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Ten Quick Tips for Harnessing the Power of ChatGPT/GPT-4 in Computational Biology

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arXiv 2023年

作者： Lubiana, Tiago Lopes, Rafael Medeiros, Pedro Silva, Juan Carlo Goncalves, Andre Nicolau Aquime Maracaja-Coutinho, Vinicius Nakaya, Helder I. School of Pharmaceutical Sciences University of São Paulo São Paulo Brazil Department of Epidemiology of Microbial Diseases Public Health Modeling Unit Yale School of Public Health New HavenCT United States TauGC Bioinformatics São Paulo Brazil Oxford Vaccine Group University of Oxford Oxford United Kingdom Advanced Center for Chronic Diseases Universidad de Chile Santiago Chile Centro de Modelamiento Molecular Biofísica y Bioinformática - CM2B2 Facultad de Ciencias Químicas y Farmacéuticas Universidad de Chile Santiago Chile ANID Anillo ACT210004 SYSTEMIX Rancagua Chile Anillo Inflammation in HIV/AIDS - InflammAIDS Santiago Chile Beagle Bioinformatics Brasil & Santiago São Paulo Brazil Hospital Israelita Albert Einstein São Paulo Brazil

The rise of advanced chatbots, such as ChatGPT, has sparked curiosity in the scientific community. ChatGPT is a general-purpose chatbot powered by large language models (LLMs) GPT-3.5 and GPT-4, with the potential to impact numerous fields, including computational biology. In this article, we offer ten tips based on our experience with ChatGPT to assist computational biologists in optimizing their workflows. We have collected relevant prompts and reviewed the nascent literature in the field, compiling tips we project to remain pertinent for future ChatGPT and LLM iterations, ranging from code refactoring to scientific writing to prompt engineering. We hope our work will help bioinformaticians to complement their workflows while staying aware of the various implications of using this technology. Additionally, to track new and creative applications for bioinformatics tools such as ChatGPT, we have established a GitHub repository at https://***/csbl-br/awesome-compbio-chatgpt. Our belief is that ethical adherence to ChatGPT and other LLMs will increase the efficiency of computational biologists, ultimately advancing the pace of scientific discovery in the life *** Codes 92-04 © 2023, CC BY-SA.

关键词： Computational efficiency

Altered Cofactor Recruitment and Nucleosome Dynamics Underlie Bisphenol A's Impact on Estrogen Receptor-Mediated Transcriptional Bursting

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iScience 2025年

作者： Christopher R. Day Pelin Yasar Gloria Adedoyin Brian D. Bennett Carson C. Chow Joseph Rodriguez Epigenetics and RNA Biology Laboratory National Institute of Environmental Health Sciences Research Triangle Park Durham NC 27709 USA Integrative Bioinformatics Support Group Biostatistics and Computational Biology Branch National Institute of Environmental Health Sciences Research Triangle Park Durham NC 27709 USA Laboratory of Biological Modeling National Institute of Diabetes and Digestive and Kidney Diseases NIH Bethesda MD 20892 USA

Transcription initiation at hormone-responsive gene promoters involves recruitment of numerous proteins that initiate stochastic events known as transcriptional bursts. Estrogen responsive genes are regulated by Estradiol (E2) that binds to the Estrogen Receptor-α (ERα), allowing ERα to interact with DNA regulatory elements, recruit cofactors and initiate transcription. Here, we utilized single-molecule imaging to determine how ERα mediated transcription is altered by non-canonical ligands such as Bisphenol A (BPA). Our analysis showed that the TFF1 gene exhibited similar burst initiation kinetics in BPA-treated cells compared to cells treated with E2. However, there was a significant reduction in the number of active alleles in the BPA-treated cells. We show that while BPA bound ERα did induce chromatin remodeling, nucleosome positioning was altered and coincided with reduced transcription factor binding. Additionally, BPA treatment impaired enhancer mediated TFF1 bursting. Together, this demonstrates that BPA disrupts transcriptional states by altering gene specific ERα cofactor recruitment.

