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Showing 1381 to 1384 of 5186 entries
Published on November 18, 2021
A Boron Delivery Antibody (BDA) with Boronated Specific Residues: New Perspectives in Boron Neutron Capture Therapy from an In Silico Investigation.
Abstract: Boron Neutron Capture Therapy (BNCT) is a tumor cell-selective radiotherapy based on a nuclear reaction that occurs when the isotope boron-10 ((10)B) is radiated by low-energy thermal neutrons or epithermal neutrons, triggering a nuclear fission response and enabling a selective administration of irradiation to cells. Hence, we need to create novel delivery agents containing (10)B with high tumor selectivity, but also exhibiting low intrinsic toxicity, fast clearance from normal tissue and blood, and no pharmaceutical effects. In the past, boronated monoclonal antibodies have been proposed using large boron-containing molecules or dendrimers, but with no investigations in relation to maintaining antibody specificity and structural and functional features. This work aims at improving the potential of monoclonal antibodies applied to BNCT therapy, identifying in silico the best native residues suitable to be substituted with a boronated one, carefully evaluating the effect of boronation on the 3D structure of the monoclonal antibody and on its binding affinity. A boronated monoclonal antibody was thus generated for specific (10)B delivery. In this context, we have developed a case study of Boron Delivery Antibody Identification Pipeline, which has been tested on cetuximab. Cetuximab is an epidermal growth factor receptor (EGFR) inhibitor used in the treatment of metastatic colorectal cancer, metastatic non-small cell lung cancer, and head and neck cancer.
Published on November 17, 2021
Structure prediction of cyclic peptides by molecular dynamics + machine learning.
Abstract: Recent computational methods have made strides in discovering well-structured cyclic peptides that preferentially populate a single conformation. However, many successful cyclic-peptide therapeutics adopt multiple conformations in solution. In fact, the chameleonic properties of some cyclic peptides are likely responsible for their high cell membrane permeability. Thus, we require the ability to predict complete structural ensembles for cyclic peptides, including the majority of cyclic peptides that have broad structural ensembles, to significantly improve our ability to rationally design cyclic-peptide therapeutics. Here, we introduce the idea of using molecular dynamics simulation results to train machine learning models to enable efficient structure prediction for cyclic peptides. Using molecular dynamics simulation results for several hundred cyclic pentapeptides as the training datasets, we developed machine-learning models that can provide molecular dynamics simulation-quality predictions of structural ensembles for all the hundreds of thousands of sequences in the entire sequence space. The prediction for each individual cyclic peptide can be made using less than 1 second of computation time. Even for the most challenging classes of poorly structured cyclic peptides with broad conformational ensembles, our predictions were similar to those one would normally obtain only after running multiple days of explicit-solvent molecular dynamics simulations. The resulting method, termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), is the first technique capable of efficiently predicting complete structural ensembles of cyclic peptides without relying on additional molecular dynamics simulations, constituting a seven-order-of-magnitude improvement in speed while retaining the same accuracy as explicit-solvent simulations.
Published on November 17, 2021
Drug-target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization.
Abstract: BACKGROUND: Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug-target interactions (DTIs), which is one of the significant points in drug discovery, has been considered by many researchers in recent years. It also reduces the search space of interactions by proposing potential interaction candidates. RESULTS: In this paper, a new approach based on unifying matrix factorization and nuclear norm minimization is proposed to find a low-rank interaction. In this combined method, to solve the low-rank matrix approximation, the terms in the DTI problem are used in such a way that the nuclear norm regularized problem is optimized by a bilinear factorization based on Rank-Restricted Soft Singular Value Decomposition (RRSSVD). In the proposed method, adjacencies between drugs and targets are encoded by graphs. Drug-target interaction, drug-drug similarity, target-target, and combination of similarities have also been used as input. CONCLUSIONS: The proposed method is evaluated on four benchmark datasets known as Enzymes (E), Ion channels (ICs), G protein-coupled receptors (GPCRs) and nuclear receptors (NRs) based on AUC, AUPR, and time measure. The results show an improvement in the performance of the proposed method compared to the state-of-the-art techniques.
Published on November 16, 2021
COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug-Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19.
