ABSTRACT
The SARS-CoV-2 variants replacing the first wave strain pose an increased threat by their potential ability to escape pre-existing humoral protection. An angiotensin converting enzyme 2 (ACE2) decoy that competes with endogenous ACE2 for binding of the SARS-CoV-2 spike receptor binding domain (S RBD) and inhibits infection may offer a therapeutic option with sustained efficacy against variants. Here, we used Molecular Dynamics (MD) simulation to predict ACE2 sequence substitutions that might increase its affinity for S RBD and screened candidate ACE2 decoys in vitro. The lead ACE2(T27Y/H34A)-IgG1FC fusion protein with enhanced S RBD affinity shows greater live SARS-CoV-2 virus neutralization capability than wild type ACE2. MD simulation was used to predict the effects of S RBD variant mutations on decoy affinity that was then confirmed by testing of an ACE2 Triple Decoy that included an additional enzyme activity-deactivating H374N substitution against mutated S RBD. The ACE2 Triple Decoy maintains high affinity for mutated S RBD, displays enhanced affinity for S RBD N501Y or L452R, and has the highest affinity for S RBD with both E484K and N501Y mutations, making it a viable therapeutic option for the prevention or treatment of SARS-CoV-2 infection with a high likelihood of efficacy against variants.
Subject(s)
Amino Acid Substitution , Angiotensin-Converting Enzyme 2/chemistry , Angiotensin-Converting Enzyme 2/metabolism , Antiviral Agents/pharmacology , COVID-19/metabolism , Drug Discovery/methods , Molecular Dynamics Simulation , SARS-CoV-2/metabolism , Signal Transduction/drug effects , Amino Acid Sequence , COVID-19/virology , Humans , Mutation , Protein Binding/drug effects , Protein Domains/genetics , Spike Glycoprotein, Coronavirus/metabolism , Virus Internalization/drug effectsABSTRACT
The protein kinase superfamily is an important group of enzymes controlling cellular signaling cascades. The increasing amount of available experimental data provides a foundation for deeper understanding of details of signaling systems and the underlying cellular processes. Here, we describe the Protein Kinase Resource, an integrated online service that provides access to information relevant to cell signaling and enables kinase researchers to visualize and analyze the data directly in an online environment. The data set is synchronized with Uniprot and Protein Data Bank (PDB) databases and is regularly updated and verified. Additional annotation includes interactive display of domain composition, cross-references between orthologs and functional mapping to OMIM records. The Protein Kinase Resource provides an integrated view of the protein kinase superfamily by linking data with their visual representation. Thus, human kinases can be mapped onto the human kinome tree via an interactive display. Sequence and structure data can be easily displayed using applications developed for the PKR and integrated with the website and the underlying database. Advanced search mechanisms, such as multiparameter lookup, sequence pattern, and blast search, enable fast access to the desired information, while statistics tools provide the ability to analyze the relationships among the kinases under study. The integration of data presentation and visualization implemented in the Protein Kinase Resource can be adapted by other online providers of scientific data and should become an effective way to access available experimental information.
Subject(s)
Computational Biology/methods , Protein Kinases/chemistry , Proteomics/methods , Amino Acid Sequence , Computer Simulation , Databases, Factual , Databases, Protein , Health Resources , Humans , Information Storage and Retrieval , Internet , Models, Molecular , Models, Statistical , Molecular Sequence Data , Phosphorylation , Protein Conformation , Sequence Alignment , Sequence Analysis, Protein , Systems IntegrationABSTRACT
It is now clear that tyrosine kinases represent attractive targets for therapeutic intervention in cancer. Recent advances in DNA sequencing technology now provide the opportunity to survey mutational changes in cancer in a high-throughput and comprehensive manner. Here we report on the sequence analysis of members of the receptor tyrosine kinase (RTK) gene family in the genomes of glioblastoma brain tumors. Previous studies have identified a number of molecular alterations in glioblastoma, including amplification of the RTK epidermal growth factor receptor. We have identified mutations in two other RTKs: (i) fibroblast growth receptor 1, including the first mutations in the kinase domain in this gene observed in any cancer, and (ii) a frameshift mutation in the platelet-derived growth factor receptor-alpha gene. Fibroblast growth receptor 1, platelet-derived growth factor receptor-alpha, and epidermal growth factor receptor are all potential entry points to the phosphatidylinositol 3-kinase and mitogen-activated protein kinase intracellular signaling pathways already known to be important for neoplasia. Our results demonstrate the utility of applying DNA sequencing technology to systematically assess the coding sequence of genes within cancer genomes.
