Publications by authors named "Rosario I Corona"

15 Publications

  • Page 1 of 1

Reprogramming of the FOXA1 cistrome in treatment-emergent neuroendocrine prostate cancer.

Nat Commun 2021 03 30;12(1):1979. Epub 2021 Mar 30.

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.

Lineage plasticity, the ability of a cell to alter its identity, is an increasingly common mechanism of adaptive resistance to targeted therapy in cancer. An archetypal example is the development of neuroendocrine prostate cancer (NEPC) after treatment of prostate adenocarcinoma (PRAD) with inhibitors of androgen signaling. NEPC is an aggressive variant of prostate cancer that aberrantly expresses genes characteristic of neuroendocrine (NE) tissues and no longer depends on androgens. Here, we investigate the epigenomic basis of this resistance mechanism by profiling histone modifications in NEPC and PRAD patient-derived xenografts (PDXs) using chromatin immunoprecipitation and sequencing (ChIP-seq). We identify a vast network of cis-regulatory elements (N~15,000) that are recurrently activated in NEPC. The FOXA1 transcription factor (TF), which pioneers androgen receptor (AR) chromatin binding in the prostate epithelium, is reprogrammed to NE-specific regulatory elements in NEPC. Despite loss of dependence upon AR, NEPC maintains FOXA1 expression and requires FOXA1 for proliferation and expression of NE lineage-defining genes. Ectopic expression of the NE lineage TFs ASCL1 and NKX2-1 in PRAD cells reprograms FOXA1 to bind to NE regulatory elements and induces enhancer activity as evidenced by histone modifications at these sites. Our data establish the importance of FOXA1 in NEPC and provide a principled approach to identifying cancer dependencies through epigenomic profiling.
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http://dx.doi.org/10.1038/s41467-021-22139-7DOI Listing
March 2021

Transcription factors in epithelial ovarian cancer: histotype-specific drivers and novel therapeutic targets.

Pharmacol Ther 2021 Apr 1;220:107722. Epub 2020 Nov 1.

Women's Cancer Research Program at the Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address:

Transcription factors (TFs) are major contributors to cancer risk and somatic development. In preclinical and clinical studies, direct or indirect inhibition of TF-mediated oncogenic gene expression profiles have proven to be effective in many tumor types, highlighting this group of proteins as valuable therapeutic targets. In spite of this, our understanding of TFs in epithelial ovarian cancer (EOC) is relatively limited. EOC is a heterogeneous disease composed of five major histologic subtypes; high-grade serous, low-grade serous, endometrioid, clear cell and mucinous. Each histology is associated with unique clinical etiologies, sensitivity to therapies, and molecular signatures - including diverse transcriptional regulatory programs. While some TFs are shared across EOC subtypes, a set of TFs are expressed in a histotype-specific manner and likely explain part of the histologic diversity of EOC subtypes. Targeting TFs present with unique opportunities for development of novel precision medicine strategies for ovarian cancer. This article reviews the critical TFs in EOC subtypes and highlights the potential of exploiting TFs as biomarkers and therapeutic targets.
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http://dx.doi.org/10.1016/j.pharmthera.2020.107722DOI Listing
April 2021

Ovarian Cancer Risk Variants Are Enriched in Histotype-Specific Enhancers and Disrupt Transcription Factor Binding Sites.

Am J Hum Genet 2020 10 17;107(4):622-635. Epub 2020 Sep 17.

Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA. Electronic address:

Quantifying the functional effects of complex disease risk variants can provide insights into mechanisms underlying disease biology. Genome-wide association studies have identified 39 regions associated with risk of epithelial ovarian cancer (EOC). The vast majority of these variants lie in the non-coding genome, where they likely function through interaction with gene regulatory elements. In this study we first estimated the heritability explained by known common low penetrance risk alleles for EOC. The narrow sense heritability (h) of EOC overall and high-grade serous ovarian cancer (HGSOCs) were estimated to be 5%-6%. Partitioned SNP heritability across broad functional categories indicated a significant contribution of regulatory elements to EOC heritability. We collated epigenomic profiling data for 77 cell and tissue types from Roadmap Epigenomics and ENCODE, and from H3K27Ac ChIP-seq data generated in 26 ovarian cancer and precursor-related cell and tissue types. We identified significant enrichment of risk single-nucleotide polymorphisms (SNPs) in active regulatory elements marked by H3K27Ac in HGSOCs. To further investigate how risk SNPs in active regulatory elements influence predisposition to ovarian cancer, we used motifbreakR to predict the disruption of transcription factor binding sites. We identified 469 candidate causal risk variants in H3K27Ac peaks that are predicted to significantly break transcription factor (TF) motifs. The most frequently broken motif was REST (p value = 0.0028), which has been reported as both a tumor suppressor and an oncogene. Overall, these systematic functional annotations with epigenomic data improve interpretation of EOC risk variants and shed light on likely cells of origin.
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http://dx.doi.org/10.1016/j.ajhg.2020.08.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536645PMC
October 2020

Prostate cancer reactivates developmental epigenomic programs during metastatic progression.

