SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules.

We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.net). Only single-step, two-reactant syntheses were calculated for this database even though the technology can execute multi-step reactions. The possibility to incorporate scoring systems in CHMTRN allowed us to subdivide the database of 1.75 billion compounds in sets according to their predicted synthesizability, with the most-synthesizable class comprising 1.09 billion synthetic products. Properties calculated for all SAVI products show that the database should be well-suited for drug discovery. It is being made publicly available for free download from https://doi.org/10.35115/37n9-5738.

Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface.

Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a web-based tool for structure activity relationship and quantitative structure activity relationship modeling to add to the services provided by CHARMMing (www.charmming.org). This new module implements some of the most recent advances in modern machine learning algorithms-Random Forest, Support Vector Machine, Stochastic Gradient Descent, Gradient Tree Boosting, so forth. A user can import training data from Pubchem Bioassay data collections directly from our interface or upload his or her own SD files which contain structures and activity information to create new models (either categorical or numerical). A user can then track the model generation process and run models on new data to predict activity.

Transforming growth factor ß signaling overcomes dasatinib resistance in lung cancer.

Lung cancer is the second most common cancer and the leading cause of cancer-related deaths. Despite recent advances in the development of targeted therapies, patients with advanced disease remain incurable, mostly because metastatic non-small cell lung carcinomas (NSCLC) eventually become resistant to tyrosine kinase inhibitors (TKIs). Kinase inhibitors have the potential for target promiscuity because the kinase super family is the largest family of druggable genes that binds to a common substrate (ATP). As a result, TKIs often developed for a specific purpose have been found to act on other targets. Drug affinity chromatography has been used to show that dasatinib interacts with the TGFß type I receptor (TßR-I), a serine-threonine kinase. To determine the potential biological relevance of this association, we studied the combined effects of dasatinib and TGFß on lung cancer cell lines. We found that dasatinib treatment alone had very little effect; however, when NSCLC cell lines were treated with a combination of TGFß and dasatinib, apoptosis was induced. Combined TGFß-1 + dasatinib treatment had no effect on the activity of Smad2 or other non-canonical TGFß intracellular mediators. Interestingly, combined TGFß and dasatinib treatment resulted in a transient increase in p-Smad3 (seen after 3 hours). In addition, when NSCLC cells were treated with this combination, the pro-apoptotic protein BIM was up-regulated. Knockdown of the expression of Smad3 using Smad3 siRNA also resulted in a decrease in BIM protein, suggesting that TGFß-1 + dasatinib-induced apoptosis is mediated by Smad3 regulation of BIM. Dasatinib is only effective in killing EGFR mutant cells, which is shown in only 10% of NSCLCs. Therefore, the observation that wild-type EGFR lung cancers can be manipulated to render them sensitive to killing by dasatinib could have important implications for devising innovative and potentially more efficacious treatment strategies for this disease.

Fragment-based docking: development of the CHARMMing Web user interface as a platform for computer-aided drug design.

Web-based user interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser. One such interface, CHARMMing (CHARMM interface and graphics), facilitates access to the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multiscale modeling, and other techniques commonly used by computational life scientists. We have extended CHARMMing's capabilities to include a fragment-based docking protocol that allows users to perform molecular docking and virtual screening calculations either directly via the CHARMMing Web server or on computing resources using the self-contained job scripts generated via the Web interface. The docking protocol was evaluated by performing a series of "re-dockings" with direct comparison to top commercial docking software. Results of this evaluation showed that CHARMMing's docking implementation is comparable to many widely used software packages and validates the use of the new CHARMM generalized force field for docking and virtual screening.

Web-based computational chemistry education with CHARMMing I: Lessons and tutorial.

This article describes the development, implementation, and use of web-based "lessons" to introduce students and other newcomers to computer simulations of biological macromolecules. These lessons, i.e., interactive step-by-step instructions for performing common molecular simulation tasks, are integrated into the collaboratively developed CHARMM INterface and Graphics (CHARMMing) web user interface (http://www.charmming.org). Several lessons have already been developed with new ones easily added via a provided Python script. In addition to CHARMMing's new lessons functionality, web-based graphical capabilities have been overhauled and are fully compatible with modern mobile web browsers (e.g., phones and tablets), allowing easy integration of these advanced simulation techniques into coursework. Finally, one of the primary objections to web-based systems like CHARMMing has been that "point and click" simulation set-up does little to teach the user about the underlying physics, biology, and computational methods being applied. In response to this criticism, we have developed a freely available tutorial to bridge the gap between graphical simulation setup and the technical knowledge necessary to perform simulations without user interface assistance.

Recent advances in proteasome inhibitor discovery.

INTRODUCTION: Proteasome inhibition is a quickly advancing subject of research and has a significant potential to become a potent therapeutic modality for many diseases and disorders. The aim of this review is to present the reader with the variety of approaches to the proteasome inhibitor discovery as well as highlight the diversity of scaffolds being considered for this task. AREAS COVERED: This review focuses on current developments in proteasome inhibitor discovery, including an account of research efforts covered in the literature from the years 2009 - 2012, although some of the earlier work is also mentioned. Specifically, presented are the type of experiments performed, the compounds and compound families investigated along with their activities and assessment for potential therapeutic value. In particular, authors highlight different paths to discovery of the proteasome inhibitors such as screening of large libraries, repurposing of existing therapeutics, development of compounds with known proteasome inhibitory activities as well as utilizing novel scaffolds. EXPERT OPINION: Discovery of therapeutically successful proteasome inhibitors depends on a number of factors and demands a multipronged approach. Screening protocols, choice of assays, desired mode of action, selection of a binding pocket, targeting and delivery strategy, all require careful consideration when attempting to target the proteasome.

Virtual target screening: validation using kinase inhibitors.

Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term "Virtual Target Screening (VTS)", a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here, we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.