Conformational changes in DNA‐binding proteins: Relationships with precomplex features and contributions to specificity and stability

Both Proteins and DNA undergo conformational changes in order to form functional complexes and also to facilitate interactions with other molecules. These changes have direct implications for the stability and specificity of the complex, as well as the cooperativity of interactions between multiple entities. In this work, we have extensively analyzed conformational changes in DNA‐binding proteins by superimposing DNA‐bound and unbound pairs of protein structures in a curated database of 90 proteins. We manually examined each of these pairs, unified the authors' annotations, and summarized our observations by classifying conformational changes into six structural categories. We explored a relationship between conformational changes and functional classes, binding motifs, target specificity, biophysical features of unbound proteins, and stability of the complex. In addition, we have also investigated the degree to which the intrinsic flexibility can explain conformational changes in a subset of 52 proteins with high quality coordinate data. Our results indicate that conformational changes in DNA‐binding proteins contribute significantly to both the stability of the complex and the specificity of targets recognized by them. We also conclude that most conformational changes occur in proteins interacting with specific DNA targets, even though unbound protein structures may have sufficient information to interact with DNA in a nonspecific manner. Proteins 2014; 82:841–857. © 2013 Wiley Periodicals, Inc.     

Surface shapes and surrounding environment analysis of single‐ and double‐stranded DNA‐binding proteins in protein‐DNA interface

Protein‐DNA bindings are critical to many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. Here, we analyzed the residues shape (peak, flat, or valley) and the surrounding environment of double‐stranded DNA‐binding proteins (DSBs) and single‐stranded DNA‐binding proteins (SSBs) in protein‐DNA interfaces. In the results, we found that the interface shapes, hydrogen bonds, and the surrounding environment present significant differences between the two kinds of proteins. Built on the investigation results, we constructed a random forest (RF) classifier to distinguish DSBs and SSBs with satisfying performance. In conclusion, we present a novel methodology to characterize protein interfaces, which will deepen our understanding of the specificity of proteins binding to ssDNA (single‐stranded DNA) or dsDNA (double‐stranded DNA). Proteins 2016; 84:979–989. © 2016 Wiley Periodicals, Inc.     

DNABind: A hybrid algorithm for structure‐based prediction of DNA‐binding residues by combining machine learning‐ and template‐based approaches

Accurate prediction of DNA‐binding residues has become a problem of increasing importance in structural bioinformatics. Here, we presented DNABind, a novel hybrid algorithm for identifying these crucial residues by exploiting the complementarity between machine learning‐ and template‐based methods. Our machine learning‐based method was based on the probabilistic combination of a structure‐based and a sequence‐based predictor, both of which were implemented using support vector machines algorithms. The former included our well‐designed structural features, such as solvent accessibility, local geometry, topological features, and relative positions, which can effectively quantify the difference between DNA‐binding and nonbinding residues. The latter combined evolutionary conservation features with three other sequence attributes. Our template‐based method depended on structural alignment and utilized the template structure from known protein–DNA complexes to infer DNA‐binding residues. We showed that the template method had excellent performance when reliable templates were found for the query proteins but tended to be strongly influenced by the template quality as well as the conformational changes upon DNA binding. In contrast, the machine learning approach yielded better performance when high‐quality templates were not available (about 1/3 cases in our dataset) or the query protein was subject to intensive transformation changes upon DNA binding. Our extensive experiments indicated that the hybrid approach can distinctly improve the performance of the individual methods for both bound and unbound structures. DNABind also significantly outperformed the state‐of‐art algorithms by around 10% in terms of Matthews's correlation coefficient. The proposed methodology could also have wide application in various protein functional site annotations. DNABind is freely available at http://mleg.cse.sc.edu/DNABind/ . Proteins 2013; 81:1885–1899. © 2013 Wiley Periodicals, Inc.     

