MoCoLo: a testing framework for motif co-localization.

Sequence-level data offers insights into biological processes through the interaction of two or more genomic features from the same or different molecular data types. Within motifs, this interaction is often explored via the co-occurrence of feature genomic tracks using fixed-segments or analytical tests that respectively require window size determination and risk of false positives from over-simplified models. Moreover, methods for robustly examining the co-localization of genomic features, and thereby understanding their spatial interaction, have been elusive. We present a new analytical method for examining feature interaction by introducing the notion of reciprocal co-occurrence, define statistics to estimate it and hypotheses to test for it. Our approach leverages conditional motif co-occurrence events between features to infer their co-localization. Using reverse conditional probabilities and introducing a novel simulation approach that retains motif properties (e.g. length, guanine-content), our method further accounts for potential confounders in testing. As a proof-of-concept, motif co-localization (MoCoLo) confirmed the co-occurrence of histone markers in a breast cancer cell line. As a novel analysis, MoCoLo identified significant co-localization of oxidative DNA damage within non-B DNA-forming regions that significantly differed between non-B DNA structures. Altogether, these findings demonstrate the potential utility of MoCoLo for testing spatial interactions between genomic features via their co-localization.

Comparative microsomal proteomics of a model lung cancer cell line NCI-H23 reveals distinct differences between molecular profiles of 3D and 2D cultured cells.

Lung cancer is the leading cause of cancer-related deaths in the USA and worldwide. Yet, about 95% of new drug candidates validated in preclinical phase eventually fail in clinical trials. Such a high attrition rate is attributed mostly to the inability of conventional two-dimensionally (2D) cultured cancer cells to mimic native three-dimensional (3D) growth of malignant cells in human tumors. To ascertain phenotypical differences between these two distinct culture conditions, we carried out a comparative proteomic analysis of a membrane fraction obtained from 3D- and 2D-cultured NSCLC model cell line NCI-H23. This analysis revealed a map of 1,166 (24%) protein species regulated in culture dependent manner, including differential regulation of a subset of cell surface-based CD molecules. We confirmed exclusive expression of CD99, CD146 and CD239 in 3D culture. Furthermore, label-free quantitation, targeting KRas proteoform-specific peptides, revealed upregulation of both wild type and monoallelic KRas4BG12C mutant at the surface of 3D cultured cells. In order to reduce the high attrition rate of new drug candidates, the results of this study strongly suggests exploiting base-line molecular profiling of a large number of patient-derived NSCLC cell lines grown in 2D and 3D culture, prior to actual drug candidate testing.

Direct molecular dissection of tumor parenchyma from tumor stroma in tumor xenograft using mass spectrometry-based glycoproteomics.

The most widely used cancer animal model is the human-murine tumor xenograft. Unbiased molecular dissection of tumor parenchyma versus stroma in human-murine xenografts is critical for elucidating dysregulated protein networks/pathways and developing therapeutics that may target these two functionally codependent compartments. Although antibody-reliant technologies (e.g., immunohistochemistry, imaging mass cytometry) are capable of distinguishing tumor-proper versus stromal proteins, the breadth or extent of targets is limited. Here, we report an antibody-free targeted cross-species glycoproteomic (TCSG) approach that enables direct dissection of human tumor parenchyma from murine tumor stroma at the molecular/protein level in tumor xenografts at a selectivity rate presently unattainable by other means. This approach was used to segment/dissect and obtain the protein complement phenotype of the tumor stroma and parenchyma of the metastatic human lung adenocarcinoma A549 xenograft, with no need for tissue microdissection prior to mass-spectrometry analysis. An extensive molecular map of the tumor proper and the associated microenvironment was generated along with the top functional N-glycosylated protein networks enriched in each compartment. Importantly, immunohistochemistry-based cross-validation of selected parenchymal and stromal targets applied on human tissue samples of lung adenocarcinoma and normal adjacent tissue is indicative of a noteworthy translational capacity for this unique approach that may facilitate identifications of novel targets for next generation antibody therapies and development of real time preclinical tumor models.

