From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions

Unveiling Sticholysin II and plasmid DNA interaction: Implications for developing non-viral vectors

Non-viral gene delivery systems offer significant potential for gene therapy due to their versatility, safety, and cost advantages over viral vectors. However, their effectiveness can be hindered by the challenge of efficiently releasing the genetic cargo from endosomes to prevent degradation in lysosomes. To overcome this obstacle, functional components can be incorporated into these systems. Sticholysin II (StII) is one of the pore-forming proteins derived from the sea anemone Stichodactyla helianthus, known for its high ability to permeabilize cellular and model membranes. In this study, we aimed to investigate the interaction between StII, and a model plasmid (pDNA) as an initial step towards designing an improved vector with enhanced endosomal escape capability. The electrophoretic mobility shift assay (EMSA) confirmed the formation of complexes between StII and pDNA. Computational predictions identified specific residues involved in the StII-DNA interaction interface, highlighting the importance of electrostatic interactions and hydrogen bonds in mediating the binding. Atomic force microscopy (AFM) of StII-pDNA complexes revealed the presence of nodular fiber and toroid shapes. These complexes were found to have a predominantly micrometer size, as confirmed by dynamic light scattering (DLS) measurements. Despite increase in the overall charge, the complexes formed at the evaluated nitrogen-to-phosphorus (N/P) ratios still maintained a negative charge. Moreover, StII retained its pore-forming capacity regardless of its binding to the complexes. These findings suggest that the potential ability of StII to permeabilize endosomal membranes could be largely maintained when combined with nucleic acid delivery systems. Additionally, the still remaining negative charge of the complexes would enable the association of another positively charged component to compact pDNA. However, to minimize non-specific cytotoxic effects, it is advisable to explore methods to regulate the protein's activity in response to the microenvironment.

Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins

Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine-learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g., identification of DNA-binding proteins or where an excellent global representation can be divined. Complete understanding of the various functions of proteins requires discovery and functional annotation at the residue level. Herein, we cast this problem into the setting of multiple-instance learning, which only requires knowledge of the protein’s function yet identifies functionally relevant residues and need not rely on homology. We developed a new multiple-instance leaning algorithm derived from AdaBoost and benchmarked this algorithm against two well-studied protein function prediction tasks: annotating proteins that bind DNA and RNA. This algorithm outperforms certain previous approaches in annotating protein function while identifying functionally relevant residues involved in binding both DNA and RNA, and on one protein-DNA benchmark, it achieves near perfect classification.

Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces

The increasing number of protein structures with uncharacterized function necessitates the development of in silico prediction methods for functional annotations on proteins. In this chapter, different kinds of computational approaches are briefly introduced to predict DNA-binding residues on surface of DNA-binding proteins, and the merits and limitations of these methods are mainly discussed. This chapter focuses on the structure-based approaches and mainly discusses the framework of machine learning methods in application to DNA-binding prediction task.

How proteins bind to DNA: target discrimination and dynamic sequence search by the telomeric protein TRF1

Target search as performed by DNA-binding proteins is a complex process, in which multiple factors contribute to both thermodynamic discrimination of the target sequence from overwhelmingly abundant off-target sites and kinetic acceleration of dynamic sequence interrogation. TRF1, the protein that binds to telomeric tandem repeats, faces an intriguing variant of the search problem where target sites are clustered within short fragments of chromosomal DNA. In this study, we use extensive (>0.5 ms in total) MD simulations to study the dynamical aspects of sequence-specific binding of TRF1 at both telomeric and non-cognate DNA. For the first time, we describe the spontaneous formation of a sequence-specific native protein-DNA complex in atomistic detail, and study the mechanism by which proteins avoid off-target binding while retaining high affinity for target sites. Our calculated free energy landscapes reproduce the thermodynamics of sequence-specific binding, while statistical approaches allow for a comprehensive description of intermediate stages of complex formation.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

Streptococcus pneumoniae Genome-wide Identification and Characterization of BOX Element-binding Domains

