Journal of Computer-Aided Molecular Design最新文献

{"title":"Computational exploration and molecular dynamics simulations for investigating the potential inhibitory mechanism of amantadine on the ion channel activity of bovine viral diarrhea virus p7","authors":"Xiao Wang,&nbsp;Ziwei Liu,&nbsp;Daolai Zhang,&nbsp;Yulong Wu,&nbsp;Yongfeng Li,&nbsp;Xiaowei Chen","doi":"10.1007/s10822-025-00643-y","DOIUrl":"10.1007/s10822-025-00643-y","url":null,"abstract":"<div><p>Bovine viral diarrhea virus (BVDV) p7 functions as a viroporin for the ion balance and membrane permeabilization. Blocking the function of the viroporin is a promising strategy for the treatment of viral infection. Previous studies have demonstrated that the antiviral drug amantadine inhibits BVDV replication by inhibiting BVDV p7 activity. However, the mechanism by which amantadine acts against BVDV p7 remains unclear. In this study, AlphaFold2, molecular docking and molecular dynamics (MD) simulations were employed to investigate the binding sites of amantadine on BVDV p7. Structural analysis by AlphaFold2 and MD simulations showed that BVDV p7 may undergo antiparallel oligomerization, forming a stable hexamer that generates a pore channel. Notably, residues E21, Y25, L28, and R34 within the channel are likely involved in ion transport. Subsequently, the interaction of amantadine with BVDV p7 hexamer was investigated by docking studies and MD simulations analysis, indicating residues Y25 and L28 by van der Waals forces, alkyl and Pi-Alkyl interactions with amantadine. Importantly, the hydrogen bonding was observed between the -NH₃⁺ group of amantadine and residue Y25. By integrating these findings with the potential hexameric assembly of BVDV p7, we further proposed a potential ion channel model in which E21, Y25 and R34 are hypothesized to selectively recruit and dehydrate ions, while residue L28 acts as a hydrophobic restrictor, limiting the free movement of water. The binding of amantadine to residues Y25 and L28 likely disrupts ion transport. Our findings provide possible structural insights into the BVDV p7 ion channel and offer a mechanistic explanation for the inhibitory of amantadine on BVDV p7-mediated ion channel conductance, though experimental validation remains necessary.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788030","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":"Targeting neurodegeneration: three machine learning methods for G9a inhibitors discovery using PubChem and scikit-learn","authors":"Mariya L. Ivanova,&nbsp;Nicola Russo,&nbsp;Konstantin Nikolic","doi":"10.1007/s10822-025-00642-z","DOIUrl":"10.1007/s10822-025-00642-z","url":null,"abstract":"<div><p>In light of the increasing interest in G9a’s role in neuroscience, three machine learning (ML) models, that are time efficient and cost effective, were developed to support researchers in this area. The models are based on data provided by PubChem and performed by algorithms interpreted by the scikit-learn Python-based ML library. The first ML model aimed to predict the efficacy magnitude of active G9a inhibitors. The ML models were trained with 3112 and tested with 778 samples. The Gradient Boosting Regressor perform the best, achieving 17.81% means relative error, 21.48% mean absolute error, 27.39% root mean squared error and 0.02 coefficient of determination (R²) error. The goal of the second ML model, called a CID_SID ML model, utilised PubChem identifiers to predict the G9a inhibition of a small biomolecule that has been primarily designed for different purposes. The ML models were trained with 58,552 samples and tested with 14,000. The most suitable classifier for this case study was the Extreme Gradient Boosting Classifier, which obtained 79.7% accuracy, 83.2% precision,67.7% recall, 74.7% F1-score and 78.4% ROC. Up to date, this methodology has been used in seven studies, achieving a mean accuracy of 82.75%, precision of 90.71%, Recall of 73.01%, F1-score of 80.79% and ROC of 80.63% across all case studies. The third ML model utilised IUPAC names. It was based on the Random Forest Classifier algorithm, trained with 19,455 samples and tested with 14,100. The probability of this prediction was 68.2% accuracy. Its feature importance list was reordered by the relative proportion of active cases in which they participate. Thus, “iodide” was identified as the one with the highest relative proportion of the active cases to all cases where this fragment participated. In addition, ‘iodo’ was identified as the most desirable fragment, and “phenylcarbamate” as the least desirable based on their participation only in active or inactive cases, respectively. The computational approach has been initially developed and demonstrated using a case study on Tyrosyl-DNA phosphodiesterase 1(TDP 1) inhibition.