1
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Kock KH, Kimes PK, Gisselbrecht SS, Inukai S, Phanor SK, Anderson JT, Ramakrishnan G, Lipper CH, Song D, Kurland JV, Rogers JM, Jeong R, Blacklow SC, Irizarry RA, Bulyk ML. DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues. Nat Commun 2024; 15:3110. [PMID: 38600112 PMCID: PMC11006913 DOI: 10.1038/s41467-024-47396-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 03/29/2024] [Indexed: 04/12/2024] Open
Abstract
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.
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Affiliation(s)
- Kian Hong Kock
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Program in Biological and Biomedical Sciences, Harvard University, Cambridge, MA, USA
| | - Patrick K Kimes
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Stephen S Gisselbrecht
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Sachi Inukai
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Sabrina K Phanor
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - James T Anderson
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Gayatri Ramakrishnan
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Boston Bangalore Biosciences Beginnings Program, Harvard University, Cambridge, MA, USA
| | - Colin H Lipper
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Dongyuan Song
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jesse V Kurland
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Julia M Rogers
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, MA, USA
| | - Raehoon Jeong
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA, USA
| | - Stephen C Blacklow
- Program in Biological and Biomedical Sciences, Harvard University, Cambridge, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
- Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, MA, USA
| | - Rafael A Irizarry
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Martha L Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
- Program in Biological and Biomedical Sciences, Harvard University, Cambridge, MA, USA.
- Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, MA, USA.
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA, USA.
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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2
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Khetan S, Bulyk ML. Overlapping binding sites underlie TF genomic occupancy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583629. [PMID: 38496549 PMCID: PMC10942454 DOI: 10.1101/2024.03.05.583629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Sequence-specific DNA binding by transcription factors (TFs) is a crucial step in gene regulation. However, current high-throughput in vitro approaches cannot reliably detect lower affinity TF-DNA interactions, which play key roles in gene regulation. Here, we developed PADIT-seq ( p rotein a ffinity to D NA by in vitro transcription and RNA seq uencing) to assay TF binding preferences to all 10-bp DNA sequences at far greater sensitivity than prior approaches. The expanded catalogs of low affinity DNA binding sites for the human TFs HOXD13 and EGR1 revealed that nucleotides flanking high affinity DNA binding sites create overlapping lower affinity sites that together modulate TF genomic occupancy in vivo . Formation of such extended recognition sequences stems from an inherent property of TF binding sites to interweave each other and expands the genomic sequence space for identifying noncoding variants that directly alter TF binding. One-Sentence Summary Overlapping DNA binding sites underlie TF genomic occupancy through their inherent propensity to interweave each other.
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3
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Liu S, Gomez-Alcala P, Leemans C, Glassford WJ, Mann RS, Bussemaker HJ. Predicting the DNA binding specificity of mutated transcription factors using family-level biophysically interpretable machine learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.24.577115. [PMID: 38352411 PMCID: PMC10862739 DOI: 10.1101/2024.01.24.577115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Sequence-specific interactions of transcription factors (TFs) with genomic DNA underlie many cellular processes. High-throughput in vitro binding assays coupled with computational analysis have made it possible to accurately define such sequence recognition in a biophysically interpretable yet mechanism-agonistic way for individual TFs. The fact that such sequence-to-affinity models are now available for hundreds of TFs provides new avenues for predicting how the DNA binding specificity of a TF changes when its protein sequence is mutated. To this end, we developed an analytical framework based on a tetrahedron embedding that can be applied at the level of a given structural TF family. Using bHLH as a test case, we demonstrate that we can systematically map dependencies between the protein sequence of a TF and base preference within the DNA binding site. We also develop a regression approach to predict the quantitative energetic impact of mutations in the DNA binding domain of a TF on its DNA binding specificity, and perform SELEX-seq assays on mutated TFs to experimentally validate our results. Our results point to the feasibility of predicting the functional impact of disease mutations and allelic variation in the cell-wide TF repertoire by leveraging high-quality functional information across sets of homologous wild-type proteins.
