Browse
Submit Data
Databases
API
Help

Dataset Information

38 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Structure-based modeling of protein: DNA specificity.

ABSTRACT: 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.

SUBMITTER: Joyce AP

PROVIDER: S-EPMC4366589 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Structure-based modeling of protein: DNA specificity.

Joyce Adam P AP Zhang Chi C Bradley Philip P Havranek James J JJ

Briefings in functional genomics 20141119 1

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 rese ...[more]

PMID: 25414269

Similar Datasets

Atomistic modeling of protein-DNA interaction specificity: progress and applications.

Project description:An accurate, predictive understanding of protein-DNA binding specificity is crucial for the successful design and engineering of novel protein-DNA binding complexes. In this review, we summarize recent studies that use atomistic representations of interfaces to predict protein-DNA binding specificity computationally. Although methods with limited structural flexibility have proven successful at recapitulating consensus binding sequences from wild-type complex structures, conformational flexibility is likely important for design and template-based modeling, where non-native conformations need to be sampled and accurately scored. A successful application of such computational modeling techniques in the construction of the TAL-DNA complex structure is discussed. With continued improvements in energy functions, solvation models, and conformational sampling, we are optimistic that reliable and large-scale protein-DNA binding prediction and engineering is a goal within reach.

| S-EPMC3425445 | biostudies-literature

Extensive protein and DNA backbone sampling improves structure-based specificity prediction for C2H2 zinc fingers.

Project description:Sequence-specific DNA recognition by gene regulatory proteins is critical for proper cellular functioning. The ability to predict the DNA binding preferences of these regulatory proteins from their amino acid sequence would greatly aid in reconstruction of their regulatory interactions. Structural modeling provides one route to such predictions: by building accurate molecular models of regulatory proteins in complex with candidate binding sites, and estimating their relative binding affinities for these sites using a suitable potential function, it should be possible to construct DNA binding profiles. Here, we present a novel molecular modeling protocol for protein-DNA interfaces that borrows conformational sampling techniques from de novo protein structure prediction to generate a diverse ensemble of structural models from small fragments of related and unrelated protein-DNA complexes. The extensive conformational sampling is coupled with sequence space exploration so that binding preferences for the target protein can be inferred from the resulting optimized DNA sequences. We apply the algorithm to predict binding profiles for a benchmark set of eleven C2H2 zinc finger transcription factors, five of known and six of unknown structure. The predicted profiles are in good agreement with experimental binding data; furthermore, examination of the modeled structures gives insight into observed binding preferences.

| S-EPMC3113574 | biostudies-literature

Template-based structure modeling of protein-protein interactions.

Project description:The structure of protein-protein complexes can be constructed by using the known structure of other protein complexes as a template. The complex structure templates are generally detected either by homology-based sequence alignments or, given the structure of monomer components, by structure-based comparisons. Critical improvements have been made in recent years by utilizing interface recognition and by recombining monomer and complex template libraries. Encouraging progress has also been witnessed in genome-wide applications of template-based modeling, with modeling accuracy comparable to high-throughput experimental data. Nevertheless, bottlenecks exist due to the incompleteness of the protein-protein complex structure library and the lack of methods for distant homologous template identification and full-length complex structure refinement.

| S-EPMC3984454 | biostudies-literature

Identification of a novel class of farnesylation targets by structure-based modeling of binding specificity.

Project description:Farnesylation is an important post-translational modification catalyzed by farnesyltransferase (FTase). Until recently it was believed that a C-terminal CaaX motif is required for farnesylation, but recent experiments have revealed larger substrate diversity. In this study, we propose a general structural modeling scheme to account for peptide binding specificity and recapitulate the experimentally derived selectivity profile of FTase in vitro. In addition to highly accurate recovery of known FTase targets, we also identify a range of novel potential targets in the human genome, including a new substrate class with an acidic C-terminal residue (CxxD/E). In vitro experiments verified farnesylation of 26/29 tested peptides, including both novel human targets, as well as peptides predicted to tightly bind FTase. This study extends the putative range of biological farnesylation substrates. Moreover, it suggests that the ability of a peptide to bind FTase is a main determinant for the farnesylation reaction. Finally, simple adaptation of our approach can contribute to more accurate and complete elucidation of peptide-mediated interactions and modifications in the cell.

| S-EPMC3188499 | biostudies-literature

A feature-based approach to modeling protein-DNA interactions.

Project description:Transcription factor (TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position specific scoring matrix (PSSM), which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. Here, we present feature motif models (FMMs), a novel probabilistic method for modeling TF-DNA interactions, based on log-linear models. Our approach uses sequence features to represent TF binding specificities, where each feature may span multiple positions. We develop the mathematical formulation of our model and devise an algorithm for learning its structural features from binding site data. We also developed a discriminative motif finder, which discovers de novo FMMs that are enriched in target sets of sequences compared to background sets. We evaluate our approach on synthetic data and on the widely used TF chromatin immunoprecipitation (ChIP) dataset of Harbison et al. We then apply our algorithm to high-throughput TF ChIP data from mouse and human, reveal sequence features that are present in the binding specificities of mouse and human TFs, and show that FMMs explain TF binding significantly better than PSSMs. Our FMM learning and motif finder software are available at http://genie.weizmann.ac.il/.

| S-EPMC2516605 | biostudies-literature

Assessment of template-based modeling of protein structure in CASP11.

