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Spirov AV, Myasnikova EM. Heuristic algorithms in evolutionary computation and modular organization of biological macromolecules: Applications to in vitro evolution. PLoS One 2022; 17:e0260497. [PMID: 35085255 PMCID: PMC8794168 DOI: 10.1371/journal.pone.0260497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 11/10/2021] [Indexed: 11/19/2022] Open
Abstract
Evolutionary computing (EC) is an area of computer sciences and applied mathematics covering heuristic optimization algorithms inspired by evolution in Nature. EC extensively study all the variety of methods which were originally based on the principles of selectionism. As a result, many new algorithms and approaches, significantly more efficient than classical selectionist schemes, were found. This is especially true for some families of special problems. There are strong arguments to believe that EC approaches are quite suitable for modeling and numerical analysis of those methods of synthetic biology and biotechnology that are known as in vitro evolution. Therefore, it is natural to expect that the new algorithms and approaches developed in EC can be effectively applied in experiments on the directed evolution of biological macromolecules. According to the John Holland's Schema theorem, the effective evolutionary search in genetic algorithms (GA) is provided by identifying short schemata of high fitness which in the further search recombine into the larger building blocks (BBs) with higher and higher fitness. The multimodularity of functional biological macromolecules and the preservation of already found modules in the evolutionary search have a clear analogy with the BBs in EC. It seems reasonable to try to transfer and introduce the methods of EC, preserving BBs and essentially accelerating the search, into experiments on in vitro evolution. We extend the key instrument of the Holland's theory, the Royal Roads fitness function, to problems of the in vitro evolution (Biological Royal Staircase, BioRS, functions). The specific version of BioRS developed in this publication arises from the realities of experimental evolutionary search for (DNA-) RNA-devices (aptazymes). Our numerical tests showed that for problems with the BioRS functions, simple heuristic algorithms, which turned out to be very effective for preserving BBs in GA, can be very effective in in vitro evolution approaches. We are convinced that such algorithms can be implemented in modern methods of in vitro evolution to achieve significant savings in time and resources and a significant increase in the efficiency of evolutionary search.
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Affiliation(s)
- Alexander V. Spirov
- I. M. Sechenov Institute of Evolutionary Physiology and Biochemistry Russian Academy of Sciences, St. Petersburg, Russia
- The Institute of Scientific Information for Social Sciences RAS, Moscow, Russia
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Zhang L, Martini GD, Rube HT, Kribelbauer JF, Rastogi C, FitzPatrick VD, Houtman JC, Bussemaker HJ, Pufall MA. SelexGLM differentiates androgen and glucocorticoid receptor DNA-binding preference over an extended binding site. Genome Res 2017; 28:111-121. [PMID: 29196557 PMCID: PMC5749176 DOI: 10.1101/gr.222844.117] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 11/22/2017] [Indexed: 11/28/2022]
Abstract
The DNA-binding interfaces of the androgen (AR) and glucocorticoid (GR) receptors are virtually identical, yet these transcription factors share only about a third of their genomic binding sites and regulate similarly distinct sets of target genes. To address this paradox, we determined the intrinsic specificities of the AR and GR DNA-binding domains using a refined version of SELEX-seq. We developed an algorithm, SelexGLM, that quantifies binding specificity over a large (31-bp) binding site by iteratively fitting a feature-based generalized linear model to SELEX probe counts. This analysis revealed that the DNA-binding preferences of AR and GR homodimers differ significantly, both within and outside the 15-bp core binding site. The relative preference between the two factors can be tuned over a wide range by changing the DNA sequence, with AR more sensitive to sequence changes than GR. The specificity of AR extends to the regions flanking the core 15-bp site, where isothermal calorimetry measurements reveal that affinity is augmented by enthalpy-driven readout of poly(A) sequences associated with narrowed minor groove width. We conclude that the increased specificity of AR is correlated with more enthalpy-driven binding than GR. The binding models help explain differences in AR and GR genomic binding and provide a biophysical rationale for how promiscuous binding by GR allows functional substitution for AR in some castration-resistant prostate cancers.