The BOX elements are short repetitive DNA sequences that distribute randomly in intergenic regions of the Streptococcus pneumoniae genome. The function and origin of such elements are still unknown, but they were found to modulate expression of neighboring genes. Evidences suggested that the modulation's mechanism can be fulfilled by sequence-specific interaction of BOX elements with transcription factor family proteins. However, the type and function of these BOX-binding proteins still remain largely unexplored to date. In the current study we described a synthetic protocol to investigate the recognition and interaction between a highly conserved site of BOX elements and the DNA-binding domains of a variety of putative transcription factors in the pneumococcal genome. With the protocol we were able to predict those high-affinity domain binders of the conserved BOX DNA site (BOX DNA) in a high-throughput manner, and analyzed sequence-specific interaction in the domainDNA recognition at molecular level. Consequently, a number of putative transcription factor domains with both high affinity and specificity for the BOX DNA were identified, from which the helix-turn-helix (HTH) motif of a small heat shock factor was selected as a case study and tested for its binding capability toward the double-stranded BOX DNA using fluorescence anisotropy analysis. As might be expected, a relatively high affinity was detected for the interaction of HTH motif with BOX DNA with dissociation constant at nanomolar level. Molecular dynamics simulation, atomic structure examination and binding energy analysis revealed a complicated network of intensive nonbonded interactions across the complex interface, which confers both stability and specificity for the complex architecture.

Critical Evaluation and Compilation of Physicochemical Determinants and Membrane Interactions of MMGP1 Antifungal Peptide

A growing issue of pathogen resistance to antibiotics has fostered the development of innovative approaches for novel drug development. Here, we report the physicochemical and biological properties of an antifungal peptide, MMGP1, based on computational analysis. Computation of physicochemical properties has revealed that the natural biological activities of MMGP1 are coordinated by its intrinsic properties such as net positive charge (+5.04), amphipathicity, high hydrophobicity, low hydrophobic moment, and higher isoelectric point (11.915). Prediction of aggregation hot spots in MMGP1 had revealed the presence of potentially aggregation-prone segments that can nucleate in vivo aggregation (on the membrane), whereas no aggregating regions were predicted for in vitro aggregation (in solutions) of MMGP1. This ability of MMGP1 to form oligomeric aggregates on membrane further substantiates its direct-cell penetrating potency. Monte Carlo simulation of the interactions of MMGP1 in the aqueous phase and different membrane environments revealed that increasing the proportion of acidic lipids on membrane had led to increase in the peptide helicity. Furthermore, the peptide adopts energetically favorable transmembrane configuration, by inserting peptide loop and helix termini into the membrane containing >60% of anionic lipids. The charged lipid-based insertion of MMGP1 into membrane might be responsible for the selectivity of peptide toward fungal cells. Additionally, MMGP1 possessed DNA-binding property. Computational docking has identified DNA-binding residues (TRP3, SER4, MET7, ARG8, PHE10, ALA11, GLY20, THR21, ARG22, MET23, TRP34, and LYS36) in MMGP1 crucial for its DNA-binding property. Furthermore, computational mutation analysis revealed that aromatic amino acids are crucial for in vivo aggregation, membrane insertion, and DNA-binding property of MMGP1. These data provide new insight into the molecular determinants of MMGP1 antifungal activity and also serves as the template for the design of novel peptide antibiotics.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

Evidence supporting a critical contribution of intrinsically disordered regions to the biochemical behavior of full-length human HP1γ

HP1γ, a non-histone chromatin protein, has elicited significant attention because of its role in gene silencing, elongation, splicing, DNA repair, cell growth, differentiation, and many other cancer-associated processes, including therapy resistance. These characteristics make it an ideal target for developing small drugs for both mechanistic experimentation and potential therapies. While high-resolution structures of the two globular regions of HP1γ, the chromo- and chromoshadow domains, have been solved, little is currently known about the conformational behavior of the full-length protein. Consequently, in the current study, we use threading, homology-based molecular modeling, molecular mechanics calculations, and molecular dynamics simulations to develop models that allow us to infer properties of full-length HP1γ at an atomic resolution level. HP1γ appears as an elongated molecule in which three Intrinsically Disordered Regions (IDRs, 1, 2, and 3) endow this protein with dynamic flexibility, intermolecular recognition properties, and the ability to integrate signals from various intracellular pathways. Our modeling also suggests that the dynamic flexibility imparted to HP1γ by the three IDRs is important for linking nucleosomes with PXVXL motif-containing proteins, in a chromatin environment. The importance of the IDRs in intermolecular recognition is illustrated by the building and study of both IDR2 HP1γ−importin-α and IDR1 and IDR2 HP1γ−DNA complexes. The ability of the three IDRs for integrating cell signals is demonstrated by combined linear motif analyses and molecular dynamics simulations showing that posttranslational modifications can generate a histone mimetic sequence within the IDR2 of HP1γ, which when bound by the chromodomain can lead to an autoinhibited state. Combined, these data underscore the importance of IDRs 1, 2, and 3 in defining the structural and dynamic properties of HP1γ, discoveries that have both mechanistic and potentially biomedical relevance.

