Journal of Chemical Information and Modeling 最新文献

{"title":"ProCeSa: Contrast-Enhanced Structure-Aware Network for Thermostability Prediction with Protein Language Models.","authors":"Feixiang Zhou, Shuo Zhang, Huifeng Zhang, Jian K Liu","doi":"10.1021/acs.jcim.4c01752","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01752","url":null,"abstract":"<p><p>Proteins play a fundamental role in biology, and their thermostability is essential for their proper functionality. The precise measurement of thermostability is crucial, traditionally relying on resource-intensive experiments. Recent advances in deep learning, particularly in protein language models (PLMs), have significantly accelerated the progress in protein thermostability prediction. These models utilize various biological characteristics or deep representations generated by PLMs to represent the protein sequences. However, effectively incorporating structural information, based on the PLM embeddings, while not considering atomic protein structures, remains an open and formidable challenge. Here, we propose a novel Protein Contrast-enhanced Structure-Aware (ProCeSa) model that seamlessly integrates both sequence and structural information extracted from PLMs to enhance thermostability prediction. Our model employs a contrastive learning scheme guided by the categories of amino acid residues, allowing it to discern intricate patterns within protein sequences. Rigorous experiments conducted on publicly available data sets establish the superiority of our method over state-of-the-art approaches, excelling in both classification and regression tasks. Our results demonstrate that ProCeSa addresses the complex challenge of predicting protein thermostability by utilizing PLM-derived sequence embeddings, without requiring access to atomic structural data.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning.","authors":"Yojana Gadiya, Olga Genilloud, Ursula Bilitewski, Mark Brönstrup, Leonie von Berlin, Marie Attwood, Philip Gribbon, Andrea Zaliani","doi":"10.1021/acs.jcim.4c02347","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02347","url":null,"abstract":"<p><p>While the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therapeutics has also slowed down. In the era of advanced computational methods, approaches like machine learning (ML) could be one potential solution to help reduce the high costs and complexity of antibiotic drug discovery and attract collaboration across organizations. In our work, we developed a large antimicrobial knowledge graph (AntiMicrobial-KG) as a repository for collecting and visualizing public <i>in vitro</i> antibacterial assay. Utilizing this data, we build ML models to efficiently scan compound libraries to identify compounds with the potential to exhibit antimicrobial activity. Our strategy involved training seven classic ML models across six compound fingerprint representations, of which the Random Forest trained on the MHFP6 fingerprint outperformed, demonstrating an accuracy of 75.9% and Cohen's Kappa score of 0.68. Finally, we illustrated the model's applicability for predicting the antimicrobial properties of two small molecule screening libraries. First, the EU-OpenScreen library was tested against a panel of Gram-positive, Gram-negative, and Fungal pathogens. Here, we unveiled that the model was able to correctly predict more than 30% of active compounds for Gram-positive, Gram-negative, and Fungal pathogens. Second, with the Enamine library, a commercially available HTS compound collection with claimed antibacterial properties, we predicted its antimicrobial activity and pathogen class specificity. These results may provide a means for accelerating research in AMR drug discovery efforts by carefully filtering out compounds from commercial libraries with lower chances of being active.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NABhClassifier Server: A Tool for the Identification of Helical Nucleic Acid-Binding Sequences in Proteins.","authors":"Rogerio Margis, Iara Macedo, Nureyev F Rodrigues, Mateus Dias-Oliveira, Fernanda Lazzarotto, Diego Trindade de Souza, Geancarlo Zanatta","doi":"10.1021/acs.jcim.4c02244","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02244","url":null,"abstract":"<p><p>Engineered proteins capable of binding and transporting nucleic acids hold significant potential for advancing disease control in both the medical and agricultural fields. However, identifying small nucleic acid-binding domains remains challenging, as existing predictors primarily classify entire proteins as binders or nonbinders rather than targeting specific binding