6,503 research outputs found

    Rational design of antibodies and development of a novel method for (1–3)-β-D glucan detection as an alternative to Limulus amebocyte lysate assay

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    With advances in medicine, increasing medical interventions have increased the risk of invasive fungal disease development. (1-3)-β-D glucan (BDG) is a common fungal biomarker in serological tests. However, the scarcity of Limulus resources for BDG detection poses a challenge. This study addresses the need for an alternative to Limulus amebocyte lysate by using BDG mutant antibody for chemiluminescence detection. The wild-type BDG antibody was obtained by immunizing rabbits. An optimal V52HI/N34L Y mutant antibody, which has increased 3.7-fold of the testing efficiency compared to the wild-type antibody, was first achieved by mutating “hot-spot” residues that contribute to strong non-covalent bonds, as determined by alanine scanning and molecular dynamics simulation. The mutant was then applied to develop the magnetic particle chemiluminescence method. 574 clinical samples were tested using the developed method, with a cutoff value of 95 pg/mL set by Limulus amebocyte lysate. The receiver operating characteristic curve demonstrated an area under the curve of 0.905 (95% CI: 0.880–0.929). Chemiluminescence detected an antigen concentration of 89.98 pg/mL, exhibiting a sensitivity of 83.33% and specificity of 89.76%. In conclusion, the results showed a good agreement with Limulus amebocyte lysate and demonstrated the feasibility of using BDG mutant antibodies for invasive fungal disease diagnosis. The new method based on chemiluminescence for detecting BDG could shorten the sample-to-result time to approximately 30 min, rescue Limulus from being endangered and is resource efficient in terms of equipment and the non-use of a skilled technician

    A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

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    The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning

    Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

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    In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery

    De novo drug design through artificial intelligence: an introduction

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    Developing new drugs is a complex and formidable challenge, intensified by rapidly evolving global health needs. De novo drug design is a promising strategy to accelerate and refine this process. The recent introduction of Generative Artificial Intelligence (AI) algorithms has brought new attention to the field and catalyzed a paradigm shift, allowing rapid and semi-automatic design and optimization of drug-like molecules. This review explores the impact of de novo drug design, highlighting both traditional methodologies and the recently introduced generative algorithms, as well as the promising development of Active Learning (AL). It places special emphasis on their application in oncological drug development, where the need for novel therapeutic agents is urgent. The potential integration of these AI technologies with established computational and experimental methods heralds a new era in the rapid development of innovative drugs. Despite the promising developments and notable successes, these technologies are not without limitations, which require careful consideration and further advancement. This review, intended for professionals across related disciplines, provides a comprehensive introduction to AI-driven de novo drug design of small organic molecules. It aims to offer a clear understanding of the current state and future prospects of these innovative techniques in drug discovery

    Cholesterol regulation of mechanosensitive ion channels

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    The purpose of this review is to evaluate the role of cholesterol in regulating mechanosensitive ion channels. Ion channels discussed in this review are sensitive to two types of mechanical signals, fluid shear stress and/or membrane stretch. Cholesterol regulates the channels primarily in two ways: 1) indirectly through localizing the channels into cholesterol-rich membrane domains where they interact with accessory proteins and/or 2) direct binding of cholesterol to the channel at specified putative binding sites. Cholesterol may also regulate channel function via changes of the biophysical properties of the membrane bilayer. Changes in cholesterol affect both mechanosensitivity and basal channel function. We focus on four mechanosensitive ion channels in this review Piezo, Kir2, TRPV4, and VRAC channels. Piezo channels were shown to be regulated by auxiliary proteins that enhance channel function in high cholesterol domains. The direct binding mechanism was shown in Kir2.1 and TRPV4 where cholesterol inhibits channel function. Finally, cholesterol regulation of VRAC was attributed to changes in the physical properties of lipid bilayer. Additional studies should be performed to determine the physiological implications of these sterol effects in complex cellular environments

    The development of bioinformatics workflows to explore single-cell multi-omics data from T and B lymphocytes

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    The adaptive immune response is responsible for recognising, containing and eliminating viral infection, and protecting from further reinfection. This antigen-specific response is driven by T and B cells, which recognise antigenic epitopes via highly specific heterodimeric surface receptors, termed T-cell receptors (TCRs) and B cell receptors (BCRs). The theoretical diversity of the receptor repertoire that can be generated via homologous recombination of V, D and J genes is large enough (>1015 unique sequences) that virtually any antigen can be recognised. However, only a subset of these are generated within the human body, and how they succeed in specifically recognising any pathogen(s) and distinguishing these from self-proteins remains largely unresolved. The recent advances in applying single-cell genomics technologies to simultaneously measure the clonality, surface phenotype and transcriptomic signature of pathogen- specific immune cells have significantly improved understanding of these questions. Single-cell multi-omics permits the accurate identification of clonally expanded populations, their differentiation trajectories, the level of immune receptor repertoire diversity involved in the response and the phenotypic and molecular heterogeneity. This thesis aims to develop a bioinformatic workflow utilising single-cell multi-omics data to explore, quantify and predict the clonal and transcriptomic signatures of the human T-cell response during and following viral infection. In the first aim, a web application, VDJView, was developed to facilitate the simultaneous analysis and visualisation of clonal, transcriptomic and clinical metadata of T and B cell multi-omics data. The application permits non-bioinformaticians to perform quality control and common analyses of single-cell genomics data integrated with other metadata, thus permitting the identification of biologically and clinically relevant parameters. The second aim pertains to analysing the functional, molecular and immune receptor profiles of CD8+ T cells in the acute phase of primary hepatitis C virus (HCV) infection. This analysis identified a novel population of progenitors of exhausted T cells, and lineage tracing revealed distinct trajectories with multiple fates and evolutionary plasticity. Furthermore, it was observed that high-magnitude IFN-γ CD8+ T-cell response is associated with the increased probability of viral escape and chronic infection. Finally, in the third aim, a novel analysis is presented based on the topological characteristics of a network generated on pathogen-specific, paired-chain, CD8+ TCRs. This analysis revealed how some cross-reactivity between TCRs can be explained via the sequence similarity between TCRs and that this property is not uniformly distributed across all pathogen-specific TCR repertoires. Strong correlations between the topological properties of the network and the biological properties of the TCR sequences were identified and highlighted. The suite of workflows and methods presented in this thesis are designed to be adaptable to various T and B cell multi-omic datasets. The associated analyses contribute to understanding the role of T and B cells in the adaptive immune response to viral-infection and cancer

    AI in drug discovery and its clinical relevance

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    The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.  Other InformationPublished in:HeliyonLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://doi.org/10.1016/j.heliyon.2023.e17575 </p
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