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Mitra R, Li J, Sagendorf JM, Jiang Y, Cohen AS, Chiu TP, Glasscock CJ, Rohs R. Geometric deep learning of protein-DNA binding specificity. Nat Methods 2024; 21:1674-1683. [PMID: 39103447 PMCID: PMC11399107 DOI: 10.1038/s41592-024-02372-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 06/14/2024] [Indexed: 08/07/2024]
Abstract
Predicting protein-DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein-DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding specificity, to a wide range of DNA sequences. This information is not directly accessible in a single structure. Here, to access this information, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity from protein-DNA structure. DeepPBS can be applied to experimental or predicted structures. Interpretable protein heavy atom importance scores for interface residues can be extracted. When aggregated at the protein residue level, these scores are validated through mutagenesis experiments. Applied to designed proteins targeting specific DNA sequences, DeepPBS was demonstrated to predict experimentally measured binding specificity. DeepPBS offers a foundation for machine-aided studies that advance our understanding of molecular interactions and guide experimental designs and synthetic biology.
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Affiliation(s)
- Raktim Mitra
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Jinsen Li
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Jared M Sagendorf
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Yibei Jiang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Ari S Cohen
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Tsu-Pei Chiu
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Cameron J Glasscock
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Remo Rohs
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
- Department of Physics and Astronomy, University of Southern California, Los Angeles, CA, USA.
- Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, CA, USA.
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2
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Zitnik M, Li MM, Wells A, Glass K, Morselli Gysi D, Krishnan A, Murali TM, Radivojac P, Roy S, Baudot A, Bozdag S, Chen DZ, Cowen L, Devkota K, Gitter A, Gosline SJC, Gu P, Guzzi PH, Huang H, Jiang M, Kesimoglu ZN, Koyuturk M, Ma J, Pico AR, Pržulj N, Przytycka TM, Raphael BJ, Ritz A, Sharan R, Shen Y, Singh M, Slonim DK, Tong H, Yang XH, Yoon BJ, Yu H, Milenković T. Current and future directions in network biology. BIOINFORMATICS ADVANCES 2024; 4:vbae099. [PMID: 39143982 PMCID: PMC11321866 DOI: 10.1093/bioadv/vbae099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 05/31/2024] [Accepted: 07/08/2024] [Indexed: 08/16/2024]
Abstract
Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation Not applicable.
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Affiliation(s)
- Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Michelle M Li
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Aydin Wells
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Deisy Morselli Gysi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Statistics, Federal University of Paraná, Curitiba, Paraná 81530-015, Brazil
- Department of Physics, Northeastern University, Boston, MA 02115, United States
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
| | - Sushmita Roy
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Wisconsin Institute for Discovery, Madison, WI 53715, United States
| | - Anaïs Baudot
- Aix Marseille Université, INSERM, MMG, Marseille, France
| | - Serdar Bozdag
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- Department of Mathematics, University of North Texas, Denton, TX 76203, United States
| | - Danny Z Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Kapil Devkota
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Morgridge Institute for Research, Madison, WI 53715, United States
| | - Sara J C Gosline
- Biological Sciences Division, Pacific Northwest National Laboratory, Seattle, WA 98109, United States
| | - Pengfei Gu
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Pietro H Guzzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, 88100, Italy
| | - Heng Huang
- Department of Computer Science, University of Maryland College Park, College Park, MD 20742, United States
| | - Meng Jiang
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Ziynet Nesibe Kesimoglu
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Mehmet Koyuturk
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, United States
| | - Nataša Pržulj
- Department of Computer Science, University College London, London, WC1E 6BT, England
- ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, 08010, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
| | - Anna Ritz
- Department of Biology, Reed College, Portland, OR 97202, United States
| | - Roded Sharan
- School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, United States
| | - Donna K Slonim
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Hanghang Tong
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Xinan Holly Yang
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, United States
| | - Byung-Jun Yoon
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, United States
| | - Haiyuan Yu
- Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, United States
| | - Tijana Milenković
