Molecular flexibility in protein–DNA interactions

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.

Local conformational changes in the DNA interfaces of proteins

When a protein binds to DNA, a conformational change is often induced so that the protein will fit into the DNA structure. Therefore, quantitative analyses were conducted to understand the conformational changes in proteins. The results showed that conformational changes in DNA interfaces are more frequent than in non-interfaces, and DNA interfaces have more conformational variations in the DNA-free form. As expected, the former indicates that interaction with DNA has some influence on protein structure. The latter suggests that the intrinsic conformational flexibility of DNA interfaces is important for adjusting their conformation for DNA. The amino acid propensities of the conformationally changed regions in DNA interfaces indicate that hydrophilic residues are preferred over the amino acids that appear in the conformationally unchanged regions. This trend is true for disordered regions, suggesting again that intrinsic flexibility is of importance not only for DNA binding but also for interactions with other molecules. These results demonstrate that fragments destined to be DNA interfaces have an intrinsic flexibility and are composed of amino acids with the capability of binding to DNA. This information suggests that the prediction of DNA binding sites may be improved by the integration of amino acid preference for DNA and one for disordered regions.

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces

Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design.

Dynamic protein-DNA recognition: beyond what can be seen

Traditionally, specific DNA recognition is thought to rely on static contacts with the bases or phosphates. Recent results, however, indicate that residues far outside the binding context can crucially influence selectivity or binding affinity via transient, dynamic interactions with the DNA binding interface. These regions usually do not adopt a well-defined structure, even when bound to DNA, and thus form a fuzzy complex. Here, we propose the existence of a dynamic DNA readout mechanism, wherein distant segments modulate conformational preferences, flexibility or spacing of the DNA binding motifs or serve as competitive partners. Despite their low sequence similarity, these intrinsically disordered regions are often conserved at the structural level, and exploited for regulation of the transcription machinery via protein-protein interactions, post-translational modifications or alternative splicing.

Structural plasticity and distinct drug-binding modes of LfrR, a mycobacterial efflux pump regulator

The TetR-like transcriptional repressor LfrR controls the expression of the gene encoding the Mycobacterium smegmatis efflux pump LfrA, which actively extrudes fluoroquinolones, cationic dyes, and anthracyclines from the cell and promotes intrinsic antibiotic resistance. The crystal structure of the apoprotein form of the repressor reveals a structurally asymmetric homodimer exhibiting local unfolding and a blocked drug-binding site, emphasizing the significant conformational plasticity of the protein necessary for DNA and multidrug recognition. Crystallographic and calorimetric studies of LfrR-drug complexes further confirm the intrinsic flexibility of the homodimer, which provides a dynamic mechanism to broaden multidrug binding specificity and may be a general property of transcriptional repressors regulating microbial efflux pump expression.

Understanding ligand binding effects on the conformation of estrogen receptor alpha-DNA complexes: a combinational quartz crystal microbalance with dissipation and surface plasmon resonance study

Estrogen receptors are ligand-activated transcription factors that regulate gene expression by binding to specific DNA sequences. To date, the effect of ligands on the conformation of estrogen receptor alpha (ERalpha)-DNA complex remains a poorly understood issue. In our study, we are introducing the quartz crystal microbalance with dissipation monitoring (QCM-D) as a new alternative to study the conformational differences in protein-DNA complexes. Specifically, we have used QCM-D, in combination with surface plasmon resonance (SPR) spectroscopy, to monitor the binding of ERalpha to a specific DNA (estrogen response element, ERE) and a nonspecific DNA in the presence of either the agonist ligand, 17b-estradiol, the partial antagonist ligand, 4-hydroxytamoxifen, or vehicle alone. Both with presence and absence of ligand, the specific ERalpha-ERE complexes are observed to adopt a more compact conformation compared to nonspecific complexes. This observation is well correlated to the biophysical changes occurring during protein-DNA interaction shown by past structural and mechanism studies. Notably, pretreatment of ERalpha with E2 and 4OHT affects not only the viscoelasticity and conformation of the protein-DNA complex but also ERalpha binding capacity to immobilized ERE. These results affirm that ligands have remarkable effects on ERalpha-DNA complexes. Understanding these effects will provide insight into how ligand binding promotes subsequent events required for gene transcription.

Design of accurate predictors for DNA-binding sites in proteins using hybrid SVM-PSSM method

In this paper, we investigate the design of accurate predictors for DNA-binding sites in proteins from amino acid sequences. As a result, we propose a hybrid method using support vector machine (SVM) in conjunction with evolutionary information of amino acid sequences in terms of their position-specific scoring matrices (PSSMs) for prediction of DNA-binding sites. Considering the numbers of binding and non-binding residues in proteins are significantly unequal, two additional weights as well as SVM parameters are analyzed and adopted to maximize net prediction (NP, an average of sensitivity and specificity) accuracy. To evaluate the generalization ability of the proposed method SVM-PSSM, a DNA-binding dataset PDC-59 consisting of 59 protein chains with low sequence identity on each other is additionally established. The SVM-based method using the same six-fold cross-validation procedure and PSSM features has NP=80.15% for the training dataset PDNA-62 and NP=69.54% for the test dataset PDC-59, which are much better than the existing neural network-based method by increasing the NP values for training and test accuracies up to 13.45% and 16.53%, respectively. Simulation results reveal that SVM-PSSM performs well in predicting DNA-binding sites of novel proteins from amino acid sequences.

Representation of target-bound drugs by computed conformers: implications for conformational libraries

BACKGROUND

The increasing number of known protein structures provides valuable information about pharmaceutical targets. Drug binding sites are identifiable and suitable lead compounds can be proposed. The flexibility of ligands is a critical point for the selection of potential drugs. Since computed 3D structures of millions of compounds are available, the knowledge of their binding conformations would be a great benefit for the development of efficient screening methods.

RESULTS

Integration of two public databases allowed superposition of conformers for 193 approved drugs with 5507 crystallised target-bound counterparts. The generation of 9600 drug conformers using an atomic force field was carried out to obtain an optimal coverage of the conformational space. Bioactive conformations are best described by a conformational ensemble: half of all drugs exhibit multiple active states, distributed over the entire range of the reachable energy and conformational space.A number of up to 100 conformers per drug enabled us to reproduce the bound states within a similarity threshold of 1.0 angstroms in 70% of all cases. This fraction rises to about 90% for smaller or average sized drugs.

CONCLUSION

Single drugs adopt multiple bioactive conformations if they interact with different target proteins. Due to the structural diversity of binding sites they adopt conformations that are distributed over a broad conformational space and wide energy range. Since the majority of drugs is well represented by a predefined low number of conformers (up to 100) this procedure is a valuable method to compare compounds by three-dimensional features or for fast similarity searches starting with pharmacophores. The underlying 9600 generated drug conformers are downloadable from the Super Drug Web site 1. All superpositions are visualised at the same source. Additional conformers (110,000) of 2400 classified WHO-drugs are also available.