{"title":"Binding of small molecule inhibitors to RNA polymerase-Spt5 complex impacts RNA and DNA stability","authors":"Adan Gallardo, Bercem Dutagaci","doi":"10.1007/s10822-023-00543-z","DOIUrl":"10.1007/s10822-023-00543-z","url":null,"abstract":"<div><p>Spt5 is an elongation factor that associates with RNA polymerase II (Pol II) during transcription and has important functions in promoter-proximal pausing and elongation processivity. Spt5 was also recognized for its roles in the transcription of expanded-repeat genes that are related to neurodegenerative diseases. Recently, a set of Spt5-Pol II small molecule inhibitors (SPIs) were reported, which selectively inhibit mutant huntingtin gene transcription. Inhibition mechanisms as well as interaction sites of these SPIs with Pol II and Spt5 are not entirely known. In this study, we predicted the binding sites of three selected SPIs at the Pol II-Spt5 interface by docking and molecular dynamics simulations. Two molecules out of three demonstrated strong binding with Spt5 and Pol II, while the other molecule was more loosely bound and sampled multiple binding sites. Strongly bound SPIs indirectly affected RNA and DNA dynamics at the exit site as DNA became more flexible while RNA was stabilized by increased interactions with Spt5. Our results suggest that the transcription inhibition mechanism induced by SPIs can be related to Spt5-nucleic acid interactions, which were altered to some extent with strong binding of SPIs.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"38 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138175193","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}
Sadra Kashef Ol Gheta, Anne Bonin, Thomas Gerlach, Andreas H. Göller
{"title":"Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state","authors":"Sadra Kashef Ol Gheta, Anne Bonin, Thomas Gerlach, Andreas H. Göller","doi":"10.1007/s10822-023-00538-w","DOIUrl":"10.1007/s10822-023-00538-w","url":null,"abstract":"<div><p>In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute–solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute–solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ (<span>({Delta }_{fus}{G}_{A}^{ominus })</span>) and mixing the artificially liquid solute into the solvent (<span>({Delta }_{m}{G}_{left(A:Bright)}^{ominus })</span>). In this approach <span>({Delta }_{fus}{G}_{A}^{ominus })</span> is predicted using machine learning models, and the <span>({Delta }_{m}{G}_{left(A:Bright)}^{ominus })</span> is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMO<i>therm</i> software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMO<i>quick</i> calculations with only marginal reduction in the quality of predicted solubility.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"765 - 789"},"PeriodicalIF":3.5,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50156780","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}
Nour Jamal Jaradat, Mamon Hatmal, Dana Alqudah, Mutasem Omar Taha
{"title":"Correction to: Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study","authors":"Nour Jamal Jaradat, Mamon Hatmal, Dana Alqudah, Mutasem Omar Taha","doi":"10.1007/s10822-023-00540-2","DOIUrl":"10.1007/s10822-023-00540-2","url":null,"abstract":"","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"679 - 679"},"PeriodicalIF":3.5,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49673023","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}
Tiago O. Pereira, Maryam Abbasi, Rita I. Oliveira, Romina A. Guedes, Jorge A. R. Salvador, Joel P. Arrais
{"title":"Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information","authors":"Tiago O. Pereira, Maryam Abbasi, Rita I. Oliveira, Romina A. Guedes, Jorge A. R. Salvador, Joel P. Arrais","doi":"10.1007/s10822-023-00539-9","DOIUrl":"10.1007/s10822-023-00539-9","url":null,"abstract":"<div><p>In this work, we develop a method for generating targeted hit compounds by applying deep reinforcement learning and attention mechanisms to predict binding affinity against a biological target while considering stereochemical information. The novelty of this work is a deep model Predictor that can establish the relationship between chemical structures and their corresponding <span>(pIC_{50})</span> values. We thoroughly study the effect of different molecular descriptors such as ECFP4, ECFP6, SMILES and RDKFingerprint. Also, we demonstrated the importance of attention mechanisms to capture long-range dependencies in molecular sequences. Due to the importance