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Affiliation(s)
- Shaoxun Liu
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Pilar Gomez-Alcala
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Christ Leemans
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - William J Glassford
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Richard S Mann
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Harmen J Bussemaker
- Department of Biological Sciences, Columbia University, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
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4
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Horton CA, Alexandari AM, Hayes MGB, Marklund E, Schaepe JM, Aditham AK, Shah N, Suzuki PH, Shrikumar A, Afek A, Greenleaf WJ, Gordân R, Zeitlinger J, Kundaje A, Fordyce PM. Short tandem repeats bind transcription factors to tune eukaryotic gene expression. Science 2023; 381:eadd1250. [PMID: 37733848 DOI: 10.1126/science.add1250] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/26/2023] [Indexed: 09/23/2023]
Abstract
Short tandem repeats (STRs) are enriched in eukaryotic cis-regulatory elements and alter gene expression, yet how they regulate transcription remains unknown. We found that STRs modulate transcription factor (TF)-DNA affinities and apparent on-rates by about 70-fold by directly binding TF DNA-binding domains, with energetic impacts exceeding many consensus motif mutations. STRs maximize the number of weakly preferred microstates near target sites, thereby increasing TF density, with impacts well predicted by statistical mechanics. Confirming that STRs also affect TF binding in cells, neural networks trained only on in vivo occupancies predicted effects identical to those observed in vitro. Approximately 90% of TFs preferentially bound STRs that need not resemble known motifs, providing a cis-regulatory mechanism to target TFs to genomic sites.
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Affiliation(s)
- Connor A Horton
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Amr M Alexandari
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Michael G B Hayes
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Emil Marklund
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Julia M Schaepe
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Arjun K Aditham
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | - Nilay Shah
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Peter H Suzuki
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Avanti Shrikumar
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Ariel Afek
- Center for Genomic and Computational Biology, Duke University School of Medicine, Durham, NC 27710, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | | | - Raluca Gordân
- Center for Genomic and Computational Biology, Duke University School of Medicine, Durham, NC 27710, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA
- Department of Computer Science, Duke University, Durham, NC 27708, USA
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Julia Zeitlinger
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
- The University of Kansas Medical Center, Kansas City, KS 66103, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Polly M Fordyce
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94110, USA
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5
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Alexandari AM, Horton CA, Shrikumar A, Shah N, Li E, Weilert M, Pufall MA, Zeitlinger J, Fordyce PM, Kundaje A. De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein-DNA binding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.11.540401. [PMID: 37214836 PMCID: PMC10197627 DOI: 10.1101/2023.05.11.540401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, in vivo genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained on in vitro TF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific, in vivo binding profiles. Conversely, deep learning models, trained on in vivo TF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models of in vitro and in vivo TF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinities de-novo from deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diverse in vitro assays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant of in vivo binding, suggest that deep learning models of in vivo binding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughput in silico experiments to explore the influence of sequence context and variation on both intrinsic affinity and in vivo occupancy.
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Affiliation(s)
- Amr M. Alexandari
- Department of Computer Science, Stanford University, Stanford, CA 94305
| | | | - Avanti Shrikumar
- Department of Earth System Science, Stanford University, Stanford, CA 94305
| | - Nilay Shah
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | - Eileen Li
- Department of Genetics, Stanford University, Stanford, CA 94305
| | - Melanie Weilert
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | - Miles A. Pufall
- Department of Biochemistry, Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242, USA
| | - Julia Zeitlinger
- Stowers Institute for Medical Research, Kansas City, MO, USA
- The University of Kansas Medical Center, Kansas City, KS, USA
| | - Polly M. Fordyce
- Department of Genetics, Stanford University, Stanford, CA 94305
- Department of Bioengineering, Stanford University, Stanford, CA 94305
- ChEM-H Institute, Stanford University, Stanford, CA 94305
- Chan Zuckerberg Biohub, San Francisco, CA 94110
| | - Anshul Kundaje
- Department of Computer Science, Stanford University, Stanford, CA 94305
- Department of Genetics, Stanford University, Stanford, CA 94305
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6
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Swint-Kruse L, Dougherty LL, Page B, Wu T, O’Neil PT, Prasannan CB, Timmons C, Tang Q, Parente DJ, Sreenivasan S, Holyoak T, Fenton AW. PYK-SubstitutionOME: an integrated database containing allosteric coupling, ligand affinity and mutational, structural, pathological, bioinformatic and computational information about pyruvate kinase isozymes. Database (Oxford) 2023; 2023:baad030. [PMID: 37171062 PMCID: PMC10176505 DOI: 10.1093/database/baad030] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/29/2023] [Accepted: 04/11/2023] [Indexed: 05/13/2023]
Abstract
Interpreting changes in patient genomes, understanding how viruses evolve and engineering novel protein function all depend on accurately predicting the functional outcomes that arise from amino acid substitutions. To that end, the development of first-generation prediction algorithms was guided by historic experimental datasets. However, these datasets were heavily biased toward substitutions at positions that have not changed much throughout evolution (i.e. conserved). Although newer datasets include substitutions at positions that span a range of evolutionary conservation scores, these data are largely derived from assays that agglomerate multiple aspects of function. To facilitate predictions from the foundational chemical properties of proteins, large substitution databases with biochemical characterizations of function are needed. We report here a database derived from mutational, biochemical, bioinformatic, structural, pathological and computational studies of a highly studied protein family-pyruvate kinase (PYK). A centerpiece of this database is the biochemical characterization-including quantitative evaluation of allosteric regulation-of the changes that accompany substitutions at positions that sample the full conservation range observed in the PYK family. We have used these data to facilitate critical advances in the foundational studies of allosteric regulation and protein evolution and as rigorous benchmarks for testing protein predictions. We trust that the collected dataset will be useful for the broader scientific community in the further development of prediction algorithms. Database URL https://github.com/djparente/PYK-DB.