Evidence supporting the existence of a NUPR1-like family of helix-loop-helix chromatin proteins related to, yet distinct from, AT hook-containing HMG proteins

NUPR1, a small chromatin protein, plays a critical role in cancer development, progression, and resistance to therapy. Here, using a combination of structural bioinformatics and molecular modeling methods, we report several novel findings that enhance our understanding of the biochemical function of this protein. We find that NUPR1 has been conserved throughout evolution, and over time it has undergone duplications and transpositions to form other transcriptional regulators. Using threading, homology-based molecular modeling, molecular mechanics calculations, and molecular dynamics simulations, we generated structural models for four of these proteins: NUPR1a, NUPR1b, NUPR2, and the NUPR-like domain of GTF2-I. Comparative analyses of these models combined with extensive linear motif identification reveal that these four proteins, though similar in their propensities for folding, differ in size, surface changes, and sites amenable for posttranslational modification. Lastly, taking NUPR1a as the paradigm for this family, we built models of a NUPR–DNA complex. Additional structural comparisons revealed that NUPR1 defines a new family of small-groove-binding proteins that share structural features with, yet are distinct from, helix-loop-helix AT-hook-containing HMG proteins. These models and inferences should lead to a better understanding of the function of this group of chromatin proteins, which play a critical role in the development of human malignant diseases.

Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome

As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions). A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC) of 0.77 with high precision (94%) and high sensitivity (65%). We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA)] is available as an on-line server at http://sparks-lab.org.

Conformational and thermodynamic hallmarks of DNA operator site specificity in the copper sensitive operon repressor from Streptomyces lividans

Metal ion homeostasis in bacteria relies on metalloregulatory proteins to upregulate metal resistance genes and enable the organism to preclude metal toxicity. The copper sensitive operon repressor (CsoR) family is widely distributed in bacteria and controls the expression of copper efflux systems. CsoR operator sites consist of G-tract containing pseudopalindromes of which the mechanism of operator binding is poorly understood. Here, we use a structurally characterized CsoR from Streptomyces lividans (CsoR^Sl) together with three specific operator targets to reveal the salient features pertaining to the mechanism of DNA binding. We reveal that CsoR^Sl binds to its operator site through a 2-fold axis of symmetry centred on a conserved 5′-TAC/GTA-3′ inverted repeat. Operator recognition is stringently dependent not only on electropositive residues but also on a conserved polar glutamine residue. Thermodynamic and circular dichroic signatures of the CsoR^Sl–DNA interaction suggest selectivity towards the A-DNA-like topology of the G-tracts at the operator site. Such properties are enhanced on protein binding thus enabling the symmetrical binding of two CsoR^Sl tetramers. Finally, differential binding modes may exist in operator sites having more than one 5′-TAC/GTA-3′ inverted repeat with implications in vivo for a mechanism of modular control.

DBSI: DNA-binding site identifier

In this study, we present the DNA-Binding Site Identifier (DBSI), a new structure-based method for predicting protein interaction sites for DNA binding. DBSI was trained and validated on a data set of 263 proteins (TRAIN-263), tested on an independent set of protein-DNA complexes (TEST-206) and data sets of 29 unbound (APO-29) and 30 bound (HOLO-30) protein structures distinct from the training data. We computed 480 candidate features for identifying protein residues that bind DNA, including new features that capture the electrostatic microenvironment within shells near the protein surface. Our iterative feature selection process identified features important in other models, as well as features unique to the DBSI model, such as a banded electrostatic feature with spatial separation comparable with the canonical width of the DNA minor groove. Validations and comparisons with established methods using a range of performance metrics clearly demonstrate the predictive advantage of DBSI, and its comparable performance on unbound (APO-29) and bound (HOLO-30) conformations demonstrates robustness to binding-induced protein conformational changes. Finally, we offer our feature data table to others for integration into their own models or for testing improved feature selection and model training strategies based on DBSI.

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.