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Prediction of Diblock Copolymer Morphology via Machine Learning

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arXiv 2023年

作者： Park, Hyun Yu, Boyuan Park, Juhae Sun, Ge Tajkhorshid, Emad de Pablo, Juan J. Schneider, Ludwig Theoretical and Computational Biophysics Group NIH Resource Center for Macromolecular Modeling and Bioinformatics Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Pritzker School of Molecular Engineering University of Chicago 5640 Ellis Ave ChicagoIL60637 United States Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States

A machine learning approach is presented to accelerate the computation of block polymer morphology evolution for large domains over long timescales. The strategy exploits the separation of characteristic times between coarse-grained particle evolution on the monomer scale and slow morphological evolution over mesoscopic scales. In contrast to empirical continuum models, the proposed approach learns stochastically driven defect annihilation processes directly from particle-based simulations. A UNet architecture that respects different boundary conditions is adopted, thereby allowing periodic and fixed substrate boundary conditions of arbitrary shape. Physical concepts are also introduced via the loss function and symmetries are incorporated via data augmentation. The model is validated using three different use cases. Explainable artificial intelligence methods are applied to visualize the morphology evolution over time. This approach enables the generation of large system sizes and long trajectories to investigate defect densities and their evolution under different types of confinement. As an application, we demonstrate the importance of accessing late-stage morphologies for understanding particle diffusion inside a single block. This work has implications for directed self-assembly and materials design in micro-electronics, battery materials, and membranes. Copyright © 2023, The Authors. All rights reserved.

关键词： Block copolymers

Isoleucine gate blocks K+ conduction in C-type inactivation

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arXiv 2024年

作者： Treptow, Werner Liu, Yichen Bassetto, Carlos A.Z. Pinto, Bernardo I. Alves, João Chipot, Christophe Bezanilla, Francisco Roux, Benoît Universidade de Brasília DF CEP 70904-970 Brazil Department of Biochemistry and Molecular Biology The University of Chicago ChicagoIL60637 United States Department of Neurobiology The University of Chicago ChicagoIL60637 United States Laboratoire International Associe Centre National de la Recherche Scientifique University of Illinois at Urbana−Champaign Université de Lorraine Université de Lorraine Unité Mixte de Recherche No. 7019 B.P. 70239 Vandœuvre-lès-Nancy cedex 54506 France NIH Center for Macromolecular Modeling and Bioinformatics Beckman Institute for Advanced Science and Technology Department of Physics University of Illinois at Urbana−Champaign UrbanaIL61801 United States Centro Interdisciplinario de Neurociencia de Valparaíso Facultad de Ciencias Universidad de Valparaíso Valparaíso Chile

Many voltage-gated potassium (Kv) channels display a time-dependent phenomenon called C-type inactivation, whereby prolonged activation by voltage leads to the inhibition of ionic conduction, a process that involves a conformational change at the selectivity filter toward a non-conductive state. Recently, a high-resolution structure of a strongly inactivating triple-mutant channel kv1.2-kv2.1-3m revealed a novel conformation of the selectivity filter that is dilated at its outer end, distinct from the well-characterized conductive state. While the experimental structure was interpreted as the elusive non-conductive state, molecular dynamics simulations and electrophysiology measurements demonstrate that the dilated filter of kv1.2-kv2.1-3m, however, is conductive and, as such, cannot completely account for the inactivation of the channel observed in functional experiments. An additional conformational change implicating isoleucine residues at position 398 along the pore lining segment S6 is required to effectively block ion conduction. It is shown that the I398 residues from the four subunits act as a state-dependent hydrophobic gate located immediately beneath the selectivity filter. As a critical piece of the C-type inactivation machinery, this structural feature is the potential target of a broad class of QA blockers and negatively charged activators thus opening new research directions towards the development of drugs that specifically modulate gating-states of Kv channels. © 2024, CC BY.