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788033","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":"Exploring Oxazolidinone scaffolds for future antibiotics: synthesis and computational insights with DFT, docking, ADME and MD simulation","authors":"Sanjay Soni,&nbsp;Khushbu Patil,&nbsp;Sharad Gavale,&nbsp;Soyeb Pathan,&nbsp;Rasana Yadav,&nbsp;Prashant R. Murumkar,&nbsp;Rahul Kadu","doi":"10.1007/s10822-025-00634-z","DOIUrl":"10.1007/s10822-025-00634-z","url":null,"abstract":"<div><p>The emergence of antibiotic-resistance is a serious concern in maintaining global health in this era, which necessitates constant advancements in antibacterial research for effective antibacterial solutions. To address this issue, a series of oxazolidinone derivatives bearing biologically significant functionalities were efficiently synthesized and screened for in vitro antimicrobial activities against four bacterial strains viz. two Gram positive strains: <i>S. aureus</i>, <i>S. pyogenes</i>, two Gram negative strains: <i>E. coli</i>, <i>P. aeruginosa</i>, and two fungal strains namely, <i>C. albicans</i>, <i>A. niger</i>, in comparison to standard drugs like Ampicillin and Nystatin, respectively. Further, DFT study were performed and these compounds were screened for their binding efficacies against the respective target proteins, followed by prediction of drug-likeness and ADME properties. Among the oxazolidinone derivatives, compounds <b>7 g</b> and <b>7i</b> display excellent activity. Interestingly, <b>7 g</b> emerged as a promising candidate, demonstrating better effectiveness against <i>E. coli</i> (12 µg/mL), <i>P. aeruginosa</i> (20 µg/mL) and <i>S. aureus</i> (50 µg/mL) compared to reference drug Ampicillin. Compound <b>7a</b> exhibited excellent activity against <i>A. niger</i> and <i>C. albicans</i> with an MIC of 50 µg/mL and 100 µg/mL compared to Nystatin (MIC = 100 µg/mL). Molecular Dynamics simulations were performed on the target protein for <b>7 g</b>, demonstrating exceptional biological activity and binding affinity in in silico studies. These results suggest that oxazolidinone derivative <b>7 g</b>,<b> 7i</b> and <b>7k</b> is a promising therapeutic candidate for drug-resistant bacterial and fungal infections.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788032","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":"Anticancer potential of 2,2′-bipyridine hydroxamic acid derivatives in head and neck cancer therapy","authors":"Manasa Gangadhar Shetty,&nbsp;Bipasa Dey,&nbsp;Padmini Pai,&nbsp;Babitha Kampa Sundara,&nbsp;Kapaettu Satyamoorthy,&nbsp;Srinivas Oruganti,&nbsp;Usha Yogendra Nayak,&nbsp;T. Ashwini","doi":"10.1007/s10822-025-00640-1","DOIUrl":"10.1007/s10822-025-00640-1","url":null,"abstract":"<div><p>The genesis of head and neck cancer (HNC) is attributed to the combined influence of genetic and epigenetic irregularities. While surgical resection and radiotherapy remain primary treatment modalities, the effectiveness of current chemotherapeutic options is often hindered by toxicity, resistance, and limited selectivity. Hydroxyurea has long been recognized for its anticancer potential; however, its clinical application is limited by a short half-life, dose-dependent toxicity, and resistance mechanisms. To address these limitations, researchers have focused on developing novel hydroxyurea derivatives with improved pharmacokinetics, target specificity, and multimodal mechanisms of action. In the present study, we report the design and synthesis of two novel 2,2′-bipyridine hydroxamic acid derivatives, including a hydroxyurea analogue aimed at enhancing chemotherapeutic efficacy and safety. Compound <b>1A</b> demonstrated selective cytotoxicity against Cal27 cells (IC₅₀ = 19.36 μM). Mechanistic investigations revealed that <b>1A</b> inhibits cancer cell migration and induces ROS-mediated apoptosis. Additionally, <b>1A</b> exhibited moderate HDAC inhibition, supported by molecular docking and dynamics simulations, which confirmed stable binding to HDAC 2 isoform through Zn²⁺ coordination. These findings place compound <b>1A</b> as a promising lead candidate, integrating epigenetic modulation and direct cytotoxic effects for potential therapeutic application in HNC.