Kincore: a web resource for structural classification of protein kinases and their inhibitors.
TL;DR: Kincore as mentioned in this paper is a web resource providing access to conformational assignments for protein kinase structures in the Protein Data Bank (PDB) and includes the conformational state and the inhibitor type (Type 1, 1.5, 2, 3, allosteric).
Abstract: The active form of kinases is shared across different family members, as are several commonly observed inactive forms. We previously performed a clustering of the conformation of the activation loop of all protein kinase structures in the Protein Data Bank (PDB) into eight classes based on the dihedral angles that place the Phe side chain of the DFG motif at the N-terminus of the activation loop. Our clusters are strongly associated with the placement of the activation loop, the C-helix, and other structural elements of kinases. We present Kincore, a web resource providing access to our conformational assignments for kinase structures in the PDB. While other available databases provide conformational states or drug type but not both, KinCore includes the conformational state and the inhibitor type (Type 1, 1.5, 2, 3, allosteric) for each kinase chain. The user can query and browse the database using these attributes or determine the conformational labels of a kinase structure using the web server or a standalone program. The database and labeled structure files can be downloaded from the server. Kincore will help in understanding the conformational dynamics of these proteins and guide development of inhibitors targeting specific states. Kincore is available at http://dunbrack.fccc.edu/kincore.
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TL;DR: The physicochemical properties of all 72 FDA-approved small molecule protein kinase inhibitors including lipophilic efficiency and ligand efficiency are summarized in this article , with the exception of netarsudil, temsirolimus, and trilaciclib.
Abstract: Owing to the dysregulation of protein kinase activity in many diseases including cancer, this enzyme family has become one of the most important drug targets in the 21st century. There are 72 FDA-approved therapeutic agents that target about two dozen different protein kinases and three of these drugs were approved in 2022. Of the approved drugs, twelve target protein-serine/threonine protein kinases, four are directed against dual specificity protein kinases (MEK1/2), sixteen block nonreceptor protein-tyrosine kinases, and 40 target receptor protein-tyrosine kinases. The data indicate that 62 of these drugs are prescribed for the treatment of neoplasms (57 against solid tumors including breast, lung, and colon, ten against nonsolid tumors such as leukemia, and four against both solid and nonsolid tumors: acalabrutinib, ibrutinib, imatinib, and midostaurin). Four drugs (abrocitinib, baricitinib, tofacitinib, upadacitinib) are used for the treatment of inflammatory diseases (atopic dermatitis, psoriatic arthritis, rheumatoid arthritis, Crohn disease, and ulcerative colitis). Of the 72 approved drugs, eighteen are used in the treatment of multiple diseases. The following three drugs received FDA approval in 2022 for the treatment of these specified diseases: abrocitinib (atopic dermatitis), futibatinib (cholangiocarcinomas), pacritinib (myelofibrosis). All of the FDA-approved drugs are orally effective with the exception of netarsudil, temsirolimus, and trilaciclib. This review summarizes the physicochemical properties of all 72 FDA-approved small molecule protein kinase inhibitors including lipophilic efficiency and ligand efficiency.
33 citations
TL;DR: The Janus kinase (JAK) family of nonreceptor protein-tyrosine kinases consists of JAK1, JAK2,JAK3, and TYK2 (Tyrosine Kinase 2).
