351 research outputs found

    Structure-guided selection of specificity determining positions in the human kinome

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    Background: The human kinome contains many important drug targets. It is well-known that inhibitors of protein kinases bind with very different selectivity profiles. This is also the case for inhibitors of many other protein families. The increased availability of protein 3D structures has provided much information on the structural variation within a given protein family. However, the relationship between structural variations and binding specificity is complex and incompletely understood. We have developed a structural bioinformatics approach which provides an analysis of key determinants of binding selectivity as a tool to enhance the rational design of drugs with a specific selectivity profile. Results: We propose a greedy algorithm that computes a subset of residue positions in a multiple sequence alignment such that structural and chemical variation in those positions helps explain known binding affinities. By providing this information, the main purpose of the algorithm is to provide experimentalists with possible insights into how the selectivity profile of certain inhibitors is achieved, which is useful for lead optimization. In addition, the algorithm can also be used to predict binding affinities for structures whose affinity for a given inhibitor is unknown. The algorithm’s performance is demonstrated using an extensive dataset for the human kinome. Conclusion: We show that the binding affinity of 38 different kinase inhibitors can be explained with consistently high precision and accuracy using the variation of at most six residue positions in the kinome binding site. We show for several inhibitors that we are able to identify residues that are known to be functionally important

    Toward a systems-level view of dynamic phosphorylation networks

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    To better understand how cells sense and respond to their environment, it is important to understand the organization and regulation of the phosphorylation networks that underlie most cellular signal transduction pathways. These networks, which are composed of protein kinases, protein phosphatases and their respective cellular targets, are highly dynamic. Importantly, to achieve signaling specificity, phosphorylation networks must be regulated at several levels, including at the level of protein expression, substrate recognition, and spatiotemporal modulation of enzymatic activity. Here, we briefly summarize some of the traditional methods used to study the phosphorylation status of cellular proteins before focusing our attention on several recent technological advances, such as protein microarrays, quantitative mass spectrometry, and genetically-targetable fluorescent biosensors, that are offering new insights into the organization and regulation of cellular phosphorylation networks. Together, these approaches promise to lead to a systems-level view of dynamic phosphorylation networks

    Using the Structural Kinome to Systematize Kinase Drug Discovery

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    Kinase-targeted drug design is challenging. It requires designing inhibitors that can bind to specific kinases, when all kinase catalytic domains share a common folding scaffold that binds ATP. Thus, obtaining the desired selectivity, given the whole human kinome, is a fundamental task during early-stage drug discovery. This begins with deciphering the kinase-ligand characteristics, analyzing the structure–activity relationships and prioritizing the desired drug molecules across the whole kinome. Currently, there are more than 300 kinases with released PDB structures, which provides a substantial structural basis to gain these necessary insights. Here, we review in silico structure-based methods – notably, a function-site interaction fingerprint approach used in exploring the complete human kinome. In silico methods can be explored synergistically with multiple cell-based or protein-based assay platforms such as KINOMEscan. We conclude with new drug discovery opportunities associated with kinase signaling networks and using machine/deep learning techniques broadly referred to as structural biomedical data science

    Detailed protein sequence alignment based on Spectral Similarity Score (SSS)

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    BACKGROUND: The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and similarity of primary protein sequences. However, character based similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a similarity score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain. RESULTS: Distance matrices of various branches of the human kinome, that is the full complement of human kinases, were developed that matched the phylogenetic tree of the human kinome establishing the efficacy of the global alignment of the algorithm. PKCd and PKCe kinases share close biological properties and structural similarities but do not give high scores with character based alignments. Detailed comparison established close similarities between subsequences that do not have any significant character identity. We compared their known 3D structures to establish that the algorithm is able to pick subsequences that are not considered similar by character based matching algorithms but share structural similarities. Similarly many subsequences with low character identity were picked between xyna-theau and xyna-clotm F/10 xylanases. Comparison of 3D structures of the subsequences confirmed the claim of similarity in structure. CONCLUSION: An algorithm is developed which is inspired by successful application of spectral similarity applied to music sequences. The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures. The Spectral Similarity Score (SSS) is an extension to the conventional similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins

    Chemoinformatics-Driven Approaches for Kinase Drug Discovery

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    Given their importance for the majority of cell physiology processes, protein kinases are among the most extensively studied protein targets in drug discovery. Inappropriate regulation of their basal levels results in pathophysiological disorders. In this regard, small-molecule inhibitors of human kinome have been developed to treat these conditions effectively and improve the survival rates and life quality of patients. In recent years, kinase-related data has become increasingly available in the public domain. These large amounts of data provide a rich knowledge source for the computational studies of kinase drug discovery concepts. This thesis aims to systematically explore properties of kinase inhibitors on the basis of publicly available data. Hence, an established "selectivity versus promiscuity" conundrum of kinase inhibitors is evaluated, close structural analogs with diverging promiscuity levels are analyzed, and machine learning is employed to classify different kinase inhibitor binding modes. In the first study, kinase inhibitor selectivity trends are explored on the kinase pair level where kinase structural features and phylogenetic relationships are used to explain the obtained selectivity information. Next, selectivity of clinical kinase inhibitors is inspected on the basis of cell-based profiling campaign results to consolidate the previous findings. Further, clinical candidates are mapped to medicinal chemistry sources and promiscuity levels of different inhibitor subsets are estimated, including designated chemical probes. Additionally, chemical probe analysis is extended to expert-curated representatives to correlate the views established by scientific community and evaluate their potential for chemical biology applications. Then, large-scale promiscuity analysis of kinase inhibitor data combining several public repositories is performed to subsequently explore promiscuity cliffs (PCs) and PC pathways and study structure-promiscuity relationships. Furthermore, an automated extraction protocol prioritizing the most informative pathways is proposed with focus on those containing promiscuity hubs. In addition, the generated promiscuity data structures including cliffs, pathways, and hubs are discussed for their potential in experimental and computational follow-ups and subsequently made publicly available. Finally, machine learning methods are used to develop classification models of kinase inhibitors with distinct experimental binding modes and their potential for the development of novel therapeutics is assessed