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12328545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular dynamics simulations reveal mechanistic insights into aptamer-induced structural rearrangements in viral capsid proteins","authors":"Chen Fei Low,&nbsp;Norazli Ghadin,&nbsp;Muhamad Arif Mohamad Jamali","doi":"10.1007/s10822-025-00633-0","DOIUrl":"10.1007/s10822-025-00633-0","url":null,"abstract":"<div><p><i>Macrobrachium rosenbergii</i> nodavirus is a major viral pathogen responsible for white tail disease in giant freshwater prawn aquaculture, leading to significant economic losses. In this study, a truncated DNA aptamer, TrAptm-1 was investigated for its binding properties against both monomeric and trimeric forms of the MrNV capsid proteins. Molecular dynamics simulations coupled with MM/PBSA binding free energy calculations revealed that TrAptm-1 exhibited a higher binding affinity to the trimeric capsid protein (-153.95 ± 6.74 kcal/mol) compared to the monomeric form (-120.77 ± 2.46 kcal/mol). TrAptm-1 binding induced significant conformational changes and structural rearrangements in the capsid protein, highlighted the antiviral potential of TrAptm-1 to interfere with the capsid protein self-assembly process. The observed structural changes demonstrated the importance of the oligomeric state in aptamer-capsid protein interactions, emphasizing that extended simulations up-to microseconds are required to capture the slow conformational rearrangements characteristic of large oligomeric protein complexes. These findings provide a molecular basis for the development of aptamer-based antiviral strategies, and the design of biosensor for early detection of MrNV in aquaculture settings.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144688533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anti-inflammatory potential of Grewialin from Grewia optiva: insights from molecular docking, ADMET, DFT, and in-vitro studies","authors":"Wali Ullah,&nbsp;Ghias Uddin,&nbsp;Abdur Rauf,&nbsp;Muhammad Umer Khan,&nbsp;Zuneera Akram,&nbsp;Chaudhry Ahmed Shabbir,&nbsp;Abdulhakeem S. Alamri,&nbsp;Walaa F. Alsanie,&nbsp;Marcello Iriti","doi":"10.1007/s10822-025-00632-1","DOIUrl":"10.1007/s10822-025-00632-1","url":null,"abstract":"<div><p><i>Grewia optiva</i>, a medicinal plant native to northern Pakistan, has traditionally been valued for managing pain and inflammation. Among its bioactive constituents, <i>Grewialin</i>, a compound isolated from the stem bark, has garnered attention as a promising candidate for anti-inflammatory drug development. This study aimed to evaluate the anti-inflammatory potential of <i>Grewialin</i> using a combination of in-silico and in-vitro approaches, focusing on its effects on lipoxygenase (LOX) and neutrophil respiratory burst activity. Furthermore, the pharmacological and toxicological profiles of <i>Grewialin</i> were assessed to support its potential as a lead compound. Structure-based virtual screening identified <i>Grewialin</i> as a potential LOX inhibitor. Molecular docking studies revealed a significant binding score of -6.874 kcal/mol, indicating strong interactions with the active site of the LOX enzyme (5-LOX). ADMET profiling demonstrated its favourable pharmacokinetic and toxicological properties, while density functional theory (DFT) calculations highlighted its balanced electrophilic and nucleophilic properties, reflecting its chemical stability and reactivity. Experimental validation through in vitro assays confirmed <i>Grewialin</i>’s potent inhibitory activity against LOX, with an IC50 value of 31.9 ± 0.03 µM. Additionally, <i>Grewialin</i> effectively inhibited neutrophil respiratory burst activity, achieving an IC50 of 317.62 ± 0.059 µM, further supporting its anti-inflammatory potential. These findings position <i>Grewialin</i> as a multifaceted anti-inflammatory agent with dual action targeting LOX and neutrophil respiratory burst activity. The combined in-silico and in-vitro results underscore its potential as a lead compound for developing anti-inflammatory drugs. Further research is warranted to explore its therapeutic mechanisms and optimize its efficacy, bridging traditional medicinal knowledge with modern pharmacological advancements. It is worth mentioning, though, that, in line with the aforementioned dual inhibitory profiles, <i>Grewialin</i> also demonstrated moderate potency (LOX and neutrophil respiratory), thus indicating that additional structural optimization or adequate formulation design is necessary to progress it toward therapeutic development. By leveraging the unique properties of <i>Grewialin</i>, this study contributes to the ongoing pursuit of novel, natural compounds for the effective management of inflammatory diseases. This comprehensive evaluation highlights the significance of <i>Grewia optiva</i> as a source of bioactive compounds, emphasizing the need for further exploration into its pharmacological application.