Pub Date : 2024-03-13DOI: 10.1007/s10822-024-00556-2
Ajay N Jain, Alexander C Brueckner, Christine Jorge, Ann E Cleves, Purnima Khandelwal, Janet Caceres Cortes, Luciano Mueller
{"title":"Correction: Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design.","authors":"Ajay N Jain, Alexander C Brueckner, Christine Jorge, Ann E Cleves, Purnima Khandelwal, Janet Caceres Cortes, Luciano Mueller","doi":"10.1007/s10822-024-00556-2","DOIUrl":"10.1007/s10822-024-00556-2","url":null,"abstract":"","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10933138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140108732","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}
Pub Date : 2024-03-12DOI: 10.1007/s10822-024-00557-1
Azam Nesabi, Jas Kalayan, Sara Al-Rawashdeh, Mohammad A Ghattas, Richard A Bryce
Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound's aggregation propensity.
{"title":"Molecular dynamics simulations as a guide for modulating small molecule aggregation.","authors":"Azam Nesabi, Jas Kalayan, Sara Al-Rawashdeh, Mohammad A Ghattas, Richard A Bryce","doi":"10.1007/s10822-024-00557-1","DOIUrl":"10.1007/s10822-024-00557-1","url":null,"abstract":"<p><p>Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound's aggregation propensity.</p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10933209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140108733","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}
Pub Date : 2024-02-16DOI: 10.1007/s10822-024-00549-1
Alan Kerstjens, Hans De Winter
Ensuring that computationally designed molecules are chemically reasonable is at best cumbersome. We present a molecule correction algorithm that morphs invalid molecular graphs into structurally related valid analogs. The algorithm is implemented as a tree search, guided by a set of policies to minimize its cost. We showcase how the algorithm can be applied to molecular design, either as a post-processing step or as an integral part of molecule generators.
{"title":"Molecule auto-correction to facilitate molecular design.","authors":"Alan Kerstjens, Hans De Winter","doi":"10.1007/s10822-024-00549-1","DOIUrl":"10.1007/s10822-024-00549-1","url":null,"abstract":"<p><p>Ensuring that computationally designed molecules are chemically reasonable is at best cumbersome. We present a molecule correction algorithm that morphs invalid molecular graphs into structurally related valid analogs. The algorithm is implemented as a tree search, guided by a set of policies to minimize its cost. We showcase how the algorithm can be applied to molecular design, either as a post-processing step or as an integral part of molecule generators.</p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10873457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139740104","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}
Pub Date : 2024-02-14DOI: 10.1007/s10822-024-00550-8
Jose R Mora, Edgar A Marquez, Noel Pérez-Pérez, Ernesto Contreras-Torres, Yunierkis Perez-Castillo, Guillermin Agüero-Chapin, Felix Martinez-Rios, Yovani Marrero-Ponce, Stephen J Barigye
Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.
