A structural-based strategy for recognition of transcription factor binding sites

Building Transcription Factor Binding Site Models to Understand Gene Regulation in Plants

Transcription factors (TFs) are key cellular components that control gene expression. They recognize specific DNA sequences, the TF binding sites (TFBSs), and thus are targeted to specific regions of the genome where they can recruit transcriptional co-factors and/or chromatin regulators to fine-tune spatiotemporal gene regulation. Therefore, the identification of TFBSs in genomic sequences and their subsequent quantitative modeling is of crucial importance for understanding and predicting gene expression. Here, we review how TFBSs can be determined experimentally, how the TFBS models can be constructed in silico, and how they can be optimized by taking into account features such as position interdependence within TFBSs, DNA shape, and/or by introducing state-of-the-art computational algorithms such as deep learning methods. In addition, we discuss the integration of context variables into the TFBS modeling, including nucleosome positioning, chromatin states, methylation patterns, 3D genome architectures, and TF cooperative binding, in order to better predict TF binding under cellular contexts. Finally, we explore the possibilities of combining the optimized TFBS model with technological advances, such as targeted TFBS perturbation by CRISPR, to better understand gene regulation, evolution, and plant diversity.

Cofunctional Subpathways Were Regulated by Transcription Factor with Common Motif, Common Family, or Common Tissue

Dissecting the characteristics of the transcription factor (TF) regulatory subpathway is helpful for understanding the TF underlying regulatory function in complex biological systems. To gain insight into the influence of TFs on their regulatory subpathways, we constructed a global TF-subpathways network (TSN) to analyze systematically the regulatory effect of common-motif, common-family, or common-tissue TFs on subpathways. We performed cluster analysis to show that the common-motif, common-family, or common-tissue TFs that regulated the same pathway classes tended to cluster together and contribute to the same biological function that led to disease initiation and progression. We analyzed the Jaccard coefficient to show that the functional consistency of subpathways regulated by the TF pairs with common motif, common family, or common tissue was significantly greater than the random TF pairs at the subpathway level, pathway level, and pathway class level. For example, HNF4A (hepatocyte nuclear factor 4, alpha) and NR1I3 (nuclear receptor subfamily 1, group I, member 3) were a pair of TFs with common motif, common family, and common tissue. They were involved in drug metabolism pathways and were liver-specific factors required for physiological transcription. In short, we inferred that the cofunctional subpathways were regulated by common-motif, common-family, or common-tissue TFs.

Structure-based modeling of protein: DNA specificity

Protein:DNA interactions are essential to a range of processes that maintain and express the information encoded in the genome. Structural modeling is an approach that aims to understand these interactions at the physicochemical level. It has been proposed that structural modeling can lead to deeper understanding of the mechanisms of protein:DNA interactions, and that progress in this field can not only help to rationalize the observed specificities of DNA-binding proteins but also to allow researchers to engineer novel DNA site specificities. In this review we discuss recent developments in the structural description of protein:DNA interactions and specificity, as well as the challenges facing the field in the future.

Ancient expansion of the hox cluster in lepidoptera generated four homeobox genes implicated in extra-embryonic tissue formation

Gene duplications within the conserved Hox cluster are rare in animal evolution, but in Lepidoptera an array of divergent Hox-related genes (Shx genes) has been reported between pb and zen. Here, we use genome sequencing of five lepidopteran species (Polygonia c-album, Pararge aegeria, Callimorpha dominula, Cameraria ohridella, Hepialus sylvina) plus a caddisfly outgroup (Glyphotaelius pellucidus) to trace the evolution of the lepidopteran Shx genes. We demonstrate that Shx genes originated by tandem duplication of zen early in the evolution of large clade Ditrysia; Shx are not found in a caddisfly and a member of the basally diverging Hepialidae (swift moths). Four distinct Shx genes were generated early in ditrysian evolution, and were stably retained in all descendent Lepidoptera except the silkmoth which has additional duplications. Despite extensive sequence divergence, molecular modelling indicates that all four Shx genes have the potential to encode stable homeodomains. The four Shx genes have distinct spatiotemporal expression patterns in early development of the Speckled Wood butterfly (Pararge aegeria), with ShxC demarcating the future sites of extraembryonic tissue formation via strikingly localised maternal RNA in the oocyte. All four genes are also expressed in presumptive serosal cells, prior to the onset of zen expression. Lepidopteran Shx genes represent an unusual example of Hox cluster expansion and integration of novel genes into ancient developmental regulatory networks.

COMPARING FOLDING MECHANISMS OF DIFFERENT PRION PROTEINS BY Gō MODEL

Prions are associated with neurodegenerative diseases induced by transmissible spongiform encephalopathies. The infectious scrapie form is referred to as PrP Sc , which has conformational change from normal prion with predominant α-helical conformation to the abnormal PrP Sc that is rich in β-sheet content. Neurodegenerative diseases have been found from both human and bovine sources, but there are no reports about infected by transmissible spongiform encephalopathies from rabbit, canine and horse sources. Here we used coarse-grained Gō model to compare the difference among human, bovine, rabbit, canine, and horse normal (cellular) prion proteins. The denatured state of normal prion has relation with the conversion from normal to abnormal prion protein, so we used all-atom Gō model to investigate the folding pathway and energy landscape for human prion protein. Through using coarse-grained Gō model, the cooperativity of the five prion proteins was characterized in terms of calorimetric criterion, sigmoidal transition, and free-energy profile. The rabbit and horse prion proteins have higher folding free-energy barrier and cooperativity, and canine prion protein has slightly higher folding free-energy barrier comparing with human and bovine prion proteins. The results from all-atom Gō model confirmed the validity of C α-Gō model. The correlations of our results with previous experimental and theoretical researches were discussed.