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144688532","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":"Comparison study of dominant molecular sequence representation based on diffusion model","authors":"Yongrui Cui,&nbsp;Dongjing Shan,&nbsp;Qiheng Lu,&nbsp;Beijia Zou,&nbsp;Huali Zhang,&nbsp;Jin Li,&nbsp;Jiashun Mao","doi":"10.1007/s10822-025-00614-3","DOIUrl":"10.1007/s10822-025-00614-3","url":null,"abstract":"<div><p>In recent years, the emergence of large language models (LLMs), particularly the advent of ChatGPT, has positioned natural language sequence-based representation learning and generative models as the dominant research paradigm in AI for science. Within the domains of drug discovery and computational chemistry, compound representation learning and molecular generation stand out as two of the most significant tasks. Currently, the predominant molecular representation sequences used for molecular characterization and generation include SMILES (Simplified Molecular-Input Line-Entry System), SELFIES (SELF-referencing Embedded Strings), SMARTS (Smiles Arbitrary Target Specification), and IUPAC (International Union of Pure and Applied Chemistry) nomenclature. In the context of AI-assisted drug design, each of these molecular languages has its own strengths and weaknesses, and the granularity of information encoded by different molecular representation forms varies significantly. However, the selection of an appropriate molecular representation as the input format for model training is crucial, yet this issue has not been thoroughly explored. Furthermore, the state-of-the-art models currently employed for molecular generation and optimization are diffusion models. Therefore, this study investigates the characteristics of the four mainstream molecular representation languages within the same diffusion model for training generative molecular sets. First, a single molecule is represented in four different ways through varying methodologies, followed by training a denoising diffusion model using identical parameters. Subsequently, thirty thousand molecules are generated for evaluation and analysis. The results indicate that the four molecular representation languages exhibit both similarities and differences in attribute distribution and spatial distribution; notably, SELFIES and SMARTS demonstrate a high degree of similarity, while IUPAC and SMILES show substantial differences. Additionally, IUPAC’s primary advantage lies in the novelty and diversity of generated molecules, whereas SMILES excels in QEPPI and SAscore metrics, with SELFIES and SMARTS performing best on the QED metric. The findings of this research will provide crucial insights into the selection of molecular representations in AI drug design tasks, thereby contributing to enhanced efficiency in drug development.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658039","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":"Design, synthesis, evaluation and molecular modeling of quinazoline derivatives bearing amino acids as small-molecule PD-L1 inhibitors","authors":"Han Liu,&nbsp;Roufen Chen,&nbsp;Dandan Yuan,&nbsp;Yidan Xing,&nbsp;Xueyan Ding,&nbsp;Xingye Wu,&nbsp;Yali Gao,&nbsp;Junjie Ma","doi":"10.1007/s10822-025-00635-y","DOIUrl":"10.1007/s10822-025-00635-y","url":null,"abstract":"<div><p>Herein, we reported a series of quinazoline derivatives bearing amino acids by introducing a rigid pyrimidine structure between the 2 and 3-positions of the biphenyl and establishing an ionic interaction with Lys124 of PD-L1. Evaluation of the PD-1/PD-L1 inhibitory activity identified compound <b>7</b>, which exhibited the most potent inhibitory activity with an IC₅₀ value of 7.21 nM. Molecular docking was performed to demonstrate that the carboxyl group of amino acid in the tail established an ionic interaction with the ε-NH₃⁺ of Lys124, enhancing the binding. Importantly, molecular dynamics study revealed that the nitrogen atom in the nicotinonitrile formed water-mediated interactions with Asn63 of PD-L1, that stabilized the binding of the compound to PD-L1, providing an important and reasonable explanation for the introduction of nicotinonitrile to enhance inhibitory activity. Our study provides valuable guidance for further design of potent quinazoline-based small-molecule PD-L1 inhibitors, and identifies the compound <b>7</b> that is a promising lead compound and deserves further investigation.