Origins of Specificity in Protein-DNA Recognition

Rohs, Remo; Jin, Xiangshu; West, Sean M.; Joshi, Rohit; Honig, Barry; Mann, Richard S.

doi:10.1146/annurev-biochem-060408-091030

Cited by 831 publications

(995 citation statements)

References 233 publications

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“…However, a detailed analysis of the WOPR-dsDNA interaction shows that Arg65 of the R loop inserts into the minor groove and specifically recognizes the first three base pairs of the DNA core motif, and compared with the ideal B-DNA, the width of the minor groove at this binding site is narrowed to about 3.5 Å and the depth is deepened to about 7.4 Å ( Figure 5B). This conforms to a typical local shape readout mechanism in the narrow minor groove [28]. In other words, the CaWor1 WOPR domain uses both the base readout mechanism in the major groove and the local shape readout mechanism in the minor groove to recognize the core motif of the sequence-specific DNA.…”

Section: The Wopr Domain Utilizes Both the Base Readout And The Shapesupporting

confidence: 66%

“…In the protein-DNA complexes, the protein can specifically recognize the DNA via two types of mechanisms: the base readout mechanism involving formation of hydrogen bonds or hydrophobic contacts with the specific bases of DNA primarily in the major groove and the shape readout mechanism involving sequence-dependent deformation of the DNA in the narrow minor groove [28]. In the structure of the WOPR-dsDNA complex, the WOPR domain uses mainly the R loop and the β3-β6 strands of WOPR to interact with the bases of a 6-bp core motif of the dsDNA.…”

Section: The Wopr Domain Utilizes Both the Base Readout And The Shapementioning

confidence: 99%

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Crystal structure of the WOPR-DNA complex and implications for Wor1 function in white-opaque switching of Candida albicans

Zhang

Yan

et al. 2014

Cell Res

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Wor1 (white-opaque switching regulator 1) is a master regulator of the white-opaque switching in Candida albicans, an opportunistic human fungal pathogen, and is associated with its pathogenicity and commensality. Wor1 contains a conserved DNA-binding region at the N-terminus, consisting of two conserved segments (WOPRa and WOPRb) connected by a non-conserved linker that can bind to specific DNA sequences of the promoter regions and then regulates the transcription. Here, we report the crystal structure of the C. albicans Wor1 WOPR segments in complex with a double-stranded DNA corresponding to one promoter region of WOR1. The sequentially separated WOPRa and WOPRb are structurally interwound together to form a compact globular domain that we term the WOPR domain. The WOPR domain represents a new conserved fungal-specific DNA-binding domain which uses primarily a conserved loop to recognize and interact specifically with a conserved 6-bp motif of the DNA in both minor and major grooves. The protein-DNA interactions are essential for WOR1 transcriptional regulation and whiteto-opaque switching. The structural and biological data together reveal the molecular basis for the recognition and binding specificity of the WOPR domain with its specific DNA sequences and the function of Wor1 in the activation of transcription.

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Section: The Wopr Domain Utilizes Both the Base Readout And The Shapesupporting

confidence: 66%

Section: The Wopr Domain Utilizes Both the Base Readout And The Shapementioning

confidence: 99%

Crystal structure of the WOPR-DNA complex and implications for Wor1 function in white-opaque switching of Candida albicans

Zhang

Yan

et al. 2014

Cell Res

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“…The sequence specificities of a protein are most commonly characterized using position weight matrices 1 (PWMs), which are easy to interpret and can be scanned over a genomic sequence to detect potential binding sites. However, growing evidence indicates that sequence specificities can be more accurately captured by more complex techniques [2][3][4][5] . Recently, 'deep learning' has achieved record-breaking performance in a variety of information technology applications 6,7 .…”

Section: A N a Ly S I Smentioning

confidence: 99%

“…Given a random mini-batch of training sequences s (1.N) and corresponding target scores t (1.N) , the performance of the network is improved by approximately minimizing the training objective where || || ⋅ 1 denotes the L1 norm (sum of absolute values), weight decay coefficients β 1 , β 2 ≥ 0, and LOSS(p,t) is a function such as the squared error (p − t) 2 or negative log-likelihood (Supplementary Notes, sec. 1.3).…”

Section: Competing Financial Interestsmentioning

confidence: 99%

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

et al. 2015

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Knowing the sequence specificities of DNA-and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.DNA-and RNA-binding proteins play a central role in gene regulation, including transcription and alternative splicing. The sequence specificities of a protein are most commonly characterized using position weight matrices 1 (PWMs), which are easy to interpret and can be scanned over a genomic sequence to detect potential binding sites. However, growing evidence indicates that sequence specificities can be more accurately captured by more complex techniques 2-5 . Recently, 'deep learning' has achieved record-breaking performance in a variety of information technology applications 6,7 . We adapted deep learning methods to the task of predicting sequence specificities and found that they compete favorably with the state of the art. Our approach, called DeepBind, is based on deep convolutional neural networks and can discover new patterns even when the locations of patterns within sequences are unknown-a task for which traditional neural networks require an exorbitant amount of training data.There are several challenging aspects in learning models of sequence specificity using modern high-throughput technologies. First, the data come in qualitatively different forms. Protein binding microarrays (PBMs) 8 and RNAcompete assays 9 provide a specificity coefficient for each probe sequence, whereas chromatin immunoprecipitation (ChIP)-seq 10 provides a ranked list of putatively bound sequences of varying length, and HT-SELEX 11 generates a set of very high affinity sequences. Second, the quantity of data is large. A typical high-throughput experiment measures between 10,000 and 100,000 sequences, and it is computationally demanding to incorporate them all. Third, each data acquisition technology has its own artifacts, biases and limitations, and we must discover the pertinent specificities despite these unwanted effects. For example, ChIP-seq reads often localize to "hyper-ChIPable" regions of the genome near highly expressed genes 12 .DeepBind (Fig. 1) addresses the above challenges. (i) It can be applied to both microarray and sequencing data; (ii) it can learn from millions of...

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“…2 Proteinnucleic acid interactions are also crucial in biological processes, ranging from replication and transcription to enzymatic events to miRNA machinery. [3][4][5] Drug compounds bind to proteins, regulating their functions so as to acquire beneficial effects to treat diseases.…”

Section: Introductionmentioning

confidence: 99%

G‐LoSA: An efficient computational tool for local structure‐centric biological studies and drug design

Lee

2016

Protein Science

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Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G-LoSA. G-LoSA aligns protein local structures in a sequence order independent way and provides a GA-score, a chemical feature-based and size-independent structure similarity score. Our benchmark validation shows the robust performance of G-LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure-centric comparative biology studies. In particular, G-LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of GLoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer-aided drug design. We hope that G-LoSA can be a useful computational method for exploring interesting biological problems through large-scale comparison of protein local structures and facilitating drug discovery research and development. G-LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/.

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Origins of Specificity in Protein-DNA Recognition

Cited by 831 publications

References 233 publications

Crystal structure of the WOPR-DNA complex and implications for Wor1 function in white-opaque switching of Candida albicans

Crystal structure of the WOPR-DNA complex and implications for Wor1 function in white-opaque switching of Candida albicans

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

G‐LoSA: An efficient computational tool for local structure‐centric biological studies and drug design

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