尽管 QSAR 建模广泛采用了 OECD 原则(或最佳实践),但硅学预测与实验结果之间经常出现差异,这表明模型预测往往过于乐观。在这些 OECD 原则中,适用域(AD)估算在一些文献报告中被认为是最具挑战性的原则之一,这意味着模型预测的实际可靠性度量往往不可靠。对文献中报道的、QsarDB 数据库中的 5 个 QSAR 模型(即雄激素受体生物活性(分别为激动剂、拮抗剂和粘合剂)和膜渗透性(最高膜渗透性和内在渗透性),我们证明了被错误地标记为可靠的预测(AD 预测错误)绝大多数对应于预测错误率最高的子空间(队列)中的实例,突出了 AD 空间的不均匀性。从这个意义上说,我们呼吁制定更严格的 AD 分析指南,要求纳入模型误差分析方案,以提供对基础 AD 算法可靠性的重要见解。此外,任何选定的 AD 方法都应经过严格验证,以证明其适用于所应用的模型空间。这些步骤最终将有助于更准确地估计模型预测的可靠性。最后,误差分析还有助于 "合理 "地完善模型,因为数据扩展工作和模型再训练都将重点放在误差率最高的队列上。
{"title":"Rethinking the applicability domain analysis in QSAR models.","authors":"Jose R Mora, Edgar A Marquez, Noel Pérez-Pérez, Ernesto Contreras-Torres, Yunierkis Perez-Castillo, Guillermin Agüero-Chapin, Felix Martinez-Rios, Yovani Marrero-Ponce, Stephen J Barigye","doi":"10.1007/s10822-024-00550-8","DOIUrl":"10.1007/s10822-024-00550-8","url":null,"abstract":"<p><p>Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in \"rational\" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.</p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139728695","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}
Pub Date : 2024-02-07DOI: 10.1007/s10822-023-00548-8
Md Fulbabu Sk, Sunanda Samanta, Sayan Poddar, Parimal Kar
The Janus kinases (JAK) are crucial targets in drug development for several diseases. However, accounting for the impact of possible structural rearrangements on the binding of different kinase inhibitors is complicated by the extensive conformational variability of their catalytic kinase domain (KD). The dynamic KD contains mainly four prominent mobile structural motifs: the phosphate-binding loop (P-loop), the αC-helix within the N-lobe, the Asp-Phe-Gly (DFG) motif, and the activation loop (A-loop) within the C-lobe. These distinct structural orientations imply a complex signal transmission path for regulating the A-loop's flexibility and conformational preference for optimal JAK function. Nevertheless, the precise dynamical features of the JAK induced by different types of inhibitors still remain elusive. We performed comparative, microsecond-long, Gaussian accelerated molecular dynamics simulations in triplicate of three phosphorylated JAK2 systems: the KD alone, type-I ATP-competitive inhibitor (CI) bound KD in the catalytically active DFG-in conformation, and the type-II inhibitor (AI) bound KD in the catalytically inactive DFG-out conformation. Our results indicate significant conformational variations observed in the A-loop and αC helix motions upon inhibitor binding. Our studies also reveal that the DFG-out inactive conformation is characterized by the closed A-loop rearrangement, open catalytic cleft of N and C-lobe, the outward movement of the αC helix, and open P-loop states. Moreover, the outward positioning of the αC helix impacts the hallmark salt bridge formation between Lys882 and Glu898 in an inactive conformation. Finally, we compared their ligand binding poses and free energy by the MM/PBSA approach. The free energy calculations suggested that the AI's binding affinity is higher than CI against JAK2 due to an increased favorable contribution from the total non-polar interactions and the involvement of the αC helix. Overall, our study provides the structural and energetic insights crucial for developing more promising type I/II JAK2 inhibitors for treating JAK-related diseases.
Janus 激酶(JAK)是多种疾病药物开发的关键靶点。然而,由于其催化激酶结构域(KD)具有广泛的构象可变性,因此考虑可能的结构重排对不同激酶抑制剂结合的影响变得非常复杂。动态 KD 主要包含四个突出的移动结构基团:磷酸结合环(P 环)、N 环内的αC-螺旋、Asp-Phe-Gly(DFG)基团和 C 环内的激活环(A 环)。这些不同的结构取向意味着有一个复杂的信号传输路径来调节 A 环的灵活性和构象偏好,以实现最佳的 JAK 功能。尽管如此,不同类型抑制剂诱导的 JAK 的精确动态特征仍然难以捉摸。我们对三个磷酸化的 JAK2 系统进行了一式三份的微秒级高斯加速分子动力学模拟比较:单独的 KD、在催化活性 DFG-in构象中与 I 型 ATP 竞争性抑制剂(CI)结合的 KD 以及在催化不活跃的 DFG-out 构象中与 II 型抑制剂(AI)结合的 KD。我们的研究结果表明,与抑制剂结合后,A 环和αC 螺旋的运动发生了明显的构象变化。我们的研究还发现,DFG-out 非活性构象的特点是闭合的 A 环重排、N 和 C 环的催化裂隙开放、αC 螺旋向外运动以及 P 环开放状态。此外,αC 螺旋的外移还影响了 Lys882 和 Glu898 在非活性构象中形成的标志性盐桥。最后,我们通过 MM/PBSA 方法比较了它们的配体结合位置和自由能。自由能计算表明,AI 与 JAK2 的结合亲和力高于 CI,这是因为总的非极性相互作用和 αC 螺旋的参与增加了有利的贡献。总之,我们的研究为开发更有前景的 I/II 型 JAK2 抑制剂以治疗 JAK 相关疾病提供了至关重要的结构和能量见解。
