Journal of Computer-Aided Molecular Design最新文献

{"title":"Investigating the role of glycans in Omicron sub-lineages XBB.1.5 and XBB.1.16 binding to host receptor using molecular dynamics and binding free energy calculations","authors":"Jaikee Kumar Singh,&nbsp;Jai Singh,&nbsp;Sandeep Kumar Srivastava","doi":"10.1007/s10822-023-00526-0","DOIUrl":"10.1007/s10822-023-00526-0","url":null,"abstract":"<div><p>Omicron derived lineages viz. BA.2, BA.3, BA.4 BA.5, BF.7 and XBBs show prominence with improved immune escape, transmissibility, infectivity, and pathogenicity in general. Sub-variants, XBB.1.5 and XBB.1.16 have shown rapid spread, with mutations embedded throughout the viral genome, including the spike protein. Changing atomic landscapes in spike contributes significantly to modulate host pathogen interactions and infections thereof. In the present work, we computationally analyzed the binding affinities of spike receptor binding domains (RBDs) of XBB.1.5 and XBB.1.16 towards human angiotensin-converting enzyme 2 (hACE2) compared to Omicron. We have employed simulations and binding energy estimation of molecular complexes of spike-hACE2 to assess the interplay of interaction pattern and effect of mutations if any in the binding mode of the RBDs of these novel mutants. We calculated the binding free energy (BFE) of the RBD of the Omicron, XBB.1.5 and XBB.1.16 spike protein to hACE2. We showed that XBB.1.5 and XBB.1.16 can bind to human cells more strongly than Omicron due to the increased charge of the RBD, which enhances the electrostatic interactions with negatively charged hACE2. The per-residue decompositions further show that the Asp339His, Asp405Asn and Asn460Lys mutations in the XBBs RBD play a crucial role in enhancing the electrostatic interactions, by acquiring positively charged residues, thereby influencing the formation/loss of interfacial bonds and thus strongly affecting the spike RBD-hACE2 binding affinity. Simulation results also indicate less interference of heterogeneous glycans of XBB.1.5 spike RBD towards binding to hACE2. Moreover, despite having less interaction at the three interfacial contacts between XBB S RBD and hACE2 compared to Omicron, variants XBB.1.5 and XBB.1.16 had higher total binding free energies (ΔG_bind) than Omicron due to the contribution of non-interfacial residues to the free energy, providing insight into the increased binding affinity of XBB1.5 and XBB.1.16. Furthermore, the presence of large positively charged surface patches in the XBBs act as drivers of electrostatic interactions, thus support the possibility of a higher binding affinity to hACE2.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 11","pages":"551 - 563"},"PeriodicalIF":3.5,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-023-00526-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6727458","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":"Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design","authors":"Ajay N. Jain,&nbsp;Alexander C. Brueckner,&nbsp;Christine Jorge,&nbsp;Ann E. Cleves,&nbsp;Purnima Khandelwal,&nbsp;Janet Caceres Cortes,&nbsp;Luciano Mueller","doi":"10.1007/s10822-023-00524-2","DOIUrl":"10.1007/s10822-023-00524-2","url":null,"abstract":"<div><p>Systematic optimization of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead-optimization using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate. We show how conformational restraints can be derived by exploiting NMR data to identify low-energy solution ensembles of a lead compound. Such restraints can be used to focus conformational search for analogs in order to accurately predict bound ligand poses through molecular docking and thereby estimate ligand strain and protein-ligand intermolecular binding energy. We also describe an analogous ligand-based approach that employs molecular similarity optimization to predict bound poses. Both approaches are shown to be effective for prioritizing lead-compound analogs. Surprisingly, relatively small ligand modifications, which may have minimal effects on predicted bound pose or intermolecular interactions, often lead to large changes in estimated strain that have dominating effects on overall binding energy estimates. Effective macrocyclic conformational search is crucial, whether in the context of NMR-based restraints, X-ray ligand refinement, partial torsional restraint for docking/ligand-similarity calculations or agnostic search for nominal global minima. Lead optimization for peptidic macrocycles can be made more productive using a multi-disciplinary approach that combines biophysical data with practical and efficient computational methods.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 11","pages":"519 - 535"},"PeriodicalIF":3.5,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-023-00524-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6727391","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":"PoseEdit: enhanced ligand binding mode communication by interactive 2D diagrams","authors":"Konrad Diedrich,&nbsp;Bennet Krause,&nbsp;Ole Berg,&nbsp;Matthias Rarey","doi":"10.1007/s10822-023-00522-4","DOIUrl":"10.1007/s10822-023-00522-4","url":null,"abstract":"<div><p>In this article, we present PoseEdit, a new, interactive frontend of the popular pose visualization tool PoseView. PoseEdit automatically produces high-quality 2D diagrams of intermolecular interactions in 3D binding sites calculated from ligands in complex with protein, DNA, and RNA. The PoseView diagrams have been improved in several aspects, most notably in their interactivity. Thanks to the easy-to-use 2D editor of PoseEdit, the diagrams are extensively editable and extendible by the user, can be merged with other diagrams, and even be created from scratch. A large variety of graphical objects in the diagram can be moved, rotated, selected and highlighted, mirrored, removed, or even newly added. Furthermore, PoseEdit enables a synchronized 2D-3D view of macromolecule-ligand complexes simplifying the analysis of structural features and interactions. The representation of individual diagram objects regarding their visualized chemical properties, like stereochemistry, and general graphical styles, like the color of interactions, can additionally be edited. The primary objective of PoseEdit is to support scientists with an enhanced way to communicate ligand binding mode information through graphical 2D representations optimized with the scientist’s input in accordance with objective criteria and individual needs. PoseEdit is freely available on the Proteins<i>Plus</i> web server (https://proteins.plus).