DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues
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PROVIDER: PRJNA978058 | ENA |
REPOSITORIES: ENA
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Project description:Homeodomains (HDs) are the second largest class of DNA binding domains (DBDs) in eukaryotic sequence-specific transcription factors (TFs) and are the TF structural class with the largest number of disease mutations in the Human Gene Mutation Database (HGMD). Despite numerous structural studies and large-scale analyses of HD DNA binding specificity, HD-DNA recognition is still not fully understood. Here, we analyzed 92 human HD mutants, including disease-associated variants and variants of unknown significance (VUS), for their effects on DNA binding activity. Many of the variants altered DNA binding affinity and/or specificity. Structural analysis identified 14 novel specificity-determining positions, 5 of which do not contact DNA. The same missense substitution at analogous positions within different HDs exhibited different effects on DNA binding activity. Variant effect prediction tools perform moderately well in distinguishing variants with altered DNA binding affinity, but poorly in identifying those with altered binding specificity. Our results highlight the need for biochemical assays of TF coding variants and promote dozens of variants for further investigations into their pathogenicity and the development of clinical diagnostics and precision therapies.
2023-12-05 | GSE233827 | GEO
Project description:Homeodomains (HDs) are the second largest class of DNA binding domains (DBDs) among eukaryotic sequence-specific transcription factors (TFs) and are the TF structural class with the largest number of disease-associated mutations in the Human Gene Mutation Database (HGMD). Despite numerous structural studies and large-scale analyses of HD DNA binding specificity, HD-DNA recognition is still not fully understood. Here, we analyze 92 human HD mutants, including disease-associated variants and variants of uncertain significance (VUS), for their effects on DNA binding activity. Many of the variants alter DNA binding affinity and/or specificity. Detailed biochemical analysis and structural modeling identifies 14 previously unknown specificity-determining positions, 5 of which do not contact DNA. The same missense substitution at analogous positions within different HDs often exhibits different effects on DNA binding activity. Variant effect prediction tools perform moderately well in distinguishing variants with altered DNA binding affinity, but poorly in identifying those with altered binding specificity. Our results highlight the need for biochemical assays of TF coding variants and prioritize dozens of variants for further investigations into their pathogenicity and the development of clinical diagnostics and precision therapies.
| S-EPMC11006913 | biostudies-literature
Project description:A subfamily of Drosophila homeodomain (HD) transcription factors (TFs) controls the identities of individual muscle founder cells (FCs). However, the molecular mechanisms by which these TFs generate unique FC genetic programs remain unknown. To investigate this problem, we first applied genome-wide mRNA expression profiling to identify genes that are activated or repressed by the muscle HD TFs Slouch (Slou) and Muscle segment homeobox (Msh). Next, we used protein binding microarrays to define the sequences that are bound by Slou, Msh and other HD TFs having mesodermal expression. These studies revealed that a large class of HDs, including Slou and Msh, predominantly recognize TAAT core sequences but that each HD also binds to unique sites that deviate from this canonical motif. To better understand the regulatory specificity of an individual FC identity HD, we evaluated the functions of atypical binding sites that are preferentially bound by Slou relative to other HDs within muscle enhancers that are either activated or repressed by this TF. These studies showed that Slou regulates the activities of particular myoblast enhancers through Slou-preferred sequences, whereas swapping these sequences for sites that are capable of binding to multiple HD family members does not support the normal regulatory functions of Slou. Moreover, atypical Slou binding sites are overrepresented in putative enhancers associated with additional Slou-responsive FC genes. Collectively, these studies provide new insights into the roles of individual HD TFs in determining cellular identity, and suggest that the diversity of HD binding preferences can confer regulatory specificity.
2012-01-28 | GSE35380 | GEO
Project description:A subfamily of Drosophila homeodomain (HD) transcription factors (TFs) controls the identities of individual muscle founder cells (FCs). However, the molecular mechanisms by which these TFs generate unique FC genetic programs remain unknown. To investigate this problem, we first applied genome-wide mRNA expression profiling to identify genes that are activated or repressed by the muscle HD TFs Slouch (Slou) and Muscle segment homeobox (Msh). Next, we used protein binding microarrays to define the sequences that are bound by Slou, Msh and other HD TFs having mesodermal expression. These studies revealed that a large class of HDs, including Slou and Msh, predominantly recognize TAAT core sequences but that each HD also binds to unique sites that deviate from this canonical motif. To better understand the regulatory specificity of an individual FC identity HD, we evaluated the functions of atypical binding sites that are preferentially bound by Slou relative to other HDs within muscle enhancers that are either activated or repressed by this TF. These studies showed that Slou regulates the activities of particular myoblast enhancers through Slou-preferred sequences, whereas swapping these sequences for sites that are capable of binding to multiple HD family members does not support the normal regulatory functions of Slou. Moreover, atypical Slou binding sites are overrepresented in putative enhancers associated with additional Slou-responsive FC genes. Collectively, these studies provide new insights into the roles of individual HD TFs in determining cellular identity, and suggest that the diversity of HD binding preferences can confer regulatory specificity. 10 Protein binding microarray (PBM) experiments of Drosophila transcription factors were performed. Briefly, the PBMs involved binding GST-tagged fly transcription factors to double-stranded 44K Agilent microarrays in order to determine their sequence preferences. The method is described in Berger et al., Nature Biotechnology 2006 (PMID: 16998473). A key feature is that the microarrays are composed of de Bruijn sequences that contain each 10-base sequence once and only once, providing an evenly balanced sequence distribution. Individual de Bruijn sequences have different properties, including representation of gapped patterns. The array probe sequences on the custom array design used in this study were reported previously in Berger et al., Cell 2008 (PMID: 18585359) and are available via an academic research use license. Here we provide the data transformed into median signal intensities (after normalization and detrending of the original array data) for all 32,896 8-base sequences, Z-scores for these intensities, and E-scores. E-scores are a modified version of AUC, and describe how well each 8-mer ranks the intensities of the spots. 'Keep fraction' (kf) parameter setting of 0.9 was used to calculate E-scores. In general the E-scores are slightly more reproducible than Z-scores, but contain less information about relative binding affinity. Additional experimental details are found in Berger et al., Nature Biotechnology 2006 (PMID: 16998473), and the accompanying Supplementary information.
