Structure-based learning to model complex protein-DNA interactions and transcription-factor co-operativity in cis -regulatory elements
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eLife assessment
This valuable study describes the development of a new structure-based learning approach to predict transcription binding specificity and its application in the modeling of regulatory complexes in cis-regulatory modules. The authors developed a structure-based learning approach to predict TF binding features and model the regulatory complex(es) in cis-regulatory modules, integrating experimental knowledge of structures of TF-DNA complexes and high-throughput TF-DNA interactions. The validation presented by the authors is currently incomplete, with a large variability in the performance of the method on the different TF families tested.
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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 state-of-art 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. Cooperativity is modelled by: i) the co-localization of TFs; and ii) the structural modeling of protein-protein interactions between TFs and with co-factors. As case examples, we apply our approach to automatically model the interferon-β enhanceosome and the pioneering complex of OCT4, SOX2 and SOX11 with a nucleosome, which are compared with the experimentally known structures.
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Author Response
Reviewer #1 (Public Review):
The paper proposes a novel approach, named ModCRE, which utilizes structure-based learning to predict the DNA binding preferences of transcription factors (TFs). The authors integrate both experimental knowledge of the structures of TF-DNA complexes and large amounts of high-throughput TF-DNA interaction data. Additionally, the authors have developed a server that automatically produces these characteristics for other TFs and their complexes with co-factors.
Strengths: The paper's integration of experimental knowledge and highthroughput data to develop statistical knowledge-based potentials to score the binding capability of TFs in cis-regulatory elements is a powerful strategy. The proposed approach can be applied to more than 80% of TF sequences, making it a general method for …
Author Response
Reviewer #1 (Public Review):
The paper proposes a novel approach, named ModCRE, which utilizes structure-based learning to predict the DNA binding preferences of transcription factors (TFs). The authors integrate both experimental knowledge of the structures of TF-DNA complexes and large amounts of high-throughput TF-DNA interaction data. Additionally, the authors have developed a server that automatically produces these characteristics for other TFs and their complexes with co-factors.
Strengths: The paper's integration of experimental knowledge and highthroughput data to develop statistical knowledge-based potentials to score the binding capability of TFs in cis-regulatory elements is a powerful strategy. The proposed approach can be applied to more than 80% of TF sequences, making it a general method for characterizing binding preferences.
Weaknesses: The paper is difficult to follow, as it contains many technical details and implementation details. The method applied is not always clear, and the paper focuses on implementation rather than the message. The results indicate that the nearest neighbors approach in Figure 4 outperforms the proposed method in many cases, and the proposed method seems to perform better only when similarity with the target is low. The same applies in Fig. 5 when using normalized ranked scores.
It appears that the authors have successfully developed a structure-based learning approach for predicting DNA binding preferences of transcription factors. However, the paper's technical language and implementation focus make it challenging to follow at times.
It seems the authors have successfully achieved most of their aims in improving predictions for TF-DNA interaction, and the results support their conclusions.
This work has the potential to significantly impact the field of TF-DNA binding and gene regulation, particularly for those interested in predicting PWMs for TFs with limited or unreliable experimental data.
General comment: We wish to thank the reviewer for his/her comments helping us to facilitate the reading, clarify the ideas and certainly improve the manuscript. We also thank his/her comments on the strengths. In the current revision we have tried to solve the faults and improve the weaknesses. Certainly, the results section contained many explanations of the method and its implementation rather than its use and application. Referred to figures 4 and 5, the reviewer is right too: Our approach can help to predict the binding motif of a transcription factor on difficult cases, when the PWMs of closest homologs are unknown, but the structure of its complex with DNA can be provided. Otherwise, when information of binding is available for close homologs, traditional state-of-the-art approaches are better than our approach and we recommend them.
Reviewer #2 (Public Review):
This work describes the development of a new structure-based learning approach to predict transcription binding specificity and its application in the modeling of regulatory complexes in cis-regulatory modules. The development of accurate computer tools to model protein-DNA complexes and to predict DNA binding specificity is a very relevant research topic with significant impact in many areas.
This article highlights the importance of transcriptional regulatory elements in gene expression regulation and the challenges in understanding their mechanisms. Traditional definitions of activating regulatory elements, such as promoters and enhancers, are becoming unclear, suggesting an updated model based on DNA accessibility and enhancer/promoter potential. Experimental techniques can assess the sequence preferences of transcription factors (TFs) for binding sites. Recent models propose a cooperative model in which regulatory elements work together to increase the local concentrations of TFs, RNA polymerase II, and other co-factors. Co-operative binding can be mediated through protein-protein or DNA interactions. The authors developed a structurebased learning approach to predict TF binding features and model the regulatory complex(es) in cis-regulatory modules, integrating experimental knowledge of structures of TF-DNA complexes and high-throughput TF-DNA interactions. They developed a server to characterize and model the binding specificity of a TF sequence or its structure, which was applied to the examples of interferon-β enhanceosome and the complex of factors SOX11/SOX2 and OCT4 with the nucleosome. The models highlight the co-operativity of TFs and suggest a potential role for nucleosome opening.
The results presented by the authors have a large variability in performance upon the different TF families tested. Therefore, it would be ideal if the performance/accuracy of the method is tested in some simple predictions and validated with prospective experimental data before applying it to model difficult scenarios such as those described here: SOX11/SOX2/OCT4 and nucleosome or interferon beta and enhanceosome. This will give more support to the models generated and thus the validity of the conclusions and hypothesis derived from them.
