Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models

The human {\it ether-a-go-go} (hERG) potassium channel (K$_\text{v}11.1$) plays a critical role in mediating cardiac action potential. The blockade of this ion channel can potentially lead fatal disorder and/or long QT syndrome. Many drugs have been withdrawn because of their serious hERG-cardiotoxicity. It is crucial to assess the hERG blockade activity in the early stage of drug discovery. We are particularly interested in the hERG-cardiotoxicity of compounds collected in the DrugBank database considering that many DrugBank compounds have been approved for therapeutic treatments or have high potential to become drugs. Machine learning-based in silico tools offer a rapid and economical platform to virtually screen DrugBank compounds. We design accurate and robust classifiers for blockers/non-blockers and then build regressors to quantitatively analyze the binding potency of the DrugBank compounds on the hERG channel. Molecular sequences are embedded with two natural language processing (NPL) methods, namely, autoencoder and transformer. Complementary three-dimensional (3D) molecular structures are embedded with two advanced mathematical approaches, i.e., topological Laplacians and algebraic graphs. With our state-of-the-art tools, we reveal that 227 out of the 8641 DrugBank compounds are potential hERG blockers, suggesting serious drug safety problems. Our predictions provide guidance for the further experimental interrogation of DrugBank compounds' hERG-cardiotoxicity

Chatbots in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with ChatGPT

The birth of ChatGPT, a cutting-edge language model chatbot developed by OpenAI, ushered in a new era in AI, and this paper vividly showcases its innovative application within the field of drug discovery. Focused specifically on developing anti-cocaine addiction drugs, the study employs GPT-4 as a virtual guide, offering strategic and methodological insights to researchers working on generative models for drug candidates. The primary objective is to generate optimal drug-like molecules with desired properties. By leveraging the capabilities of ChatGPT, the study introduces a novel approach to the drug discovery process. This symbiotic partnership between AI and researchers transforms how drug development is approached. Chatbots become facilitators, steering researchers towards innovative methodologies and productive paths for creating effective drug candidates. This research sheds light on the collaborative synergy between human expertise and AI assistance, wherein ChatGPT's cognitive abilities enhance the design and development of potential pharmaceutical solutions. This paper not only explores the integration of advanced AI in drug discovery but also reimagines the landscape by advocating for AI-powered chatbots as trailblazers in revolutionizing therapeutic innovation

SVSBI: Sequence-based virtual screening of biomolecular interactions

Virtual screening (VS) is an essential technique for understanding biomolecular interactions, particularly, drug design and discovery. The best-performing VS models depend vitally on three-dimensional (3D) structures, which are not available in general but can be obtained from molecular docking. However, current docking accuracy is relatively low, rendering unreliable VS models. We introduce sequence-based virtual screening (SVS) as a new generation of VS models for modeling biomolecular interactions. The SVS model utilizes advanced natural language processing (NLP) algorithms and optimizes deep $K$-embedding strategies to encode biomolecular interactions without invoking 3D structure-based docking. We demonstrate the state-of-art performance of SVS for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for the protein-protein interactions in five biological species. SVS has the potential to dramatically change the current practice in drug discovery and protein engineering

Knot data analysis using multiscale Gauss link integral

In the past decade, topological data analysis (TDA) has emerged as a powerful approach in data science. The main technique in TDA is persistent homology, which tracks topological invariants over the filtration of point cloud data using algebraic topology. Although knot theory and related subjects are a focus of study in mathematics, their success in practical applications is quite limited due to the lack of localization and quantization. We address these challenges by introducing knot data analysis (KDA), a new paradigm that incorporating curve segmentation and multiscale analysis into the Gauss link integral. The resulting multiscale Gauss link integral (mGLI) recovers the global topological properties of knots and links at an appropriate scale but offers multiscale feature vectors to capture the local structures and connectivities of each curve segment at various scales. The proposed mGLI significantly outperforms other state-of-the-art methods in benchmark protein flexibility analysis, including earlier persistent homology-based methods. Our approach enables the integration of artificial intelligence (AI) and KDA for general curve-like objects and data

Antiferromagnetic magnonic charge current generation via ultrafast optical excitation

