ACS Publications最新文献

{"title":"HIV OctaScanner: A Machine Learning Approach to Unveil Proteolytic Cleavage Dynamics in HIV-1 Protease Substrates.","authors":"Kashif Iqbal Sahibzada, Shumaila Shahid, Mohsina Akhter, Rizwan Abid, Muteeba Azhar, Yuansen Hu, Dong-Qing Wei","doi":"10.1021/acs.jcim.4c01808","DOIUrl":"10.1021/acs.jcim.4c01808","url":null,"abstract":"<p><p>The rise of resistance to antiretroviral drugs due to mutations in human immunodeficiency virus-1 (HIV-1) protease is a major obstacle to effective treatment. These mutations alter the drug-binding pocket of the protease and reduce the drug efficacy by disrupting interactions with inhibitors. Traditional methods, such as biochemical assays and structural biology, are crucial for studying enzyme function but are time-consuming and labor-intensive. To address these challenges, we developed HIV OctaScanner, a machine learning algorithm that predicts the proteolytic cleavage activity of octameric substrates at the HIV-1 protease cleavage sites. The algorithm uses a Random Forest (RF) classifier and achieves a prediction accuracy of 89% in the identification of cleavable octamers. This innovative approach facilitates the rapid screening of potential substrates for HIV-1 protease, providing critical insights into enzyme function and guiding the development of more effective therapeutic strategies. By improving the accuracy of substrate identification, HIV OctaScanner has the potential to support the development of next generation antiretroviral treatments.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"640-648"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976865","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":"Computational Study of Organotin Oxide Systems for Extreme Ultraviolet Photoresist.","authors":"Jingbin Li, Zhefeng Wang, Han Wang","doi":"10.1021/acs.jpca.4c07585","DOIUrl":"https://doi.org/10.1021/acs.jpca.4c07585","url":null,"abstract":"<p><p>With the advancement of extreme ultraviolet (EUV) lithography technology, the demand for high-performance EUV photoresists has surged. Traditional photoresists struggle to meet the stringent requirements for increasingly smaller feature sizes in semiconductor manufacturing. Among emerging candidates, tin-based materials, particularly Sn₁₂-oxo photoresists, have shown promise due to their superior EUV light absorption properties. Modifying these clusters offers a potential pathway to tailoring their properties for specific lithographic applications. In this study, we investigate the relationship between the photosensitivity of experimentally synthesized Sn₁₂-oxo photoresists and their calculable parameters with quantum chemistry calculations. Key parameters such as bonding energies between metal atoms and organic ligands, molecular ionization potential, electrostatic potential, and HOMO-LUMO gap are identified as critical for predicting photosensitivity. While current research predominantly focuses on replacing counter-anions in Sn₁₂-oxo clusters, there is limited exploration of modifications through the replacement of organic ligands. We examined the effects of electron-withdrawing and electron-donating groups as ligands on the Sn₁₂-oxo cluster's ionization potential and Sn-ligand bonding energy. Our findings suggest a strategy for designing high-performance photoresists, thereby illuminating the path to discovering novel photoresist materials.</p>","PeriodicalId":59,"journal":{"name":"The Journal of Physical Chemistry A","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044905","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":"Mechanistic Cooperation of the Two Pore-Forming Transmembrane Motifs Regulates the β-Barrel Pore Formation by Listeriolysin O.","authors":"Kusum Lata, Koyel Nandy, Geetika, Kausik Chattopadhyay","doi":"10.1021/acs.biochem.4c00592","DOIUrl":"https://doi.org/10.1021/acs.biochem.4c00592","url":null,"abstract":"<p><p>Listeriolysin O (LLO) is a potent membrane-damaging pore-forming toxin (PFT) secreted by the bacterial pathogen <i>Listeria monocytogenes</i>. LLO belongs to the family of cholesterol-dependent cytolysins (CDCs), which specifically target cholesterol-containing cell membranes to form oligomeric pores and induce membrane damage. CDCs, including LLO, harbor designated pore-forming motifs. In the soluble monomeric state, these motifs are present as helical segments (two transmembrane helices (TMHs); TMH1 and TMH2), and in the course of oligomeric pore formation, they convert into transmembrane β-hairpins to form the β-barrel scaffold of the CDC pores. Despite their well-established role in forming the β-barrel pore scaffold, precise structural implications of the two distinct TMH motifs and their membrane-insertion mechanism still remain obscure. Here, we show that the two TMH motifs of LLO contribute differently to maintaining the structural integrity of the toxin. While the deletion