ACS Publications最新文献

{"title":"Exploring Binding Sites in Chagas Disease Protein TcP21 Using Integrated Mixed Solvent Molecular Dynamics Approaches.","authors":"William Oliveira Soté, Moacyr Comar Junior","doi":"10.1021/acs.jcim.4c01927","DOIUrl":"10.1021/acs.jcim.4c01927","url":null,"abstract":"<p><p>Chagas disease, caused by the protozoan Trypanosoma cruzi, remains a significant global health burden, particularly in Latin America, where millions are at risk. This disease predominantly affects socioeconomically vulnerable populations, aggravating economic inequality, marginalization, and low political visibility. Despite extensive research, effective treatments are still lacking, partly due to the complex biology of the parasite and its infection mechanisms. This study focuses on TcP21, a novel 21 kDa protein secreted by extracellular amastigotes, which has been implicated in <i>T. cruzi</i> infection via an alternative infective pathway. Although the potential of TcP21 for understanding Chagas disease is promising, further exploration is necessary, particularly in identifying potential binding sites on its surface. Computational tools offer a versatile and effective strategy for preliminary binding site assessment, facilitating a more cost-efficient allocation of experimental resources. In this study, we employed three independent computational approaches─mixed solvent molecular dynamics simulations (MSMD), fragment-based molecular docking, and pharmacophore model docking coupled with molecular dynamics simulations─to identify potential binding sites and provide comprehensive insights into TcP21. The three methodologies converged on a common site located on the external surface of the protein, characterized by key residues such as GLU55, ASP52, VAL70, ILE62, and TRP77. The protonated amino, acetamido, and phenyl groups of the pharmacophore probe were consistently observed to interact with the site via a network of salt bridges, hydrogen bonds, charge-charge interactions, and alkyl-π interactions, suggesting these groups play a significant role in ligand binding. This study does not aim to propose specific therapeutic hits but to highlight a still unknown and unexplored protein involved in <i>T. cruzi</i> cell invasion. In this regard, given the strong correlation between the three distinct approaches used for mapping, we consider this study offers valuable insights for further research into P21 and its role in Chagas disease.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"363-377"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142833216","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":"MGT: Machine Learning Accelerates Performance Prediction of Alloy Catalytic Materials.","authors":"Lei Geng, Yue Feng, Yaxi Niu, Fang Zhang, Huaqing Yin","doi":"10.1021/acs.jcim.4c01065","DOIUrl":"10.1021/acs.jcim.4c01065","url":null,"abstract":"<p><p>The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and selectivity of catalytic materials are mainly influenced by the properties and positions of active sites and adsorption sites. However, current deep learning models have not sufficiently focused on the importance of active atoms and adsorbates, instead placing more emphasis on the overall structure of the catalytic materials. In this paper, the overall molecular graph and a masked graph, which ignores fixed atoms, are separately input into the Masked Graph Transformer (MGT) network to enhance the model's ability to recognize key sites in catalytic reactions. Second, we introduce a nonlinear message-passing mechanism to improve the dot-product attention in the Transformer and capture the directional information on the relative positions of nodes by integrating molecular geometric information through deep tensor products. Subsequently, we constructed the NLMP-TransNet framework, which combines MPNN and Transformer and optimizes the model's learning and prediction capabilities through weight sharing and residual connections. The MGT achieves an error rate of 0.5447 eV on the small data set OC20-Ni, surpassing existing technologies. Ablation studies confirm the necessity of focusing on site features for accurate adsorption energy prediction. Code is available at https://github.com/KristinSun/OCP-MGT.git.