Project description:MolOptimizer is a user-friendly computational toolkit designed to streamline the hit-to-lead optimization process in drug discovery. MolOptimizer extracts features and trains machine learning models using a user-provided, labeled, and small-molecule dataset to accurately predict the binding values of new small molecules that share similar scaffolds with the target in focus. Hosted on the Azure web-based server, MolOptimizer emerges as a vital resource, accelerating the discovery and development of novel drug candidates with improved binding properties.
Project description:BackgroundThe human leukocyte antigen (HLA) proteins play a fundamental role in the adaptive immune system as they present peptides to T cells. Mass-spectrometry-based immunopeptidomics is a promising and powerful tool for characterizing the immunopeptidomic landscape of HLA proteins, that is the peptides presented on HLA proteins. Despite the growing interest in the technology, and the recent rise of immunopeptidomics-specific identification pipelines, there is still a gap in data-analysis and software tools that are specialized in analyzing and visualizing immunopeptidomics data.ResultsWe present the IPTK library which is an open-source Python-based library for analyzing, visualizing, comparing, and integrating different omics layers with the identified peptides for an in-depth characterization of the immunopeptidome. Using different datasets, we illustrate the ability of the library to enrich the result of the identified peptidomes. Also, we demonstrate the utility of the library in developing other software and tools by developing an easy-to-use dashboard that can be used for the interactive analysis of the results.ConclusionIPTK provides a modular and extendable framework for analyzing and integrating immunopeptidomes with different omics layers. The library is deployed into PyPI at https://pypi.org/project/IPTKL/ and into Bioconda at https://anaconda.org/bioconda/iptkl , while the source code of the library and the dashboard, along with the online tutorials are available at https://github.com/ikmb/iptoolkit .
Project description:Advances in CRISPR technology have immensely improved our ability to manipulate nucleic acids, and the recent discovery of the RNA-targeting endonuclease Cas13 adds even further functionality. Here, we show that Cas13 works efficiently in Drosophila, both ex vivo and in vivo. We test 44 different Cas13 variants to identify enzymes with the best overall performance and show that Cas13 could target endogenous Drosophila transcripts in vivo with high efficiency and specificity. We also develop Cas13 applications to edit mRNAs and target mitochondrial transcripts. Our vector collection represents a versatile tool collection to manipulate gene expression at the post-transcriptional level.
Project description:Fragment-based drug discovery (FBDD) is a powerful method to develop potent small-molecule compounds starting from fragments binding weakly to targets. As FBDD exhibits several advantages over high-throughput screening campaigns, it becomes an attractive strategy in target-based drug discovery. Many potent compounds/inhibitors of diverse targets have been developed using this approach. Methods used in fragment screening and understanding fragment-binding modes are critical in FBDD. This review elucidates fragment libraries, methods utilized in fragment identification/confirmation, strategies applied in growing the identified fragments into drug-like lead compounds, and applications of FBDD to different targets. As FBDD can be readily carried out through different biophysical and computer-based methods, it will play more important roles in drug discovery.
Project description:Molecular dynamics (MD) simulations reveal molecular motions at atomic resolution. Recent advances in high-performance computing now enable microsecond-long simulations capable of sampling a wide range of biologically relevant events. But the disk space required to store an MD trajectory increases with simulation length and system size, complicating collaborative sharing and visualization. To overcome these limitations, we created PCAViz, an open-source toolkit for sharing and visualizing MD trajectories via the web browser. PCAViz includes two components: the PCAViz Compressor, which compresses and saves simulation data; and the PCAViz Interpreter, which decompresses the data in users' browsers and feeds it to any of several browser-based molecular-visualization libraries (e.g., 3Dmol.js, NGL Viewer, etc.). An easy-to-install WordPress plugin enables "plug-and-play" trajectory visualization. PCAViz will appeal to a broad audience of researchers and educators. The source code is available at http://durrantlab.com/pcaviz/ , and the WordPress plugin is available via the official WordPress Plugin Directory.