Project description:We present the assessment of predictions submitted in the template-based modeling (TBM) category of CASP11 (Critical Assessment of Protein Structure Prediction). Model quality was judged on the basis of global and local measures of accuracy on all atoms including side chains. The top groups on 39 human-server targets based on model 1 predictions were LEER, Zhang, LEE, MULTICOM, and Zhang-Server. The top groups on 81 targets by server groups based on model 1 predictions were Zhang-Server, nns, BAKER-ROSETTASERVER, QUARK, and myprotein-me. In CASP11, the best models for most targets were equal to or better than the best template available in the Protein Data Bank, even for targets with poor templates. The overall performance in CASP11 is similar to the performance of predictors in CASP10 with slightly better performance on the hardest targets. For most targets, assessment measures exhibited bimodal probability density distributions. Multi-dimensional scaling of an RMSD matrix for each target typically revealed a single cluster with models similar to the target structure, with a mode in the GDT-TS density between 40 and 90, and a wide distribution of models highly divergent from each other and from the experimental structure, with density mode at a GDT-TS value of ∼20. The models in this peak in the density were either compact models with entirely the wrong fold, or highly non-compact models. The results argue for a density-driven approach in future CASP TBM assessments that accounts for the bimodal nature of these distributions instead of Z scores, which assume a unimodal, Gaussian distribution. Proteins 2016; 84(Suppl 1):200-220. © 2016 Wiley Periodicals, Inc.

| S-EPMC5030193 | biostudies-literature

Sequence and Structure-Based Analysis of Specificity Determinants in Eukaryotic Protein Kinases.

Project description:Protein kinases lie at the heart of cell-signaling processes and are often mutated in disease. Kinase target recognition at the active site is in part determined by a few amino acids around the phosphoacceptor residue. However, relatively little is known about how most preferences are encoded in the kinase sequence or how these preferences evolved. Here, we used alignment-based approaches to predict 30 specificity-determining residues (SDRs) for 16 preferences. These were studied with structural models and were validated by activity assays of mutant kinases. Cancer mutation data revealed that kinase SDRs are mutated more frequently than catalytic residues. We have observed that, throughout evolution, kinase specificity has been strongly conserved across orthologs but can diverge after gene duplication, as illustrated by the G protein-coupled receptor kinase family. The identified SDRs can be used to predict kinase specificity from sequence and aid in the interpretation of evolutionary or disease-related genomic variants.

| S-EPMC7809594 | biostudies-literature

DNA structure specificity of Rap endonuclease.

Project description:The Rap protein of phage lambda is an endonuclease that nicks branched DNA structures. It has been proposed that Rap can nick D-loops formed during phage recombination to generate splice products without the need for the formation of a 4-strand (Holliday) junction. The structure specificity of Rap was investigated using a variety of branched DNA molecules made by annealing partially complementary oligo-nucleotides. On Holliday junctions, Rap endonuclease shows a requirement for magnesium or manganese ions, with Mn(2+)supporting 5-fold more cleavage than Mg(2+). The location of endonuclease incisions was determined on 3'-tailed D-loop, bubble, flayed duplex, 5'-flap and Y junction DNA substrates. In all cases, Rap preferentially cleaves at the branch point of these molecules. With a flayed duplex, incisions are made in the duplex adjacent to the single-strand arms. Comparison of binding and cleavage specificities revealed that Rap is highly structure-specific and exhibits a clear preference for 4- and 3-stranded DNA over Y and flayed duplex DNA. Almost no binding or cleavage was detected with duplex, partial duplex and single-stranded DNA. Thus Rap endonuclease shows a bias for structures that resemble D-loop and Holliday junction recombination intermediates.

| S-EPMC148684 | biostudies-other

Engineering altered protein-DNA recognition specificity.

Project description:Protein engineering is used to generate novel protein folds and assemblages, to impart new properties and functions onto existing proteins, and to enhance our understanding of principles that govern protein structure. While such approaches can be employed to reprogram protein-protein interactions, modifying protein-DNA interactions is more difficult. This may be related to the structural features of protein-DNA interfaces, which display more charged groups, directional hydrogen bonds, ordered solvent molecules and counterions than comparable protein interfaces. Nevertheless, progress has been made in the redesign of protein-DNA specificity, much of it driven by the development of engineered enzymes for genome modification. Here, we summarize the creation of novel DNA specificities for zinc finger proteins, meganucleases, TAL effectors, recombinases and restriction endonucleases. The ease of re-engineering each system is related both to the modularity of the protein and the extent to which the proteins have evolved to be capable of readily modifying their recognition specificities in response to natural selection. The development of engineered DNA binding proteins that display an ideal combination of activity, specificity, deliverability, and outcomes is not a fully solved problem, however each of the current platforms offers unique advantages, offset by behaviors and properties requiring further study and development.

| S-EPMC6007267 | biostudies-literature

Physics-Based Potentials for Coarse-Grained Modeling of Protein-DNA Interactions.

Project description:Physics-based potentials have been developed for the interactions between proteins and DNA for simulations with the UNRES + NARES-2P force field. The mean-field interactions between a protein and a DNA molecule can be divided into eight categories: (1) nonpolar side chain–DNA base, (2) polar uncharged side chain–DNA base, (3) charged side chain–DNA base, (4) peptide group–phosphate group, (5) peptide group–DNA base, (6) nonpolar side chain–phosphate group, (7) polar uncharged side chain–phosphate group, and (8) charged side chain–phosphate group. Umbrella-sampling molecular dynamics simulations in explicit TIP3P water using the AMBER force field were carried out to determine the potentials of mean force (PMF) for all 105 pairs of interacting components. Approximate analytical expressions for the mean-field interaction energy of each pair of the different kinds of interacting molecules were then fitted to the PMFs to obtain the parameters of the analytical expressions. These analytical expressions can reproduce satisfactorily the PMF curves corresponding to different orientations of the interacting molecules. The results suggest that the physics-based mean-field potentials of amino acid–nucleotide interactions presented here can be used in coarse-grained simulation of protein–DNA interactions.

| S-EPMC4455907 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data