New insights into DNA recognition by zinc fingers revealed by structural analysis of the oncoprotein ZNF217

Classical zinc fingers (ZFs) are one of the most abundant and best characterized DNA-binding domains. Typically, tandem arrays of three or more ZFs bind DNA target sequences with high affinity and specificity, and the mode of DNA recognition is sufficiently well understood that tailor-made ZF-based DNA-binding proteins can be engineered. We have shown previously that a two-zinc finger unit found in the transcriptional coregulator ZNF217 recognizes DNA but with an affinity and specificity that is lower than other ZF arrays. To investigate the basis for these differences, we determined the structure of a ZNF217-DNA complex. We show that although the overall position of the ZFs on the DNA closely resembles that observed for other ZFs, the side-chain interaction pattern differs substantially from the canonical model. The structure also reveals the presence of two methyl-π interactions, each featuring a tyrosine contacting a thymine methyl group. To our knowledge, interactions of this type have not previously been described in classical ZF-DNA complexes. Finally, we investigated the sequence specificity of this two-ZF unit and discuss how ZNF217 might discriminate its target DNA sites in the cell.

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

Scanning through genomes for potential transcription factor binding sites (TFBSs) is becoming increasingly important in this post-genomic era. The position weight matrix (PWM) is the standard representation of TFBSs utilized when scanning through sequences for potential binding sites. However, many transcription factor (TF) motifs are short and highly degenerate, and methods utilizing PWMs to scan for sites are plagued by false positives. Furthermore, many important TFs do not have well-characterized PWMs, making identification of potential binding sites even more difficult. One approach to the identification of sites for these TFs has been to use the 3D structure of the TF to predict the DNA structure around the TF and then to generate a PWM from the predicted 3D complex structure. However, this approach is dependent on the similarity of the predicted structure to the native structure. We introduce here a novel approach to identify TFBSs utilizing structure information that can be applied to TFs without characterized PWMs, as long as a 3D complex structure (TF/DNA) exists. This approach utilizes an energy function that is uniquely trained on each structure. Our approach leads to increased prediction accuracy and robustness compared with those using a more general energy function. The software is freely available upon request.

Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters

We present a set of four parameters that in combination can predict DNA-binding residues on protein structures to a high degree of accuracy. These are the number of evolutionary conserved residues (N_cons) and their spatial clustering (ρ_e), hydrogen bond donor capability (D_p) and residue propensity (R_p). We first used these parameters to characterize 130 interfaces in a set of 126 DNA-binding proteins (DBPs). The applicability of these parameters both individually and in combination, to distinguish the true binding region from the rest of the protein surface was then analyzed. R_p shows the best performance identifying the true interface with the top rank in 83% cases. Importantly, we also used the unbound-bound test cases of the protein–DNA docking benchmark to test the efficacy of our method. When applied to the unbound form of the DBPs, R_p can distinguish 86% cases. Finally, we have applied the SVM approach for recognizing the interface region using the above parameters along with the individual amino acid composition as attributes. The accuracy of prediction is 90.5% for the bound structures and 93.6% for the unbound form of the proteins.

Exploiting a reduced set of weighted average features to improve prediction of DNA-binding residues from 3D structures

Predicting DNA-binding residues from a protein three-dimensional structure is a key task of computational structural proteomics. In the present study, based on machine learning technology, we aim to explore a reduced set of weighted average features for improving prediction of DNA-binding residues on protein surfaces. Via constructing the spatial environment around a DNA-binding residue, a novel weighting factor is first proposed to quantify the distance-dependent contribution of each neighboring residue in determining the location of a binding residue. Then, a weighted average scheme is introduced to represent the surface patch of the considering residue. Finally, the classifier is trained on the reduced set of these weighted average features, consisting of evolutionary profile, interface propensity, betweenness centrality and solvent surface area of side chain. Experimental results on 5-fold cross validation and independent tests indicate that the new feature set are effective to describe DNA-binding residues and our approach has significantly better performance than two previous methods. Furthermore, a brief case study suggests that the weighted average features are powerful for identifying DNA-binding residues and are promising for further study of protein structure-function relationship. The source code and datasets are available upon request.

Benchmarks for flexible and rigid transcription factor-DNA docking

BACKGROUND

Structural insight from transcription factor-DNA (TF-DNA) complexes is of paramount importance to our understanding of the affinity and specificity of TF-DNA interaction, and to the development of structure-based prediction of TF binding sites. Yet the majority of the TF-DNA complexes remain unsolved despite the considerable experimental efforts being made. Computational docking represents a promising alternative to bridge the gap. To facilitate the study of TF-DNA docking, carefully designed benchmarks are needed for performance evaluation and identification of the strengths and weaknesses of docking algorithms.