Abstract: The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19. SciBiteAI ontological tagging of the COVID Open Research Data set (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of the target protein and drug terms, and confidence scores were calculated for each entity pair. COKE processing of the current CORD-19 database identified about 3000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. The COKE repository and web application can serve as a useful resource for drug repurposing against SARS-CoV-2. COKE is freely available at https://coke.mml.unc.edu/, and the code is available at https://github.com/DnlRKorn/CoKE.
Published on November 16, 2021
Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example.
Abstract: Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.
Published on November 11, 2021
Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets.
Abstract: Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln4 as the target of N-pyrimidinyl-beta-thiophenylacrylamide (NP-BTA), an antifungal compound.
Published on November 9, 2021
Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features.
Abstract: The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict DDI types for histamine antagonist drugs using simplified molecular-input line-entry systems (SMILES) combined with interaction features based on CYP450 group as inputs. The data used in our research consisted of approved drugs of histamine antagonists that are connected to 26,344 DDI pairs from the DrugBank database. Various classification algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost were used with 5-fold cross-validation to approach a large-scale DDIs prediction among histamine antagonist drugs. The prediction performance shows that our model outperformed previously published works on DDI prediction with the best precision of 0.788, a recall of 0.921, and an F1-score of 0.838 among 19 given DDIs types. An important finding of the study is that our prediction is based solely on the SMILES and CYP450 and thus can be applied at the early stage of drug development.
Published on November 9, 2021
Characterizing the Genomic Profile in High-Grade Gliomas: From Tumor Core to Peritumoral Brain Zone, Passing through Glioma-Derived Tumorspheres.
Abstract: Glioblastoma is an extremely heterogeneous disease. Treatment failure and tumor recurrence primarily reflect the presence in the tumor core (TC) of the glioma stem cells (GSCs), and secondly the contribution, still to be defined, of the peritumoral brain zone (PBZ). Using the array-CGH platform, we deepened the genomic knowledge about the different components of GBM and we identified new specific biomarkers useful for new therapies. We firstly investigated the genomic profile of 20 TCs of GBM; then, for 14 cases and 7 cases, respectively, we compared these genomic profiles with those of the related GSC cultures and PBZ biopsies. The analysis on 20 TCs confirmed the intertumoral heterogeneity and a high percentage of copy number alterations (CNAs) in GBM canonical pathways. Comparing the genomic profiles of 14 TC-GSC pairs, we evidenced a robust similarity among the two samples of each patient. The shared imbalanced genes are related to the development and progression of cancer and in metabolic pathways, as shown by bioinformatic analysis using DAVID. Finally, the comparison between 7 TC-PBZ pairs leads to the identification of PBZ-unique alterations that require further investigation.
Published on November 9, 2021
Machine Learning and Network Medicine approaches for Drug Repositioning for COVID-19.
Abstract: We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorisation algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 Repositioning Explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.
Published on November 9, 2021
Identification of potential pan-coronavirus therapies using a computational drug repurposing platform.
Abstract: In the past 20 years, there have been several infectious disease outbreaks in humans for which the causative agent has been a zoonotic coronavirus. Novel infectious disease outbreaks, as illustrated by the current coronavirus disease 2019 (COVID-19) pandemic, demand a rapid response in terms of identifying effective treatments for seriously ill patients. The repurposing of approved drugs from other therapeutic areas is one of the most practical routes through which to approach this. Here, we present a systematic network-based drug repurposing methodology, which interrogates virus-human, human protein-protein and drug-protein interactome data. We identified 196 approved drugs that are appropriate for repurposing against COVID-19 and 102 approved drugs against a related coronavirus, severe acute respiratory syndrome (SARS-CoV). We constructed a protein-protein interaction (PPI) network based on disease signatures from COVID-19 and SARS multi-omics datasets. Analysis of this PPI network uncovered key pathways. Of the 196 drugs predicted to target COVID-19 related pathways, 44 (hypergeometric p-value: 1.98e-04) are already in COVID-19 clinical trials, demonstrating the validity of our approach. Using an artificial neural network, we provide information on the mechanism of action and therapeutic value for each of the identified drugs, to facilitate their rapid repurposing into clinical trials.
Showing 1381 to 1384 of 5186 entries