Browse
Submit Data
Databases
API
Help

Dataset Information

44 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface.

ABSTRACT: The current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking and structure-activity relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different docking protocols on the example of the search for bioactivity of compounds containing N-N-C(S)-N scaffold at the S-protein of SARS-CoV-2 virus with ACE2 human receptor interface. Based on over 1800 structures in the training set we have predicted binding properties of the complete set of nearly 600000 structures from the same class using the Machine Learning Random Forest Regressor approach.

SUBMITTER: Rola M

PROVIDER: S-EPMC8428716 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface.

Rola Monika M Krassowski Jakub J Górska Julita J Grobelna Anna A Płonka Wojciech W Paneth Agata A Paneth Piotr P

PloS one 20210909 9

The current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking and structure-activity relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different dock ...[more]

PMID: 34499662

Similar Datasets

Docking and QSAR of Aminothioureas at the SARS-CoV-2 S-Protein-Human ACE2 Receptor Interface.

Project description:Docking of over 160 aminothiourea derivatives at the SARS-CoV-2 S-protein-human ACE2 receptor interface, whose structure became available recently, has been evaluated for its complex stabilizing potency and subsequently subjected to quantitative structure-activity relationship (QSAR) analysis. The structural variety of the studied compounds, that include 3 different forms of the N-N-C(S)-N skeleton and combinations of 13 different substituents alongside the extensive length of the interface, resulted in the failure of the QSAR analysis, since different molecules were binding to different parts of the interface. Subsequently, absorption, distribution, metabolism, and excretion (ADME) analysis on all studied compounds, followed by a toxicity analysis using statistical models for selected compounds, was carried out to evaluate their potential use as lead compounds for drug design. Combined, these studies highlighted two molecules among the studied compounds, i.e., 5-(pyrrol-2-yl)-2-(2-methoxyphenylamino)-1,3,4-thiadiazole and 1-(cyclopentanoyl)-4-(3-iodophenyl)-thiosemicarbazide, as the best candidates for the development of future drugs.

| S-EPMC7656307 | biostudies-literature

Machine learning guided design of high affinity ACE2 decoys for SARS-CoV-2 neutralization.

Project description:A potential therapeutic candidate for neutralizing SARS-CoV-2 infection is engineering high-affinity soluble ACE2 decoy proteins to compete for binding of the viral spike (S) protein. Previously, a deep mutational scan of ACE2 was performed and has led to the identification of a triple mutant ACE2 variant, named ACE2 2 .v.2.4, that exhibits nanomolar affinity binding to the RBD domain of S. Using a recently developed transfer learning algorithm, TLmutation, we sought to identified other ACE2 variants, namely double mutants, that may exhibit similar binding affinity with decreased mutational load. Upon training a TLmutation model on the effects of single mutations, we identified several ACE2 double mutants that bind to RBD with tighter affinity as compared to the wild type, most notably, L79V;N90D that binds RBD with similar affinity to ACE2 2 .v.2.4. The successful experimental validation of the double mutants demonstrated the use transfer and supervised learning approaches for engineering protein-protein interactions and identifying high affinity ACE2 peptides for targeting SARS-CoV-2.

| S-EPMC8722601 | biostudies-literature

Machine Learning Guided Design of High-Affinity ACE2 Decoys for SARS-CoV-2 Neutralization.

Project description:A potential therapeutic strategy for neutralizing SARS-CoV-2 infection is engineering high-affinity soluble ACE2 decoy proteins to compete for binding to the viral spike (S) protein. Previously, a deep mutational scan of ACE2 was performed and has led to the identification of a triple mutant variant, named sACE22.v.2.4, that exhibits subnanomolar affinity to the receptor-binding domain (RBD) of S. Using a recently developed transfer learning algorithm, TLmutation, we sought to identify other ACE2 variants that may exhibit similar binding affinity with decreased mutational load. Upon training a TLmutation model on the effects of single mutations, we identified multiple ACE2 double mutants that bind SARS-CoV-2 S with tighter affinity as compared to the wild type, most notably L79V;N90D that binds RBD similarly to ACE22.v.2.4. The experimental validation of the double mutants successfully demonstrates the use of machine learning approaches for engineering protein-protein interactions and identifying high-affinity ACE2 peptides for targeting SARS-CoV-2.

| S-EPMC9999943 | biostudies-literature

Molecular docking, molecular dynamics simulations and reactivity, studies on approved drugs library targeting ACE2 and SARS-CoV-2 binding with ACE2.

Project description:The recent new contagion coronavirus 2019 (COVID-19) disease is a new generation of severe acute respiratory syndrome coronavirus-2 SARS-CoV-2 which infected millions confirmed cases and hundreds of thousands death cases around the world so far. Molecular docking combined with molecular dynamics is one of the most important tools of drug discovery and drug design, which it used to examine the type of binding between the ligand and its protein enzyme. Global reactivity has important properties, which enable chemists to understand the chemical reactivity and kinetic stability of compounds. In this study, molecular docking and reactivity were applied for eighteen drugs, which are similar in structure to chloroquine and hydroxychloroquine, the potential inhibitors to angiotensin-converting enzyme (ACE2). Those drugs were selected from DrugBank. The reactivity, molecular docking and molecular dynamics were performed for two receptors ACE2 and [SARS-CoV-2/ACE2] complex receptor in two active sites to find a ligand, which may inhibit COVID-19. The results obtained from this study showed that Ramipril, Delapril and Lisinopril could bind with ACE2 receptor and [SARS-CoV-2/ACE2] complex better than chloroquine and hydroxychloroquine. This new understanding should help to improve predictions of the impact of such alternatives on COVID-19.Communicated by Ramaswamy H. Sarma.

| S-EPMC7484571 | biostudies-literature

Machine learning prediction of 3CL<sup>pro</sup> SARS-CoV-2 docking scores.