Somatic Genomics and Clinical Features of Lung Adenocarcinoma: A Retrospective Study.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer and has a high risk of distant metastasis at every disease stage. We aimed to characterize the genomic landscape of LUAD and identify mutation signatures associated with tumor progression. METHODS AND FINDINGS: We performed an integrative genomic analysis, incorporating whole exome sequencing (WES), determination of DNA copy number and DNA methylation, and transcriptome sequencing for 101 LUAD samples from the Environment And Genetics in Lung cancer Etiology (EAGLE) study. We detected driver genes by testing whether the nonsynonymous mutation rate was significantly higher than the background mutation rate and replicated our findings in public datasets with 724 samples. We performed subclonality analysis for mutations based on mutant allele data and copy number alteration data. We also tested the association between mutation signatures and clinical outcomes, including distant metastasis, survival, and tumor grade. We identified and replicated two novel candidate driver genes, POU class 4 homeobox 2 (POU4F2) (mutated in 9 [8.9%] samples) and ZKSCAN1 (mutated in 6 [5.9%] samples), and characterized their major deleterious mutations. ZKSCAN1 was part of a mutually exclusive gene set that included the RTK/RAS/RAF pathway genes BRAF, EGFR, KRAS, MET, and NF1, indicating an important driver role for this gene. Moreover, we observed strong associations between methylation in specific genomic regions and somatic mutation patterns. In the tumor evolution analysis, four driver genes had a significantly lower fraction of subclonal mutations (FSM), including TP53 (p = 0.007), KEAP1 (p = 0.012), STK11 (p = 0.0076), and EGFR (p = 0.0078), suggesting a tumor initiation role for these genes. Subclonal mutations were significantly enriched in APOBEC-related signatures (p < 2.5×10-50). The total number of somatic mutations (p = 0.0039) and the fraction of transitions (p = 5.5×10-4) were associated with increased risk of distant metastasis. Our study's limitations include a small number of LUAD patients for subgroup analyses and a single-sample design for investigation of subclonality. CONCLUSIONS: These data provide a genomic characterization of LUAD pathogenesis and progression. The distinct clonal and subclonal mutation signatures suggest possible diverse carcinogenesis pathways for endogenous and exogenous exposures, and may serve as a foundation for more effective treatments for this lethal disease. LUAD's high heterogeneity emphasizes the need to further study this tumor type and to associate genomic findings with clinical outcomes.

Comparative proteomics of a model MCF10A-KRasG12V cell line reveals a distinct molecular signature of the KRasG12V cell surface.

Oncogenic Ras mutants play a major role in the etiology of most aggressive and deadly carcinomas in humans. In spite of continuous efforts, effective pharmacological treatments targeting oncogenic Ras isoforms have not been developed. Cell-surface proteins represent top therapeutic targets primarily due to their accessibility and susceptibility to different modes of cancer therapy. To expand the treatment options of cancers driven by oncogenic Ras, new targets need to be identified and characterized at the surface of cancer cells expressing oncogenic Ras mutants. Here, we describe a mass spectrometry-based method for molecular profiling of the cell surface using KRasG12V transfected MCF10A (MCF10A-KRasG12V) as a model cell line of constitutively activated KRas and native MCF10A cells transduced with an empty vector (EV) as control. An extensive molecular map of the KRas surface was achieved by applying, in parallel, targeted hydrazide-based cell-surface capturing technology and global shotgun membrane proteomics to identify the proteins on the KRasG12V surface. This method allowed for integrated proteomic analysis that identified more than 500 cell-surface proteins found unique or upregulated on the surface of MCF10A-KRasG12V cells. Multistep bioinformatic processing was employed to elucidate and prioritize targets for cross-validation. Scanning electron microscopy and phenotypic cancer cell assays revealed changes at the cell surface consistent with malignant epithelial-to-mesenchymal transformation secondary to KRasG12V activation. Taken together, this dataset significantly expands the map of the KRasG12V surface and uncovers potential targets involved primarily in cell motility, cellular protrusion formation, and metastasis.

Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene.