of TMH1 imposed a more serious defect, truncation of TMH2 was found to have a less severe effect on the structural integrity. Despite showing membrane-binding and oligomerization ability, the TMH2-deleted LLO variant displayed drastically abrogated pore-forming activity, presumably due to compromised membrane-insertion efficacy of the pore-forming TMH motifs. When probed for the membrane-insertion mechanism, we found slower membrane-insertion kinetics for TMH2 than for TMH1. Interestingly, deletion of TMH2 arrested membrane insertion of TMH1, thus suggesting a stringent cooperation between the two TMH motifs in regulating the pore-formation mechanism of LLO. Taken together, our study provides new mechanistic insights regarding the membrane-damaging action of LLO, in the CDC family of PFTs.</p>","PeriodicalId":28,"journal":{"name":"Biochemistry Biochemistry","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Complementary Peptide Interactions Support the Ultra-Rigidity of Polymers of De Novo Designed Click-Functionalized Bundlemers.","authors":"Dai-Bei Yang, Tianren Zhang, Jacquelyn E Blum, Christopher J Kloxin, Darrin J Pochan, Jeffery G Saven","doi":"10.1021/acs.jpcb.4c06403","DOIUrl":"https://doi.org/10.1021/acs.jpcb.4c06403","url":null,"abstract":"<p><p>Computationally designed 29-residue peptides yield tetra-α-helical bundles with <i>D</i>₂ symmetry. The \"bundlemers\" can be bifunctionally linked via thiol-maleimide cross-links at their N-termini, yielding supramolecular polymers with unusually large, micrometer-scale persistence lengths. To provide a molecularly resolved understanding of these systems, all-atom molecular modeling and simulations of linked bundlemers in explicit solvent are presented. A search over relative orientations of the bundlemers identifies a structure, wherein at the bundlemer-bundlemer interface, interior hydrophobic residues are in contact, and α-helices are aligned with a pseudocontiguous α-helix that spans the interface. Calculation of a potential of mean force confirms that the structure in which the bundlemers are in contact and colinearly aligned is a stable minimum. Analyses of hydrogen bonds and hydrophobic complementarity highlight the complementary interactions at the interface. The molecular insight provided reveals the molecular origins of bundlemer alignment within the supramolecular polymers.</p>","PeriodicalId":60,"journal":{"name":"The Journal of Physical Chemistry B","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051076","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":"Free Energy of Membrane Pore Formation and Stability from Molecular Dynamics Simulations.","authors":"Timothée Rivel, Denys Biriukov, Ivo Kabelka, Robert Vácha","doi":"10.1021/acs.jcim.4c01960","DOIUrl":"10.1021/acs.jcim.4c01960","url":null,"abstract":"<p><p>Understanding the molecular mechanisms of pore formation is crucial for elucidating fundamental biological processes and developing therapeutic strategies, such as the design of drug delivery systems and antimicrobial agents. Although experimental methods can provide valuable information, they often lack the temporal and spatial resolution necessary to fully capture the dynamic stages of pore formation. In this study, we present two novel collective variables (CVs) designed to characterize membrane pore behavior, particularly its energetics, through molecular dynamics (MD) simulations. The first CV─termed Full-Path─effectively tracks both the nucleation and expansion phases of pore formation. The second CV─called Rapid─is tailored to accurately assess pore expansion in the limit of large pores, providing quick and reliable method for evaluating membrane line tension under various conditions. Our results clearly demonstrate that the line tension predictions from both our CVs are in excellent agreement. Moreover, these predictions align qualitatively with available experimental data. Specifically, they reflect higher line tension of 1-palmitoyl-2-oleoyl-<i>sn</i>-glycero-3-phosphocholine (POPC) membranes containing 1-palmitoyl-2-oleoyl-<i>sn</i>-glycero-3-phospho-l-serine (POPS) lipids compared to pure POPC, the decrease in line tension of POPC vesicles as the 1-palmitoyl-2-oleoyl-<i>sn</i>-glycero-3-phosphoglycerol (POPG) content increases, and higher line tension when ionic concentration is increased. Notably, these experimental trends are accurately captured only by the all-atom CHARMM36 and prosECCo75 force fields. In contrast, the all-atom Slipids force field, along with the coarse-grained Martini 2.2, Martini 2.2 polarizable, and Martini 3 models, show varying degrees of agreement with experiments. Our developed CVs can be adapted to various MD simulation engines for studying pore formation, with potential implications in membrane biophysics. They are also applicable to simulations involving external agents, offering an efficient alternative to existing methodologies.