    Functional characterisation of the FIKK kinase family of Plasmodium falciparum

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    Key to P. falciparum virulence is its capacity to remodel the host erythrocyte. Infected erythrocytes become rigid and cytoadhere to the vascular endothelium leading to the disease symptoms and preventing their filtration by the spleen. Unlike other human- infecting Plasmodium species, P. falciparum exports a family of 18 FIKK kinases into the host cell. Here, a conditional knockout strategy based on the DiCre/LoxPint technology was used to study 4 FIKK kinases (FIKK4.1, FIKK7.1, FIKK10.1 and FIKK11) and identify their potential targets by quantitative phosphoproteome analysis. The deletion of FIKK4.1 led to a significant reduction in the phosphorylation of host cytoskeletal proteins and parasite proteins involved in remodelling. The characterisation of FIKK4.1 KO parasites confirmed its role both in the rigidification of the infected erythrocytes and in the trafficking of the adherence-mediating virulence factor PfEMP1 to the host cell surface. Additionally, recombinant versions of several FIKK kinase domains were used to identify potential pan-FIKK inhibitors. When tested in vitro, these compounds showed activity on both P. falciparum and P. knowlesi, raising concerns regarding their specificity. A whole genome sequencing on drug-resistant parasites did not allow to identify additional targets. Moreover, it was shown that the compounds were not active on the FIKK kinases in culture due to the high intra-erythrocytic ATP concentration. Using the recombinant FIKK kinase domains it was also shown that FIKK kinases possess distinct substrate specificity. Whereas most of them conserved the ancestral basophilicity, some evolved to phosphorylate preferentially acidic motifs. Strikingly, FIKK13 was found to be a tyrosine kinase, a feature supposed to be absent in Plasmodium. Finally, by studying the FIKK kinases from another Plasmodium species closely related to P. falciparum, it was shown that FIKK kinases substrate specificity is conserved across species of the Laverania clade.Open Acces

    Conditional protein degradation with novel PROteolysis-TArgeting Chimeras (PROTACs)

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    PROteolysis-TArgeting Chimeras (PROTACs) are novel heterobifunctional degraders that catalytically induce targeted protein degradation through the Ubiquitin-Proteasome System (UPS). Offering unique advantages over conventional small molecule inhibitors, PROTACs have successfully degraded a wide range of oncogenic proteins and showed potential as a promising paradigm in drug discovery. Despite the rapid expansion of the field, achieving conditional activation control of PROTAC-mediated protein degradation remains relatively unexplored. In this thesis, two novel PROTAC design strategies were developed to enhance spatiotemporal control and tissue specificity in PROTAC-mediated protein degradation. In the first design, a novel photoswitchable multi-kinase PROTAC, AP-PROTAC-2, was developed to enable conditional light-mediated control of protein degradation. This design incorporates a novel arylazopyrazole photoswitchable linker, combined with a multi-kinase inhibitor capable of engaging approximately 40% of the kinome. AP-PROTAC-2 can be reversibly switched between E and Z isomer-enriched states and exhibits superior photochemical properties compared to previous photoswitchable PROTACs. Multiplexed proteomics studies demonstrated that AP-PROTAC-2 selectively depleted four protein kinases in vitro in a light-switchable manner. This research marks the first instance of simultaneous photoswitchable degradation of multiple proteins, achieving selective spatiotemporal modulation of targeted kinase degradation. In the second design, peptide-based PROTACs were conjugated to monoclonal antibodies to design antibody-peptide degrader conjugates (Ab-peptides), building upon the concept of antibody-drug conjugates (ADCs). These Ab-peptides were designed to utilise ADC's antibody-mediated internalisation pathways for the targeted delivery of peptide payloads to antigen-positive cells. This approach aimed to enhance tissue specificity, cellular uptake, and intracellular degradation potency of peptide-based degraders. The development of three types of Ab-peptides targeting distinct proteins and employing different ADC linkers was reported. The resulting Ab-peptides exhibited enhanced target degradation efficacy surpassing that of unconjugated peptides, underscoring their promising potential. Collectively, these novel strategies offer valuable perspectives and insights into conditional protein degradation with a focus on photoswitchable multi-target PROTACs and peptide-based PROTACs.Open Acces
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