Hodgkin lymphoma shows strong familial aggregation but no major susceptibility genes have been identified to date. The goal of this study was to identify high-penetrance variants using whole exome sequencing in 17 Hodgkin lymphoma prone families with three or more affected cases or obligate carriers (69 individuals), followed by targeted sequencing in an additional 48 smaller HL families (80 individuals). Alignment and variant calling were performed using standard methods. Dominantly segregating, rare, coding or potentially functional variants were further prioritized based on predicted deleteriousness, conservation, and potential importance in lymphoid malignancy pathways. We selected 23 genes for targeted sequencing. Only the p.A1065T variant in KDR (kinase insert domain receptor) also known as VEGFR2 (vascular endothelial growth factor receptor 2) was replicated in two independent Hodgkin lymphoma families. KDR is a type III receptor tyrosine kinase, the main mediator of vascular endothelial growth factor induced proliferation, survival, and migration. Its activity is associated with several diseases including lymphoma. Functional experiments have shown that p.A1065T, located in the activation loop, can promote constitutive autophosphorylation on tyrosine in the absence of vascular endothelial growth factor and that the kinase activity was abrogated after exposure to kinase inhibitors. A few other promising mutations were identified but appear to be "private". In conclusion, in the largest sequenced cohort of Hodgkin lymphoma families to date, we identified a causal mutation in the KDR gene. While independent validation is needed, this mutation may increase downstream tumor cell proliferation activity and might be a candidate for targeted therapy.

Clonally expanded CD4+ T cells can produce infectious HIV-1 in vivo.

Reservoirs of infectious HIV-1 persist despite years of combination antiretroviral therapy and make curing HIV-1 infections a major challenge. Most of the proviral DNA resides in CD4(+)T cells. Some of these CD4(+)T cells are clonally expanded; most of the proviruses are defective. It is not known if any of the clonally expanded cells carry replication-competent proviruses. We report that a highly expanded CD4(+) T-cell clone contains an intact provirus. The highly expanded clone produced infectious virus that was detected as persistent plasma viremia during cART in an HIV-1-infected patient who had squamous cell cancer. Cells containing the intact provirus were widely distributed and significantly enriched in cancer metastases. These results show that clonally expanded CD4(+)T cells can be a reservoir of infectious HIV-1.

Whole exome sequencing reveals a C-terminal germline variant in CEBPA-associated acute myeloid leukemia: 45-year follow up of a large family.

Familial acute myeloid leukemia is rare and linked to germline mutations in RUNX1, GATA2 or CCAAT/enhancer binding protein-α (CEBPA). We re-evaluated a large family with acute myeloid leukemia originally seen at NIH in 1969. We used whole exome sequencing to study this family, and conducted in silico bioinformatics analysis, protein structural modeling and laboratory experiments to assess the impact of the identified CEBPA Q311P mutation. Unlike most previously identified germline mutations in CEBPA, which were N-terminal frameshift mutations, we identified a novel Q311P variant that was located in the C-terminal bZip domain of C/EBPα. Protein structural modeling suggested that the Q311P mutation alters the ability of the CEBPA dimer to bind DNA. Electrophoretic mobility shift assays showed that the Q311P mu-tant had attenuated binding to DNA, as predicted by the protein modeling. Consistent with these findings, we found that the Q311P mutation has reduced transactivation, consistent with a loss-of-function mutation. From 45 years of follow up, we observed incomplete penetrance (46%) of CEBPA Q311P. This study of a large multi-generational pedigree reveals that a germline mutation in the C-terminal bZip domain can alter the ability of C/EBP-α to bind DNA and reduces transactivation, leading to acute myeloid leukemia.

LEDGF/p75 interacts with mRNA splicing factors and targets HIV-1 integration to highly spliced genes.

The host chromatin-binding factor LEDGF/p75 interacts with HIV-1 integrase and directs integration to active transcription units. To understand how LEDGF/p75 recognizes transcription units, we sequenced 1 million HIV-1 integration sites isolated from cultured HEK293T cells. Analysis of integration sites showed that cancer genes were preferentially targeted, raising concerns about using lentivirus vectors for gene therapy. Additional analysis led to the discovery that introns and alternative splicing contributed significantly to integration site selection. These correlations were independent of transcription levels, size of transcription units, and length of the introns. Multivariate analysis with five parameters previously found to predict integration sites showed that intron density is the strongest predictor of integration density in transcription units. Analysis of previously published HIV-1 integration site data showed that integration density in transcription units in mouse embryonic fibroblasts also correlated strongly with intron number, and this correlation was absent in cells lacking LEDGF. Affinity purification showed that LEDGF/p75 is associated with a number of splicing factors, and RNA sequencing (RNA-seq) analysis of HEK293T cells lacking LEDGF/p75 or the LEDGF/p75 integrase-binding domain (IBD) showed that LEDGF/p75 contributes to splicing patterns in half of the transcription units that have alternative isoforms. Thus, LEDGF/p75 interacts with splicing factors, contributes to exon choice, and directs HIV-1 integration to transcription units that are highly spliced.