Switches of hydrogen bonds during ligand–protein association processes determine binding kinetics

Revealing the processes of ligand–protein associations deepens our understanding of molecular recognition and binding kinetics. Hydrogen bonds (H‐bonds) play a crucial role in optimizing ligand–protein interactions and ligand specificity. In addition to the formation of stable H‐bonds in the final bound state, the formation of transient H‐bonds during binding processes contributes binding kinetics that define a ligand as a fast or slow binder, which also affects drug action. However, the effect of forming the transient H‐bonds on the kinetic properties is little understood. Guided by results from coarse‐grained Brownian dynamics simulations, we used classical molecular dynamics simulations in an implicit solvent model and accelerated molecular dynamics simulations in explicit waters to show that the position and distribution of the H‐bond donor or acceptor of a drug result in switching intermolecular and intramolecular H‐bond pairs during ligand recognition processes. We studied two major types of HIV‐1 protease ligands: a fast binder, xk263, and a slow binder, ritonavir. The slow association rate in ritonavir can be attributed to increased flexibility of ritonavir, which yields multistep transitions and stepwise entering patterns and the formation and breaking of complex H‐bond pairs during the binding process. This model suggests the importance of conversions of spatiotemporal H‐bonds during the association of ligands and proteins, which helps in designing inhibitors with preferred binding kinetics. Copyright © 2014 John Wiley & Sons, Ltd.     

RBRDetector: Improved prediction of binding residues on RNA‐binding protein structures using complementary feature‐ and template‐based strategies

Computational prediction of RNA‐binding residues is helpful in uncovering the mechanisms underlying protein‐RNA interactions. Traditional algorithms individually applied feature‐ or template‐based prediction strategy to recognize these crucial residues, which could restrict their predictive power. To improve RNA‐binding residue prediction, herein we propose the first integrative algorithm termed RBRDetector (RNA‐Binding Residue Detector) by combining these two strategies. We developed a feature‐based approach that is an ensemble learning predictor comprising multiple structure‐based classifiers, in which well‐defined evolutionary and structural features in conjunction with sequential or structural microenvironment were used as the inputs of support vector machines. Meanwhile, we constructed a template‐based predictor to recognize the putative RNA‐binding regions by structurally aligning the query protein to the RNA‐binding proteins with known structures. The final RBRDetector algorithm is an ingenious fusion of our feature‐ and template‐based approaches based on a piecewise function. By validating our predictors with diverse types of structural data, including bound and unbound structures, native and simulated structures, and protein structures binding to different RNA functional groups, we consistently demonstrated that RBRDetector not only had clear advantages over its component methods, but also significantly outperformed the current state‐of‐the‐art algorithms. Nevertheless, the major limitation of our algorithm is that it performed relatively well on DNA‐binding proteins and thus incorrectly predicted the DNA‐binding regions as RNA‐binding interfaces. Finally, we implemented the RBRDetector algorithm as a user‐friendly web server, which is freely accessible at http://ibi.hzau.edu.cn/rbrdetector . Proteins 2014; 82:2455–2471. © 2014 Wiley Periodicals, Inc.     

HDX‐MS reveals orthosteric and allosteric changes in apolipoprotein‐D structural dynamics upon binding of progesterone

Apolipoprotein‐D is a glycosylated tetrameric lipocalin that binds and transports small hydrophobic molecules such as progesterone and arachidonic acid. Like other lipocalins, apolipoprotein‐D adopts an eight‐stranded β‐barrel fold stabilized by two intramolecular disulphide bonds, with an adjacent α‐helix. Crystallography studies of recombinant apolipoprotein‐D demonstrated no major conformational changes upon progesterone binding. Amide hydrogen‐deuterium exchange mass spectrometry (HDX‐MS) reports structural changes of proteins in solution by monitoring exchange of amide hydrogens in the protein backbone with deuterium. HDX‐MS detects changes in conformation and structural dynamics in response to protein function such as ligand binding that may go undetected in X‐ray crystallography, making HDX‐MS an invaluable orthogonal technique. Here, we report an HDX‐MS protocol for apolipoprotein‐D that solved challenges of high protein rigidity and low pepsin cleavage using rigorous quenching conditions and longer deuteration times, yielding 85% sequence coverage and 50% deuterium exchange. The relative fractional deuterium exchange of ligand‐free apolipoprotein‐D revealed apolipoprotein‐D to be a highly structured protein. Progesterone binding was detected by significant reduction in deuterium exchange in eight peptides. Stabilization of apolipoprotein‐D dynamics can be interpreted as a combined orthosteric effect in the ligand binding pocket and allosteric effect at the N‐terminus and C‐terminus. Together, our experiments provide insight into apolipoprotein‐D structural dynamics and map the effects of progesterone binding that are relayed to distal parts of the protein. The observed stabilization of apolipoprotein‐D dynamics upon progesterone binding demonstrates a common behaviour in the lipocalin family and may have implications for interactions of apolipoprotein‐D with receptors or lipoprotein particles. Statement: We reveal for the first time how apolipoprotein‐D, which is protective in Alzheimer's disease, becomes more ordered when bound to a molecule of steroid hormone. These results significantly extend the understanding of apolipoprotein‐D structure from X‐ray crystallography studies by incorporating information on how protein motion changes over time. To achieve these results an improved protocol was developed, suitable for proteins similar to apolipoprotein‐D, to elucidate how proteins change flexibility when binding to small molecules.     