</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 10","pages":"491 - 503"},"PeriodicalIF":3.5,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-023-00522-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5117959","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":"Exploring binding positions and backbone conformations of peptide ligands of proteins with a backbone-centred statistical energy function","authors":"Lu Zhang,&nbsp;Haiyan Liu","doi":"10.1007/s10822-023-00518-0","DOIUrl":"10.1007/s10822-023-00518-0","url":null,"abstract":"<div><p>When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 10","pages":"463 - 478"},"PeriodicalIF":3.5,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5048225","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":"Enhancement by pyrazolones of colistin efficacy against mcr-1-expressing E. coli: an in silico and in vitro investigation","authors":"Chonnikan Hanpaibool,&nbsp;Puey Ounjai,&nbsp;Sirilata Yotphan,&nbsp;Adrian J. Mulholland,&nbsp;James Spencer,&nbsp;Natharin Ngamwongsatit,&nbsp;Thanyada Rungrotmongkol","doi":"10.1007/s10822-023-00519-z","DOIUrl":"10.1007/s10822-023-00519-z","url":null,"abstract":"<div><p>Owing to the emergence of antibiotic resistance, the polymyxin colistin has been recently revived to treat acute, multidrug-resistant Gram-negative bacterial infections. Positively charged colistin binds to negatively charged lipids and damages the outer membrane of Gram-negative bacteria. However, the MCR-1 protein, encoded by the mobile colistin resistance (<i>mcr</i>) gene, is involved in bacterial colistin resistance by catalysing phosphoethanolamine (PEA) transfer onto lipid A, neutralising its negative charge, and thereby reducing its interaction with colistin. Our preliminary results showed that treatment with a reference pyrazolone compound significantly reduced colistin minimal inhibitory concentrations in <i>Escherichia coli</i> expressing <i>mcr-1</i> mediated colistin resistance (Hanpaibool et al. in ACS Omega, 2023). A docking-MD combination was used in an ensemble-based docking approach to identify further pyrazolone compounds as candidate MCR-1 inhibitors. Docking simulations revealed that 13/28 of the pyrazolone compounds tested are predicted to have lower binding free energies than the reference compound. Four of these were chosen for in vitro testing, with the results demonstrating that all the compounds tested could lower colistin MICs in an <i>E. coli</i> strain carrying the <i>mcr-1</i> gene. Docking of pyrazolones into the MCR-1 active site reveals residues that are implicated in ligand–protein interactions, particularly E246, T285, H395, H466, and H478, which are located in the MCR-1 active site and which participate in interactions with MCR-1 in ≥ 8/10 of the lowest energy complexes. This study establishes pyrazolone-induced colistin susceptibility in <i>E</i>. <i>coli</i> carrying the <i>mcr-1</i> gene, providing a method for the development of novel treatments against colistin-resistant bacteria.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 10","pages":"479 - 489"},"PeriodicalIF":3.5,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4935933","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":"Correction to: Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors","authors":"Wemenes José Lima Silva,&nbsp;Renato Ferreira de Freitas","doi":"10.1007/s10822-023-00521-5","DOIUrl":"10.1007/s10822-023-00521-5","url":null,"abstract":"","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 10","pages":"505 - 505"},"PeriodicalIF":3.5,"publicationDate":"2023-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4861346","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":"Polygalic acid inhibits african swine fever virus polymerase activity: findings from machine learning and in vitro testing","authors":"Jiwon Choi,&nbsp;Hyundo Lee,&nbsp;Soyoung Cho,&nbsp;Yorim Choi,&nbsp;Thuy X. Pham,&nbsp;Trang T. X. Huynh,&nbsp;Yun-Sook Lim,&nbsp;Soon B. Hwang","doi":"10.1007/s10822-023-00520-6","DOIUrl":"10.1007/s10822-023-00520-6","url":null,"abstract":"<div><p>African swine fever virus (ASFV), an extremely contagious virus with high mortality rates, causes severe hemorrhagic viral disease in both domestic and wild pigs. Fortunately, ASFV cannot be transmitted from pigs to humans. However, ongoing ASFV outbreaks could have severe economic consequences for global food security. Although ASFV was discovered several years ago, no vaccines or treatments are commercially available yet; therefore, the identification of new anti-ASFV drugs is urgently warranted. Using molecular docking and machine learning, we have previously identified pentagastrin, cangrelor, and fostamatinib as potential antiviral drugs against ASFV. Here, using machine learning combined with docking simulations, we identified natural products with a high affinity for <i>Asfv</i>PolX proteins. We selected five natural products (NPs) that are located close in chemical space to the six known natural flavonoids that possess anti-ASFV activity. Polygalic acid markedly reduced <i>Asfv</i>PolX polymerase activity in a dose-dependent manner. We propose an efficient protocol for identifying NPs as potential antiviral drugs by identifying chemical spaces containing high-affinity binders against ASFV in NP databases.