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"908-920"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941461","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":"SPONGE-FEP: An Automated Relative Binding Free Energy Calculation Accelerated by Selective Integrated Tempering Sampling.","authors":"Yijie Xia, Xiaohan Lin, Jinyuan Hu, Lijiang Yang, Yi Qin Gao","doi":"10.1021/acs.jctc.4c01486","DOIUrl":"https://doi.org/10.1021/acs.jctc.4c01486","url":null,"abstract":"<p><p>Computer-aided drug discovery (CADD) utilizes computational methods to accelerate the identification and optimization of potential drug candidates. Free energy perturbation (FEP) and thermodynamic integration (TI) play a critical role in predicting differences in protein binding affinities between drug molecules. Here, we implement SPONGE-FEP, which incorporates selective integrated tempering sampling (SITS) to enhance sampling efficiency and contains an automated workflow for relative binding free energy (RBFE) calculations. We first provide an overview of the workflow, which encompasses the generation of a perturbation map, alchemical free energy calculations, and cycle closure analysis. Two case studies were then performed to demonstrate the enhanced sampling of conformational states of ligands and proteins during the alchemical transformation process. The results show that the refined SITS method in SPONGE-FEP can significantly improve the sampling efficiency of rare events and the performance of RBFE predictions. Three series of comparative RBFE tests were conducted to demonstrate the accuracy of SPONGE-FEP, which is comparable to FEP+, using an average computation time of 4 h for a pair of ligands on an A100 GPU device.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Ubiquitin Ligase CHIP Accelerates Papillary Thyroid Carcinoma Metastasis via the Transgelin-Matrix Metalloproteinase-9 Axis.","authors":"Shaohua Zhan, Yan Yang, Shuwei Deng, Xinnan Liu, Liyan Cui, Tianxiao Wang","doi":"10.1021/acs.jproteome.4c00726","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00726","url":null,"abstract":"<p><p>The carboxyl-terminus of Hsp70-interacting protein (CHIP) plays crucial roles in tumorigenesis and immunity, with previous studies suggesting a double-edged sword in thyroid cancer. However, its precise functions and underlying molecular mechanisms in thyroid cancer remained unclear. Here, we demonstrate through immunohistochemistry (IHC) that CHIP expression progressively increases from normal thyroid tissue to primary papillary thyroid carcinoma (PTC) and lymph node metastases, with CHIP levels positively correlating with lymph node metastasis (<i>P</i> = 0.006). Moreover, CHIP overexpression enhanced thyroid cancer cell migration and invasion without significantly affecting cell viability. Tandem mass tag (TMT)-based LC-MS/MS analysis revealed that CHIP-regulated differentially expressed proteins, notably transgelin, were predominantly associated with metastasis-related pathways. Western blot, qPCR, and TCGA-THCA cohort data confirmed that CHIP regulates transgelin expression at the protein but not the genetic level. Mechanistically, CHIP promotes extracellular matrix degradation through the transgelin-matrix metalloproteinase-9 (MMP-9) axis, thereby facilitating PTC progression. Collectively, our findings indicate that CHIP expression was closely related to the progression and metastasis of PTC, suggesting that CHIP functions as a novel tumor oncoprotein in PTC via the transgelin-MMP-9 signaling axis.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"End-Point Affinity Estimation of Galectin Ligands by Classical and Semiempirical Quantum Mechanical Potentials.","authors":"Jan Choutka, Jakub Kaminský, Ercheng Wang, Kamil Parkan, Radek Pohl","doi":"10.1021/acs.jcim.4c01659","DOIUrl":"10.1021/acs.jcim.4c01659","url":null,"abstract":"<p><p>The use of quantum mechanical potentials in protein-ligand affinity prediction is becoming increasingly feasible with growing computational power. To move forward, validation of such potentials on real-world challenges is necessary. To this end, we have collated an extensive set of over a thousand galectin inhibitors with known affinities and docked them into galectin-3. The docked poses were then used to systematically evaluate several modern force fields and semiempirical quantum mechanical (SQM) methods up to the tight-binding level under consistent computational workflow. Implicit solvation models available with the tested methods were used to simulate solvation effects. Overall, the best methods in this study achieved a Pearson correlation of 0.7-0.8 between the computed and experimental affinities. There were differences between the tested methods in their ability to rank ligands across the entire ligand set as well as within subsets of structurally similar ligands. A major discrepancy was observed for a subset of ligands that bind to the protein via a halogen bond, which was clearly challenging for all the tested methods. The inclusion of an entropic term calculated by the rigid-rotor-harmonic-oscillator approximation at SQM level slightly worsened correlation with experiment but brought the calculated affinities closer to experimental values. We also found that the success of the prediction strongly depended on the solvation model. Furthermore, we provide an in-depth analysis of the individual energy terms and their effect on the overall prediction accuracy.