Nat Genet 2020 08 20;52(8):790-799. Epub 2020 Jul 20.

Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.

Epigenetic processes govern prostate cancer (PCa) biology, as evidenced by the dependency of PCa cells on the androgen receptor (AR), a prostate master transcription factor. We generated 268 epigenomic datasets spanning two state transitions-from normal prostate epithelium to localized PCa to metastases-in specimens derived from human tissue. We discovered that reprogrammed AR sites in metastatic PCa are not created de novo; rather, they are prepopulated by the transcription factors FOXA1 and HOXB13 in normal prostate epithelium. Reprogrammed regulatory elements commissioned in metastatic disease hijack latent developmental programs, accessing sites that are implicated in prostate organogenesis. Analysis of reactivated regulatory elements enabled the identification and functional validation of previously unknown metastasis-specific enhancers at HOXB13, FOXA1 and NKX3-1. Finally, we observed that prostate lineage-specific regulatory elements were strongly associated with PCa risk heritability and somatic mutation density. Examining prostate biology through an epigenomic lens is fundamental for understanding the mechanisms underlying tumor progression.
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http://dx.doi.org/10.1038/s41588-020-0664-8DOI Listing
August 2020

Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer.

Nat Commun 2020 04 24;11(1):2020. Epub 2020 Apr 24.

Cedars-Sinai Women's Cancer Program at the Samuel Oschin Cancer Center, Los Angeles, CA, USA.

The functional consequences of somatic non-coding mutations in ovarian cancer (OC) are unknown. To identify regulatory elements (RE) and genes perturbed by acquired non-coding variants, here we establish epigenomic and transcriptomic landscapes of primary OCs using H3K27ac ChIP-seq and RNA-seq, and then integrate these with whole genome sequencing data from 232 OCs. We identify 25 frequently mutated regulatory elements, including an enhancer at 6p22.1 which associates with differential expression of ZSCAN16 (P = 6.6 × 10-4) and ZSCAN12 (P = 0.02). CRISPR/Cas9 knockout of this enhancer induces downregulation of both genes. Globally, there is an enrichment of single nucleotide variants in active binding sites for TEAD4 (P = 6 × 10-11) and its binding partner PAX8 (P = 2×10-10), a known lineage-specific transcription factor in OC. In addition, the collection of cis REs associated with PAX8 comprise the most frequently mutated set of enhancers in OC (P = 0.003). These data indicate that non-coding somatic mutations disrupt the PAX8 transcriptional network during OC development.
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http://dx.doi.org/10.1038/s41467-020-15951-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7181647PMC
April 2020

A Study of High-Grade Serous Ovarian Cancer Origins Implicates the SOX18 Transcription Factor in Tumor Development.

Cell Rep 2019 12;29(11):3726-3735.e4

Department of Neurosurgery, Henry Ford Hospital, Detroit, MI, USA; Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil. Electronic address:

Fallopian tube secretory epithelial cells (FTSECs) are likely the main precursor cell type of high-grade serous ovarian cancers (HGSOCs), but these tumors may also arise from ovarian surface epithelial cells (OSECs). We profiled global landscapes of gene expression and active chromatin to characterize molecular similarities between OSECs (n = 114), FTSECs (n = 74), and HGSOCs (n = 394). A one-class machine learning algorithm predicts that most HGSOCs derive from FTSECs, with particularly high FTSEC scores in mesenchymal-type HGSOCs (p < 8 × 10). However, a subset of HGSOCs likely derive from OSECs, particularly HGSOCs of the proliferative type (p < 2 × 10), suggesting a dualistic model for HGSOC origins. Super-enhancer (SE) landscapes were also more similar between FTSECs and HGSOCs than between OSECs and HGSOCs (p < 2.2 × 10). The SOX18 transcription factor (TF) coincided with a HGSOC-specific SE, and ectopic overexpression of SOX18 in FTSECs caused epithelial-to-mesenchymal transition, indicating that SOX18 plays a role in establishing the mesenchymal signature of fallopian-derived HGSOCs.
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http://dx.doi.org/10.1016/j.celrep.2019.10.122DOI Listing
December 2019

Super-Enhancer-Associated LncRNA UCA1 Interacts Directly with AMOT to Activate YAP Target Genes in Epithelial Ovarian Cancer.

iScience 2019 Jul 20;17:242-255. Epub 2019 Jun 20.

Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Center for Bioinformatics and Functional Genomics, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address:

Long noncoding RNAs (lncRNAs) have emerged as critical regulators of tumorigenesis, and yet their mechanistic roles remain challenging to characterize. Here, we integrate functional proteomics with lncRNA-interactome profiling to characterize Urothelial Cancer Associated 1 (UCA1), a candidate driver of ovarian cancer development. Reverse phase protein array (RPPA) analysis indicates that UCA1 activates transcription coactivator YAP and its target genes. In vivo RNA antisense purification (iRAP) of UCA1 interacting proteins identified angiomotin (AMOT), a known YAP regulator, as a direct binding partner. Loss-of-function experiments show that AMOT mediates YAP activation by UCA1, as UCA1 enhances the AMOT-YAP interaction to promote YAP dephosphorylation and nuclear translocation. Together, we characterize UCA1 as a lncRNA regulator of Hippo-YAP signaling and highlight the UCA1-AMOT-YAP signaling axis in ovarian cancer development.
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http://dx.doi.org/10.1016/j.isci.2019.06.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6629722PMC
July 2019

Genome-wide association studies identify susceptibility loci for epithelial ovarian cancer in east Asian women.

Gynecol Oncol 2019 05 19;153(2):343-355. Epub 2019 Mar 19.

Cancer Prevention and Control, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Community and Population Health Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Objective: Genome-wide association studies (GWASs) for epithelial ovarian cancer (EOC) have focused largely on populations of European ancestry. We aimed to identify common germline variants associated with EOC risk in Asian women.

Methods: Genotyping was performed as part of the OncoArray project. Samples with >60% Asian ancestry were included in the analysis. Genotyping was performed on 533,631 SNPs in 3238 Asian subjects diagnosed with invasive or borderline EOC and 4083 unaffected controls. After imputation, genotypes were available for 11,595,112 SNPs to identify associations.

Results: At chromosome 6p25.2, SNP rs7748275 was associated with risk of serous EOC (odds ratio [OR] = 1.34, P = 8.7 × 10) and high-grade serous EOC (HGSOC) (OR = 1.34, P = 4.3 × 10). SNP rs6902488 at 6p25.2 (r = 0.97 with rs7748275) lies in an active enhancer and is predicted to impact binding of STAT3, P300 and ELF1. We identified additional risk loci with low Bayesian false discovery probability (BFDP) scores, indicating they are likely to be true risk associations (BFDP <10%). At chromosome 20q11.22, rs74272064 was associated with HGSOC risk (OR = 1.27, P = 9.0 × 10). Overall EOC risk was associated with rs10260419 at chromosome 7p21.3 (OR = 1.33, P = 1.2 × 10) and rs74917072 at chromosome 2q37.3 (OR = 1.25, P = 4.7 × 10). At 2q37.3, expression quantitative trait locus analysis in 404 HGSOC tissues identified ESPNL as a putative candidate susceptibility gene (P = 1.2 × 10).

Conclusion: While some risk loci were shared between East Asian and European populations, others were population-specific, indicating that the landscape of EOC risk in Asian women has both shared and unique features compared to women of European ancestry.
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http://dx.doi.org/10.1016/j.ygyno.2019.02.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6754211PMC
May 2019

An SVM-based method for assessment of transcription factor-DNA complex models.

BMC Bioinformatics 2018 Dec 21;19(Suppl 20):506. Epub 2018 Dec 21.

Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.

Background: Atomic details of protein-DNA complexes can provide insightful information for better understanding of the function and binding specificity of DNA binding proteins. In addition to experimental methods for solving protein-DNA complex structures, protein-DNA docking can be used to predict native or near-native complex models. A docking program typically generates a large number of complex conformations and predicts the complex model(s) based on interaction energies between protein and DNA. However, the prediction accuracy is hampered by current approaches to model assessment, especially when docking simulations fail to produce any near-native models.