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
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3
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Oriol F, Alberto M, Joachim AP, Patrick G, M BP, Ruben MF, Jaume B, Altair CH, Ferran P, Oriol G, Narcis FF, Baldo O. Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity in cis-regulatory elements. NAR Genom Bioinform 2024; 6:lqae068. [PMID: 38867914 PMCID: PMC11167492 DOI: 10.1093/nargab/lqae068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/18/2024] [Accepted: 05/23/2024] [Indexed: 06/14/2024] Open
Abstract
Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ∼25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. We introduce a structure-based learning approach to predict the binding preferences of TFs and the automated modelling of TF regulatory complexes. We show the advantage of using our approach over the classical nearest-neighbor prediction in the limits of remote homology. Starting from a TF sequence or structure, we predict binding preferences in the form of motifs that are then used to scan a DNA sequence for occurrences. The best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA. Co-operativity is modelled by: (i) the co-localization of TFs and (ii) the structural modeling of protein-protein interactions between TFs and with co-factors. We have applied our approach to automatically model the interferon-β enhanceosome and the pioneering complexes of OCT4, SOX2 (or SOX11) and KLF4 with a nucleosome, which are compared with the experimentally known structures.
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Affiliation(s)
- Fornes Oriol
- Centre for Molecular Medicine and Therapeutics. BC Children's Hospital Research Institute. Department of Medical Genetics. University of British Columbia, Vancouver, BC V5Z 4H4, Canada
| | - Meseguer Alberto
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | | | - Gohl Patrick
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Bota Patricia M
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Molina-Fernández Ruben
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Bonet Jaume
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
- Laboratory of Protein Design & Immunoengineering. School of Engineering. Ecole Polytechnique Federale de Lausanne. Lausanne 1015, Vaud, Switzerland
| | - Chinchilla-Hernandez Altair
- Live-Cell Structural Biology. Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Pegenaute Ferran
- Live-Cell Structural Biology. Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Gallego Oriol
- Live-Cell Structural Biology. Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Fernandez-Fuentes Narcis
- Institute of Biological, Environmental and Rural Science. Aberystwyth University, SY23 3DA Aberystwyth, UK
| | - Oliva Baldo
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
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4
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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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5
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Girard C. The tri-flow adaptiveness of codes in major evolutionary transitions. Biosystems 2024; 237:105133. [PMID: 38336225 DOI: 10.1016/j.biosystems.2024.105133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/26/2024] [Accepted: 01/27/2024] [Indexed: 02/12/2024]
Abstract
Life codes increase in both number and variety with biological complexity. Although our knowledge of codes is constantly expanding, the evolutionary progression of organic, neural, and cultural codes in response to selection pressure remains poorly understood. Greater clarification of the selective mechanisms is achieved by investigating how major evolutionary transitions reduce spatiotemporal and energetic constraints on transmitting heritable code to offspring. Evolution toward less constrained flows is integral to enduring flow architecture everywhere, in both engineered and natural flow systems. Beginning approximately 4 billion years ago, the most basic level for transmitting genetic material to offspring was initiated by protocell division. Evidence from ribosomes suggests that protocells transmitted comma-free or circular codes, preceding the evolution of standard genetic code. This rudimentary information flow within protocells is likely to have first emerged within the geo-energetic and geospatial constraints of hydrothermal vents. A broad-gauged hypothesis is that major evolutionary transitions overcame such constraints with tri-flow adaptations. The interconnected triple flows incorporated energy-converting, spatiotemporal, and code-based informational dynamics. Such tri-flow adaptations stacked sequence splicing code on top of protein-DNA recognition code in eukaryotes, prefiguring the transition to sexual reproduction. Sex overcame the spatiotemporal-energetic constraints of binary fission with further code stacking. Examples are tubulin code and transcription initiation code in vertebrates. In a later evolutionary transition, language reduced metabolic-spatiotemporal constraints on inheritance by stacking phonetic, phonological, and orthographic codes. In organisms that reproduce sexually, each major evolutionary transition is shown to be a tri-flow adaptation that adds new levels of code-based informational exchange. Evolving biological complexity is also shown to increase the nongenetic transmissibility of code.