Abstract: The Janus kinase (JAK) family of nonreceptor protein-tyrosine kinases consists of JAK1, JAK2, JAK3, and TYK2 (Tyrosine Kinase 2). Each of these proteins contains a JAK homology pseudokinase (JH2) domain that interacts with and regulates the activity of the adjacent protein kinase domain (JH1). The Janus kinase family is regulated by numerous cytokines including interferons, interleukins, and hormones such as erythropoietin and thrombopoietin. Ligand binding to cytokine receptors leads to the activation of associated Janus kinases, which then catalyze the phosphorylation of the receptors. The SH2 domain of signal transducers and activators of transcription (STAT) binds to the cytokine receptor phosphotyrosines thereby promoting STAT phosphorylation and activation by the Janus kinases. STAT dimers are then translocated into the nucleus where they participate in the regulation and expression of dozens of proteins. JAK1/3 signaling participates in the pathogenesis of inflammatory disorders while JAK1/2 signaling contributes to the development of myeloproliferative neoplasms as well as several malignancies including leukemias and lymphomas. An activating JAK2 V617F mutation occurs in 95% of people with polycythemia vera and about 50% of cases of myelofibrosis and essential thrombocythemia. Abrocitinib, ruxolitinib, and upadacitinib are JAK inhibitors that are FDA-approved for the treatment of atopic dermatitis. Baricitinib is used for the treatment of rheumatoid arthritis and covid 19. Tofacitinib and upadacitinib are JAK antagonists that are used for the treatment of rheumatoid arthritis and ulcerative colitis. Additionally, ruxolitinib is approved for the treatment of polycythemia vera while fedratinib, pacritinib, and ruxolitinib are approved for the treatment of myelofibrosis.
17 citations
TL;DR: For a review of the key regulatory features of the protein kinase family, describe the different types of kinase inhibitors, and highlight examples where the understanding of the kinase regulatory mechanisms has gone hand in hand with the development of inhibitors, see as discussed by the authors .
14 citations
TL;DR: The KLIFS (kinase-ligand interaction fingerprint and structure) catalog includes 85 ligand binding-site residues occurring in both the small and large protein kinase lobes as mentioned in this paper.
13 citations
TL;DR: It is demonstrated that B-cell activation reprograms the tricarboxylic acid cycle and boosts the expression of fumarate hydratase (FH), leading to decreased cellularfumarate abundance, uncovering a previously unappreciated metabolic regulation of B cells.
6 citations
References
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TL;DR: A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original.
Abstract: The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSIBLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
70,111 citations
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
10,601 citations
TL;DR: The protein kinase complement of the human genome is catalogued using public and proprietary genomic, complementary DNA, and expressed sequence tag sequences to provide a starting point for comprehensive analysis of protein phosphorylation in normal and disease states and a detailed view of the current state of human genome analysis through a focus on one large gene family.
Abstract: We have catalogued the protein kinase complement of the human genome (the "kinome") using public and proprietary genomic, complementary DNA, and expressed sequence tag (EST) sequences. This provides a starting point for comprehensive analysis of protein phosphorylation in normal and disease states, as well as a detailed view of the current state of human genome analysis through a focus on one large gene family. We identify 518 putative protein kinase genes, of which 71 have not previously been reported or described as kinases, and we extend or correct the protein sequences of 56 more kinases. New genes include members of well-studied families as well as previously unidentified families, some of which are conserved in model organisms. Classification and comparison with model organism kinomes identified orthologous groups and highlighted expansions specific to human and other lineages. We also identified 106 protein kinase pseudogenes. Chromosomal mapping revealed several small clusters of kinase genes and revealed that 244 kinases map to disease loci or cancer amplicons.
7,486 citations
TL;DR: An annotation score for all entries in UniProt is introduced to represent the relative amount of knowledge known about each protein to help identify which proteins are the best characterized and most informative for comparative analysis.
Abstract: UniProt is an important collection of protein sequences and their annotations, which has doubled in size to 80 million sequences during the past year. This growth in sequences has prompted an extension of UniProt accession number space from 6 to 10 characters. An increasing fraction of new sequences are identical to a sequence that already exists in the database with the majority of sequences coming from genome sequencing projects. We have created a new proteome identifier that uniquely identifies a particular assembly of a species and strain or subspecies to help users track the provenance of sequences. We present a new website that has been designed using a user-experience design process. We have introduced an annotation score for all entries in UniProt to represent the relative amount of knowledge known about each protein. These scores will be helpful in identifying which proteins are the best characterized and most informative for comparative analysis. All UniProt data is provided freely and is available on the web at http://www.uniprot.org/.
4,050 citations
TL;DR: Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.
Abstract: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence. le formats and multiple sequence alignments, dealing with 3D macromolecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.
3,855 citations