N\'eel spin-orbit torque allows a charge current pulse to efficiently manipulate the N\'eel vector in antiferromagnets, which offers a unique opportunity for ultrahigh density information storage with high speed. However, the reciprocal process of N\'eel spin-orbit torque, the generation of ultrafast charge current in antiferromagnets has not been demonstrated. Here, we report the experimental observation of charge current generation in antiferromagnetic metallic Mn2Au thin films using ultrafast optical excitation. The ultrafast laser pulse excites antiferromagnetic magnons, resulting in instantaneous non-equilibrium spin polarization at the antiferromagnetic spin sublattices with broken spatial symmetry. Then the charge current is generated directly via spin-orbit fields at the two sublattices, which is termed as the reciprocal phenomenon of N\'eel spin-orbit torque, and the associated THz emission can be detected at room temperature. Besides the fundamental significance on the Onsager reciprocity, the observed magnonic charge current generation in antiferromagnet would advance the development of antiferromagnetic THz emitter.Comment: 15 pages, 4 figures, this work was submitted to Nature Communications on Jan. 4th, 2023, now is under the 3rd review proces

DCs Pulsed with Novel HLA-A2-Restricted CTL Epitopes against Hepatitis C Virus Induced a Broadly Reactive Anti-HCV-Specific T Lymphocyte Response

OBJECTIVE: To determine the capacity of dendritic cells (DCs) loaded with single or multiple-peptide mixtures of novel hepatitis C virus (HCV) epitopes to stimulate HCV-specific cytotoxic T lymphocyte (CTL) effector functions. METHODS: A bioinformatics approach was used to predict HLA-A2-restricted HCV-specific CTL epitopes, and the predicted peptides identified from this screen were synthesized. Subsequent IFN-γ ELISPOT analysis detected the stimulating function of these peptides in peripheral blood mononuclear cells (PBMCs) from both chronic and self-limited HCV infected subjects (subjects exhibiting spontaneous HCV clearance). Mature DCs, derived in vitro from CD14(+) monocytes harvested from the study subjects by incubation with appropriate cytokine cocktails, were loaded with novel peptide or epitope peptide mixtures and co-cultured with autologous T lymphocytes. Granzyme B (GrB) and IFN-γ ELISPOT analysis was used to test for epitope-specific CTL responses. T-cell-derived cytokines contained in the co-cultured supernatant were detected by flow cytometry. RESULTS: We identified 7 novel HLA-A2-restricted HCV-specific CTL epitopes that increased the frequency of IFN-γ-producing T cells compared to other epitopes, as assayed by measuring spot forming cells (SFCs). Two epitopes had the strongest stimulating capability in the self-limited subjects, one found in the E2 and one in the NS2 region of HCV; five epitopes had a strong stimulating capacity in both chronic and self-limited HCV infection, but were stronger in the self-limited subjects. They were distributed in E2, NS2, NS3, NS4, and NS5 regions of HCV, respectively. We also found that mDCs loaded with novel peptide mixtures could significantly increase GrB and IFN-γ SFCs as compared to single peptides, especially in chronic HCV infection subjects. Additionally, we found that DCs pulsed with multiple epitope peptide mixtures induced a Th1-biased immune response. CONCLUSIONS: Seven novel and strongly stimulating HLA-A2-restricted HCV-specific CTL epitopes were identified. Furthermore, DCs loaded with multiple-epitope peptide mixtures induced epitope-specific CTLs responses

Numerical simulation to the FitzHugh-Nagumo model with strong reaction

The Fitzhugh-Nagumo model is a mathematical model derived from the simulation of propagating pulses in multicellular organisms. Since its creation, this model has drawn great attentions from academics and industry. To better understand the properties underlying this system, suitable numerical methods are needed to study it. In this thesis, numerical methods including the finite difference method, the finite element method, and the least-squares finite element method are applied to approximate its traveling wave solutions. In particular, since the FitzHugh-Nagumo model with strong reaction has a significant role in application, appropriate numerical scheme is designed to study it. Consistency and stability of the methods will been investigated. Numerical results are provided to illustrate the performances of the methods on the FitzHugh-Nagumo model under different cases

A finite difference method for the Fitzhugh-Nagumo equations

In this paper, we propose a special finite difference method to approximate traveling wave solutions of the FitzHugh-Nagumo equations. Consistency and stability of the method have been investigated. Numerical results are provided to illustrate the performance of the method. The threshold phenomenon of the neural system have also been studied numerically