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Affiliation(s)
- Chris Girard
- Department of Global and Sociocultural Studies, Florida International University, Miami, FL 33199, United States.
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6
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Augustijn HE, Roseboom AM, Medema MH, van Wezel GP. Harnessing regulatory networks in Actinobacteria for natural product discovery. J Ind Microbiol Biotechnol 2024; 51:kuae011. [PMID: 38569653 PMCID: PMC10996143 DOI: 10.1093/jimb/kuae011] [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: 01/22/2024] [Accepted: 04/02/2024] [Indexed: 04/05/2024]
Abstract
Microbes typically live in complex habitats where they need to rapidly adapt to continuously changing growth conditions. To do so, they produce an astonishing array of natural products with diverse structures and functions. Actinobacteria stand out for their prolific production of bioactive molecules, including antibiotics, anticancer agents, antifungals, and immunosuppressants. Attention has been directed especially towards the identification of the compounds they produce and the mining of the large diversity of biosynthetic gene clusters (BGCs) in their genomes. However, the current return on investment in random screening for bioactive compounds is low, while it is hard to predict which of the millions of BGCs should be prioritized. Moreover, many of the BGCs for yet undiscovered natural products are silent or cryptic under laboratory growth conditions. To identify ways to prioritize and activate these BGCs, knowledge regarding the way their expression is controlled is crucial. Intricate regulatory networks control global gene expression in Actinobacteria, governed by a staggering number of up to 1000 transcription factors per strain. This review highlights recent advances in experimental and computational methods for characterizing and predicting transcription factor binding sites and their applications to guide natural product discovery. We propose that regulation-guided genome mining approaches will open new avenues toward eliciting the expression of BGCs, as well as prioritizing subsets of BGCs for expression using synthetic biology approaches. ONE-SENTENCE SUMMARY This review provides insights into advances in experimental and computational methods aimed at predicting transcription factor binding sites and their applications to guide natural product discovery.
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Affiliation(s)
- Hannah E Augustijn
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
- Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Anna M Roseboom
- Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Marnix H Medema
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
- Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Gilles P van Wezel
- Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
- Netherlands Institute for Ecology (NIOO-KNAW), Wageningen, The Netherlands
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7
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Li X, Chen Z, Ye W, Yu J, Zhang X, Li Y, Niu Y, Ran S, Wang S, Luo Z, Zhao J, Hao Y, Zong J, Xia C, Xia J, Wu J. High-throughput CRISPR technology: a novel horizon for solid organ transplantation. Front Immunol 2024; 14:1295523. [PMID: 38239344 PMCID: PMC10794540 DOI: 10.3389/fimmu.2023.1295523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 12/12/2023] [Indexed: 01/22/2024] Open
Abstract
Organ transplantation is the gold standard therapy for end-stage organ failure. However, the shortage of available grafts and long-term graft dysfunction remain the primary barriers to organ transplantation. Exploring approaches to solve these issues is urgent, and CRISPR/Cas9-based transcriptome editing provides one potential solution. Furthermore, combining CRISPR/Cas9-based gene editing with an ex vivo organ perfusion system would enable pre-implantation transcriptome editing of grafts. How to determine effective intervention targets becomes a new problem. Fortunately, the advent of high-throughput CRISPR screening has dramatically accelerated the effective targets. This review summarizes the current advancements, utilization, and workflow of CRISPR screening in various immune and non-immune cells. It also discusses the ongoing applications of CRISPR/Cas-based gene editing in transplantation and the prospective applications of CRISPR screening in solid organ transplantation.