RESULTS

We constructed two benchmarks for flexible and rigid TF-DNA docking respectively using a unified non-redundant set of 38 test cases. The test cases encompass diverse fold families and are classified into easy and hard groups with respect to the degrees of difficulty in TF-DNA docking. The major parameters used to classify expected docking difficulty in flexible docking are the conformational differences between bound and unbound TFs and the interaction strength between TFs and DNA. For rigid docking in which the starting structure is a bound TF conformation, only interaction strength is considered.

CONCLUSIONS

We believe these benchmarks are important for the development of better interaction potentials and TF-DNA docking algorithms, which bears important implications to structure-based prediction of transcription factor binding sites and drug design.

Using a structural and logics systems approach to infer bHLH-DNA binding specificity determinants

Numerous efforts are underway to determine gene regulatory networks that describe physical relationships between transcription factors (TFs) and their target DNA sequences. Members of paralogous TF families typically recognize similar DNA sequences. Knowledge of the molecular determinants of protein–DNA recognition by paralogous TFs is of central importance for understanding how small differences in DNA specificities can dictate target gene selection. Previously, we determined the in vitro DNA binding specificities of 19 Caenorhabditis elegans basic helix-loop-helix (bHLH) dimers using protein binding microarrays. These TFs bind E-box (CANNTG) and E-box-like sequences. Here, we combine these data with logics, bHLH–DNA co-crystal structures and computational modeling to infer which bHLH monomer can interact with which CAN E-box half-site and we identify a critical residue in the protein that dictates this specificity. Validation experiments using mutant bHLH proteins provide support for our inferences. Our study provides insights into the mechanisms of DNA recognition by bHLH dimers as well as a blueprint for system-level studies of the DNA binding determinants of other TF families in different model organisms and humans.

The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement

Exhaustive exploration of molecular interactions at the level of complete proteomes requires efficient and reliable computational approaches to protein function inference. Ligand docking and ranking techniques show considerable promise in their ability to quantify the interactions between proteins and small molecules. Despite the advances in the development of docking approaches and scoring functions, the genome-wide application of many ligand docking/screening algorithms is limited by the quality of the binding sites in theoretical receptor models constructed by protein structure prediction. In this study, we describe a new template-based method for the local refinement of ligand-binding regions in protein models using remotely related templates identified by threading. We designed a Support Vector Regression (SVR) model that selects correct binding site geometries in a large ensemble of multiple receptor conformations. The SVR model employs several scoring functions that impose geometrical restraints on the Cα positions, account for the specific chemical environment within a binding site and optimize the interactions with putative ligands. The SVR score is well correlated with the RMSD from the native structure; in 47% (70%) of the cases, the Pearson's correlation coefficient is >0.5 (>0.3). When applied to weakly homologous models, the average heavy atom, local RMSD from the native structure of the top-ranked (best of top five) binding site geometries is 3.1Å (2.9Å) for roughly half of the targets; this represents a 0.1 (0.3)Å average improvement over the original predicted structure. Focusing on the subset of strongly conserved residues, the average heavy atom RMSD is 2.6Å (2.3Å). Furthermore, we estimate the upper bound of template-based binding site refinement using only weakly related proteins to be ∼2.6Å RMSD. This value also corresponds to the plasticity of the ligand-binding regions in distant homologues. The Binding Site Refinement (BSR) approach is available to the scientific community as a web server that can be accessed at http://cssb.biology.gatech.edu/bsr/.

DNABINDPROT: fluctuation-based predictor of DNA-binding residues within a network of interacting residues

DNABINDPROT is designed to predict DNA-binding residues, based on the fluctuations of residues in high-frequency modes by the Gaussian network model. The residue pairs that display high mean-square distance fluctuations are analyzed with respect to DNA binding, which are then filtered with their evolutionary conservation profiles and ranked according to their DNA-binding propensities. If the analyses are based on the exact outcome of fluctuations in the highest mode, using a conservation threshold of 5, the results have a sensitivity, specificity, precision and accuracy of 9.3%, 90.5%, 18.1% and 78.6%, respectively, on a dataset of 36 unbound–bound protein structure pairs. These values increase up to 24.3%, 93.4%, 45.3% and 83.3% for the respective cases, when the neighboring two residues are considered. The relatively low sensitivity appears with the identified residues being selective and susceptible more for the binding core residues rather than all DNA-binding residues. The predicted residues that are not tagged as DNA-binding residues are those whose fluctuations are coupled with DNA-binding sites. They are in close proximity as well as plausible for other functional residues, such as ligand and protein–protein interaction sites. DNABINDPROT is free and open to all users without login requirement available at: http://www.prc.boun.edu.tr/appserv/prc/dnabindprot/.