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Affiliation(s)
- Liyang Zhang
- Department of Biochemistry, Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242, USA
| | - Gabriella D Martini
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA.,Department of Systems Biology, Columbia University Medical Center, New York, New York 10032, USA
| | - H Tomas Rube
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA.,Department of Systems Biology, Columbia University Medical Center, New York, New York 10032, USA
| | - Judith F Kribelbauer
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA.,Department of Systems Biology, Columbia University Medical Center, New York, New York 10032, USA
| | - Chaitanya Rastogi
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA.,Department of Systems Biology, Columbia University Medical Center, New York, New York 10032, USA
| | - Vincent D FitzPatrick
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA.,Department of Systems Biology, Columbia University Medical Center, New York, New York 10032, USA
| | - Jon C Houtman
- Department of Immunology, Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242, USA
| | - Harmen J Bussemaker
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA.,Department of Systems Biology, Columbia University Medical Center, New York, New York 10032, USA
| | - Miles A Pufall
- Department of Biochemistry, Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242, USA
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Brand LH, Henneges C, Schüssler A, Kolukisaoglu HÜ, Koch G, Wallmeroth N, Hecker A, Thurow K, Zell A, Harter K, Wanke D. Screening for protein-DNA interactions by automatable DNA-protein interaction ELISA. PLoS One 2013; 8:e75177. [PMID: 24146751 PMCID: PMC3795721 DOI: 10.1371/journal.pone.0075177] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Accepted: 08/12/2013] [Indexed: 12/22/2022] Open
Abstract
DNA-binding proteins (DBPs), such as transcription factors, constitute about 10% of the protein-coding genes in eukaryotic genomes and play pivotal roles in the regulation of chromatin structure and gene expression by binding to short stretches of DNA. Despite their number and importance, only for a minor portion of DBPs the binding sequence had been disclosed. Methods that allow the de novo identification of DNA-binding motifs of known DBPs, such as protein binding microarray technology or SELEX, are not yet suited for high-throughput and automation. To close this gap, we report an automatable DNA-protein-interaction (DPI)-ELISA screen of an optimized double-stranded DNA (dsDNA) probe library that allows the high-throughput identification of hexanucleotide DNA-binding motifs. In contrast to other methods, this DPI-ELISA screen can be performed manually or with standard laboratory automation. Furthermore, output evaluation does not require extensive computational analysis to derive a binding consensus. We could show that the DPI-ELISA screen disclosed the full spectrum of binding preferences for a given DBP. As an example, AtWRKY11 was used to demonstrate that the automated DPI-ELISA screen revealed the entire range of in vitro binding preferences. In addition, protein extracts of AtbZIP63 and the DNA-binding domain of AtWRKY33 were analyzed, which led to a refinement of their known DNA-binding consensi. Finally, we performed a DPI-ELISA screen to disclose the DNA-binding consensus of a yet uncharacterized putative DBP, AtTIFY1. A palindromic TGATCA-consensus was uncovered and we could show that the GATC-core is compulsory for AtTIFY1 binding. This specific interaction between AtTIFY1 and its DNA-binding motif was confirmed by in vivo plant one-hybrid assays in protoplasts. Thus, the value and applicability of the DPI-ELISA screen for de novo binding site identification of DBPs, also under automatized conditions, is a promising approach for a deeper understanding of gene regulation in any organism of choice.
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Affiliation(s)
- Luise H. Brand
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
| | - Carsten Henneges
- Cognitive Systems, Center for Bioinformatics, University of Tuebingen, Tuebingen, Germany
| | - Axel Schüssler
- Cognitive Systems, Center for Bioinformatics, University of Tuebingen, Tuebingen, Germany
| | - H. Üner Kolukisaoglu
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
- Center for Life Science Automation, Rostock, Germany
| | - Grit Koch
- Center for Life Science Automation, Rostock, Germany
| | - Niklas Wallmeroth
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
| | - Andreas Hecker
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
| | | | - Andreas Zell
- Cognitive Systems, Center for Bioinformatics, University of Tuebingen, Tuebingen, Germany
| | - Klaus Harter
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
| | - Dierk Wanke
- Plant Physiology, Center for Plant Molecular Biology, University of Tuebingen, Tuebingen, Germany
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