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Affiliation(s)
- Xiaohan Li
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhang Chen
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weicong Ye
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jizhang Yu
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xi Zhang
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuan Li
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuqing Niu
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuan Ran
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Song Wang
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zilong Luo
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiulu Zhao
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanglin Hao
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junjie Zong
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chengkun Xia
- Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiahong Xia
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education, National Health Commission (NHC) Key Laboratory of Organ Transplantation, Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
| | - Jie Wu
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Translational Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Organ Transplantation, Ministry of Education, National Health Commission (NHC) Key Laboratory of Organ Transplantation, Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China
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Mitra R, Li J, Sagendorf JM, Jiang Y, Chiu TP, Rohs R. DeepPBS: Geometric deep learning for interpretable prediction of protein-DNA binding specificity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.15.571942. [PMID: 38293168 PMCID: PMC10827229 DOI: 10.1101/2023.12.15.571942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Predicting specificity in protein-DNA interactions is a challenging yet essential task for understanding gene regulation. Here, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity across protein families based on protein-DNA structures. The DeepPBS architecture allows investigation of different family-specific recognition patterns. DeepPBS can be applied to predicted structures, and can aid in the modeling of protein-DNA complexes. DeepPBS is interpretable and can be used to calculate protein heavy atom-level importance scores, demonstrated as a case-study on p53-DNA interface. When aggregated at the protein residue level, these scores conform well with alanine scanning mutagenesis experimental data. The inference time for DeepPBS is sufficiently fast for analyzing simulation trajectories, as demonstrated on a molecular-dynamics simulation of a Drosophila Hox-DNA tertiary complex with its cofactor. DeepPBS and its corresponding data resources offer a foundation for machine-aided protein-DNA interaction studies, guiding experimental choices and complex design, as well as advancing our understanding of molecular interactions.
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Nora LC, Cassiano MHA, Santana ÍP, Guazzaroni ME, Silva-Rocha R, da Silva RR. Mining novel cis-regulatory elements from the emergent host Rhodosporidium toruloides using transcriptomic data. Front Microbiol 2023; 13:1069443. [PMID: 36687612 PMCID: PMC9853887 DOI: 10.3389/fmicb.2022.1069443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 12/14/2022] [Indexed: 01/07/2023] Open
Abstract
The demand for robust microbial cell factories that produce valuable biomaterials while resisting stresses imposed by current bioprocesses is rapidly growing. Rhodosporidium toruloides is an emerging host that presents desirable features for bioproduction, since it can grow in a wide range of substrates and tolerate a variety of toxic compounds. To explore R. toruloides suitability for application as a cell factory in biorefineries, we sought to understand the transcriptional responses of this yeast when growing under experimental settings that simulated those used in biofuels-related industries. Thus, we performed RNA sequencing of the oleaginous, carotenogenic yeast in different contexts. The first ones were stress-related: two conditions of high temperature (37 and 42°C) and two ethanol concentrations (2 and 4%), while the other used the inexpensive and abundant sugarcane juice as substrate. Differential expression and functional analysis were implemented using transcriptomic data to select differentially expressed genes and enriched pathways from each set-up. A reproducible bioinformatics workflow was developed for mining new regulatory elements. We then predicted, for the first time in this yeast, binding motifs for several transcription factors, including HAC1, ARG80, RPN4, ADR1, and DAL81. Most putative transcription factors uncovered here were involved in stress responses and found in the yeast genome. Our method for motif discovery provides a new realm of possibilities in studying gene regulatory networks, not only for the emerging host R. toruloides, but for other organisms of biotechnological importance.
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Affiliation(s)
- Luísa Czamanski Nora
- Cell and Molecular Biology Department, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil,*Correspondence: Luísa Czamanski Nora,
| | | | - Ítalo Paulino Santana
- Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - María-Eugenia Guazzaroni
- Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Rafael Silva-Rocha
- Cell and Molecular Biology Department, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Ricardo Roberto da Silva
- Faculty of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil,Ricardo Roberto da Silva,
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