Dynamic and Structural Modeling of the Specificity in Protein–DNA Interactions Guided by Binding Assay and Structure Data

How transcription factors (TFs) recognize their DNA sequences is often investigated complementarily by high-throughput protein binding assays and by structural biology experiments. The former quantifies the specificity of TF binding sites for numerous DNA sequences, often represented as the position-weight-matrix (PWM). The latter provides mechanistic insights into the interactions via the protein–DNA complex structures. However, these two types of data are not readily integrated. Here, we propose and test a new modeling method that incorporates the PWM with complex structure data. On the basis of pretuned coarse-grained models for proteins and DNAs, we model the specific protein–DNA interactions, PWMcos, in terms of an orientation-dependent potential function, which enables us to perform molecular dynamics simulations at unprecedentedly large scales. We show that the PWMcos model reproduces subtle specificity in the protein–DNA recognition. During the target search in genomic sequences, TF moves on highly rugged landscapes and occasionally flips on DNA depending on the sequence. The TATA-binding protein exhibits two remarkably distinct binding modes, of which frequencies differ between TATA-containing and TATA-less promoters. The PWMcos is general and can be applied to any protein–DNA interactions given their PWMs and complex structure data are available

Dynamic and Structural Modeling of the Specificity in Protein–DNA Interactions Guided by Binding Assay and Structure Data

How transcription factors (TFs) recognize their DNA sequences is often investigated complementarily by high-throughput protein binding assays and by structural biology experiments. The former quantifies the specificity of TF binding sites for numerous DNA sequences, often represented as the position-weight-matrix (PWM). The latter provides mechanistic insights into the interactions via the protein–DNA complex structures. However, these two types of data are not readily integrated. Here, we propose and test a new modeling method that incorporates the PWM with complex structure data. On the basis of pretuned coarse-grained models for proteins and DNAs, we model the specific protein–DNA interactions, PWMcos, in terms of an orientation-dependent potential function, which enables us to perform molecular dynamics simulations at unprecedentedly large scales. We show that the PWMcos model reproduces subtle specificity in the protein–DNA recognition. During the target search in genomic sequences, TF moves on highly rugged landscapes and occasionally flips on DNA depending on the sequence. The TATA-binding protein exhibits two remarkably distinct binding modes, of which frequencies differ between TATA-containing and TATA-less promoters. The PWMcos is general and can be applied to any protein–DNA interactions given their PWMs and complex structure data are available

Localized Frustration and Binding-Induced Conformational Change in Recognition of 5S RNA by TFIIIA Zinc Finger

Protein TFIIIA is composed of nine tandemly arranged Cys₂His₂ zinc fingers. It can bind either to the 5S RNA gene as a transcription factor or to the 5S RNA transcript as a chaperone. Although structural and biochemical data provided valuable information on the recognition between the TFIIIIA and the 5S DNA/RNA, the involved conformational motions and energetic factors contributing to the binding affinity and specificity remain unclear. In this work, we conducted MD simulations and MM/GBSA calculations to investigate the binding-induced conformational changes in the recognition of the 5S RNA by the central three zinc fingers of TFIIIA and the energetic factors that influence the binding affinity and specificity at an atomistic level. Our results revealed drastic interdomain conformational changes between these three zinc fingers, involving the exposure/burial of several crucial DNA/RNA binding residues, which can be related to the competition between DNA and RNA for the binding of TFIIIA. We also showed that the specific recognition between finger 4/finger 6 and the 5S RNA introduces frustrations to the nonspecific interactions between finger 5 and the 5S RNA, which may be important to achieve optimal binding affinity and specificity

Dynamic Coupling among Protein Binding, Sliding, and DNA Bending Revealed by Molecular Dynamics

Protein binding to DNA changes the DNA’s structure, and altered DNA structure can, in turn, modulate the dynamics of protein binding. This mutual dependency is poorly understood. Here we investigated dynamic couplings among protein binding to DNA, protein sliding on DNA, and DNA bending by applying a coarse-grained simulation method to the bacterial architectural protein HU and 14 other DNA-binding proteins. First, we verified our method by showing that the simulated HU exhibits a weak preference for A/T-rich regions of DNA and a much higher affinity for gapped and nicked DNA, consistent with biochemical experiments. The high affinity was attributed to a local DNA bend, but not the specific chemical moiety of the gap/nick. The long-time dynamic analysis revealed that HU sliding is associated with the movement of the local DNA bending site. Deciphering single sliding steps, we found the coupling between HU sliding and DNA bending is akin to neither induced-fit nor population-shift; instead they moved concomitantly. This is reminiscent of a cation transfer on DNA and can be viewed as a protein version of polaron-like sliding. Interestingly, on shorter time scales, HU paused when the DNA was highly bent at the bound position and escaped from pauses once the DNA spontaneously returned to a less bent structure. The HU sliding is largely regulated by DNA bending dynamics. With 14 other proteins, we explored the generality and versatility of the dynamic coupling and found that 6 of the 15 assayed proteins exhibit the polaron-like sliding