Project description:High-dimensional data are pervasive in this bigdata era. To avoid the curse of the dimensionality problem, various dimensionality reduction (DR) algorithms have been proposed. To facilitate systematic DR quality comparison and assessment, this paper reviews related metrics and develops an open-source Python package pyDRMetrics. Supported metrics include reconstruction error, distance matrix, residual variance, ranking matrix, co-ranking matrix, trustworthiness, continuity, co-k-nearest neighbor size, LCMC (local continuity meta criterion), and rank-based local/global properties. pyDRMetrics provides a native Python class and a web-oriented API. A case study of mass spectra is conducted to demonstrate the package functions. A web GUI wrapper is also published to support user-friendly B/S applications.
Project description:BackgroundScripting languages such as Python are ideally suited to common programming tasks in cheminformatics such as data analysis and parsing information from files. However, for reasons of efficiency, cheminformatics toolkits such as the OpenBabel toolkit are often implemented in compiled languages such as C++. We describe Pybel, a Python module that provides access to the OpenBabel toolkit.ResultsPybel wraps the direct toolkit bindings to simplify common tasks such as reading and writing molecular files and calculating fingerprints. Extensive use is made of Python iterators to simplify loops such as that over all the molecules in a file. A Pybel Molecule can be easily interconverted to an OpenBabel OBMol to access those methods or attributes not wrapped by Pybel.ConclusionPybel allows cheminformaticians to rapidly develop Python scripts that manipulate chemical information. It is open source, available cross-platform, and offers the power of the OpenBabel toolkit to Python programmers.
Project description:Fragment-based drug discovery (FBDD) is an innovative approach, progressively more applied in the academic and industrial context, to enhance hit identification for previously considered undruggable biological targets. In particular, FBDD discovers low-molecular-weight (LMW) ligands (<300Da) able to bind to therapeutically relevant macromolecules in an affinity range from the micromolar (?M) to millimolar (mM). X-ray crystallography (XRC) and nuclear magnetic resonance (NMR) spectroscopy are commonly the methods of choice to obtain 3D information about the bound ligand-protein complex, but this can occasionally be problematic, mainly for early, low-affinity fragments. The recent development of computational fragment-based approaches provides a further strategy for improving the identification of fragment hits. In this review, we summarize the state of the art of molecular dynamics simulations approaches used in FBDD, and discuss limitations and future perspectives for these approaches.
Project description:Recombinant adeno-associated virus (AAV) is a promising delivery vehicle for in vivo gene therapy and has been widely used in >200 clinical trials globally. There are already several approved gene therapy products, e.g., Luxturna and Zolgensma, highlighting the remarkable potential of AAV delivery. In the past, AAV has been seen as a relatively non-immunogenic vector associated with low risk of toxicity. However, an increasing number of recent studies indicate that immune responses against AAV and transgene products could be the bottleneck of AAV gene therapy. In clinical studies, pre-existing antibodies against AAV capsids exclude many patients from receiving the treatment as there is high prevalence of antibodies among humans. Moreover, immune response could lead to loss of efficacy over time and severe toxicity, manifested as liver enzyme elevations, kidney injury, and thrombocytopenia, resulting in deaths of non-human primates and patients. Therefore, extensive efforts have been attempted to address these issues, including capsid engineering, plasmapheresis, IgG proteases, CpG depletion, empty capsid decoy, exosome encapsulation, capsid variant switch, induction of regulatory T cells, and immunosuppressants. This review will discuss these methods in detail and highlight important milestones along the way.
Project description:The relevance of personalized medicine, aimed at a more individualized drug therapy, has inspired research into nano-based concerted diagnosis and therapeutics (theranostics). As the intention is to "kill two birds with one stone", scientists have already described the emerging concept as a treasured tailor for the future of cancer therapy, wherein the main idea is to design "smart" nanosystems to concurrently discharge both therapeutic and diagnostic roles. These nanosystems are expected to offer a relatively clearer view of the ingenious cellular trafficking pathway, in-situ diagnosis, and therapeutic efficacy. We herein present a detailed review of versatile nanosystems, with prominent examples of recently developed intelligent delivery strategies which have gained attention in the field of theranostics. These nanotheranostics include various mechanisms programmed in novel platforms to enable predetermined delivery of cargo to specific sites, as well as techniques to overcome the notable challenges involved in the efficacy of theranostics.