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Affiliation(s)
- Liskin Swint-Kruse
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Larissa L Dougherty
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Braelyn Page
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Tiffany Wu
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Pierce T O’Neil
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Charulata B Prasannan
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Cody Timmons
- Chemistry Department, Southwestern Oklahoma State University, 100 Campus Dr., Weatherford, OK 73096, USA
| | - Qingling Tang
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Daniel J Parente
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
- Department of Family Medicine and Community Health, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Shwetha Sreenivasan
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Todd Holyoak
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
- Department of Biology, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
| | - Aron W Fenton
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
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7
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Sweeney K, McClean MN. Transcription factor localization dynamics and DNA binding drive distinct promoter interpretations. Cell Rep 2023; 42:112426. [PMID: 37087734 DOI: 10.1016/j.celrep.2023.112426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 02/17/2023] [Accepted: 04/07/2023] [Indexed: 04/24/2023] Open
Abstract
Environmental information may be encoded in the temporal dynamics of transcription factor (TF) activation and subsequently decoded by gene promoters to enact stimulus-specific gene expression programs. Previous studies of this behavior focused on the encoding and decoding of information in TF nuclear localization dynamics, yet cells control the activity of TFs in myriad ways, including by regulating their ability to bind DNA. Here, we use light-controlled mutants of the yeast TF Msn2 as a model system to investigate how promoter decoding of TF localization dynamics is affected by changes in the ability of the TF to bind DNA. We find that yeast promoters directly decode the light-controlled localization dynamics of Msn2 and that the effects of changing Msn2 affinity on that decoding behavior are highly promoter dependent, illustrating how cells could regulate TF localization dynamics and DNA binding in concert for improved control of gene expression.
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Affiliation(s)
- Kieran Sweeney
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Megan N McClean
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA; University of Wisconsin Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
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8
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Luan Y, Tang Z, He Y, Xie Z. Intra-Domain Residue Coevolution in Transcription Factors Contributes to DNA Binding Specificity. Microbiol Spectr 2023; 11:e0365122. [PMID: 36943132 PMCID: PMC10100741 DOI: 10.1128/spectrum.03651-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 02/22/2023] [Indexed: 03/23/2023] Open
Abstract
Understanding the basis of the DNA-binding specificity of transcription factors (TFs) has been of long-standing interest. Despite extensive efforts to map millions of putative TF binding sequences, identifying the critical determinants for DNA binding specificity remains a major challenge. The coevolution of residues in proteins occurs due to a shared evolutionary history. However, it is unclear how coevolving residues in TFs contribute to DNA binding specificity. Here, we systematically collected publicly available data sets from multiple large-scale high-throughput TF-DNA interaction screening experiments for the major TF families with large numbers of TF members. These families included the Homeobox, HLH, bZIP_1, Ets, HMG_box, ZF-C4, and Zn_clus TFs. We detected TF subclass-determining sites (TSDSs) and showed that the TSDSs were more likely to coevolve with other TSDSs than with non-TSDSs, particularly for the Homeobox, HLH, Ets, bZIP_1, and HMG_box TF families. By in silico modeling, we showed that mutation of the highly coevolving residues could significantly reduce the stability of the TF-DNA complex. The distant residues from the DNA interface also contributed to TF-DNA binding activity. Overall, our study gave evidence that coevolved residues relate to transcriptional regulation and provided insights into the potential application of engineered DNA-binding domains and proteins. IMPORTANCE While unraveling DNA-binding specificity of TFs is the key to understanding the basis and molecular mechanism of gene expression regulation, identifying the critical determinants that contribute to DNA binding specificity remains a major challenge. In this study, we provided evidence showing that coevolving residues in TF domains contributed to DNA binding specificity. We demonstrated that the TSDSs were more likely to coevolve with other TSDSs than with non-TSDSs. Mutation of the coevolving residue pairs (CRPs) could significantly reduce the stability of THE TF-DNA complex, and even the distant residues from the DNA interface contribute to TF-DNA binding activity. Collectively, our study expands our knowledge of the interactions among coevolved residues in TFs, tertiary contacting, and functional importance in refined transcriptional regulation. Understanding the impact of coevolving residues in TFs will help understand the details of transcription of gene regulation and advance the application of engineered DNA-binding domains and protein.