关键词： Electrophysiology

Deciphering the therapeutic role of Kigelia africana fruit in erectile dysfunction through metabolite profiling and molecular modelling

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Informatics in Medicine Unlocked 2023年 37卷

作者： Olawale, Femi Olofinsan, Kolawole Ogunyemi, Oludare M. Karigidi, Kayode O. Gyebi, Gideon A. Ibrahim, Ibrahim M. Iwaloye, Opeyemi Department of Biochemistry University of Lagos Nigeria Department of Biochemistry University of KwaZulu South Africa Department of Biochemistry Nile University of Nigeria Nigeria Computational Biology and Biomolecular Modeling Laboratories Department of Biochemistry School of Basic Medical Sciences Babcock University Ogun State Illishan-Remo Nigeria Department of Biochemistry Olusegun Agagu University of Science and Technology Okitipupa Nigeria Department of Biochemistry Faculty of Science and Technology Bingham University Nasarawa Karu Nigeria Department of Biophysics Faculty of Sciences Cairo University Giza Egypt Bioinformatics and Molecular Biology Unit Department of Biochemistry Federal University of Technology Akure P.M.B. 704 Ondo State Akure 360001 Nigeria

Kigelia africana herbal products are often used traditionally to treat erectile dysfunction and other sexual complaints, but the underlying mechanism is not yet understood. This study focused on profiling the bioactive constituents of Kigelia africana fruit (KAF) using spectroscopic techniques and providing the computational models of their interactions with phosphodiesterase (PDE5) and Rho-associated coiled-coil containing protein kinase 2 (ROCK2) targets associated with erectile dysfunction. Integrated FT-IR, HPLC-MS, and GC-MS analysis revealed 152 (C1–C152) KAF compounds with highly diverse functional groups and chemical properties. Molecular docking showed several hit compounds as potential inhibitors of PDE5 and ROCK2. Post docking MMGBSA, QSAR, predictive physicochemical analysis and AdmetSAR analysis revealed that most of the hit compounds are potential drug leads. Among these, hydroxydoxepin (C3) and ritodrine (C131) exhibited the strongest interactions with PDE5, while epigallocatechin 3-O-p-coumarate (C9) and chlorogenic acid (C91) had the strongest interaction with ROCK2. The thermodynamic parameters and trajectory clusters computed from the trajectories obtained from the 100 ns full atomistic molecular dynamic (MD) simulation indicated the structural stability and conformational flexibility of the selected complexes. The MD simulation-based MMPBSA calculation further revealed strong binding affinity and energy contribution by active site residues of PDE5 towards binding the selected KAF compounds. Various computational analyses employed revealed that the catalytic residues Gln817, Val782 and Phe786 of PDE5 exhibited high interaction potential and flexibility towards C3 and C131. Overall, hydroxydoxepin, ritodrine and other phytochemicals in Kigelia africana may account for the therapeutic role of this plant in erectile dysfunction. © 2023

关键词： Erectile dysfunction Kigelia africana Molecular docking and dynamics Phytochemical analysis QSAR

Metabolite profiling of potential bioactive fractions from ethanol extract of Boehmeria nivea flowers by GC–MS/MS analysis

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Phytomedicine Plus 2024年第2期4卷 100557-100557页

作者： Wulandari, Asri Peni Nafisa, Zahra Khira Herlina, Tati Maharani, Rani Darmawan, Gumgum Parikesit, Arli Aditya Zainul, Rahadian Department of Biology Faculty of Mathematics and Natural Science Padjadjaran University Sumedang 45363 Indonesia Center for Bioprospection of Natural Fibers and Biological Resources Faculty of Mathematics and Natural Science Padjadjaran University Bandung 40132 Indonesia Department of Chemistry Faculty of Mathematics and Natural Science Padjadjaran University Sumedang 45363 Indonesia Center for Natural Products and Synthesis Faculty of Mathematics and Natural Sciences Padjadjaran University Sumedang 45363 Indonesia Department of Statistics Faculty of Mathematics and Natural Science Padjadjaran University Sumedang 45363 Indonesia Center for Modeling and Computational Faculty of Mathematics and Natural Sciences Sumedang Padjadjaran University 45363 Indonesia Department of Bioinformatics Indonesia International Institute for Life Sciences East Jakarta 13210 Indonesia Department of Chemistry Faculty of Mathematics and Natural Science Padang State University Padang 25171 Indonesia