UNDERSTANDING THE MOLECULAR MECHANISM OF BINDING MODES OF AURORA A INHIBITORS BY LONG TIME SCALE GPU DYNAMICS

Inhibition of Aurora A kinase interaction is considered to be a promising approach for the discovery of new molecularly targeted cancer therapeutics. In this study, the binding mechanisms of two different inhibitors with a contrasting binding affinity to Aurora A were investigated by long time scale GPU molecular dynamics (MD) simulations coupled with molecular mechanics-Poisson–Boltzmann/generalized Born surface area (MM-PB/GBSA) method. The results showed that the predicted binding free energies of these two complexes were consistent with the experimental data. Through analyzing the individual energy components of binding free energy, we found that the van der Waals contribution was the main force to drive the inhibitor–protein binding and the electrostatic contribution was also a crucial factor for the inhibitor–Aurora A binding. The structural analysis demonstrated that the inhibitor HPM could produce more hydrophobic interaction contacts with Aurora A than that of 2JZ, and the loss of key hydrogen bonds between the inhibitor and residue Arg137 in the hinge region of Aurora A was another important reason for the weaker binding affinity of 2JZ to Aurora A. This study sheds more light on the development of the efficient inhibitors targeting the Aurora A.

Variation in one residue associated with the metal ion-dependent adhesion site regulates αIIbβ3 integrin ligand binding affinity

The Asp of the RGD motif of the ligand coordinates with the β I domain metal ion dependent adhesion site (MIDAS) divalent cation, emphasizing the importance of the MIDAS in ligand binding. There appears to be two distinct groups of integrins that differ in their ligand binding affinity and adhesion ability. These differences may be due to a specific residue associated with the MIDAS, particularly the β3 residue Ala²⁵² and corresponding Ala in the β1 integrin compared to the analogous Asp residue in the β2 and β7 integrins. Interestingly, mutations in the adjacent to MIDAS (ADMIDAS) of integrins α4β7 and αLβ2 increased the binding and adhesion abilities compared to the wild-type, while the same mutations in the α2β1, α5β1, αVβ3, and αIIbβ3 integrins demonstrated decreased ligand binding and adhesion. We introduced a mutation in the αIIbβ3 to convert this MIDAS associated Ala²⁵² to Asp. By combination of this mutant with mutations of one or two ADMIDAS residues, we studied the effects of this residue on ligand binding and adhesion. Then, we performed molecular dynamics simulations on the wild-type and mutant αIIbβ3 integrin β I domains, and investigated the dynamics of metal ion binding sites in different integrin-RGD complexes. We found that the tendency of calculated binding free energies was in excellent agreement with the experimental results, suggesting that the variation in this MIDAS associated residue accounts for the differences in ligand binding and adhesion among different integrins, and it accounts for the conflicting results of ADMIDAS mutations within different integrins. This study sheds more light on the role of the MIDAS associated residue pertaining to ligand binding and adhesion and suggests that this residue may play a pivotal role in integrin-mediated cell rolling and firm adhesion.

PiDNA: Predicting protein-DNA interactions with structural models

Predicting binding sites of a transcription factor in the genome is an important, but challenging, issue in studying gene regulation. In the past decade, a large number of protein–DNA co-crystallized structures available in the Protein Data Bank have facilitated the understanding of interacting mechanisms between transcription factors and their binding sites. Recent studies have shown that both physics-based and knowledge-based potential functions can be applied to protein–DNA complex structures to deliver position weight matrices (PWMs) that are consistent with the experimental data. To further use the available structural models, the proposed Web server, PiDNA, aims at first constructing reliable PWMs by applying an atomic-level knowledge-based scoring function on numerous in silico mutated complex structures, and then using the PWM constructed by the structure models with small energy changes to predict the interaction between proteins and DNA sequences. With PiDNA, the users can easily predict the relative preference of all the DNA sequences with limited mutations from the native sequence co-crystallized in the model in a single run. More predictions on sequences with unlimited mutations can be realized by additional requests or file uploading. Three types of information can be downloaded after prediction: (i) the ranked list of mutated sequences, (ii) the PWM constructed by the favourable mutated structures, and (iii) any mutated protein–DNA complex structure models specified by the user. This study first shows that the constructed PWMs are similar to the annotated PWMs collected from databases or literature. Second, the prediction accuracy of PiDNA in detecting relatively high-specificity sites is evaluated by comparing the ranked lists against in vitro experiments from protein-binding microarrays. Finally, PiDNA is shown to be able to select the experimentally validated binding sites from 10 000 random sites with high accuracy. With PiDNA, the users can design biological experiments based on the predicted sequence specificity and/or request mutated structure models for further protein design. As well, it is expected that PiDNA can be incorporated with chromatin immunoprecipitation data to refine large-scale inference of in vivo protein–DNA interactions. PiDNA is available at: http://dna.bime.ntu.edu.tw/pidna.