Results: We present here a Support Vector Machine (SVM)-based approach for quality assessment of the predicted transcription factor (TF)-DNA complex models. Besides a knowledge-based protein-DNA interaction potential DDNA3, we applied several structural features that have been shown to play important roles in binding specificity between transcription factors and DNA molecules to quality assessment of complex models. To address the issue of unbalanced positive and negative cases in the training dataset, we applied hard-negative mining, an iterative training process that selects an initial training dataset by combining all of the positive cases and a random sample from the negative cases. Results show that the SVM model greatly improves prediction accuracy (84.2%) over two knowledge-based protein-DNA interaction potentials, orientation potential (60.8%) and DDNA3 (68.4%). The improvement is achieved through reducing the number of false positive predictions, especially for the hard docking cases, in which a docking algorithm fails to produce any near-native complex models.

Conclusions: A learning-based SVM scoring model with structural features for specific protein-DNA binding and an atomic-level protein-DNA interaction potential DDNA3 significantly improves prediction accuracy of complex models by successfully identifying cases without near-native structural models.
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http://dx.doi.org/10.1186/s12859-018-2538-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302363PMC
December 2018

The PAX8 cistrome in epithelial ovarian cancer.

Oncotarget 2017 Dec 27;8(65):108316-108332. Epub 2017 Nov 27.

Center for Bioinformatics and Functional Genomics, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.

PAX8 is a lineage-restricted transcription factor that is expressed in epithelial ovarian cancer (EOC) precursor tissues, and in the major EOC histotypes. Frequent overexpression of PAX8 in primary EOCs suggests this factor functions as an oncogene during tumorigenesis, however, the biological role of PAX8 in EOC development is poorly understood. We found that stable knockdown of PAX8 in EOC models significantly reduced cell proliferation and anchorage dependent growth and attenuated tumorigenicity . Chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) and transcriptional profiling were used to create genome-wide maps of PAX8 binding and putative target genes. PAX8 binding sites were significantly enriched in promoter regions ( < 0.05) and superenhancers ( < 0.05). MEME-ChIP analysis revealed that PAX8 binding sites overlapping superenhancers or enhancers, but not promoters, were enriched for JUND/B and ARNT/AHR motifs. Integrating PAX8 ChIP-seq and gene expression data identified PAX8 target genes through their associations within shared topological association domains. Across two EOC models we identified 62 direct regulatory targets based on PAX8 binding in promoters and 1,330 putative enhancer regulatory targets which is involved in oxidation-reduction, was identified as a PAX8 target gene in both cell line models. While the PAX8 cistrome exhibits a high degree of cell-type specificity, analyses of PAX8 target genes and putative cofactors identified common molecular targets and partners as candidate therapeutic targets for EOC.
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http://dx.doi.org/10.18632/oncotarget.22718DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5752447PMC
December 2017

Protein side-chain packing problem: is there still room for improvement?

Brief Bioinform 2017 Nov;18(6):1033-1043

The protein side-chain packing problem (PSCPP) is an important subproblem of both protein structure prediction and protein design. During the past two decades, a large number of methods have been proposed to tackle this problem. These methods consist of three main components: a rotamer library, a scoring function and a search strategy. The average overall accuracy level obtained by these methods is approximately 87%. Whether a better accuracy level could be achieved remains to be answered. To address this question, we calculated the maximum accuracy level attainable using a simple rotamer library, independently of the energy function or the search method. Using 2883 different structures from the Protein Data Bank, we compared this accuracy level with the accuracy level of five state-of-the-art methods. These comparisons indicated that, for buried residues in the protein, we are already close to the best possible accuracy results. In addition, for exposed residues, we found that a significant gap exists between the possible improvement and the maximum accuracy level achievable with current methods. After determining that an improvement is possible, the next step is to understand what limitations are preventing us from obtaining such an improvement. Previous works on protein structure prediction and protein design have shown that scoring function inaccuracies may represent the main obstacle to achieving better results for these problems. To show that the same is true for the PSCPP, we evaluated the quality of two scoring functions used by some state-of-the-art algorithms. Our results indicate that neither of these scoring functions can guide the search method correctly, thereby reinforcing the idea that efforts to solve the PSCPP must also focus on developing better scoring functions.
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http://dx.doi.org/10.1093/bib/bbw079DOI Listing
November 2017

Statistical analysis of structural determinants for protein-DNA-binding specificity.