Monte Carlo replica‐exchange based ensemble docking of protein conformations

A replica‐exchange Monte Carlo (REMC) ensemble docking approach has been developed that allows efficient exploration of protein–protein docking geometries. In addition to Monte Carlo steps in translation and orientation of binding partners, possible conformational changes upon binding are included based on Monte Carlo selection of protein conformations stored as ordered pregenerated conformational ensembles. The conformational ensembles of each binding partner protein were generated by three different approaches starting from the unbound partner protein structure with a range spanning a root mean square deviation of 1–2.5 Å with respect to the unbound structure. Because MC sampling is performed to select appropriate partner conformations on the fly the approach is not limited by the number of conformations in the ensemble compared to ensemble docking of each conformer pair in ensemble cross docking. Although only a fraction of generated conformers was in closer agreement with the bound structure the REMC ensemble docking approach achieved improved docking results compared to REMC docking with only the unbound partner structures or using docking energy minimization methods. The approach has significant potential for further improvement in combination with more realistic structural ensembles and better docking scoring functions. Proteins 2017; 85:924–937. © 2016 Wiley Periodicals, Inc.     

Probing the role of interfacial waters in protein–DNA recognition using a hybrid implicit/explicit solvation model

When proteins bind to their DNA target sites, ordered water molecules are often present at the protein–DNA interface bridging protein and DNA through hydrogen bonds. What is the role of these ordered interfacial waters? Are they important determinants of the specificity of DNA sequence recognition, or do they act in binding in a primarily nonspecific manner, by improving packing of the interface, shielding unfavorable electrostatic interactions, and solvating unsatisfied polar groups that are inaccessible to bulk solvent? When modeling details of structure and binding preferences, can fully implicit solvent models be fruitfully applied to protein–DNA interfaces, or must the individualistic properties of these interfacial waters be accounted for? To address these questions, we have developed a hybrid implicit/explicit solvation model that specifically accounts for the locations and orientations of small numbers of DNA‐bound water molecules, while treating the majority of the solvent implicitly. Comparing the performance of this model with that of its fully implicit counterpart, we find that explicit treatment of interfacial waters results in a modest but significant improvement in protein side‐chain placement and DNA sequence recovery. Base‐by‐base comparison of the performance of the two models highlights DNA sequence positions whose recognition may be dependent on interfacial water. Our study offers large‐scale statistical evidence for the role of ordered water for protein–DNA recognition, together with detailed examination of several well‐characterized systems. In addition, our approach provides a template for modeling explicit water molecules at interfaces that should be extensible to other systems. Proteins 2013; 81:1318–1329. © 2013 Wiley Periodicals, Inc.     

Carbohydrate binding module recognition of xyloglucan defined by polar contacts with branching xyloses and CH‐Π interactions

Engineering of novel carbohydrate‐binding proteins that can be utilized in various biochemical and biotechnical applications would benefit from a deeper understanding of the biochemical interactions that determine protein‐carbohydrate specificity. In an effort to understand further the basis for specificity we present the crystal structure of the multi‐specific carbohydrate‐binding module (CBM) X‐2 L110F bound to a branched oligomer of xyloglucan (XXXG). X‐2 L110F is an engineered CBM that can recognize xyloglucan, xylans and β‐glucans. The structural observations of the present study compared with previously reported structures of X‐2 L110F in complex with linear oligomers, show that the π‐surface of a phenylalanine, F110, allows for interactions with hydrogen atoms on both linear (xylopentaose and cellopentaose) and branched ligands (XXXG). Furthermore, X‐2 L110F is shown to have a relatively flexible binding cleft, as illustrated in binding to XXXG. This branched ligand requires a set of reorientations of protein side chains Q72, N31, and R142, although these residues have previously been determined as important for binding to xylose oligomers by mediating polar contacts. The loss of these polar contacts is compensated for in binding to XXXG by polar interactions mediated by other protein residues, T74, R115, and Y149, which interact mainly with the branching xyloses of the xyloglucan oligomer. Taken together, the present study illustrates in structural detail how CH‐π interactions can influence binding specificity and that flexibility is a key feature for the multi‐specificity displayed by X‐2 L110F, allowing for the accommodation of branched ligands. Proteins 2014; 82:3466–3475. © 2014 Wiley Periodicals, Inc.     