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"31-40"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851613","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":"Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow.","authors":"Son Gyo Jung, Guwon Jung, Jacqueline M Cole","doi":"10.1021/acs.jcim.4c01862","DOIUrl":"10.1021/acs.jcim.4c01862","url":null,"abstract":"<p><p>Machine learning (ML) methods provide a pathway to accurately predict molecular properties, leveraging patterns derived from structure-property relationships within materials databases. This approach holds significant importance in drug discovery and materials design, where the rapid, efficient screening of molecules can accelerate the development of new pharmaceuticals and chemical materials for highly specialized target application. Unsupervised and self-supervised learning methods applied to graph-based or geometric models have garnered considerable traction. More recently, transformer-based language models have emerged as powerful tools. Nevertheless, their application entails considerable computational resources, owing to the need for an extensive pretraining process on a vast corpus of unlabeled chemical data sets. To this end, we present a semisupervised strategy that harnesses substructure vector embeddings in conjunction with a ML-based feature selection workflow to predict various molecular and drug properties. We evaluate the efficacy of our modeling methodology across a diverse range of data sets, encompassing both regression and classification tasks. Our findings demonstrate superior performance compared to most existing state-of-the-art algorithms, while offering advantages in terms of balancing model accuracy with computational requirements. Moreover, our approach provides deeper insights into feature interactions that are essential for model interpretability. A case study is conducted to predict the lipophilicity of chemical molecules, exemplifying the robustness of our strategy. The result underscores the importance of meticulous feature analysis and selection over a mere reliance on predictive modeling with a high degree of algorithmic complexity.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"133-152"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880701","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":"Ranking Single Fluorescent Protein-Based Calcium Biosensor Performance by Molecular Dynamics Simulations.","authors":"Melike Berksoz, Canan Atilgan","doi":"10.1021/acs.jcim.4c01478","DOIUrl":"10.1021/acs.jcim.4c01478","url":null,"abstract":"<p><p>Genetically encoded fluorescent biosensors (GEFBs) have become indispensable tools for visualizing biological processes <i>in vivo.</i> A typical GEFB is composed of a sensory domain (SD) that undergoes a conformational change upon ligand binding or enzymatic reaction; the SD is genetically fused with a fluorescent protein (FP). The changes in the SD allosterically modulate the chromophore environment whose spectral properties are changed. Single fluorescent (FP)-based biosensors, a subclass of GEFBs, offer a simple experimental setup; they are easy to produce in living cells, structurally stable, and simple to use due to their single-wavelength operation. However, they pose a significant challenge for structure optimization, especially concerning the length and residue content of linkers between the FP and SD, which affect how well the chromophore responds to conformational change in the SD. In this work, we use all-atom molecular dynamics simulations to analyze the dynamic properties of a series of calmodulin-based calcium biosensors, all with different FP-SD interaction interfaces and varying degrees of calcium binding-dependent fluorescence change. Our results indicate that biosensor performance can be predicted based on distribution of water molecules around the chromophore and shifts in hydrogen bond occupancies between the ligand-bound and ligand-free sensor structures.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"338-350"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890673","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":"Interpretable Deep-Learning p<i>K</i>_a Prediction for Small Molecule Drugs via Atomic Sensitivity Analysis.","authors":"Joseph DeCorte, Benjamin Brown, Rathmell Jeffrey, Jens Meiler","doi":"10.1021/acs.jcim.4c01472","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01472","url":null,"abstract":"<p><p>Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociation constant (p<i>K</i>_a). Despite recent architectural advances, these models often generalize poorly to novel compounds due to a scarcity of ground-truth data. Further, these models lack interpretability. To this end, with deliberate molecular embeddings, atomic-resolution information is accessible in chemical structures by observing the model response to atomic perturbations of an input molecule. Here, we present BCL-XpKa, a deep neural network (DNN)-based multitask classifier for p<i>K</i>_a prediction that encodes local atomic environments through Mol2D descriptors. BCL-XpKa outputs a discrete distribution for each molecule, which stores the p<i>K</i>_a prediction and the model's uncertainty for that molecule. BCL-XpKa generalizes well to novel small molecules. BCL-XpKa performs competitively with modern ML p<i>K</i>_a predictors, outperforms several models in