protein-diseases and protein-drugs interaction, we used NetworkAnalyst. Result: Based on the prior knowledge on the interaction of NS1 with GAB1 and PTBP1 human proteins, we found several core proteins that drive dengue pathogenesis. The proteins EED, NXF1, and MOV10, are the mediators between PTBP1 and CTSL. Similarly, DNM2, GRB2, PXN, PTPRC, and NTRK1 mediate GAB1 and PTBP1. The common first neighbors of MOV10, NXF1, and EED were identified, and the common primary pathways in all three subnetworks were mRNA processing and protein translation. The common interaction partners were considered for drug and disease network analysis. These proteins were; PARP1, NFKB2, HDAC2, SLC25A4, ATP5A1, EPN1, CTSL, UBR4, CLK3, and ARPC4. PARP1 was the highly connected node in the protein-drug network. The highest degree protein, LMNA, was associated with many diseases. The NXF1 is connected with LMNA. Here, we reported one essential protein, namely, NXF1 protein, which links PTBP1 with CTSL. The NXF1 is also connected with TPM3, which is connected to CTSL. Conclusion: We listed functionally important proteins which are involved in cathepsin L activation. Based on network properties, we proposed, NXF1 and TPM3 are the important high centrality proteins in dengue infection.

Rethinking protein drug design with highly accurate structure prediction of anti-CRISPR proteins

Protein therapeutics play an important role in controlling the functions and activities of disease-causing proteins in modern medicine. Despite protein therapeutics having several advantages over traditional small-molecule therapeutics, further development has been hindered by drug complexity and delivery issues. However, recent progress in deep learning-based protein structure prediction approaches such as AlphaFold opens new opportunities to exploit the complexity of these macro-biomolecules for highly-specialised design to inhibit, regulate or even manipulate specific disease-causing proteins. Anti-CRISPR proteins are small proteins from bacteriophages that counter-defend against the prokaryotic adaptive immunity of CRISPR-Cas systems. They are unique examples of natural protein therapeutics that have been optimized by the host-parasite evolutionary arms race to inhibit a wide variety of host proteins. Here, we show that these Anti-CRISPR proteins display diverse inhibition mechanisms through accurate structural prediction and functional analysis. We find that these phage-derived proteins are extremely distinct in structure, some of which have no homologues in the current protein structure domain. Furthermore, we find a novel family of Anti-CRISPR proteins which are structurally homologous to the recently-discovered mechanism of manipulating host proteins through enzymatic activity, rather than through direct inference. Using highly accurate structure prediction, we present a wide variety of protein-manipulating strategies of anti-CRISPR proteins for future protein drug design.