The somatic autosomal mutation matrix in cancer genomes.

DNA damage in somatic cells originates from both environmental and endogenous sources, giving rise to mutations through multiple mechanisms. When these mutations affect the function of critical genes, cancer may ensue. Although identifying genomic subsets of mutated genes may inform therapeutic options, a systematic survey of tumor mutational spectra is required to improve our understanding of the underlying mechanisms of mutagenesis involved in cancer etiology. Recent studies have presented genome-wide sets of somatic mutations as a 96-element vector, a procedure that only captures the immediate neighbors of the mutated nucleotide. Herein, we present a 32 × 12 mutation matrix that captures the nucleotide pattern two nucleotides upstream and downstream of the mutation. A somatic autosomal mutation matrix (SAMM) was constructed from tumor-specific mutations derived from each of 909 individual cancer genomes harboring a total of 10,681,843 single-base substitutions. In addition, mechanistic template mutation matrices (MTMMs) representing oxidative DNA damage, ultraviolet-induced DNA damage, (5m)CpG deamination, and APOBEC-mediated cytosine mutation, are presented. MTMMs were mapped to the individual tumor SAMMs to determine the maximum contribution of each mutational mechanism to the overall mutation pattern. A Manhattan distance across all SAMM elements between any two tumor genomes was used to determine their relative distance. Employing this metric, 89.5% of all tumor genomes were found to have a nearest neighbor from the same tissue of origin. When a distance-dependent 6-nearest neighbor classifier was used, 10.4% of the SAMMs had an Undetermined tissue of origin, and 92.2% of the remaining SAMMs were assigned to the correct tissue of origin. [corrected]. Thus, although tumors from different tissues may have similar mutation patterns, their SAMMs often display signatures that are characteristic of specific tissues.

Can structural features of kinase receptors provide clues on selectivity and inhibition? A molecular modeling study.

Cancer is a complex disease resulting from the uncontrolled proliferation of cell signaling events. Protein kinases have been identified as central molecules that participate overwhelmingly in oncogenic events, thus becoming key targets for anticancer drugs. A majority of studies converged on the idea that ligand-binding pockets of kinases retain clues to the inhibiting abilities and cross-reacting tendencies of inhibitor drugs. Even though these ideas are critical for drug discovery, validating them using experiments is not only difficult, but also in some cases infeasible. To overcome these limitations and to test these ideas at the molecular level, we present here the results of receptor-focused in-silico docking of nine marketed drugs to 19 different wild-type and mutated kinases chosen from a wide range of families. This investigation highlights the need for using relevant models to explain the correct inhibition trends and the results are used to make predictions that might be able to influence future experiments. Our simulation studies are able to correctly predict the primary targets for each drug studied in majority of cases and our results agree with the existing findings. Our study shows that the conformations a given receptor acquires during kinase activation, and their micro-environment, defines the ligand partners. Type II drugs display high compatibility and selectivity for DFG-out kinase conformations. On the other hand Type I drugs are less selective and show binding preferences for both the open and closed forms of selected kinases. Using this receptor-focused approach, it is possible to capture the observed fold change in binding affinities between the wild-type and disease-centric mutations in ABL kinase for Imatinib and the second-generation ABL drugs. The effects of mutation are also investigated for two other systems, EGFR and B-Raf. Finally, by including pathway information in the design it is possible to model kinase inhibitors with potentially fewer side-effects.

Identification of gene signatures used to recognize biological characteristics of gastric cancer upon gene expression data.

High-throughput gene expression microarrays can be examined by machine-learning algorithms to identify gene signatures that recognize the biological characteristics of specific human diseases, including cancer, with high sensitivity and specificity. A previous study compared 20 gastric cancer (GC) samples against 20 normal tissue (NT) samples and identified 1,519 differentially expressed genes (DEGs). In this study, Classification Information Index (CII), Information Gain Index (IGI), and RELIEF algorithms are used to mine the previously reported gene expression profiling data. In all, 29 of these genes are identified by all three algorithms and are treated as GC candidate biomarkers. Three biomarkers, COL1A2, ATP4B, and HADHSC, are selected and further examined using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) staining in two independent sets of GC and normal adjacent tissue (NAT) samples. Our study shows that COL1A2 and HADHSC are the two best biomarkers from the microarray data, distinguishing all GC from the NT, whereas ATP4B is diagnostically significant in lab tests because of its wider range of fold-changes in expression. Herein, a data-mining model applicable for small sample sizes is presented and discussed. Our result suggested that this mining model may be useful in small sample-size studies to identify putative biomarkers and potential biological features of GC.

