Phenotypic Screens, Chemical Genomics, and Antimalarial Lead Discovery

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Requirements for a lead compound to become a clinical candidate

A drug candidate suitable for clinical testing is expected to bind selectively to the receptor site on the target, to elicit the desired functional response of the target molecule, and to have adequate bioavailability and biodistribution to elicit the desired responses in animals and humans; it must also pass formal toxicity evaluation in animals. The path from lead to clinical drug candidate represents the most idiosyncratic segment of drug discovery and development. Each program is unique and setbacks are common, making it difficult to predict accurately the duration or costs of this segment. Because of incidents of unpredicted human toxicity seen in recent years, the regulatory agencies and public demands for safety of new drug candidates have become very strict, and safety issues are dominant when identifying a clinical drug candidate

Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates

Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus replication is via protease inhibition. We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2's main protease. Based on the SELFIES representation the EMOA maximizes the binding of candidate ligands to the protein using the docking tool QuickVina 2, while at the same time taking into account further objectives like drug-likeliness or the fulfillment of filter constraints. The experimental part analyzes the evolutionary process and discusses the inhibitor candidates.Comment: 15 pages, 7 figures, submitted to PPSN 202

Semantic Web integration of Cheminformatics resources with the SADI framework

<p>Abstract</p> <p>Background</p> <p>The diversity and the largely independent nature of chemical research efforts over the past half century are, most likely, the major contributors to the current poor state of chemical computational resource and database interoperability. While open software for chemical format interconversion and database entry cross-linking have partially addressed database interoperability, computational resource integration is hindered by the great diversity of software interfaces, languages, access methods, and platforms, among others. This has, in turn, translated into limited reproducibility of computational experiments and the need for application-specific computational workflow construction and semi-automated enactment by human experts, especially where emerging interdisciplinary fields, such as systems chemistry, are pursued. Fortunately, the advent of the Semantic Web, and the very recent introduction of RESTful Semantic Web Services (SWS) may present an opportunity to integrate all of the existing computational and database resources in chemistry into a machine-understandable, unified system that draws on the entirety of the Semantic Web.</p> <p>Results</p> <p>We have created a prototype framework of Semantic Automated Discovery and Integration (SADI) framework SWS that exposes the QSAR descriptor functionality of the Chemistry Development Kit. Since each of these services has formal ontology-defined input and output classes, and each service consumes and produces RDF graphs, clients can automatically reason about the services and available reference information necessary to complete a given overall computational task specified through a simple SPARQL query. We demonstrate this capability by carrying out QSAR analysis backed by a simple formal ontology to determine whether a given molecule is drug-like. Further, we discuss parameter-based control over the execution of SADI SWS. Finally, we demonstrate the value of computational resource envelopment as SADI services through service reuse and ease of integration of computational functionality into formal ontologies.</p> <p>Conclusions</p> <p>The work we present here may trigger a major paradigm shift in the distribution of computational resources in chemistry. We conclude that envelopment of chemical computational resources as SADI SWS facilitates interdisciplinary research by enabling the definition of computational problems in terms of ontologies and formal logical statements instead of cumbersome and application-specific tasks and workflows.</p

Bioinformatics in crosslinking chemistry of collagen with selective cross linkers

<p>Abstract</p> <p>Background</p> <p>Identifying the molecular interactions using bioinformatics tools before venturing into wet lab studies saves the energy and time considerably. The present study summarizes, molecular interactions and binding energy calculations made for major structural protein, collagen of Type I and Type III with the chosen cross-linkers, namely, coenzyme Q₁₀, dopaquinone, embelin, embelin complex-1 & 2, idebenone, 5-O-methyl embelin, potassium embelate and vilangin.</p> <p>Results</p> <p>Molecular descriptive analyses suggest, dopaquinone, embelin, idebenone, 5-O-methyl embelin, and potassium embelate display nil violations. And results of docking analyses revealed, best affinity for Type I (- 4.74 kcal/mol) and type III (-4.94 kcal/mol) collagen was with dopaquinone.</p> <p>Conclusions</p> <p>Among the selected cross-linkers, dopaquinone, embelin, potassium embelate and 5-O-methyl embelin were the suitable cross-linkers for both Type I and Type III collagen and stabilizes the collagen at the expected level.</p