generalization tasks, and accurately models the effects of common molecular modifications on a molecule's ionizability. We then leverage BCL-XpKa's granular descriptor set and distribution-centered output through atomic sensitivity analysis (ASA), which decomposes a molecule's predicted p<i>K</i>_a value into its respective atomic contributions without model retraining. ASA reveals that BCL-XpKa has implicitly learned high-resolution information about molecular substructures. We further demonstrate ASA's utility in structure preparation for protein-ligand docking by identifying ionization sites in 93.2% and 87.8% of complex small molecule acids and bases. We then applied ASA with BCL-XpKa to identify and optimize the physicochemical liabilities of a recently published KRAS-degrading PROTAC.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 1","pages":"101-113"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968671","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":"Development of Pluronic-Based Micelles from Palm Oil Bioactive Compounds Incorporated by a Dissolvable Microarray Patch to Enhance the Efficacy of Atopic Dermatitis Therapy.","authors":"Khusnul Humayatul Jannah, Christopher Kosasi Ko, Felicia Virginia Thios, Jihan Nabilah Isma, Anugerah Yaumil Ramadhani Aziz, Andi Dian Permana","doi":"10.1021/acs.molpharmaceut.4c00990","DOIUrl":"https://doi.org/10.1021/acs.molpharmaceut.4c00990","url":null,"abstract":"<p><p>The high content of vitamin E, including tocopherols and tocotrienols (TCF-TTE), in palm oil (<i>Elaeis guineensis</i>) has made it a promising candidate for the alternative treatment of atopic dermatitis (AD). However, the limited solubility of TCF-TTE has restricted its therapeutic efficacy. In this study, pluronic-based micelles (MCs) encapsulating palm oil-derived TCF-TTE were formulated with dissolvable microarray patch-micelles (DMP-MC) using carboxymethyl cellulose (CMC) synthesized from empty fruit bunches of palm to optimize its delivery for AD. The MC was prepared using a direct dissolution method using Pluronic F68 and F127. The results showed that MC increased the solubility of TCF-TTE, which was further confirmed by an <i>in vitro</i> study where 90.23 ± 2.07% TCF and 4.56 ± 1.36% TTE were released compared to the unencapsulated TCF-TTE extract. Furthermore, CMC biopolymers and MC integrated into DMP-MC with polyvinylpyrrolidone (PVP) exhibited favorable physical properties, such as mechanical strength and penetration ability. DMP-MC also exhibited a better platform with lower permeation, indicating higher retention and increased localized effects on AD skin than cream-MC. Additionally, dermatokinetic profile parameters showed significant improvement. The mean residence time (MRT) parameter indicated that TCF-TTE was retained for longer times 19.28 ± 0.02 h and 20.68 ± 0.01 h. Moreover, an <i>in vivo</i> study revealed that DMP-MC could relieve AD symptoms more rapidly than oral doses and cream-MC, indicating that DMP-MC proved to be more efficient. Furthermore, DMP-MC showed no tissue destruction (granulation and fibrosis) in rats treated with DMP-MC on the seventh day. Therefore, this study successfully developed the MC formula in DMP-MC formulation using synthesized CMC, which could potentially improve AD's therapeutic efficacy.</p>","PeriodicalId":52,"journal":{"name":"Molecular Pharmaceutics","volume":" ","pages":""},"PeriodicalIF":4.5,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>Biomacromolecules'</i> Year of Celebration.","authors":"Graham Michael Smeddle, Sébastien Lecommandoux","doi":"10.1021/acs.biomac.4c01672","DOIUrl":"10.1021/acs.biomac.4c01672","url":null,"abstract":"","PeriodicalId":30,"journal":{"name":"Biomacromolecules","volume":" ","pages":"1-4"},"PeriodicalIF":5.5,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142833209","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":"Silica-Biomacromolecule Interactions: Toward a Mechanistic Understanding of Silicification.","authors":"Christina A McCutchin, Kevin J Edgar, Chun-Long Chen, Patricia M Dove","doi":"10.1021/acs.biomac.4c00674","DOIUrl":"10.1021/acs.biomac.4c00674","url":null,"abstract":"<p><p>Silica-organic composites are receiving renewed attention for their versatility and environmentally benign compositions. Of particular interest is how macromolecules interact with aqueous silica to produce functional materials that confer remarkable physical properties to living organisms. This Review first examines silicification in organisms and the biomacromolecule properties proposed to modulate these reactions. We then highlight findings from silicification studies organized by major classes of biomacromolecules. Most investigations are qualitative, using disparate experimental and analytical methods and minimally characterized materials. Many findings are