{"title":"Deciphering the molecular choreography of Janus kinase 2 inhibition via Gaussian accelerated molecular dynamics simulations: a dynamic odyssey.","authors":"Md Fulbabu Sk, Sunanda Samanta, Sayan Poddar, Parimal Kar","doi":"10.1007/s10822-023-00548-8","DOIUrl":"10.1007/s10822-023-00548-8","url":null,"abstract":"<p><p>The Janus kinases (JAK) are crucial targets in drug development for several diseases. However, accounting for the impact of possible structural rearrangements on the binding of different kinase inhibitors is complicated by the extensive conformational variability of their catalytic kinase domain (KD). The dynamic KD contains mainly four prominent mobile structural motifs: the phosphate-binding loop (P-loop), the αC-helix within the N-lobe, the Asp-Phe-Gly (DFG) motif, and the activation loop (A-loop) within the C-lobe. These distinct structural orientations imply a complex signal transmission path for regulating the A-loop's flexibility and conformational preference for optimal JAK function. Nevertheless, the precise dynamical features of the JAK induced by different types of inhibitors still remain elusive. We performed comparative, microsecond-long, Gaussian accelerated molecular dynamics simulations in triplicate of three phosphorylated JAK2 systems: the KD alone, type-I ATP-competitive inhibitor (CI) bound KD in the catalytically active DFG-in conformation, and the type-II inhibitor (AI) bound KD in the catalytically inactive DFG-out conformation. Our results indicate significant conformational variations observed in the A-loop and αC helix motions upon inhibitor binding. Our studies also reveal that the DFG-out inactive conformation is characterized by the closed A-loop rearrangement, open catalytic cleft of N and C-lobe, the outward movement of the αC helix, and open P-loop states. Moreover, the outward positioning of the αC helix impacts the hallmark salt bridge formation between Lys882 and Glu898 in an inactive conformation. Finally, we compared their ligand binding poses and free energy by the MM/PBSA approach. The free energy calculations suggested that the AI's binding affinity is higher than CI against JAK2 due to an increased favorable contribution from the total non-polar interactions and the involvement of the αC helix. Overall, our study provides the structural and energetic insights crucial for developing more promising type I/II JAK2 inhibitors for treating JAK-related diseases.</p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139696630","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}
Pub Date : 2024-01-31DOI: 10.1007/s10822-023-00547-9
Florian Führer, Andrea Gruber, Holger Diedam, Andreas H Göller, Stephan Menz, Sebastian Schneckener
An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893-4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.
{"title":"A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat.","authors":"Florian Führer, Andrea Gruber, Holger Diedam, Andreas H Göller, Stephan Menz, Sebastian Schneckener","doi":"10.1007/s10822-023-00547-9","DOIUrl":"10.1007/s10822-023-00547-9","url":null,"abstract":"<p><p>An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893-4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.</p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139641396","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}
Pub Date : 2024-01-24DOI: 10.1007/s10822-023-00546-w
Maria Lazou, Jonathan R Hutton, Arijit Chakravarty, Diane Joseph-McCarthy
SARS-CoV-2, the virus that causes COVID-19, led to a global health emergency that claimed the lives of millions. Despite the widespread availability of vaccines, the virus continues to exist in the population in an endemic state which allows for the continued emergence of new variants. Most of the current vaccines target the spike glycoprotein interface of SARS-CoV-2, creating a selection pressure favoring viral immune evasion. Antivirals targeting other molecular interactions of SARS-CoV-2 can help slow viral evolution by providing orthogonal selection pressures on the virus. GRP78 is a host auxiliary factor that mediates binding of the SARS-CoV-2 spike protein to human cellular ACE2, the primary pathway of cell infection. As GRP78 forms a ternary complex with SARS-CoV-2 spike protein and ACE2, disrupting the formation of this complex is expected to hinder viral entry into host cells. Here, we developed a model of the GRP78-Spike RBD-ACE2 complex. We then used that model together with hot spot mapping of the GRP78 structure to identify the putative binding site for spike protein on GRP78. Next, we performed structure-based virtual screening of known drug/candidate drug libraries to identify binders to GRP78 that are expected to disrupt spike protein binding to the GRP78, and thereby preventing viral entry to the host cell. A subset of these compounds has previously been shown to have some activity against SARS-CoV-2. The identified hits are starting points for the further development of novel SARS-CoV-2 therapeutics, potentially serving as proof-of-concept for GRP78 as a potential drug target for other viruses.