Subject(s)
Brain Neoplasms/genetics , Evolution, Molecular , Glioblastoma/genetics , Models, Molecular , Mutation/genetics , Receptor Protein-Tyrosine Kinases/genetics , Adult , Amino Acid Sequence , Base Sequence , Child , Female , Genomics/methods , Humans , Male , Models, Genetic , Molecular Sequence Data , Receptor, Fibroblast Growth Factor, Type 1/genetics , Receptor, Platelet-Derived Growth Factor alpha/genetics , Sequence Analysis, DNAABSTRACT
BACKGROUND: The large amount of data that are currently produced in the biological sciences can no longer be explored and visualized efficiently with traditional, specialized software. Instead, new capabilities are needed that offer flexibility, rapid application development and deployment as standalone applications or available through the Web. RESULTS: We describe a new software toolkit--the Molecular Biology Toolkit (MBT; http://mbt.sdsc.edu)--that enables fast development of applications for protein analysis and visualization. The toolkit is written in Java, thus offering platform-independence and Internet delivery capabilities. Several applications of the toolkit are introduced to illustrate the functionality that can be achieved. CONCLUSIONS: The MBT provides a well-organized assortment of core classes that provide a uniform data model for the description of biological structures and automate most common tasks associated with the development of applications in the molecular sciences (data loading, derivation of typical structural information, visualization of sequence and standard structural entities).
Subject(s)
Computational Biology/methods , Molecular Biology/methods , Software , Algorithms , Computer Graphics , Computer Simulation , Database Management Systems , Databases, Genetic , Databases, Protein , Humans , Information Systems , Internet , Ligands , Models, Molecular , Molecular Conformation , Programming Languages , Protein Conformation , Proteins/chemistry , Software Design , User-Computer InterfaceABSTRACT
UNLABELLED: The Kinase Sequence Database (KSD) located at http://kinase.ucsf.edu/ksd contains information on 290 protein kinase families derived by profile-based clustering of the non-redundant list of sequences obtained from a GenBank-wide search. Included in the database are a total of 5,041 protein kinases from over 100 organisms. Clustering into families is based on the extent of homology within the kinase catalytic domain (250-300 residues in length). Alignments of the families are viewed by interactive Excel-based sequence spreadsheets. In addition, KSD features evolutionary trees derived for each family and detailed information on each sequence as well as links to the corresponding GenBank entries. Sequence manipulation tools, such as evolutionary tree generation, novel sequence assignment, and statistical analysis, are also provided. AVAILABILITY: The kinase sequence database is a web-based service accessible at http://kinase.ucsf.edu/ksd CONTACT: buzko@cmp.ucsf.edu; shokat@cmp.ucsf.edu/ksd
Subject(s)
Database Management Systems , Databases, Protein , Protein Kinases/classification , Protein Kinases/genetics , Sequence Alignment/methods , Sequence Analysis, Protein/methods , Cluster Analysis , Information Storage and Retrieval/methods , Internet , Sequence HomologyABSTRACT
Protein kinases are an important class of enzymes controlling virtually all cellular signaling pathways. Consequently, selective inhibitors of protein kinases have attracted significant interest as potential new drugs for many diseases. Computational methods, including molecular docking, have increasingly been used in the inhibitor design process [1]. We have considered several docking packages in order to strengthen our kinase inhibitor work with computational capabilities. In our experience, AutoDock offered a reasonable combination of accuracy and speed, as opposed to methods that specialize either in fast database searches or detailed and computationally intensive calculations. However, AutoDock did not perform well in cases where extensive hydrophobic contacts were involved, such as docking of SB203580 to its target protein kinase p38. Another shortcoming was a hydrogen bonding energy function, which underestimated the attraction component and, thus, did not allow for sufficiently accurate modeling of the key hydrogen bonds in the kinase-inhibitor complexes. We have modified the parameter set used to model hydrogen bonds, which increased the accuracy of AutoDock and appeared to be generally applicable to many kinase-inhibitor pairs without customization. Binding to largely hydrophobic sites, such as the active site of p38, was significantly improved by introducing a correction factor selectively affecting only carbon and hydrogen energy grids, thus, providing an effective, although approximate, treatment of solvation.