contradictory and, altogether, demonstrate that a consistent picture of biomacromolecule-Si interactions has not emerged. However, the collective evidence shows that functional groups, rather than molecular classes, are key to understanding macromolecule controls on mineralization. With recent advances in biopolymer chemistry, there are new opportunities for hypothesis-based studies that use quantitative experimental methods to decipher how macromolecule functional group chemistry and configuration influence thermodynamic and kinetic barriers to silicification. Harnessing the principles of silica-macromolecule interactions holds promise for biocomposites with specialized applications from biomedical and clean energy industries to other material-dependent industries.</p>","PeriodicalId":30,"journal":{"name":"Biomacromolecules","volume":" ","pages":"43-84"},"PeriodicalIF":5.5,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142386383","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":"Efficient Recycling of Glucose from Cellulose in Textiles Waste by Solid Catalysts.","authors":"Qiangqiang Cui, Jing Yu, Jing Li, Cheng Zeng, Fan Bu, Xiaohong Liao, Hongzhi Hu, Zihui Liang, Chao Chen, Changhai Yi","doi":"10.1021/acs.biomac.4c01382","DOIUrl":"10.1021/acs.biomac.4c01382","url":null,"abstract":"<p><p>The efficient conversion of cellulose into glucose is critical for advancing sustainable biofuels and bioproducts. Traditional methods face significant challenges, including inefficiencies and environmental concerns, highlighting the need for innovative catalytic systems. In this study, we successfully synthesized three hydroxyl-rich carbon-based solid acid catalysts─S-catalyzer, P-catalyzer, and C-catalyze. Utilizing an aqueous hydrothermal system, the S-catalyzer, characterized by high hydroxyl content and -SO₃H groups, effectively mimicked cellulase activity, breaking glycosidic bonds and achieving a glucose yield of 68% with a cellulose conversion rate of 97.2% within 120 min. The catalysts also demonstrated remarkable recyclability, maintaining over 90% conversion efficiency across multiple cycles. This stability is attributed to the robustness of hydroxyl and -SO₃H groups and the recycling of glucose as a carbonation substrate in a closed-loop system. Our findings provide a novel, environmentally sustainable method for cellulose hydrolysis, offering significant potential for scalable biofuel production and broader biotechnological applications.</p>","PeriodicalId":30,"journal":{"name":"Biomacromolecules","volume":" ","pages":"591-600"},"PeriodicalIF":5.5,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142764568","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":"l-Proline Enhanced Whole Ovary Cryopreservation by Inhibiting Ice Crystal Growth and Reducing Oxidative Stress.","authors":"Mengqiao Chi, Zhongrong Chen, Qi Feng, Mengfei Zhu, Dengyao Yi, Liyuan Zhang, Yue Cheng, Gang Zhao","doi":"10.1021/acsbiomaterials.4c01403","DOIUrl":"10.1021/acsbiomaterials.4c01403","url":null,"abstract":"<p><p>Cryopreservation and transplantation of ovaries are considered to be effective methods for preserving the fertility of female cancer patients. However, ice crystal and oxidative damage occur during the freeze-thaw cycle, significantly reducing the effectiveness of cryopreservation and limiting its clinical application. Thus, new technologies or agents must be explored to enhance ovarian cryopreservation. Recently, l-proline, a natural amino acid, has been proven to have good biocompatibility and can clear reactive oxygen species produced during cryopreservation. Whether l-proline can play a positive role in ovarian cryopreservation has not yet been explored. Here, the effect of l-proline on ovarian cryopreservation was investigated. The oxidative antioxidant system, mitochondrial function, and cell apoptosis and proliferation after thawing were systematically evaluated. Moreover, the ice crystal inhibition of l-proline was examined. Furthermore, the morphology and function of oocytes in ovaries, as well as the state of the ovaries after heterotopic renal capsule transplantation, were evaluated to validate the feasibility and reliability of this study. The above results confirm that l-proline can effectively inhibit ice crystal growth, reduce reactive oxygen species production, and enhance cryopreservation effects at the optimal concentration of 20 mM. Altogether, l-proline can significantly improve the cryopreservation effect of ovaries, which is expected to provide a new perspective for the cryopreservation of female fertility.</p>","PeriodicalId":8,"journal":{"name":"ACS Biomaterials Science & Engineering","volume":" ","pages":"463-475"},"PeriodicalIF":5.4,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142764511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}