Guanine holes are prominent targets for mutation in cancer and inherited disease.

Single base substitutions constitute the most frequent type of human gene mutation and are a leading cause of cancer and inherited disease. These alterations occur non-randomly in DNA, being strongly influenced by the local nucleotide sequence context. However, the molecular mechanisms underlying such sequence context-dependent mutagenesis are not fully understood. Using bioinformatics, computational and molecular modeling analyses, we have determined the frequencies of mutation at G • C bp in the context of all 64 5'-NGNN-3' motifs that contain the mutation at the second position. Twenty-four datasets were employed, comprising >530,000 somatic single base substitutions from 21 cancer genomes, >77,000 germline single-base substitutions causing or associated with human inherited disease and 16.7 million benign germline single-nucleotide variants. In several cancer types, the number of mutated motifs correlated both with the free energies of base stacking and the energies required for abstracting an electron from the target guanines (ionization potentials). Similar correlations were also evident for the pathological missense and nonsense germline mutations, but only when the target guanines were located on the non-transcribed DNA strand. Likewise, pathogenic splicing mutations predominantly affected positions in which a purine was located on the non-transcribed DNA strand. Novel candidate driver mutations and tissue-specific mutational patterns were also identified in the cancer datasets. We conclude that electron transfer reactions within the DNA molecule contribute to sequence context-dependent mutagenesis, involving both somatic driver and passenger mutations in cancer, as well as germline alterations causing or associated with inherited disease.

Trypsin-mediated ¹8O/¹6O labeling for biomarker discovery.

Differential (18)O/(16)O stable isotopic labeling that relies on post-digestion (18)O exchange is a simple and efficient method for the relative quantitation of proteins in complex mixtures. This method incorporates two (18)O atoms onto the C-termini of proteolytic peptides resulting in a 4 Da mass-tag difference between (18)O- and (16)O-labeled peptides. This allows for wide-range relative quantitation of proteins in complex mixtures using shotgun proteomics. Because of minimal sample consumption and unrestricted peptide tagging, the post-digestion (18)O exchange is suitable for labeling of low-abundance membrane proteins enriched from cancer cell lines or clinical specimens, including tissues and body fluids. This chapter describes a protocol that applies post-digestion (18)O labeling to elucidate putative endogenous tumor hypoxia markers in the plasma membrane fraction enriched from a hypoxia-adapted malignant melanoma cell line. Plasma membrane proteins from hypoxic and normoxic cells were differentially tagged using (18)O/(16)O stable isotopic labeling. The initial tryptic digestion and solubilization of membrane proteins were carried out in a buffer containing 60 % methanol followed by post-digestion (18)O exchange/labeling in buffered 20 % methanol. The differentially labeled peptides were mixed in a 1:1 ratio and fractionated using off-line strong cation exchange (SCX) liquid chromatography followed by on-line reversed-phase nano-flow RPLC-MS identification and quantitation of peptides/proteins in respective SCX fractions. The present protocol illustrates the utility of (18)O/(16)O stable isotope labeling in the context of quantitative shotgun proteomics that provides a basis for the discovery of hypoxia-induced membrane protein markers in malignant melanoma cell lines.

Genes affected by mouse mammary tumor virus (MMTV) proviral insertions in mouse mammary tumors are deregulated or mutated in primary human mammary tumors.