Protein-DNA interactions: A structural analysis

A detailed analysis of the DNA-binding sites of 26 proteins is presented using data from the Nucleic Acid Database (NDB) and the Protein Data Bank (PDB). Chemical and physical properties of the protein-DNA interface, such as polarity, size, shape, and packing, were analysed. The DNA-binding sites shared common features, comprising many discontinuous sequence segments forming hydrophilic surfaces capable of direct and water-mediated hydrogen bonds. These interface sites were compared to those of protein-protein binding sites, revealing them to be more polar, with many more intermolecular hydrogen bonds and buried water molecules than the protein-protein interface sites. By looking at the number and positioning of protein residue-DNA base interactions in a series of interaction footprints, three modes of DNA binding were identified (single-headed, double-headed and enveloping). Six of the eight enzymes in the data set bound in the enveloping mode, with the protein presenting a large interface area effectively wrapped around the DNA.A comparison of structural parameters of the DNA revealed that some values for the bound DNA (including twist, slide and roll) were intermediate of those observed for the unbound B-DNA and A-DNA. The distortion of bound DNA was evaluated by calculating a root-mean-square deviation on fitting to a canonical B-DNA structure. Major distortions were commonly caused by specific kinks in the DNA sequence, some resulting in the overall bending of the helix. The helix bending affected the dimensions of the grooves in the DNA, allowing the binding of protein elements that would otherwise be unable to make contact. From this structural analysis a preliminary set of rules that govern the bending of the DNA in protein-DNA complexes, are proposed.     

C‐H…O hydrogen bonds in FK506‐binding protein–ligand interactions

Hydrogen bonds are important interaction forces observed in protein structures. They can be classified as stronger or weaker depending on their energy, thereby reflecting on the type of donor. The contribution of weak hydrogen bonds is deemed as an important factor toward structure stability along with the stronger bonds. One such bond, the C‐H…O type hydrogen bond, is shown to make a contribution in maintaining three dimensional structures of proteins. Apart from their presence within protein structures, the role of these bonds in protein–ligand interactions is also noteworthy. In this study, we present a statistical analysis on the presence of C‐H…O hydrogen bonds observed between FKBPs and their cognate ligands. The FK506‐binding proteins (FKBPs) carry peptidyl cis–trans isomerase activity apart from the immunosuppressive property by binding to the immunosuppressive drugs FK506 or rapamycin. Because the active site of FKBPs is lined up by many hydrophobic residues, we speculated that the prevalence of C‐H…O hydrogen bonds will be considerable. In a total of 25 structures analyzed, a higher frequency of C‐H…O hydrogen bonds is observed in comparison with the stronger hydrogen bonds. These C‐H…O hydrogen bonds are dominated by a highly conserved donor, the C^α/β of Val55 and an acceptor, the backbone oxygen of Glu54. Both these residues are positioned in the β4‐α1 loop, whereas the other residues Tyr26, Phe36 and Phe99 with higher frequencies are lined up at the opposite face of the active site. These preferences could be implicated in FKBP pharmacophore models toward enhancing the ligand affinity. This study could be a prelude to studying other proteins with hydrophobic pockets to gain better insights into ligand recognition. Copyright © 2013 John Wiley & Sons, Ltd.     

A code governing specific binding of regulatory proteins to DNA and structure of stereospecific sites of regulatory proteins]