Proteins 2016 08 15;84(8):1147-61. Epub 2016 Jun 15.

Department of Bioinformatics and Genomics, College of Computing and Informatics, The University of North Carolina at Charlotte, Charlotte, North Carolina, 28223.

DNA-binding proteins play critical roles in biological processes including gene expression, DNA packaging and DNA repair. They bind to DNA target sequences with different degrees of binding specificity, ranging from highly specific (HS) to nonspecific (NS). Alterations of DNA-binding specificity, due to either genetic variation or somatic mutations, can lead to various diseases. In this study, a comparative analysis of protein-DNA complex structures was carried out to investigate the structural features that contribute to binding specificity. Protein-DNA complexes were grouped into three general classes based on degrees of binding specificity: HS, multispecific (MS), and NS. Our results show a clear trend of structural features among the three classes, including amino acid binding propensities, simple and complex hydrogen bonds, major/minor groove and base contacts, and DNA shape. We found that aspartate is enriched in HS DNA binding proteins and predominately binds to a cytosine through a single hydrogen bond or two consecutive cytosines through bidentate hydrogen bonds. Aromatic residues, histidine and tyrosine, are highly enriched in the HS and MS groups and may contribute to specific binding through different mechanisms. To further investigate the role of protein flexibility in specific protein-DNA recognition, we analyzed the conformational changes between the bound and unbound states of DNA-binding proteins and structural variations. The results indicate that HS and MS DNA-binding domains have larger conformational changes upon DNA-binding and larger degree of flexibility in both bound and unbound states. Proteins 2016; 84:1147-1161. © 2016 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/prot.25061DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945413PMC
August 2016

A knowledge-based orientation potential for transcription factor-DNA docking.

Bioinformatics 2013 Feb 5;29(3):322-30. Epub 2012 Dec 5.

Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA.

Motivation: Computational modeling of protein-DNA complexes remains a challenging problem in structural bioinformatics. One of the key factors for a successful protein-DNA docking is a potential function that can accurately discriminate the near-native structures from decoy complexes and at the same time make conformational sampling more efficient. Here, we developed a novel orientation-dependent, knowledge-based, residue-level potential for improving transcription factor (TF)-DNA docking.

Results: We demonstrated the performance of this new potential in TF-DNA binding affinity prediction, discrimination of native protein-DNA complex from decoy structures, and most importantly in rigid TF-DNA docking. The rigid TF-DNA docking with the new orientation potential, on a benchmark of 38 complexes, successfully predicts 42% of the cases with root mean square deviations lower than 1 Å and 55% of the cases with root mean square deviations lower than 3 Å. The results suggest that docking with this new orientation-dependent, coarse-grained statistical potential can achieve high-docking accuracy and can serve as a crucial first step in multi-stage flexible protein-DNA docking.

Availability And Implementation: The new potential is available at http://bioinfozen.uncc.edu/Protein_DNA_orientation_potential.tar.
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http://dx.doi.org/10.1093/bioinformatics/bts699DOI Listing
February 2013

Benchmarks for flexible and rigid transcription factor-DNA docking.

BMC Struct Biol 2011 Nov 1;11:45. Epub 2011 Nov 1.

Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.

Background: Structural insight from transcription factor-DNA (TF-DNA) complexes is of paramount importance to our understanding of the affinity and specificity of TF-DNA interaction, and to the development of structure-based prediction of TF binding sites. Yet the majority of the TF-DNA complexes remain unsolved despite the considerable experimental efforts being made. Computational docking represents a promising alternative to bridge the gap. To facilitate the study of TF-DNA docking, carefully designed benchmarks are needed for performance evaluation and identification of the strengths and weaknesses of docking algorithms.

Results: We constructed two benchmarks for flexible and rigid TF-DNA docking respectively using a unified non-redundant set of 38 test cases. The test cases encompass diverse fold families and are classified into easy and hard groups with respect to the degrees of difficulty in TF-DNA docking. The major parameters used to classify expected docking difficulty in flexible docking are the conformational differences between bound and unbound TFs and the interaction strength between TFs and DNA. For rigid docking in which the starting structure is a bound TF conformation, only interaction strength is considered.

Conclusions: We believe these benchmarks are important for the development of better interaction potentials and TF-DNA docking algorithms, which bears important implications to structure-based prediction of transcription factor binding sites and drug design.
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http://dx.doi.org/10.1186/1472-6807-11-45DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3262759PMC
November 2011