General comment: We wish to thank the reviewer for his/her comments, we really appreciate them and the opportunity to have new tests with our approach. Some of his/her comments coincide with those of reviewer 1. When this is the case, we will refer to our previous answers and modifications in the manuscript. In this revision we have included new tests to validate the approach using available and published experiments different than the ones used in the original submission. We hope the new information is sufficient to support our approach.
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eLife assessment
This valuable study describes the development of a new structure-based learning approach to predict transcription binding specificity and its application in the modeling of regulatory complexes in cis-regulatory modules. The authors developed a structure-based learning approach to predict TF binding features and model the regulatory complex(es) in cis-regulatory modules, integrating experimental knowledge of structures of TF-DNA complexes and high-throughput TF-DNA interactions. The validation presented by the authors is currently incomplete, with a large variability in the performance of the method on the different TF families tested.
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Reviewer #1 (Public Review):
The paper proposes a novel approach, named ModCRE, which utilizes structure-based learning to predict the DNA binding preferences of transcription factors (TFs). The authors integrate both experimental knowledge of the structures of TF-DNA complexes and large amounts of high-throughput TF-DNA interaction data. Additionally, the authors have developed a server that automatically produces these characteristics for other TFs and their complexes with co-factors.
Strengths: The paper's integration of experimental knowledge and high-throughput data to develop statistical knowledge-based potentials to score the binding capability of TFs in cis-regulatory elements is a powerful strategy. The proposed approach can be applied to more than 80% of TF sequences, making it a general method for characterizing binding …
Reviewer #1 (Public Review):
The paper proposes a novel approach, named ModCRE, which utilizes structure-based learning to predict the DNA binding preferences of transcription factors (TFs). The authors integrate both experimental knowledge of the structures of TF-DNA complexes and large amounts of high-throughput TF-DNA interaction data. Additionally, the authors have developed a server that automatically produces these characteristics for other TFs and their complexes with co-factors.
Strengths: The paper's integration of experimental knowledge and high-throughput data to develop statistical knowledge-based potentials to score the binding capability of TFs in cis-regulatory elements is a powerful strategy. The proposed approach can be applied to more than 80% of TF sequences, making it a general method for characterizing binding preferences.
Weaknesses: The paper is difficult to follow, as it contains many technical details and implementation details. The method applied is not always clear, and the paper focuses on implementation rather than the message. The results indicate that the nearest neighbors approach in Figure 4 outperforms the proposed method in many cases, and the proposed method seems to perform better only when similarity with the target is low. The same applies in Fig. 5 when using normalized ranked scores.
It appears that the authors have successfully developed a structure-based learning approach for predicting DNA binding preferences of transcription factors. However, the paper's technical language and implementation focus make it challenging to follow at times.
It seems the authors have successfully achieved most of their aims in improving predictions for TF-DNA interaction, and the results support their conclusions.
This work has the potential to significantly impact the field of TF-DNA binding and gene regulation, particularly for those interested in predicting PWMs for TFs with limited or unreliable experimental data.
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Reviewer #2 (Public Review):
This work describes the development of a new structure-based learning approach to predict transcription binding specificity and its application in the modeling of regulatory complexes in cis-regulatory modules. The development of accurate computer tools to model protein-DNA complexes and to predict DNA binding specificity is a very relevant research topic with significant impact in many areas.
This article highlights the importance of transcriptional regulatory elements in gene expression regulation and the challenges in understanding their mechanisms. Traditional definitions of activating regulatory elements, such as promoters and enhancers, are becoming unclear, suggesting an updated model based on DNA accessibility and enhancer/promoter potential. Experimental techniques can assess the sequence …
Reviewer #2 (Public Review):
This work describes the development of a new structure-based learning approach to predict transcription binding specificity and its application in the modeling of regulatory complexes in cis-regulatory modules. The development of accurate computer tools to model protein-DNA complexes and to predict DNA binding specificity is a very relevant research topic with significant impact in many areas.
This article highlights the importance of transcriptional regulatory elements in gene expression regulation and the challenges in understanding their mechanisms. Traditional definitions of activating regulatory elements, such as promoters and enhancers, are becoming unclear, suggesting an updated model based on DNA accessibility and enhancer/promoter potential. Experimental techniques can assess the sequence preferences of transcription factors (TFs) for binding sites. Recent models propose a cooperative model in which regulatory elements work together to increase the local concentrations of TFs, RNA polymerase II, and other co-factors. Co-operative binding can be mediated through protein-protein or DNA interactions. The authors developed a structure-based learning approach to predict TF binding features and model the regulatory complex(es) in cis-regulatory modules, integrating experimental knowledge of structures of TF-DNA complexes and high-throughput TF-DNA interactions. They developed a server to characterize and model the binding specificity of a TF sequence or its structure, which was applied to the examples of interferon-β enhanceosome and the complex of factors SOX11/SOX2 and OCT4 with the nucleosome. The models highlight the co-operativity of TFs and suggest a potential role for nucleosome opening.
The results presented by the authors have a large variability in performance upon the different TF families tested. Therefore, it would be ideal if the performance/accuracy of the method is tested in some simple predictions and validated with prospective experimental data before applying it to model difficult scenarios such as those described here: SOX11/SOX2/OCT4 and nucleosome or interferon beta and enhanceosome. This will give more support to the models generated and thus the validity of the conclusions and hypothesis derived from them.
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