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658040","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":"Integrated machine learning and deep learning-based virtual screening framework identifies novel natural GSK-3β inhibitors for Alzheimer’s disease","authors":"Ya Zhou,&nbsp;Ben-Rong Mu,&nbsp;Xing-Yi Chen,&nbsp;Li Liu,&nbsp;Qing-Lin Wu,&nbsp;Mei-Hong Lu,&nbsp;Feng-Ling Qiao","doi":"10.1007/s10822-025-00637-w","DOIUrl":"10.1007/s10822-025-00637-w","url":null,"abstract":"<div><p>Alzheimer’s disease (AD) is a progressive neurodegenerative disorder lacking effective therapies. Glycogen synthase kinase-3β (GSK-3β), a key regulator of Aβ aggregation and Tau hyperphosphorylation, has emerged as a promising therapeutic target. Here, we present a novel two-stage virtual screening (VS) framework that integrates an interpretable random forest (RF) model (AUC = 0.99) with a deep learning-based molecular docking platform, KarmaDock (NEF_0.5% = 1.0), to identify potential GSK-3β inhibitors from natural products. The model’s interpretability was enhanced using SHAP analysis to uncover key fingerprint features driving activity predictions. A curated natural compound library (n = 25,000) from TCMBank and HERB was constructed under drug-likeness constraints, and validated using multi-level decoy sets. Three compounds derived from <i>Clausena</i> and <i>Psoralea</i> exhibited favorable pharmacokinetic profiles in silico, including blood–brain barrier permeability and low neurotoxicity. Molecular docking, pharmacophore modeling, and molecular dynamics simulations confirmed their stable interactions with critical GSK-3β binding sites. Notably, our approach combines explainability and deep learning to enhance screening accuracy and interpretability, addressing limitations in traditional black-box models. While current findings are computational, they offer theoretical support and provide actionable leads for future experimental validation of natural GSK-3β inhibitors.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144641363","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":"Mechanistic insights into PROTAC-mediated degradation through an integrated framework of molecular dynamics, free energy landscapes, and quantum mechanics: A case study on kinase degraders","authors":"Ashis Nandy,&nbsp;Kiran Boppana,&nbsp;Samiron Phukan","doi":"10.1007/s10822-025-00630-3","DOIUrl":"10.1007/s10822-025-00630-3","url":null,"abstract":"<div><p>Targeted protein degradation by proteolysis-targeting chimeras (PROTAC) is dependent on formation and plasticity of ternary complexes enabling ubiquitination. In this study, we employed long-timescale molecular dynamics (MD) simulations, free energy landscape analysis, and quantum mechanical (QM) calculations to investigate the molecular determinants of PROTAC efficacy. Using three model systems (FAK-VHL, BTK-CRBN, and TTK-CRBN), each with three PROTACs of varying potencies, we analyzed a total of nine ternary complexes over 500 ns MD simulations each. Simulation events analysis revealed that potent PROTACs maintain stable and important interactions between the protein of interest (POI) and the E3 ligase, while weaker PROTACs exhibit diminished or no interactions. Conformational dynamics and changes in the interaction patterns between the POI-E3-ligase complexes highlighted the importance of ternary complex plasticity in degradation efficiency. These findings were also supported by the distribution of free energy landscape during simulations. Distributions of the free energy landscape offer insights into the stability of population states and open new avenues for understanding their degradation potential. Additionally, to overcome the limitation of conventional docking models, we highlight the importance of QM and DFT based methods to predict the impact of binding at the E3-ligase site which corelates with the degradation potentials of PROTACs. These insights provide a new computational framework for rational PROTAC design.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"39 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144641364","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}