of stereochemical information for the binding mechanism, this information was employed both in the prediction and generation processes. To identify the most promising hits, we apply the self-adaptive multi-objective optimization strategy. Moreover, to ensure the existence of stereochemical information, we consider all the possible enumerated stereoisomers to provide the most appropriate 3D structures. We evaluated this approach against the Ubiquitin-Specific Protease 7 (USP7) by generating putative inhibitors for this target. The predictor with SMILES notations as descriptor plus bidirectional recurrent neural network using attention mechanism has the best performance. Additionally, our methodology identify the regions of the generated molecules that are important for the interaction with the receptor’s active site. Also, the obtained results demonstrate that it is possible to discover synthesizable molecules with high biological affinity for the target, containing the indication of their optimal stereochemical conformation.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"791 - 806"},"PeriodicalIF":3.5,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41231479","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}
Gabriel Corrêa Veríssimo, Simone Queiroz Pantaleão, Philipe de Olveira Fernandes, Jadson Castro Gertrudes, Thales Kronenberger, Kathia Maria Honorio, Vinícius Gonçalves Maltarollo
{"title":"MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling","authors":"Gabriel Corrêa Veríssimo, Simone Queiroz Pantaleão, Philipe de Olveira Fernandes, Jadson Castro Gertrudes, Thales Kronenberger, Kathia Maria Honorio, Vinícius Gonçalves Maltarollo","doi":"10.1007/s10822-023-00536-y","DOIUrl":"10.1007/s10822-023-00536-y","url":null,"abstract":"<div><p>QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset’s preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"735 - 754"},"PeriodicalIF":3.5,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41094801","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}
Martina Hrast Rambaher, Irena Zdovc, Nina Kočevar Glavač, Stanislav Gobec, Rok Frlan
{"title":"Mur ligase F as a new target for the flavonoids quercitrin, myricetin, and (–)-epicatechin","authors":"Martina Hrast Rambaher, Irena Zdovc, Nina Kočevar Glavač, Stanislav Gobec, Rok Frlan","doi":"10.1007/s10822-023-00535-z","DOIUrl":"10.1007/s10822-023-00535-z","url":null,"abstract":"<div><p>MurC, D, E, and F are ATP-dependent ligases involved in the stepwise assembly of the tetrapeptide stem of forming peptidoglycan. As highly conserved targets found exclusively in bacterial cells, they are of significant interest for antibacterial drug discovery. In this study, we employed a computer-aided molecular design approach to identify potential inhibitors of MurF. A biochemical inhibition assay was conducted, screening twenty-four flavonoids and related compounds against MurC-F, resulting in the identification of quercitrin, myricetin, and (–)-epicatechin as MurF inhibitors with IC<sub>50</sub> values of 143 µM, 139 µM, and 92 µM, respectively. Notably, (–)-epicatechin demonstrated mixed type inhibition with ATP and uncompetitive inhibition with <span>d</span>-Ala-<span>d</span>-Ala dipeptide and UM3DAP substrates. Furthermore, <i>in silico</i> analysis using Sitemap and subsequent docking analysis using Glide revealed two plausible binding sites for (–)-epicatechin. The study also investigated the crucial structural features required for activity, with a particular focus on the substitution pattern and hydroxyl group positions, which were found to be important for the activity. The study highlights the significance of computational approaches in targeting essential enzymes involved in bacterial peptidoglycan synthesis.</p><h3>Graphical abstract</h3>\u0000 <div><figure><div><div><picture><source><img></source></picture></div></div></figure></div>\u0000 </div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"721 - 733"},"PeriodicalIF":3.5,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41097969","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}
Sohaib Habiballah, Janice Chambers, Edward Meek, Brad Reisfeld