Background: Ramie (Boehmeria nivea) has been reported to have a wide variety of secondary metabolite compounds in its vegetative organ that have distinct organic activities. Regarding the chemical composition of flowers, there have been no study considering their pharmacological effects. Purpose: This research aims to determine profile metabolites contained in B. nivea flowers employing a metabolomics approach. Study Design: This research was designed to obtain a metabolite database from five dissolved fractions of ramie flower using a Gas Chromatography-Mass Spectrometry (GC–MS/MS) instrument. This study includes metabolite profiles from each fraction of the spectrum obtained, which are then investigated for potential bioactivity based on previously reported studies. Metabolite profiles were developed using Principal Component Analysis (PCA) to obtain information on clustering of the compounds and determination of marker compounds from each fraction analyzed. Methods: The active compounds of the flowers were separated utilizing the maceration method with ethanol solvent and multilevel fractionation using n-hexane, dichloromethane, ethyl acetic acid derivation, butanol, and water employing a GC–MS/MS approach. Principal Component Analysis (PCA) was optimized with a total represented data diversity range of 60 % - 65 %. This allowed for the identification of characteristic compounds and similarities in compound substances accross the fractions. Result: GC–MS/MS interpretation showed the dominant compounds contained in each fraction of n-hexane, dichloromethane, n-butanol, ethyl acetate, and water are 9,12-Octadecadienoic acid, ethanol, 2-phenoxy-, butyl ester, and hexanedioic acid, bis(2-ethylhexyl) ester. PCA clustering showed combined similarity of the compound for ethyl acetate fraction with butanol, and also butanol with n-hexane. Meanwhile, the dichloromethane and water fractions have very small content similarities. The score plot indicated marker compounds of

关键词： Bioprospecting Boehmeria nivea GC-MS/MS PCA Secondary metabolite

Novel ligand-based docking;molecular dynamic simulations;and absorption, distribution, metabolism, and excretion approach to analyzing potential acetylcholinesterase inhibitors for Alzheimer's disease

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Journal of Pharmaceutical Analysis 2018年第6期8卷 413-420页

作者： Subramaniyan Vijayakumar Palani Manogar Srinivasan Prabhu Ram Avadhar Sanjeevkumar Singh Computational Phytochemistry Laboratory PG and Research Department of Botany and Microbiology AVVM Sri Pushpam College (Autonomous) Poondi Computer Aided Drug Design and Molecular Modeling Laboratory Department of Bioinformatics Alagappa University

Acetylcholinesterase(AChE) plays an important role in Alzheimer's disease(AD). The excessive activity of AChE causes various neuronal problems, particularly dementia and neuronal cell deaths. Generally, antiAChE drugs induce some serious neuronal side effects in humans. Therefore, this study sought to identify alternative drug molecules from natural products with fewer side effects than those of conventional drugs for treating AD. To achieve this, we developed computational methods for predicting drug and target binding affinities using the Schrodinger suite. The target and ligand molecules were retrieved from established databases. The target enzyme has 539 amino acid residues in its sequence alignment. Ligand molecules of 20 bioactive molecules were obtained from different kinds of plants, after which we performed critical analyses such as molecular docking; molecular dynamic(MD) simulations; and absorption, distribution, metabolism, and excretion(ADME) analysis. In the docking studies, the natural compound rutin showed a superior docking score of à 12.335 with a good binding energy value ofà73.313 kcal/mol. Based on these findings, rutin and the target complex was used to perform MD simulations to analyze rutin stability at 30 ns. In conclusion, our study demonstrates that rutin is a superior drug candidate for AD. Therefore, we propose that this molecule is worth further investigation using in vitro studies.