A model is proposed for the structure of stereospecific sites in regulatory proteins. On its basis a possible code is suggested that governs the binding of regulatory proteins at specific control sites on DNA. Stereospecific sites of regulatory proteins are assumed to contain pairs of antiparallel polypeptide chain segments which form a right-hand twisted antiparallel beta-sheet, with single-stranded regions at the ends of the beta-structure. The model predicts that binding reaction between a regulatory protein and double-helical DNA is a cooperative phenomenon and is accompanied by significant structural alteration at the stereospecific site of the protein. Half of hydrogen bonds normally existing in beta-structure are broken upon complex formation with DNA and a new set of hydrogen bonds is formed between polypeptide amide groups and DNA base pairs. In a stereospecific site, one chain (t-chain) is attached through hydrogen bonds to the carbonyl oxygens of pyramides and N3 adenines lying in one DNA strand, while the second polypeptide chain (g chain) is hydrogen bonded to the 2-amino groups of guanine residues lying in the opposite DNA strand. The amide groups serve as specific reaction sites being hydrogen bond acceptors in g-chain and hydrogen bond donors in t-chain. The single-stranded portions of t- and g-chains lying in neighbouring subunits of regulatory protein interact with each other forming deformed beta-sheets. The recognition of regulatory sequences by proteins is based on the structural complementarity between stereospecific sites of regulatory proteins and base pairs sequences at the control sites. An essential feature of these sequences is the asymmetrical distribution of guanine residues between the two DNA strands. The code predicts that there are six fundamental amino acid residues (serine, threonine, asparagine, histidine, glutamine and cysteine) whose sequence in stereospecific site determines the base pair sequence to which a given regulatory protein would bind preferentially. The code states a correspondence between four amino acid residues at the stereospecific site of regulatory protein with the two residues being in t- and g-segments, respectively, and AT(GC) base pair at the control site. It is thus possible to determine which amino acid residues in the repressor and which base pairs in the operator DNA are involved in specific interactions with each other, as exemplified by lac repressor binding to lac operator.     

Mechanism of DNA Recognition by the Restriction Enzyme EcoRV

EcoRV, a restriction enzyme in Escherichia coli, destroys invading foreign DNA by cleaving it at the center step of a GATATC sequence. In the EcoRV-cognate DNA crystallographic complex, a sharp kink of 50° has been found at the center base-pair step (TA). Here, we examine the interplay between the intrinsic propensity of the cognate sequence to kink and the induction by the enzyme by performing all-atom molecular dynamics simulations of EcoRV unbound and interacting with three DNA sequences: the cognate sequence, GATATC (TA); the non-cognate sequence, GAATTC (AT); and with the cognate sequence methylated on the first adenine GA_CH3TATC (TA-CH₃). In the unbound EcoRV, the cleft between the two C-terminal subdomains is found to be open. Binding to AT narrows the cleft and forms a partially bound state. However, the intrinsic bending propensity of AT is insufficient to allow tight binding. In contrast, the cognate TA sequence is easier to bend, allowing specific, high-occupancy hydrogen bonds to form in the complex. The absence of cleavage for this methylated sequence is found to arise from the loss of specific hydrogen bonds between the first adenine of the recognition sequence and Asn185. On the basis of the results, we suggest a three-step recognition mechanism. In the first step, EcoRV, in an open conformation, binds to the DNA at a random sequence and slides along it. In the second step, when the two outer base pairs, GAxxTC, are recognized, the R loops of the protein become more ordered, forming strong hydrogen-bonding interactions, resulting in a partially bound EcoRV-DNA complex. In the third step, the flexibility of the center base pair is probed, and in the case of the full cognate sequence the DNA bends, the complex strengthens and the protein and DNA interact more closely, allowing cleavage.     

Molecular details of spontaneous insertion and interaction of HCV non‐structure 3 protease protein domain with PIP2‐containing membrane

Hepatitis C virus (HCV), known as the leading cause of liver cirrhosis, viral hepatitis, and hepatocellular carcinoma, has been affecting more than 150 million people globally. The HCV non‐structure 3 (NS3) protease protein domain plays a key role in HCV replication and pathogenesis; and is currently a primary target for HCV antiviral therapy. Through unbiased molecular dynamics simulations which take advantage of the novel highly mobile membrane mimetic model, we constructed the membrane‐bound state of the protein domain at the atomic level. Our results indicated that protease domain of HCV NS3 protein can spontaneously bind and penetrate to an endoplasmic reticulum complex membrane containing phosphatidylinositol 4,5‐bisphosphate (PIP2). An amphipathic helix α₀ and loop S1 show their anchoring role to keep the protein on the membrane surface. Proper orientation of the protein domain at membrane surface was identified through measuring tilt angles of two specific vectors, wherein residue R161 plays a crucial role in its final orientation. Remarkably, PIP2 molecules were observed to bind to three main sites of the protease domain via specific electrostatic contacts and hydrogen bonds. PIP2‐interaction determines the protein orientation at the membrane while both hydrophobic interplay and PIP2‐interaction can stabilize the NS3 ‐ membrane complex. Simulated results provide us with a detailed characterization of insertion, orientation and PIP2‐interaction of NS3 protease domain at membrane environment, thus enhancing our understanding of structural functions and mechanism for the association of HCV non‐structure 3 protein with respect to ER membranes.