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Affiliation(s)
- Yizhao Luan
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zehua Tang
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yao He
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
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9
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Shahein A, López-Malo M, Istomin I, Olson EJ, Cheng S, Maerkl SJ. Systematic analysis of low-affinity transcription factor binding site clusters in vitro and in vivo establishes their functional relevance. Nat Commun 2022; 13:5273. [PMID: 36071116 PMCID: PMC9452512 DOI: 10.1038/s41467-022-32971-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/25/2022] [Indexed: 11/10/2022] Open
Abstract
Binding to binding site clusters has yet to be characterized in depth, and the functional relevance of low-affinity clusters remains uncertain. We characterized transcription factor binding to low-affinity clusters in vitro and found that transcription factors can bind concurrently to overlapping sites, challenging the notion of binding exclusivity. Furthermore, small clusters with binding sites an order of magnitude lower in affinity give rise to high mean occupancies at physiologically-relevant transcription factor concentrations. To assess whether the observed in vitro occupancies translate to transcriptional activation in vivo, we tested low-affinity binding site clusters in a synthetic and native gene regulatory network in S. cerevisiae. In both systems, clusters of low-affinity binding sites generated transcriptional output comparable to single or even multiple consensus sites. This systematic characterization demonstrates that clusters of low-affinity binding sites achieve substantial occupancies, and that this occupancy can drive expression in eukaryotic promoters.
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Affiliation(s)
- Amir Shahein
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maria López-Malo
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Ivan Istomin
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Evan J Olson
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Shiyu Cheng
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Sebastian J Maerkl
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
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10
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Mokhtari DA, Appel MJ, Fordyce PM, Herschlag D. High throughput and quantitative enzymology in the genomic era. Curr Opin Struct Biol 2021; 71:259-273. [PMID: 34592682 PMCID: PMC8648990 DOI: 10.1016/j.sbi.2021.07.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 07/23/2021] [Indexed: 12/28/2022]
Abstract
Accurate predictions from models based on physical principles are the ultimate metric of our biophysical understanding. Although there has been stunning progress toward structure prediction, quantitative prediction of enzyme function has remained challenging. Realizing this goal will require large numbers of quantitative measurements of rate and binding constants and the use of these ground-truth data sets to guide the development and testing of these quantitative models. Ground truth data more closely linked to the underlying physical forces are also desired. Here, we describe technological advances that enable both types of ground truth measurements. These advances allow classic models to be tested, provide novel mechanistic insights, and place us on the path toward a predictive understanding of enzyme structure and function.
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Affiliation(s)
- D A Mokhtari
- Department of Biochemistry, Stanford University, Stanford, CA, 94305, USA
| | - M J Appel
- Department of Biochemistry, Stanford University, Stanford, CA, 94305, USA
| | - P M Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA, 94305, USA; Department of Genetics, Stanford University, Stanford, CA, 94305, USA; Chan Zuckerberg Biohub San Francisco, CA, 94110, USA.
| | - D Herschlag
- Department of Biochemistry, Stanford University, Stanford, CA, 94305, USA; Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA; ChEM-H Institute, Stanford University, Stanford, CA, 94305, USA.