{"title":"The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases","authors":"Sohaib Habiballah, Janice Chambers, Edward Meek, Brad Reisfeld","doi":"10.1007/s10822-023-00537-x","DOIUrl":"10.1007/s10822-023-00537-x","url":null,"abstract":"<div><p>Owing to their potential to cause serious adverse health effects, significant efforts have been made to develop antidotes for organophosphate (OP) anticholinesterases, such as nerve agents. To be optimally effective, antidotes must not only reactivate inhibited target enzymes, but also have the ability to cross the blood–brain barrier (BBB). Progress has been made toward brain-penetrating acetylcholinesterase reactivators through the development of a new group of substituted phenoxyalkyl pyridinium oximes. To help in the selection and prioritization of compounds for future synthesis and testing within this class of chemicals, and to identify candidate broad-spectrum molecules, an in silico framework was developed to systematically generate structures and screen them for reactivation efficacy and BBB penetration potential.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"755 - 764"},"PeriodicalIF":3.5,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41098000","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}
Anacleto Silva de Souza, Robson Francisco de Souza, Cristiane Rodrigues Guzzo
{"title":"Cooperative and structural relationships of the trimeric Spike with infectivity and antibody escape of the strains Delta (B.1.617.2) and Omicron (BA.2, BA.5, and BQ.1)","authors":"Anacleto Silva de Souza, Robson Francisco de Souza, Cristiane Rodrigues Guzzo","doi":"10.1007/s10822-023-00534-0","DOIUrl":"10.1007/s10822-023-00534-0","url":null,"abstract":"<div><p>Herein, we conducted simulations of trimeric Spike from several SARS-CoV-2 variants of concern (Delta and Omicron sub-variants BA.2, BA.5, and BQ.1) and investigated the mechanisms by which specific mutations confer resistance to neutralizing antibodies. We observed that the mutations primarily affect the cooperation between protein domains within and between protomers. The substitutions K417N and L452R expand hydrogen bonding interactions, reducing their interaction with neutralizing antibodies. By interacting with nearby residues, the K444T and N460K mutations in the Spike<sup>BQ.1</sup> variant potentially reduces solvent exposure, thereby promoting resistance to antibodies. We also examined the impact of D614G, P681R, and P681H substitutions on Spike protein structure that may be related to infectivity. The D614G substitution influences communication between a glycine residue and neighboring domains, affecting the transition between up- and -down RBD states. The P681R mutation, found in the Delta variant, enhances correlations between protein subunits, while the P681H mutation in Omicron sub-variants weakens long-range interactions that may be associated with reduced fusogenicity. Using a multiple linear regression model, we established a connection between inter-protomer communication and loss of sensitivity to neutralizing antibodies. Our findings underscore the importance of structural communication between protein domains and provide insights into potential mechanisms of immune evasion by SARS-CoV-2. Overall, this study deepens our understanding of how specific mutations impact SARS-CoV-2 infectivity and shed light on how the virus evades the immune system.</p><h3>Graphical abstract</h3>\u0000 <div><figure><div><div><picture><source><img></source></picture></div></div></figure></div>\u0000 </div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"585 - 606"},"PeriodicalIF":3.5,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41097395","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":"TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection","authors":"Tanya Liyaqat, Tanvir Ahmad, Chandni Saxena","doi":"10.1007/s10822-023-00533-1","DOIUrl":"10.1007/s10822-023-00533-1","url":null,"abstract":"<div><p>Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.</p></div>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"573 - 584"},"PeriodicalIF":3.5,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41104275","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}
Ioannis Stylianakis, Nikolaos Zervos, Jenn-Huei Lii, Dimitrios A. Pantazis, Antonios Kolocouris
{"title":"Correction to: Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory","authors":"Ioannis Stylianakis, Nikolaos Zervos, Jenn-Huei Lii, Dimitrios A. Pantazis, Antonios Kolocouris","doi":"10.1007/s10822-023-00531-3","DOIUrl":"10.1007/s10822-023-00531-3","url":null,"abstract":"","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":"37 12","pages":"657 - 657"},"PeriodicalIF":3.5,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41100576","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}