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"762-777"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925803","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":"Identifying Protein-Nucleotide Binding Residues via Grouped Multi-task Learning and Pre-trained Protein Language Models.","authors":"Jiashun Wu, Yan Liu, Ying Zhang, Xiaoyu Wang, He Yan, Yiheng Zhu, Jiangning Song, Dong-Jun Yu","doi":"10.1021/acs.jcim.4c02092","DOIUrl":"10.1021/acs.jcim.4c02092","url":null,"abstract":"<p><p>The accurate identification of protein-nucleotide binding residues is crucial for protein function annotation and drug discovery. Numerous computational methods have been proposed to predict these binding residues, achieving remarkable performance. However, due to the limited availability and high variability of nucleotides, predicting binding residues for diverse nucleotides remains a significant challenge. To address these, we propose NucGMTL, a new grouped deep multi-task learning approach designed for predicting binding residues of all observed nucleotides in the BioLiP database. NucGMTL leverages pre-trained protein language models to generate robust sequence embedding and incorporates multi-scale learning along with scale-based self-attention mechanisms to capture a broader range of feature dependencies. To effectively harness the shared binding patterns across various nucleotides, deep multi-task learning is utilized to distill common representations, taking advantage of auxiliary information from similar nucleotides selected based on task grouping. Performance evaluation on benchmark data sets shows that NucGMTL achieves an average area under the Precision-Recall curve (AUPRC) of 0.594, surpassing other state-of-the-art methods. Further analyses highlight that the predominant advantage of NucGMTL can be reflected by its effective integration of grouped multi-task learning and pre-trained protein language models. The data set and source code are freely accessible at: https://github.com/jerry1984Y/NucGMTL.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"1040-1052"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941364","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":"deep-AMPpred: A Deep Learning Method for Identifying Antimicrobial Peptides and Their Functional Activities.","authors":"Jun Zhao, Hangcheng Liu, Leyao Kang, Wanling Gao, Quan Lu, Yuan Rao, Zhenyu Yue","doi":"10.1021/acs.jcim.4c01913","DOIUrl":"10.1021/acs.jcim.4c01913","url":null,"abstract":"<p><p>Antimicrobial peptides (AMPs) are small peptides that play an important role in disease defense. As the problem of pathogen resistance caused by the misuse of antibiotics intensifies, the identification of AMPs as alternatives to antibiotics has become a hot topic. Accurately identifying AMPs using computational methods has been a key issue in the field of bioinformatics in recent years. Although there are many machine learning-based AMP identification tools, most of them do not focus on or only focus on a few functional activities. Predicting the multiple activities of antimicrobial peptides can help discover candidate peptides with broad-spectrum antimicrobial ability. We propose a two-stage AMP predictor deep-AMPpred, in which the first stage distinguishes AMP from other peptides, and the second stage solves the multilabel problem of 13 common functional activities of AMP. deep-AMPpred combines the ESM-2 model to encode the features of AMP and integrates CNN, BiLSTM, and CBAM models to discover AMP and its functional activities. The ESM-2 model captures the global contextual features of the peptide sequence, while CNN, BiLSTM, and CBAM combine local feature extraction, long-term and short-term dependency modeling, and attention mechanisms to improve the performance of deep-AMPpred in AMP and its function prediction. Experimental results demonstrate that deep-AMPpred performs well in accurately identifying AMPs and predicting their functional activities. This confirms the effectiveness of using the ESM-2 model to capture meaningful peptide sequence features and integrating multiple deep learning models for AMP identification and activity prediction.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"997-1008"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941457","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}