regions. Here, we introduce NABhClassifier, a highly efficient and precise web server designed to detect small helical sequences with nucleic acid-binding potential. Featuring an intuitive interface and a fully automated prediction pipeline, NABhClassifier integrates eight machine learning models for rapid analysis, delivering results in seconds per protein sequence. Predictions are summarized in the NABh index, a consensus score that combines outputs from all models for enhanced reliability. The server's accuracy has been validated on data sets of DNA-binding and single- and double-stranded RNA-binding proteins from various species. NABhClassifier provides a powerful tool for identifying small helices with nucleic acid-binding capacity, facilitating the discovery of novel biotechnological applications. The server, along with tutorials, is freely accessible at http://143.54.25.149.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143475879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MLR Data-Driven for the Prediction of Infinite Dilution Activity Coefficient of Water in Ionic Liquids (ILs) Using QSPR-Based COSMO Descriptors.","authors":"Ali Ebrahimpoor Gorji, Juho-Pekka Laakso, Ville Alopaeus, Petri Uusi-Kyyny","doi":"10.1021/acs.jcim.4c02095","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02095","url":null,"abstract":"<p><p>To predict the partial molar excess enthalpy, entropy at infinite dilution, and phase equilibria, the availability of an infinite dilution activity coefficient is vital. The \"quantitative structure-activity/property relationship\" (QSAR/QSPR) approach has been used for the prediction of infinite dilution activity coefficient of water in ionic liquids using an extensive data set. The data set comprised 380 data points including 68 unique ILs at a wide range of temperatures, which is more extensive than previously published data sets. Moreover, new predictive QSAR/QSPR models including novel molecular descriptors, called \"COSMO-RS descriptors\", have been developed. Using two different techniques of external validation, the data set was divided to the training set for the development of models and to the validation set for external validation. Unlike former available models, internal validation using leave one/multi out-cross validations (LOO-CV/LMO-CV) and Y-scrambling methods were performed on the models using statistical parameters for further assessment. According to the obtained results of statistical parameters (<i>R</i>² = 0.99 and <i>Q</i>²_LOO-CV = 0.99), the predictive capability of the developed QSPR model was excellent for training set. Regarding the external validation, other statistical parameters such as AAD = 0.283 and AARD % = 30 were also satisfactory for the validation set. While the values of γ_H₂_O^∞ increase or decrease with increasing temperature, the QSAR/QSPR models based on the van't Hoff equation takes into account the negative and positive effects of temperature on the γ_H₂_O^∞ in ILs well, depending on the nature of ILs. It was also shown that γ_H₂_O^∞ in some new ILs which had not been experimentally studied before can be predicted using the QSPR model.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EC2Vec: A Machine Learning Method to Embed Enzyme Commission (EC) Numbers into Vector Representations.","authors":"Mengmeng Liu, Xialong Ni, J Ramanujam, Michal Brylinski","doi":"10.1021/acs.jcim.4c02161","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02161","url":null,"abstract":"<p><p>Enzyme commission (EC) numbers play a vital role in classifying enzymes and understanding their functions in enzyme-related research. Although accurate and informative encoding of EC numbers is essential for enhancing the effectiveness of machine learning applications, simple EC encoding approaches suffer from limitations such as false numerical order and high sparsity. To address these issues, we developed EC2Vec, a multimodal autoencoder that preserves the categorical nature of EC numbers and leverages their hierarchical relationships, resulting in more meaningful and informative representations. EC2Vec encodes each digit of the EC number as a categorical token and then processes these embeddings through a 1D convolutional layer to capture their relationships. Comprehensive benchmarking against a large collection of EC numbers indicates that EC2Vec outperforms simple encoding methods. The t-SNE visualization of EC2Vec embeddings revealed distinct clusters corresponding to different enzyme classes, demonstrating that the hierarchical structure of the EC numbers is effectively captured. In downstream machine learning applications, EC2Vec embeddings outperformed other EC encoding methods in the reaction-EC pair classification task, underscoring its robustness and utility for enzyme-related research and bioinformatics applications.