关键词： Alzheimer's disease Acetylcholinesterase Phytocompounds Molecular docking Free energy calculations Molecular dynamic simulations

TARGET SPECIFIC DE NOVO DESIGN OF DRUG CANDIDATE MOLECULES WITH GRAPH TRANSFORMER-BASED GENERATIVE ADVERSARIAL NETWORKS

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arXiv 2023年

作者： Ünlü, Atabey Çevrim, Elif Sarıgün, Ahmet Yiğit, Melih Gökay Çelikbilek, Hayriye Bayram, Osman Güvenilir, Heval Ataş Koyaş, Altay Kahraman, Deniz Cansen Olğaç, Abdurrahman Rifaioğlu, Ahmet Banoğlu, Erden Doğan, Tunca Biological Data Science Lab Dept. of Computer Engineering Hacettepe University Turkey Dept. of Bioinformatics Graduate School of Health Sciences Hacettepe University Turkey Dept. of Chemistry Middle East Technical University Turkey Dept. of Physics Middle East Technical University Turkey Dept. of Computer Engineering Middle East Technical University Turkey Dept. Of Artificial Intelligence Engineering Bahcesehir University Turkey Cancer Systems Biology Lab Graduate School of Informatics Middle East Technical University Turkey Dept. of Pharmaceutical Chemistry Faculty of Pharmacy Gazi University Turkey Laboratory of Molecular Modeling Evias Pharmaceutical R&D Ltd Turkey Dept. of Electrical and Electronics Engineering Iskenderun Technical University Turkey Institute for Computational Biomedicine Heidelberg University Germany

Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, offer a high potential for designing de novo molecules. However, for them to be useful in real-life drug development pipelines, these models should be able to design drug-like and target-centric molecules. In this study, we propose an end-to-end generative system, DrugGEN, for the de novo design of drug candidate molecules that interact with intended target proteins. The proposed method represents molecules as graphs and processes them via a generative adversarial network comprising graph transformer layers. The system is trained using a large dataset of drug-like compounds and target-specific bioactive molecules to design effective inhibitory molecules against the AKT1 protein, which is critically important in developing treatments for various types of cancer. We conducted molecular docking and dynamics to assess the target-centric generation performance of the model, as well as attention score visualisation to examine model interpretability. Results indicate that our de novo molecules have a high potential for interacting with the AKT1 protein at the level of its native ligands. Using the open-access DrugGEN codebase, it is possible to easily train models for other druggable proteins, given a dataset of experimentally known bioactive molecules. © 2023, CC BY-NC-SA.

关键词： Generative adversarial networks

APPROXIMATE BAYESIAN INFERENCE OF DIRECTED ACYCLIC GRAPHS IN BIOLOGY WITH FLEXIBLE PRIORS ON EDGE STATES

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arXiv 2019年

作者： Martin, Evan A. Fu, Audrey Qiuyan The Bioinformatics and Computational Biology Program University of Idaho United States The Bioinformatics and Computational Biology Program Department of Mathematics and Statistical Science Institute for Modeling Collaboration and Innovation Institute for Interdisciplinary Data Sciences University of Idaho United States

Graphical models or networks describe the statistical dependence among multiple variables and are widely used in biology (e.g., gene regulatory networks). Under appropriate assumptions, directed edges may represent causal relationships. A key feature of a biological network is sparsity, defined by how likely an edge is present, of which we often have some knowledge. However, most existing Bayesian methods use priors for the entire graph, making it difficult to specify the level of sparsity. The few methods that use priors on edges estimate the two directions independently;the sum of the two probabilities can exceed 1. Here, we present baycn (BAYesian Causal Network), a novel approximate Bayesian method that represents a graph in terms of three states of edges: the two directions and edge absence, and specifies priors on these edge states. We design a pseudo Bayesian sampling algorithm for efficient inference. We apply baycn to two genomic problems: i) distinguishing direct and indirect target genes of genetic variants, using these variants as instrumental variables, and ii) inferring combinatorial binding of highly-correlated transcription factors in Drosophila. In both cases and in extensive simulations, our method demonstrates much improved accuracy over existing methods for the whole graph and for individual edges. Copyright © 2019, The Authors. All rights reserved.

关键词： Inference engines