2012-01-28 | E-GEOD-35380 | biostudies-arrayexpress
Project description:MotivationWithin a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular functional specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs.ResultsWe combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolutionary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs.AvailabilityDatasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC2718669 | biostudies-literature
Project description:Bauxite residues Gardanne Metabarcoding
| PRJNA748554 | ENA
Project description:Genes encoding RNA splicing factors are frequently mutated in clonal hematopoesis and diverse cancers. Most spliceosomal mutations affect specific hotspot residues, causing changes in exon and splice site recognition that promote disease. However, a subset of patients carry splicesomal mutations in non-hotspot residues, and their contribution to disease are unknown. Here we systematically characterized the function of several rare and private spliceosomal mutations in order to determine their potential disease relevance. We discovered that several rare and private spliceosomal mutations phenocopy the mechanistic splicing alterations induced by their "hotspot" counterparts. Our data suggest that many rare and private spliceosomal mutations contribute to disease pathogenesis.
2019-12-02 | GSE135732 | GEO
Project description:Predicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most likely to be functionally important. Most often these approaches do not consider many residues that act to define specific sub-functions within a family, or they make no distinction between residues important for function and those more relevant for maintaining structure (e.g. in the hydrophobic core). Many protein families bind and/or act on a variety of ligands, meaning that conserved residues often only bind a common ligand sub-structure or perform general catalytic activities.Here we present a novel method for functional site prediction based on identification of conserved positions, as well as those responsible for determining ligand specificity. We define Specificity-Determining Positions (SDPs), as those occupied by conserved residues within sub-groups of proteins in a family having a common specificity, but differ between groups, and are thus likely to account for specific recognition events. We benchmark the approach on enzyme families of known 3D structure with bound substrates, and find that in nearly all families residues predicted by SDPsite are in contact with the bound substrate, and that the addition of SDPs significantly improves functional site prediction accuracy. We apply SDPsite to various families of proteins containing known three-dimensional structures, but lacking clear functional annotations, and discusse several illustrative examples.The results suggest a better means to predict functional details for the thousands of protein structures determined prior to a clear understanding of molecular function.
| S-EPMC2709924 | biostudies-literature
Project description:The specificity-determining residue database (SDR database) presents residue positions where mutations are predicted to have changed protein function in large protein families. Because the database pre-calculates predictions on existing protein sequence alignments, users can quickly find the predictions by selecting the appropriate protein family or searching by protein sequence. Predictions can be used to guide mutagenesis or to gain a better understanding of specificity changes in a protein family. The database is available on the web at http://paradox.harvard.edu/sdr.
| S-EPMC2686543 | biostudies-literature
Project description:BackgroundDesigning small-molecule kinase inhibitors with desirable selectivity profiles is a major challenge in drug discovery. A high-throughput screen for inhibitors of a given kinase will typically yield many compounds that inhibit more than one kinase. A series of chemical modifications are usually required before a compound exhibits an acceptable selectivity profile. Rationalizing the selectivity profile for a small-molecule inhibitor in terms of the specificity-determining kinase residues for that molecule can be an important step toward the goal of developing selective kinase inhibitors.ResultsHere we describe S-Filter, a method that combines sequence and structural information to predict specificity-determining residues for a small molecule and its kinase selectivity profile. Analysis was performed on seven selective kinase inhibitors where a structural basis for selectivity is known. S-Filter correctly predicts specificity determinants that were described by independent groups. S-Filter also predicts a number of novel specificity determinants that can often be justified by further structural comparison.ConclusionS-Filter is a valuable tool for analyzing kinase selectivity profiles. The method identifies potential specificity determinants that are not readily apparent, and provokes further investigation at the structural level.
| S-EPMC2655090 | biostudies-literature