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues.","authors":"Greta Grassmann, Lorenzo Di Rienzo, Giancarlo Ruocco, Mattia Miotto, Edoardo Milanetti","doi":"10.1021/acs.jcim.4c02286","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02286","url":null,"abstract":"<p><p>Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregates formation. Despite the many advancements made so far, the performance of docking protocols is deeply dependent on their capability to identify binding regions. From this, the importance of developing low-cost and computationally efficient methods in this field. We present an integrated novel protocol mainly based on compact modeling of protein surface patches via sets of orthogonal polynomials to identify regions of high shape/electrostatic complementarity. By incorporating both hydrophilic and hydrophobic contributions, we define new binding matrices, which serve as effective inputs for training a neural network. In this work, we propose a new Neural Network (NN)-based architecture, Core Interacting Residues Network (CIRNet), which achieves a performance in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC) of approximately 0.87 in identifying pairs of core interacting residues on a balanced data set. In a blind search for core interacting residues, CIRNet distinguishes them from random decoys with an ROC AUC of 0.72. We test this protocol to enhance docking algorithms by filtering the proposed poses, addressing one of the still open problems in computational biology. Notably, when applied to the top ten models from three widely used docking servers, CIRNet improves docking outcomes, significantly reducing the average RMSD between the selected poses and the native state. Compared to another state-of-the-art tool for rescaling docking poses, CIRNet more efficiently identified the worst poses generated by the three docking servers under consideration and achieved superior rescaling performance in two cases.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models as Tools for Molecular Toxicity Prediction: AI Insights into Cardiotoxicity.","authors":"Hengzheng Yang, Jian Xiu, Weiqi Yan, Kaifeng Liu, Huizi Cui, Zhibang Wang, Qizheng He, Yilin Gao, Weiwei Han","doi":"10.1021/acs.jcim.4c01371","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01371","url":null,"abstract":"<p><p>The importance of drug toxicity assessment lies in ensuring the safety and efficacy of the pharmaceutical compounds. Predicting toxicity is crucial in drug development and risk assessment. This study compares the performance of GPT-4 and GPT-4o with traditional deep-learning and machine-learning models, WeaveGNN, MorganFP-MLP, SVC, and KNN, in predicting molecular toxicity, focusing on bone, neuro, and reproductive toxicity. The results indicate that GPT-4 is comparable to deep-learning and machine-learning models in certain areas. We utilized GPT-4 combined with molecular docking techniques to study the cardiotoxicity of three specific targets, examining traditional Chinese medicinal materials listed as both food and medicine. This approach aimed to explore the potential cardiotoxicity and mechanisms of action. The study found that components in Black Sesame, Ginger, Perilla, Sichuan Pagoda Tree Fruit, Galangal, Turmeric, Licorice, Chinese Yam, Amla, and Nutmeg exhibit toxic effects on cardiac target Cav1.2. The docking results indicated significant binding affinities, supporting the hypothesis of potential cardiotoxic effects.This research highlights the potential of ChatGPT in predicting molecular properties and its significance in medicinal chemistry, demonstrating its facilitation of a new research paradigm: with a data set, high-accuracy learning models can be generated without requiring computational knowledge or coding skills, making it accessible and easy to use.