Contributions of the histidine side chain and the N-terminal alpha-amino group to the binding thermodynamics of oligopeptides to nucleic acids as a function of pH

Interactions of histidine with nucleic acid phosphates and histidine pK(a) shifts make important contributions to many protein-nucleic acid binding processes. To characterize these phenomena in simplified systems, we quantified binding of a histidine-containing model peptide HWKK ((+)NH(3)-His-Trp-Lys-Lys-NH(2)) and its lysine analogue KWKK ((+)NH(3)-Lys-Trp-Lys-Lys-NH(2)) to a single-stranded RNA model, polyuridylate (polyU), by changes in tryptophan fluorescence as a function of salt concentration and pH. For both HWKK and KWKK, equilibrium binding constants, K(obs), and magnitudes of log-log salt derivatives, SK(obs) identical with (partial differential logK(obs)/partial differential log[Na(+)]), decreased with increasing pH in the manner expected for a titration curve model in which deprotonation of the histidine and alpha-amino groups weakens binding and reduces its salt-dependence. Fully protonated HWKK and KWKK exhibit the same K(obs) and SK(obs) within uncertainty, and these SK(obs) values are consistent with limiting-law polyelectrolyte theory for +4 cationic oligopeptides binding to single-stranded nucleic acids. The pH-dependence of HWKK binding to polyU provides no evidence for pK(a) shifts nor any requirement for histidine protonation, in stark contrast to the thermodynamics of coupled protonation often seen for these cationic residues in the context of native protein structure where histidine protonation satisfies specific interactions (e.g., salt-bridge formation) within highly complementary binding interfaces. The absence of pK(a) shifts in our studies indicates that additional Coulombic interactions across the nonspecific-binding interface between RNA and protonated histidine or the alpha-amino group are not sufficient to promote proton uptake for these oligopeptides. We present our findings in the context of hydration models for specific vs nonspecific nucleic acid binding.

Boosting the prediction and understanding of DNA-binding domains from sequence

DNA-binding proteins perform vital functions related to transcription, repair and replication. We have developed a new sequence-based machine learning protocol to identify DNA-binding proteins. We compare our method with an extensive benchmark of previously published structure-based machine learning methods as well as a standard sequence alignment technique, BLAST. Furthermore, we elucidate important feature interactions found in a learned model and analyze how specific rules capture general mechanisms that extend across DNA-binding motifs. This analysis is carried out using the malibu machine learning workbench available at http://proteomics.bioengr.uic.edu/malibu and the corresponding data sets and features are available at http://proteomics.bioengr.uic.edu/dna.

A threading-based method for the prediction of DNA-binding proteins with application to the human genome

Diverse mechanisms for DNA-protein recognition have been elucidated in numerous atomic complex structures from various protein families. These structural data provide an invaluable knowledge base not only for understanding DNA-protein interactions, but also for developing specialized methods that predict the DNA-binding function from protein structure. While such methods are useful, a major limitation is that they require an experimental structure of the target as input. To overcome this obstacle, we develop a threading-based method, DNA-Binding-Domain-Threader (DBD-Threader), for the prediction of DNA-binding domains and associated DNA-binding protein residues. Our method, which uses a template library composed of DNA-protein complex structures, requires only the target protein's sequence. In our approach, fold similarity and DNA-binding propensity are employed as two functional discriminating properties. In benchmark tests on 179 DNA-binding and 3,797 non-DNA-binding proteins, using templates whose sequence identity is less than 30% to the target, DBD-Threader achieves a sensitivity/precision of 56%/86%. This performance is considerably better than the standard sequence comparison method PSI-BLAST and is comparable to DBD-Hunter, which requires an experimental structure as input. Moreover, for over 70% of predicted DNA-binding domains, the backbone Root Mean Square Deviations (RMSDs) of the top-ranked structural models are within 6.5 A of their experimental structures, with their associated DNA-binding sites identified at satisfactory accuracy. Additionally, DBD-Threader correctly assigned the SCOP superfamily for most predicted domains. To demonstrate that DBD-Threader is useful for automatic function annotation on a large-scale, DBD-Threader was applied to 18,631 protein sequences from the human genome; 1,654 proteins are predicted to have DNA-binding function. Comparison with existing Gene Ontology (GO) annotations suggests that approximately 30% of our predictions are new. Finally, we present some interesting predictions in detail. In particular, it is estimated that approximately 20% of classic zinc finger domains play a functional role not related to direct DNA-binding.