Project description:Molecular docking results of two training sets containing 866 and 8,696 compounds were used to train three different machine learning (ML) approaches. Neural network approaches according to Keras and TensorFlow libraries and the gradient boosted decision trees approach of XGBoost were used with DScribe's Smooth Overlap of Atomic Positions molecular descriptors. In addition, neural networks using the SchNetPack library and descriptors were used. The ML performance was tested on three different sets, including compounds for future organic synthesis. The final evaluation of the ML predicted docking scores was based on the ZINC in vivo set, from which 1,200 compounds were randomly selected with respect to their size. The results obtained showed a consistent ML prediction capability of docking scores, and even though compounds with more than 60 atoms were found slightly overestimated they remain valid for a subsequent evaluation of their drug repurposing suitability.

| S-EPMC8881816 | biostudies-literature

Dynamics of the ACE2-SARS-CoV-2/SARS-CoV spike protein interface reveal unique mechanisms.

Project description:The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a major public health concern. A handful of static structures now provide molecular insights into how SARS-CoV-2 and SARS-CoV interact with its host target, which is the angiotensin converting enzyme 2 (ACE2). Molecular recognition, binding and function are dynamic processes. To evaluate this, multiple 500 ns or 1 μs all-atom molecular dynamics simulations were performed to better understand the structural stability and interfacial interactions between the receptor binding domain of the spike (S) protein of SARS-CoV-2 and SARS-CoV bound to ACE2. Several contacts were observed to form, break and reform in the interface during the simulations. Our results indicate that SARS-CoV-2 and SARS-CoV utilizes unique strategies to achieve stable binding to ACE2. Several differences were observed between the residues of SARS-CoV-2 and SARS-CoV that consistently interacted with ACE2. Notably, a stable salt bridge between Lys417 of SARS-CoV-2 S protein and Asp30 of ACE2 as well as three stable hydrogen bonds between Tyr449, Gln493 and Gln498 of SARS-CoV-2 and Asp38, Glu35 and Lys353 of ACE2 were observed, which were absent in the ACE2-SARS-CoV interface. Some previously reported residues, which were suggested to enhance the binding affinity of SARS-CoV-2, were not observed to form stable interactions in these simulations. Molecular mechanics-generalized Born surface area based free energy of binding was observed to be higher for SARS-CoV-2 in all simulations. Stable binding to the host receptor is crucial for virus entry. Therefore, special consideration should be given to these stable interactions while designing potential drugs and treatment modalities to target or disrupt this interface.

| S-EPMC7449962 | biostudies-literature

Machine Learning Reveals the Critical Interactions for SARS-CoV-2 Spike Protein Binding to ACE2.

Project description:SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Herein, we have used molecular dynamics (MD) simulations, machine learning (ML), and free-energy perturbation (FEP) calculations to elucidate the differences in binding by the two viruses. Although only subtle differences were observed from the initial MD simulations of the two RBD-ACE2 complexes, ML identified the individual residues with the most distinctive ACE2 interactions, many of which have been highlighted in previous experimental studies. FEP calculations quantified the corresponding differences in binding free energies to ACE2, and examination of MD trajectories provided structural explanations for these differences. Lastly, the energetics of emerging SARS-CoV-2 mutations were studied, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but is slightly decreased by K417N.

| S-EPMC8204752 | biostudies-literature

mouse ACE2 receptor bound to SARS-CoV-2 variant Beta Spike

Project description: Not available

| EMPIAR-11181 | biostudies-other

Expert-augmented machine learning.

Project description:Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications.

| S-EPMC7060733 | biostudies-literature

The ACE2-binding interface of SARS-CoV-2 Spike inherently deflects immune recognition.

Project description:The COVID-19 pandemic remains a global threat, and host immunity remains the main mechanism of protection against the disease. The spike protein on the surface of SARS-CoV-2 is a major antigen and its engagement with human ACE2 receptor plays an essential role in viral entry into host cells. Consequently, antibodies targeting the ACE2-interacting surface (ACE2IS) located in the receptor-binding domain (RBD) of the spike protein can neutralize the virus. However, the understanding of immune responses to SARS-CoV-2 is still limited, and it is unclear how the virus protects this surface from recognition by antibodies. Here, we designed an RBD mutant that disrupts the ACE2IS and used it to characterize the prevalence of antibodies directed to the ACE2IS from convalescent sera of 94 COVID19-positive patients. We found that only a small fraction of RBD-binding antibodies targeted the ACE2IS. To assess the immunogenicity of different parts of the spike protein, we performed in vitro antibody selection for the spike and the RBD proteins using both unbiased and biased selection strategies. Intriguingly, unbiased selection yielded antibodies that predominantly targeted regions outside the ACE2IS, whereas ACE2IS-binding antibodies were readily identified from biased selection designed to enrich such antibodies. Furthermore, antibodies from an unbiased selection using the RBD preferentially bound to the surfaces that are inaccessible in the context of whole spike protein. These results suggest that the ACE2IS has evolved less immunogenic than the other regions of the spike protein, which has important implications in the development of vaccines against SARS-CoV-2.

| S-EPMC7654858 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data