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11
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Appel M, Longwell SA, Morri M, Neff N, Herschlag D, Fordyce PM. uPIC-M: Efficient and Scalable Preparation of Clonal Single Mutant Libraries for High-Throughput Protein Biochemistry. ACS OMEGA 2021; 6:30542-30554. [PMID: 34805683 PMCID: PMC8600632 DOI: 10.1021/acsomega.1c04180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 10/05/2021] [Indexed: 06/13/2023]
Abstract
New high-throughput biochemistry techniques complement selection-based approaches and provide quantitative kinetic and thermodynamic data for thousands of protein variants in parallel. With these advances, library generation rather than data collection has become rate-limiting. Unlike pooled selection approaches, high-throughput biochemistry requires mutant libraries in which individual sequences are rationally designed, efficiently recovered, sequence-validated, and separated from one another, but current strategies are unable to produce these libraries at the needed scale and specificity at reasonable cost. Here, we present a scalable, rapid, and inexpensive approach for creating User-designed Physically Isolated Clonal-Mutant (uPIC-M) libraries that utilizes recent advances in oligo synthesis, high-throughput sample preparation, and next-generation sequencing. To demonstrate uPIC-M, we created a scanning mutant library of SpAP, a 541 amino acid alkaline phosphatase, and recovered 94% of desired mutants in a single iteration. uPIC-M uses commonly available equipment and freely downloadable custom software and can produce a 5000 mutant library at 1/3 the cost and 1/5 the time of traditional techniques.
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Affiliation(s)
- Mason
J. Appel
- Department
of Biochemistry, Stanford University, Stanford, California 94305, United States
| | - Scott A. Longwell
- Department
of Bioengineering, Stanford University, Stanford, California 94305, United States
| | - Maurizio Morri
- Chan
Zuckerberg Biohub, San Francisco, California 94110, United States
| | - Norma Neff
- Chan
Zuckerberg Biohub, San Francisco, California 94110, United States
| | - Daniel Herschlag
- Department
of Biochemistry, Stanford University, Stanford, California 94305, United States
| | - Polly M. Fordyce
- Department
of Bioengineering, Stanford University, Stanford, California 94305, United States
- Chan
Zuckerberg Biohub, San Francisco, California 94110, United States
- Department
of Genetics, Stanford University, Stanford, California 94305, United States
- ChEM-H
Institute, Stanford University, Stanford, California 94305, United States
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12
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Hein J, Cyert MS, Fordyce PM. MRBLE-pep Measurements Reveal Accurate Binding Affinities for B56, a PP2A Regulatory Subunit. ACS MEASUREMENT SCIENCE AU 2021; 1:56-64. [PMID: 35128539 PMCID: PMC8809670 DOI: 10.1021/acsmeasuresciau.1c00008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Signal transduction pathways rely on dynamic interactions between protein globular domains and short linear motifs (SLiMs). The weak affinities of these interactions are essential to allow fast rewiring of signaling pathways and downstream responses but also pose technical challenges for interaction detection and measurement. We recently developed a technique (MRBLE-pep) that leverages spectrally encoded hydrogel beads to measure binding affinities between a single protein of interest and 48 different peptide sequences in a single small volume. In prior work, we applied it to map the binding specificity landscape between calcineurin and the PxIxIT SLiM (Nguyen, H. Q. et al. Elife 2019, 8). Here, using peptide sequences known to bind the PP2A regulatory subunit B56α, we systematically compare affinities measured by MRBLE-pep or isothermal calorimetry (ITC) and confirm that MRBLE-pep accurately quantifies relative affinity over a wide dynamic range while using a fraction of the material required for traditional methods such as ITC.
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Affiliation(s)
- Jamin
B. Hein
- Department
of Biology, Stanford University, Stanford, California 94305, United States
- Department
of Bioengineering, Stanford University, Stanford, California 94305, United States
- The
Novo Nordisk Foundation Center for Protein Research, Faculty of Health
and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen, Denmark
| | - Martha S. Cyert
- Department
of Biology, Stanford University, Stanford, California 94305, United States
| | - Polly M. Fordyce
- Department
of Bioengineering, Stanford University, Stanford, California 94305, United States
- Department
of Genetics, Stanford University, Stanford, California 94305, United States
- ChEM-H
Institute, Stanford University, Stanford, California 94305, United States
- Chan
Zuckerberg
Biohub, San Francisco, California 94110, United States
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13
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Rodriguez-Rivera FP, Levi SM. Unifying Catalysis Framework to Dissect Proteasomal Degradation Paradigms. ACS CENTRAL SCIENCE 2021; 7:1117-1125. [PMID: 34345664 PMCID: PMC8323112 DOI: 10.1021/acscentsci.1c00389] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Indexed: 06/13/2023]
Abstract
Diverging from traditional target inhibition, proteasomal protein degradation approaches have emerged as novel therapeutic modalities that embody distinct pharmacological profiles and can access previously undrugged targets. Small molecule degraders have the potential to catalytically destroy target proteins at substoichiometric concentrations, thus lowering administered doses and extending pharmacological effects. With this mechanistic premise, research efforts have advanced the development of small molecule degraders that benefit from stable and increased affinity ternary complexes. However, a holistic framework that evaluates different degradation modes from a catalytic perspective, including focusing on kinetically favored degradation mechanisms, is lacking. In this Outlook, we introduce the concept of an induced cooperativity spectrum as a unifying framework to mechanistically understand catalytic degradation profiles. This framework is bolstered by key examples of published molecular degraders extending from molecular glues to bivalent degraders. Critically, we discuss remaining challenges and future opportunities in drug discovery to rationally design and phenotypically screen for efficient degraders.