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.","authors":"Talant Ruzmetov, Ta I Hung, Saisri Padmaja Jonnalagedda, Si-Han Chen, Parisa Fasihianifard, Zhefeng Guo, Bir Bhanu, Chia-En A Chang","doi":"10.1021/acs.jcim.4c01838","DOIUrl":"10.1021/acs.jcim.4c01838","url":null,"abstract":"<p><p>Proteins are inherently dynamic, and their conformational ensembles play a crucial role in biological function. Large-scale motions may govern the protein structure-function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging, both experimentally and computationally. In this paper, we first introduce a deep learning-based model, termed Internal Coordinate Net (ICoN), which learns the physical principles of conformational changes from molecular dynamics simulation data. Second, we selected data points through interpolation in the learned latent space to rapidly identify novel synthetic conformations with sophisticated and large-scale side chains and backbone arrangements. Third, with the highly dynamic amyloid-β_1-42 (Aβ42) monomer, our deep learning model provided a comprehensive sampling of Aβ42's conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that could be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct side chain rearrangements that are probed by our electron paramagnetic resonance and amino acid substitution studies. This approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability of deep learning to utilize natural atomistic motions in protein conformation sampling.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GRADE and X-GRADE: Unveiling Novel Protein-Ligand Interaction Fingerprints Based on GRAIL Scores.","authors":"Christian Fellinger, Thomas Seidel, Benjamin Merget, Klaus-Juergen Schleifer, Thierry Langer","doi":"10.1021/acs.jcim.4c01902","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01902","url":null,"abstract":"<p><p>Nonbonding molecular interactions, such as hydrogen bonding, hydrophobic contacts, ionic interactions, etc., are at the heart of many biological processes, and their appropriate treatment is essential for the successful application of numerous computational drug design methods. This paper introduces GRADE, a novel interaction fingerprint (IFP) descriptor that quantifies these interactions using floating point values derived from GRAIL scores, encoding both the presence and quality of interactions. GRADE is available in two versions: a basic 35-element variant and an extended 177-element variant. Three case studies demonstrate GRADE's utility: (1) dimensionality reduction for visualizing the chemical space of protein-ligand complexes using Uniform Manifold Approximation and Projection (UMAP), showing competitive performance with complex descriptors; (2) binding affinity prediction, where GRADE achieved reasonable accuracy with minimal machine learning optimization; and (3) three-dimensional-quantitative structure-activity relationship (3D-QSAR) modeling for a specific protein target, where GRADE enhanced the performance of Morgan Fingerprints.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing a Novel Ultrashort Cyclic [R₃W₄V] Antimicrobial Peptide with Superior Antimicrobial Potential Based on the Transmembrane Structure to Facilitate Pore Formation.","authors":"Lei Liu, Zhihong Shi, Mingqiong Tong, Yaqing Fang, Dongying Yang, Jiafeng Yu, Zanxia Cao","doi":"10.1021/acs.jcim.4c02113","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02113","url":null,"abstract":"<p><p>The clinical application of antimicrobial peptides (AMPs) is frequently hindered by the inherent limitations of linear peptides. Previous studies have primarily focused on the physicochemical properties of AMPs, and there is a scarcity of information regarding the transmembrane structure and interactions of AMPs with cell membranes and their antimicrobial activity. The present study is the first to propose that the backbone cyclization of linear R₃W₄V (l(R₃W₄V)) into the cyclic R₃W₄V (c[R₃W₄V]) form can enhance the stability of its transmembrane structure and consequently improve its antibacterial activity. The results of the bacterial inhibition assays performed herein demonstrated that the antibacterial activity of c[R₃W₄V] against <i>Staphylococcus aureus</i> and <i>Bacillus subtilis</i> was approximately 17-fold and 19-fold higher than that of l(R₃W₄V). The effect of c[R₃W₄V] on the structure of the bilayer membrane was further assessed via well-tempered bias-exchange metadynamics simulations and long-time conventional unbiased molecular dynamics simulations. This study demonstrated that the single c[R₃W₄V] peptide assumes a stable transmembrane configuration. Consequently, as the number of peptides accumulating in the membrane core increases at higher peptide-lipid ratios, a higher number of phospholipid headgroups embedded into the hydrophobic lipid core, leading to membrane fusion, permeabilization, and deformation of the upper and lower leaflets of the bilayer. The study provides a novel computational perspective on enhancing the antimicrobial efficacy of AMPs and highlights the importance of peptide-membrane structures, dynamics, and interactions in promoting the membrane-disruptive potential of peptides.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}