Towards the development of novel Trypanosoma brucei RNA editing ligase 1 inhibitors

Abstract Background Trypanosoma brucei (T. brucei) is an infectious agent for which drug development has been largely neglected. We here use a recently developed computer program called AutoGrow to add interacting molecular fragments to S5, a known inhibitor of the validated T. brucei drug target RNA editing ligase 1, in order to improve its predicted binding affinity. Results The proposed binding modes of the resulting compounds mimic that of ATP, the native substrate, and provide insights into novel protein-ligand interactions that may be exploited in future drug-discovery projects. Conclusions We are hopeful that these new predicted inhibitors will aid medicinal chemists in developing novel therapeutics to fight human African trypanosomiasis

Highly Parallel Translation of DNA Sequences into Small Molecules

A large body of in vitro evolution work establishes the utility of biopolymer libraries comprising 1010 to 1015 distinct molecules for the discovery of nanomolar-affinity ligands to proteins.[1], [2], [3], [4], [5] Small-molecule libraries of comparable complexity will likely provide nanomolar-affinity small-molecule ligands.[6], [7] Unlike biopolymers, small molecules can offer the advantages of cell permeability, low immunogenicity, metabolic stability, rapid diffusion and inexpensive mass production. It is thought that such desirable in vivo behavior is correlated with the physical properties of small molecules, specifically a limited number of hydrogen bond donors and acceptors, a defined range of hydrophobicity, and most importantly, molecular weights less than 500 Daltons.[8] Creating a collection of 1010 to 1015 small molecules that meet these criteria requires the use of hundreds to thousands of diversity elements per step in a combinatorial synthesis of three to five steps. With this goal in mind, we have reported a set of mesofluidic devices that enable DNA-programmed combinatorial chemistry in a highly parallel 384-well plate format. Here, we demonstrate that these devices can translate DNA genes encoding 384 diversity elements per coding position into corresponding small-molecule gene products. This robust and efficient procedure yields small molecule-DNA conjugates suitable for in vitro evolution experiments

miRNA Expression Profiling in Migrating Glioblastoma Cells: Regulation of Cell Migration and Invasion by miR-23b via Targeting of Pyk2

Glioblastoma (GB) is the most common and lethal type of primary brain tumor. Clinical outcome remains poor and is essentially palliative due to the highly invasive nature of the disease. A more thorough understanding of the molecular mechanisms that drive glioma invasion is required to limit dispersion of malignant glioma cells.We investigated the potential role of differential expression of microRNAs (miRNA) in glioma invasion by comparing the matched large-scale, genome-wide miRNA expression profiles of migrating and migration-restricted human glioma cells. Migratory and migration-restricted cell populations from seven glioma cell lines were isolated and profiled for miRNA expression. Statistical analyses revealed a set of miRNAs common to all seven glioma cell lines that were significantly down regulated in the migrating cell population relative to cells in the migration-restricted population. Among the down-regulated miRNAs, miR-23b has been reported to target potential drivers of cell migration and invasion in other cell types. Over-expression of miR-23b significantly inhibited glioma cell migration and invasion. A bioinformatics search revealed a conserved target site within the 3' untranslated region (UTR) of Pyk2, a non-receptor tyrosine kinase previously implicated in the regulation of glioma cell migration and invasion. Increased expression of miR-23b reduced the protein expression level of Pyk2 in glioma cells but did not significantly alter the protein expression level of the related focal adhesion kinase FAK. Expression of Pyk2 via a transcript variant missing the 3'UTR in miR-23b-expressing cells partially rescued cell migration, whereas expression of Pyk2 via a transcript containing an intact 3'UTR failed to rescue cell migration.Reduced expression of miR-23b enhances glioma cell migration in vitro and invasion ex vivo via modulation of Pyk2 protein expression. The data suggest that specific miRNAs may regulate glioma migration and invasion to influence the progression of this disease