TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data

Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, and easy-to-share pipelines. While documentation for such tools is available, there are only a few freely accessible examples that teach the underlying concepts focused on CADD, especially addressing users new to the field. Here, we present TeachOpenCADD, a teaching platform developed by students for students, using open source compound and protein data as well as basic and CADD-related Python packages. We provide interactive Jupyter notebooks for central CADD topics, integrating theoretical background and practical code. TeachOpenCADD is freely available on GitHub: https://github.com/volkamerlab/TeachOpenCAD

MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest

Olfaction in mosquitoes

Female mosquitoes are vectors of diseases, affecting both livestock and humans. The host-seeking and identification behaviors of mosquitoes are mediated mainly by olfactory cues. The peripheral olfactory organs of mosquitoes which perceive olfactory cues are the antennae and maxillary palps. These appendages bear numerous hair shaped structures, sensilla, in which olfactory receptor neurons (ORNs) are housed. The ORNs detect and discriminate various odorant molecules and send information regarding odor quality, quantity and spatio-temporal patterns to the central olfactory system in the brain for further analysis. The first goal of this study was to investigate the neuroanatomy of the mosquito central olfactory system. Using different staining techniques, the neuronal architecture of the deutocerebrum as well as 3D reconstructions of antennal lobe (AL) glomeruli were depicted for both sexes of the Afrcian malaria mosquito, Anopheles gambiae and the yellow fever mosquito, Aedes aegypti. To study how mosquitoes detect olfactory cues, single sensillum recordings (SSRs) were performed, which allowed me to investigate electrophysiological properties of individual ORNs housed in four morphological types of the most abundant olfactory sensilla, s. trichodea. I was able to identify 11 functional types which their ORNs displayed distinct responses to a set of compounds. As part of this study, axons of functionally defined ORNs were traced by neurobiotin to indicate which glomeruli they targeted. This resulted in a functional map of AL glomeruli. The map indicated that different functional types of ORNs converged onto different spatially fixed glomeruli. My next step was to identify novel biologically active compounds for the ORNs using gas chromatography coupled SSRs (GC-SSRs). Headspace odors from different human body parts, i.e. armpit, feet and trunk regions as well as from a plant used as a mosquito repellent (Nepeta faassenii) were collected, extracted and eventually injected onto the GC-column. I found that some of the extract components elicited responses in previously defined ORNs as well as in ORNs of the intermediate sensilla. Some of the compounds, which were subsequently identified by using GC-mass spectrometry (GC-MS) were heptanal, octanal, nonanal and decanal

Novel translational approaches to the search for precision therapies for acute respiratory distress syndrome.

In the 50 years since acute respiratory distress syndrome (ARDS) was first described, substantial progress has been made in identifying the risk factors for and the pathogenic contributors to the syndrome and in characterising the protein expression patterns in plasma and bronchoalveolar lavage fluid from patients with ARDS. Despite this effort, however, pharmacological options for ARDS remain scarce. Frequently cited reasons for this absence of specific drug therapies include the heterogeneity of patients with ARDS, the potential for a differential response to drugs, and the possibility that the wrong targets have been studied. Advances in applied biomolecular technology and bioinformatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma, particularly when a precision medicine paradigm, wherein a biomarker or gene expression pattern indicates a patient's likelihood of responding to a treatment, has been pursued. In this Review, we consider the biological and analytical techniques that could facilitate a precision medicine approach for ARDS

Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence

The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates’ innate immunity

Artificial intelligence, machine learning, and drug repurposing in cancer

Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means. Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication. Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.Peer reviewe

High-Throughput Screening for Drug Discovery

The book focuses on various aspects and properties of high-throughput screening (HTS), which is of great importance in the development of novel drugs to treat communicable and non-communicable diseases. Chapters in this volume discuss HTS methodologies, resources, and technologies and highlight the significance of HTS in personalized and precision medicine