Abstract: Compared to adult carcinomas, there is a paucity of targeted treatments for solid tumors in children, adolescents, and young adults (C-AYA). The impact of germline genomic signatures has implications for heritability, but its impact on targeted therapies has not been fully appreciated. Performing variant-prioritization analysis on germline DNA of 1,507 C-AYA patients with solid tumors, we show 12% of these patients carrying germline pathogenic and/or likely pathogenic variants (P/LP) in known cancer-predisposing genes (KCPG). An additional 61% have germline pathogenic variants in non-KCPG genes, including PRKN, SMARCAL1, SMAD7, which we refer to as candidate genes. Despite germline variants in a broad gene spectrum, pathway analysis leads to top networks centering around p53. Our drug-target analysis shows 1/3 of patients with germline P/LP variants have at least one druggable alteration, while more than half of them are from our candidate gene group, which would otherwise go unidentified in routine clinical care.

Abstract: Silymarin is the standardized extract from the fruits of Silybum marianum (L.) Gaertn., a well-known hepatoprotectant and antioxidant. Recently, bioactive compounds of silymarin, i.e., silybins and their 2,3-dehydro derivatives, have been shown to exert anticancer activities, yet with unclear mechanisms. This study combines in silico and in vitro methods to reveal the potential interactions of optically pure silybins and dehydrosilybins with novel protein targets. The shape and chemical similarity with approved drugs were evaluated in silico, and the potential for interaction with the Hedgehog pathway receptor Smoothened (SMO) and BRAF kinase was confirmed by molecular docking. In vitro studies on SMO and BRAF V600E kinase activity and in BRAF V600E A-375 human melanoma cell lines were further performed to examine their effects on these proteins and cancer cell lines and to corroborate computational predictions. Our in silico results direct to new potential targets of silymarin constituents as dual inhibitors of BRAF and SMO, two major targets in anticancer therapy. The experimental studies confirm that BRAF kinase and SMO may be involved in mechanisms of anticancer activities, demonstrating dose-dependent profiles, with dehydrosilybins showing stronger effects than silybins. The results of this work outline the dual SMO/BRAF effect of flavonolignans from Silybum marianum with potential clinical significance. Our approach can be applied to other natural products to reveal their potential targets and mechanism of action.

Abstract: To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is needed. In contrast, extant predictive methods do not comprehensively address these aspects of drug discovery and rely on features derived from extensive, often unavailable experimental information for novel molecules. To address these issues, we developed MEDICASCY, a multilabel-based boosted random forest machine learning method that only requires the small molecule's chemical structure for the drug side effect, indication, efficacy, and probable mode of action target predictions; however, it has comparable or even significantly better performance than existing approaches requiring far more information. In retrospective benchmarking on high confidence predictions, MEDICASCY shows about 78% precision and recall for predicting at least one severe side effect and 72% precision drug efficacy. Experimental validation of MEDICASCY's efficacy predictions on novel molecules shows close to 80% precision for the inhibition of growth in ovarian, breast, and prostate cancer cell lines. Thus, MEDICASCY should improve the success rate for new drug approval. A web service for academic users is available at http://pwp.gatech.edu/cssb/MEDICASCY.

Abstract: Discovery of drugs against newly emerged pathogenic agents like the SARS-CoV-2 coronavirus (CoV) must be based on previous research against related species. Scientists need to get acquainted with and develop a global oversight over so-far tested molecules. Chemography (herein used Generative Topographic Mapping, in particular) places structures on a human-readable 2D map (obtained by dimensionality reduction of the chemical space of molecular descriptors) and is thus well suited for such an audit. The goal is to map medicinal chemistry efforts so far targeted against CoVs. This includes comparing libraries tested against various virus species/genera, predicting their polypharmacological profiles and highlighting often encountered chemotypes. Maps are challenged to provide predictive activity landscapes against viral proteins. Definition of "anti-CoV" map zones led to selection of therein residing 380 potential anti-CoV agents, out of a vast pool of 800 M organic compounds.

Abstract: The process of drug discovery and drug development consumes billions of dollars to bring a new drug to the market. Drug development is time consuming and sometimes, the failure rates are high. Thus, the pharmaceutical industry is looking for a better option for new drug discovery. Drug repositioning is a good alternative technology that has demonstrated many advantages over de novo drug development, the most important one being shorter drug development timelines. In the last two decades, drug repositioning has made tremendous impact on drug development technologies. In this review, we focus on the recent advances in drug repositioning technologies and discuss the repositioned drugs used for inflammatory diseases such as sepsis, asthma, and atopic dermatitis.

Abstract: MOTIVATION: Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug-target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs. RESULTS: In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol). AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF.

Abstract: Coot is a tool widely used for model building, refinement, and validation of macromolecular structures. It has been extensively used for crystallography and, more recently, improvements have been introduced to aid in cryo-EM model building and refinement, as cryo-EM structures with resolution ranging 2.5-4 A are now routinely available. Model building into these maps can be time-consuming and requires experience in both biochemistry and building into low-resolution maps. To simplify and expedite the model building task, and minimize the needed expertise, new tools are being added in Coot. Some examples include morphing, Geman-McClure restraints, full-chain refinement, and Fourier-model based residue-type-specific Ramachandran restraints. Here, we present the current state-of-the-art in Coot usage.

Abstract: Hardware and software advancements along with the accumulation of large amounts of data in recent years have together spurred a remarkable growth in the application of neural networks to various scientific fields. Machine learning based on neural networks with multiple (hidden) layers is becoming an extremely powerful approach for analyzing data. With the accumulation of large amounts of protein data such as structural and functional assay data, the effects of such approaches within the field of protein informatics are increasing. Here, we introduce our recent studies based on applications of neural networks for protein structure and function prediction and dynamic analysis involving: (i) inter-residue contact prediction based on a multiple sequence alignment (MSA) of amino acid sequences, (ii) prediction of protein-compound interaction using assay data, and (iii) detection of protein allostery from trajectories of molecular dynamic (MD) simulation.

Abstract: Viral diseases are one of the leading causes of morbidity and mortality in the world. Virus-specific vaccines and antiviral drugs are the most powerful tools to combat viral diseases. However, broad-spectrum antiviral agents (BSAAs, i.e. compounds targeting viruses belonging to two or more viral families) could provide additional protection of the general population from emerging and re-emerging viral diseases, reinforcing the arsenal of available antiviral options. Here, we review discovery and development of BSAAs and summarize the information on 120 safe-in-man agents in a freely accessible database (https://drugvirus.info/). Future and ongoing pre-clinical and clinical studies will increase the number of BSAAs, expand the spectrum of their indications, and identify drug combinations for treatment of emerging and re-emerging viral infections as well as co-infections.

Abstract: The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol-water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol-water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol-water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 +/- 0.13, 0.48 +/- 0.06, 0.47 +/- 0.05, and 0.50 +/- 0.06, respectively.