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Affiliation(s)
- Frances P. Rodriguez-Rivera
- Discovery
Chemistry, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Samuel M. Levi
- Pfizer
Worldwide Research and Development, Pfizer,
Inc., 1 Portland Street, Cambridge, Massachusetts 02139, United States
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14
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Markin CJ, Mokhtari DA, Sunden F, Appel MJ, Akiva E, Longwell SA, Sabatti C, Herschlag D, Fordyce PM. Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics. Science 2021; 373:373/6553/eabf8761. [PMID: 34437092 DOI: 10.1126/science.abf8761] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 05/24/2021] [Indexed: 12/21/2022]
Abstract
Systematic and extensive investigation of enzymes is needed to understand their extraordinary efficiency and meet current challenges in medicine and engineering. We present HT-MEK (High-Throughput Microfluidic Enzyme Kinetics), a microfluidic platform for high-throughput expression, purification, and characterization of more than 1500 enzyme variants per experiment. For 1036 mutants of the alkaline phosphatase PafA (phosphate-irrepressible alkaline phosphatase of Flavobacterium), we performed more than 670,000 reactions and determined more than 5000 kinetic and physical constants for multiple substrates and inhibitors. We uncovered extensive kinetic partitioning to a misfolded state and isolated catalytic effects, revealing spatially contiguous regions of residues linked to particular aspects of function. Regions included active-site proximal residues but extended to the enzyme surface, providing a map of underlying architecture not possible to derive from existing approaches. HT-MEK has applications that range from understanding molecular mechanisms to medicine, engineering, and design.
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Affiliation(s)
- C J Markin
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - D A Mokhtari
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - F Sunden
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - M J Appel
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - E Akiva
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
| | - S A Longwell
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - C Sabatti
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.,Department of Statistics, Stanford University, Stanford, CA 94305, USA
| | - D Herschlag
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA. .,Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.,ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | - P M Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA. .,ChEM-H Institute, Stanford University, Stanford, CA 94305, USA.,Department of Genetics, Stanford University, Stanford, CA 94305, USA.,Chan Zuckerberg Biohub; San Francisco, CA 94110, USA
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15
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Atsavapranee B, Stark CD, Sunden F, Thompson S, Fordyce PM. Fundamentals to function: Quantitative and scalable approaches for measuring protein stability. Cell Syst 2021; 12:547-560. [PMID: 34139165 DOI: 10.1016/j.cels.2021.05.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/16/2021] [Accepted: 05/07/2021] [Indexed: 12/11/2022]
Abstract
Folding a linear chain of amino acids into a three-dimensional protein is a complex physical process that ultimately confers an impressive range of diverse functions. Although recent advances have driven significant progress in predicting three-dimensional protein structures from sequence, proteins are not static molecules. Rather, they exist as complex conformational ensembles defined by energy landscapes spanning the space of sequence and conditions. Quantitatively mapping the physical parameters that dictate these landscapes and protein stability is therefore critical to develop models that are capable of predicting how mutations alter function of proteins in disease and informing the design of proteins with desired functions. Here, we review the approaches that are used to quantify protein stability at a variety of scales, from returning multiple thermodynamic and kinetic measurements for a single protein sequence to yielding indirect insights into folding across a vast sequence space. The physical parameters derived from these approaches will provide a foundation for models that extend beyond the structural prediction to capture the complexity of conformational ensembles and, ultimately, their function.
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Affiliation(s)
| | - Catherine D Stark
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA; ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Fanny Sunden
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - Samuel Thompson
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Polly M Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; ChEM-H, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94110, USA.
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