{"title":"Identification of a druggable site on GRP78 at the GRP78-SARS-CoV-2 interface and virtual screening of compounds to disrupt that interface.","authors":"Maria Lazou, Jonathan R Hutton, Arijit Chakravarty, Diane Joseph-McCarthy","doi":"10.1007/s10822-023-00546-w","DOIUrl":"10.1007/s10822-023-00546-w","url":null,"abstract":"<p><p>SARS-CoV-2, the virus that causes COVID-19, led to a global health emergency that claimed the lives of millions. Despite the widespread availability of vaccines, the virus continues to exist in the population in an endemic state which allows for the continued emergence of new variants. Most of the current vaccines target the spike glycoprotein interface of SARS-CoV-2, creating a selection pressure favoring viral immune evasion. Antivirals targeting other molecular interactions of SARS-CoV-2 can help slow viral evolution by providing orthogonal selection pressures on the virus. GRP78 is a host auxiliary factor that mediates binding of the SARS-CoV-2 spike protein to human cellular ACE2, the primary pathway of cell infection. As GRP78 forms a ternary complex with SARS-CoV-2 spike protein and ACE2, disrupting the formation of this complex is expected to hinder viral entry into host cells. Here, we developed a model of the GRP78-Spike RBD-ACE2 complex. We then used that model together with hot spot mapping of the GRP78 structure to identify the putative binding site for spike protein on GRP78. Next, we performed structure-based virtual screening of known drug/candidate drug libraries to identify binders to GRP78 that are expected to disrupt spike protein binding to the GRP78, and thereby preventing viral entry to the host cell. A subset of these compounds has previously been shown to have some activity against SARS-CoV-2. The identified hits are starting points for the further development of novel SARS-CoV-2 therapeutics, potentially serving as proof-of-concept for GRP78 as a potential drug target for other viruses.</p>","PeriodicalId":621,"journal":{"name":"Journal of Computer-Aided Molecular Design","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139540966","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}
Pub Date : 2023-12-16DOI: 10.1007/s10822-023-00541-1
Aleksei Kabedev, Christel A. S. Bergström, Per Larsson
Theoretical predictions of the solubilizing capacity of micelles and vesicles present in intestinal fluid are important for the development of new delivery techniques and bioavailability improvement. A balance between accuracy and computational cost is a key factor for an extensive study of numerous compounds in diverse environments. In this study, we aimed to determine an optimal molecular dynamics (MD) protocol to evaluate small-molecule interactions with micelles composed of bile salts and phospholipids. MD simulations were used to produce free energy profiles for three drug molecules (danazol, probucol, and prednisolone) and one surfactant molecule (sodium caprate) as a function of the distance from the colloid center of mass. To address the challenges associated with such tasks, we compared different simulation setups, including freely assembled colloids versus pre-organized spherical micelles, full free energy profiles versus only a few points of interest, and a coarse-grained model versus an all-atom model. Our findings demonstrate that combining these techniques is advantageous for achieving optimal performance and accuracy when evaluating the solubilization capacity of micelles.
Graphical abstract
All-atom (AA) and coarse-grained (CG) umbrella sampling (US) simulations and point-wise free energy (FE) calculations were compared to their efficiency to computationally analyze the solubilization of active pharmaceutical ingredients in intestinal fluid colloids.