The accumulation of mutations is a contributing factor in the initiation of premalignant mammary lesions and their progression to malignancy and metastasis. We have used a mouse model in which the carcinogen is the mouse mammary tumor virus (MMTV) which induces clonal premalignant mammary lesions and malignant mammary tumors by insertional mutagenesis. Identification of the genes and signaling pathways affected in MMTV-induced mouse mammary lesions provides a rationale for determining whether genetic alteration of the human orthologues of these genes/pathways may contribute to human breast carcinogenesis. A high-throughput platform for inverse PCR to identify MMTV-host junction fragments and their nucleotide sequences in a large panel of MMTV-induced lesions was developed. Validation of the genes affected by MMTV-insertion was carried out by microarray analysis. Common integration site (CIS) means that the gene was altered by an MMTV proviral insertion in at least two independent lesions arising in different hosts. Three of the new genes identified as CIS for MMTV were assayed for their capability to confer on HC11 mouse mammary epithelial cells the ability for invasion, anchorage independent growth and tumor development in nude mice. Analysis of MMTV induced mammary premalignant hyperplastic outgrowth (HOG) lines and mammary tumors led to the identification of CIS restricted to 35 loci. Within these loci members of the Wnt, Fgf and Rspo gene families plus two linked genes (Npm3 and Ddn) were frequently activated in tumors induced by MMTV. A second group of 15 CIS occur at a low frequency (2-5 observations) in mammary HOGs or tumors. In this latter group the expression of either Phf19 or Sdc2 was shown to increase HC11 cells invasion capability. Foxl1 expression conferred on HC11 cells the capability for anchorage-independent colony formation in soft agar and tumor development in nude mice. The published transcriptome and nucleotide sequence analysis of gene expression in primary human breast tumors was interrogated. Twenty of the human orthologues of MMTV CIS associated genes are deregulated and/or mutated in human breast tumors.

Post-digestion ¹8O exchange/labeling for quantitative shotgun proteomics of membrane proteins.

The role of membrane proteins is critical for regulation of physiologic and pathologic cellular processes. Hence it is not surpassing that membrane proteins make ∼70% of contemporary drug targets. Quantitative profiling of membrane proteins using mass spectrometry (MS)-based proteomics is critical in a quest for disease biomarkers and novel cancer drugs. Post-digestion (18)O exchange is a simple and efficient method for differential (18)O/(16)O stable isotope labeling of two biologically distinct specimens, allowing relative quantitation of proteins in complex mixtures when coupled with shotgun MS-based proteomics. Due to minimal sample consumption and unrestricted peptide tagging, (18)O/(16)O stable isotope labeling is particularly suitable for amount-limited protein specimens typically encountered in membrane and clinical proteomics. This chapter describes a protocol that relies on shotgun proteomics for quantitative profiling of the detergent-insoluble membrane proteins isolated from HeLa cells, differentially transfected with plasmids expressing HIV Gag protein and its myristylation-defective N-terminal mutant. Whilst this protocol depicts solubilization of detergent-insoluble membrane proteins coupled with post-digestion (18)O labeling, it is amenable to any complex membrane protein mixture. Described approach relies on solubilization and tryptic digestion of membrane proteins in a buffer containing 60% (v/v) methanol followed by differential (18)O/(16)O labeling of protein digests in 20% (v/v) methanol buffer. After mixing, the differentially labeled peptides are fractionated using off-line strong cation exchange (SCX) followed by on-line reversed phase nanoflow reversed-phase liquid chromatography (nanoRPLC)-MS identification/quantiation of peptides/proteins. The use of methanol-based buffers in the context of the post-digestion (18)O exchange/labeling eliminates the need for detergents or chaotropes that interfere with LC separations and peptide ionization. Sample losses are minimized because solubilization, digestion, and stable isotope labeling are carried out in a single tube, avoiding any sample transfer or buffer exchange between these steps.

The role of methylation in the intrinsic dynamics of B- and Z-DNA.

Methylation of cytosine at the 5-carbon position (5 mC) is observed in both prokaryotes and eukaryotes. In humans, DNA methylation at CpG sites plays an important role in gene regulation and has been implicated in development, gene silencing, and cancer. In addition, the CpG dinucleotide is a known hot spot for pathologic mutations genome-wide. CpG tracts may adopt left-handed Z-DNA conformations, which have also been implicated in gene regulation and genomic instability. Methylation facilitates this B-Z transition but the underlying mechanism remains unclear. Herein, four structural models of the dinucleotide d(GC)(5) repeat sequence in B-, methylated B-, Z-, and methylated Z-DNA forms were constructed and an aggregate 100 nanoseconds of molecular dynamics simulations in explicit solvent under physiological conditions was performed for each model. Both unmethylated and methylated B-DNA were found to be more flexible than Z-DNA. However, methylation significantly destabilized the BII, relative to the BI, state through the Gp5mC steps. In addition, methylation decreased the free energy difference between B- and Z-DNA. Comparisons of α/γ backbone torsional angles showed that torsional states changed marginally upon methylation for B-DNA, and Z-DNA. Methylation-induced conformational changes and lower energy differences may contribute to the transition to Z-DNA by methylated, over unmethylated, B-DNA and may be a contributing factor to biological function.