Role of Titanium Dioxide Nanoparticles in the Elevated Uptake and Retention of Cadmium and Zinc in <i>Daphnia magna</i>

Titanium dioxide nanoparticles (nano-TiO₂) are now widely applied in consumer products, and the dispersion of nano-TiO₂ may adsorb metals and modify their behavior and bioavailability in the aquatic environment. In the present study, the aqueous uptake, dietary assimilation efficiency (AE), and efflux rate constant (<i>k</i>_e) of two toxic metals (cadmium-Cd, and zinc-Zn) adsorbed on nano-TiO₂ in a freshwater zooplankton <i>Daphnia magna</i> were quantified. The biokinetics was then compared to daphnids that were exposed only to dissolved metals as controls. The aqueous uptake of Cd and Zn involved an initial rapid uptake and then an apparent saturation, and the uptake of metals was accompanied by an ingestion of nano-TiO₂. The AEs of Cd and Zn adsorbed on nano-TiO₂ were 24.6 ± 2.4–44.5 ± 3.7% and 30.4 ± 3.4–51.8 ± 5.0%, respectively, and decreased with increasing concentrations of nano-TiO₂. Furthermore, the difference between the AEs of Cd and Zn indicated that the desorption of Cd and Zn from nano-TiO₂ may have occurred within the gut of daphnids. With the use of algae as carrier, the AEs of Cd and Zn adsorbed on nano-TiO₂ were significantly higher than those of Cd and Zn directly from nano-TiO₂. The efflux rate constants of Cd and Zn adsorbed on nano-TiO₂ in the zooplankton were significantly lower than those of Cd and Zn not adsorbed on nano-TiO₂. Our study shows that the uptake and retention of toxic metals is enhanced when they are adsorbed on nano-TiO₂, and suggests more attention be paid to the potential influences of nano-TiO₂ on the bioavailability and toxicity of other contaminants

Galvanic skin response of the participants at different time points.

<p>Notes: The mean values of different time points (pre-HDBR, HDBR11, HDBR20, HDBR32, HDBR40, and post-HDBR) were 8.89, 0.012, 0.015, 0.015, 3.75 and 4.19µmho.</p

Average genomic similarity measures and distances between the four largest habitats^a.

<p>Average genomic similarity measures and distances between the four largest habitats<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160496#t005fn001" target="_blank">^a</a>.</p

Sequence-dependent nucleosome sliding in rotation-coupled and uncoupled modes revealed by molecular simulations

<div><p>While nucleosome positioning on eukaryotic genome play important roles for genetic regulation, molecular mechanisms of nucleosome positioning and sliding along DNA are not well understood. Here we investigated thermally-activated spontaneous nucleosome sliding mechanisms developing and applying a coarse-grained molecular simulation method that incorporates both long-range electrostatic and short-range hydrogen-bond interactions between histone octamer and DNA. The simulations revealed two distinct sliding modes depending on the nucleosomal DNA sequence. A uniform DNA sequence showed frequent sliding with one base pair step in a rotation-coupled manner, akin to screw-like motions. On the contrary, a strong positioning sequence, the so-called 601 sequence, exhibits rare, abrupt transitions of five and ten base pair steps without rotation. Moreover, we evaluated the importance of hydrogen bond interactions on the sliding mode, finding that strong and weak bonds favor respectively the rotation-coupled and -uncoupled sliding movements.</p></div

Genomic inbreeding coefficients of the 49 pandas including 34 wild pandas sampled from six habitats.

<p><b>A:</b> Genomic inbreeding coefficients of all 49 pandas using the 150K SNP set. <b>B:</b> Average genomic inbreeding coefficients of all 48 pandas with known habitat origin using the 150K SNP set. <b>C:</b> Genomic inbreeding coefficients of all 49 pandas using the 15K SNP set. <b>D:</b> Average genomic inbreeding coefficients of all 48 pandas with known habitat origin using the 15K SNP set. The genomic inbreeding coefficient of each panda was calculated using three methods: <i>f</i>-I, <i>f</i>-IV and <i>f</i>-IVb based on Definitions I, IV and IVb of genomic additive relationships implemented by GVCBLUP [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160496#pone.0160496.ref022" target="_blank">22</a>]. Habitat abbreviations are: DXL = Daxiangling, LS = Liangshan, MIN = Minshan, QIN = Qinling, QIO = Qionglai, XXL = Xiaoxiangling.</p

Changes in the resting heart rate and the heart rate variability of the participants at different test times.

<p>Notes: high-frequency and low-frequency data that are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052160#pone-0052160-t002" target="_blank">table 2</a> were magnified 1000 times relative to the raw data so that they could be observed clearly.</p