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 9","pages":"453 - 461"},"PeriodicalIF":3.5,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-023-00520-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4615206","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":"ADis-QSAR: a machine learning model based on biological activity differences of compounds","authors":"Gyoung Jin Park,&nbsp;Nam Sook Kang","doi":"10.1007/s10822-023-00517-1","DOIUrl":"10.1007/s10822-023-00517-1","url":null,"abstract":"<div><p>Drug candidates identified by the pharmaceutical industry typically have unique structural characteristics to ensure they interact strongly and specifically with their biological targets. Identifying these characteristics is a key challenge for developing new drugs, and quantitative structure-activity relationship (QSAR) analysis has generally been used to perform this task. QSAR models with good predictive power improve the cost and time efficiencies invested in compound development. Generating these good models depends on how well differences between “active” and “inactive” compound groups can be conveyed to the model to be learned. Efforts to solve this difference issue have been made, including generating a “molecular descriptor” that compressively expresses the structural characteristics of compounds. From the same perspective, we succeeded in developing the Activity Differences-Quantitative Structure-Activity Relationship (ADis-QSAR) model by generating molecular descriptors that more explicitly convey features of the group through a pair system that performs direct connections between active and inactive groups. We used popular machine learning algorithms, such as Support Vector Machine, Random Forest, XGBoost and Multi-Layer Perceptron for model learning and evaluated the model using scores such as accuracy, area under curve, precision and specificity. The results showed that the Support Vector Machine performed better than the others. Notably, the ADis-QSAR model showed significant improvements in meaningful scores such as precision and specificity compared to the baseline model, even in datasets with dissimilar chemical spaces. This model reduces the risk of selecting false positive compounds, improving the efficiency of drug development.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 9","pages":"435 - 451"},"PeriodicalIF":3.5,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5125030","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":"Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors","authors":"Wemenes José Lima Silva,&nbsp;Renato Ferreira de Freitas","doi":"10.1007/s10822-023-00515-3","DOIUrl":"10.1007/s10822-023-00515-3","url":null,"abstract":"<div><p>Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔG_bind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R² ≥ 0.5) with experimental values of ΔG_bind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 9","pages":"407 - 418"},"PeriodicalIF":3.5,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10822-023-00515-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5086055","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":"COSMO-RS blind prediction of distribution coefficients and aqueous pKa values from the SAMPL8 challenge","authors":"Michael Diedenhofen,&nbsp;Frank Eckert,&nbsp;Selman Terzi","doi":"10.1007/s10822-023-00514-4","DOIUrl":"10.1007/s10822-023-00514-4","url":null,"abstract":"<div><p>The SAMPL8 blind prediction challenge, which addresses the acid/base dissociation constants (pKa) and the distribution coefficients (logD), was addressed by the Conductor like Screening Model for Realistic Solvation (COSMO-RS). Using the COSMOtherm implementation of COSMO-RS together with a rigorous conformational sampling, yielded logD predictions with a root mean square deviation (RMSD) of 1.36 log units over all 11 compounds and seven bi-phasic systems of the data set, which was the most accurate of all contest submissions (logD).</p><p>For the SAMPL8 pKa competition, participants were asked to report the standard state free energies of all microstates, which were then used to calculate the macroscopic pKa. We have used COSMO-RS based linear free energy fit models to calculate the requested energies. The assignment of the calculated and experimental pKa values was made on the basis of the popular transitions, i.e. the transition hat was predicted by the majority of the submissions. With this assignment and a model that covers both, pKa and base pKa, we achieved an RMSD of 3.44 log units (18 pKa values of 14 molecules), which is the second place of the six ranked submissions. By changing to an assignment that is based on the experimental transition curves, the RMSD reduces to 1.65. In addition to the ranked contribution, we submitted two more data sets, one for the standard pKa model and one or the standard base pKa model of COSMOtherm. Using the experiment based assignment with the predictions of the two sets we received a RMSD of 1.42 log units (25 pKa values of 20 molecules). The deviation mainly arises from a single outlier compound, the omission of which leads to an RMSD of 0.89 log units.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 8","pages":"395 - 405"},"PeriodicalIF":3.5,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5482620","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}