Pushing the limits of what is achievable in protein-DNA docking: benchmarking HADDOCK's performance

Accurate structure prediction of biomolecular interactions with AlphaFold 3

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.

The NS1 protein of influenza B virus binds 5'-triphosphorylated dsRNA to suppress RIG-I activation and the host antiviral response

Influenza A and B viruses overcome the host antiviral response to cause a contagious and often severe human respiratory disease. Here, integrative structural biology and biochemistry studies on non-structural protein 1 of influenza B virus (NS1B) reveal a previously unrecognized viral mechanism for innate immune evasion. Conserved basic groups of its C-terminal domain (NS1B-CTD) bind 5'triphosphorylated double-stranded RNA (5'-ppp-dsRNA), the primary pathogen-associated feature that activates the host retinoic acid-inducible gene I protein (RIG-I) to initiate interferon synthesis and the cellular antiviral response. Like RIG-I, NS1B-CTD preferentially binds blunt-end 5'ppp-dsRNA. NS1B-CTD also competes with RIG-I for binding 5'ppp-dsRNA, and thus suppresses activation of RIG-I's ATPase activity. Although the NS1B N-terminal domain also binds dsRNA, it utilizes a different binding mode and lacks 5'ppp-dsRNA end preferences. In cells infected with wild-type influenza B virus, RIG-I activation is inhibited. In contrast, RIG-I activation and the resulting phosphorylation of transcription factor IRF-3 are not inhibited in cells infected with a mutant virus encoding NS1B with a R208A substitution it its CTD that eliminates its 5'ppp-dsRNA binding activity. These results reveal a novel mechanism in which NS1B binds 5'ppp-dsRNA to inhibit the RIG-I antiviral response during influenza B virus infection, and open the door to new avenues for antiviral drug discovery.

Structural predictions of protein-DNA binding: MELD-DNA

Structural, regulatory and enzymatic proteins interact with DNA to maintain a healthy and functional genome. Yet, our structural understanding of how proteins interact with DNA is limited. We present MELD-DNA, a novel computational approach to predict the structures of protein-DNA complexes. The method combines molecular dynamics simulations with general knowledge or experimental information through Bayesian inference. The physical model is sensitive to sequence-dependent properties and conformational changes required for binding, while information accelerates sampling of bound conformations. MELD-DNA can: (i) sample multiple binding modes; (ii) identify the preferred binding mode from the ensembles; and (iii) provide qualitative binding preferences between DNA sequences. We first assess performance on a dataset of 15 protein-DNA complexes and compare it with state-of-the-art methodologies. Furthermore, for three selected complexes, we show sequence dependence effects of binding in MELD predictions. We expect that the results presented herein, together with the freely available software, will impact structural biology (by complementing DNA structural databases) and molecular recognition (by bringing new insights into aspects governing protein-DNA interactions).

pyDockDNA: A new web server for energy-based protein-DNA docking and scoring

Proteins and nucleic acids are essential biological macromolecules for cell life. Indeed, interactions between proteins and DNA regulate many biological processes such as protein synthesis, signal transduction, DNA storage, or DNA replication and repair. Despite their importance, less than 4% of total structures deposited in the Protein Data Bank (PDB) correspond to protein-DNA complexes, and very few computational methods are available to model their structure. We present here the pyDockDNA web server, which can successfully model a protein-DNA complex with a reasonable predictive success rate (as benchmarked on a standard dataset of protein-DNA complex structures, where DNA is in B-DNA conformation). The server implements the pyDockDNA program, as a module of pyDock suite, thus including third-party programs, modules, and previously developed tools, as well as new modules and parameters to handle the DNA properly. The user is asked to enter Protein Data Bank files for protein and DNA input structures (or suitable models) and select the chains to be docked. The server calculations are mainly divided into three steps: sampling by FTDOCK, scoring with new energy-based parameters and the possibility of applying external restraints. The user can select different options for these steps. The final output screen shows a 3D representation of the top 10 models and a table sorting the model according to the scoring function selected previously. All these output files can be downloaded, including the top 100 models predicted by pyDockDNA. The server can be freely accessed for academic use (https://model3dbio.csic.es/pydockdna).

Searching for Low Probability Opening Events in a DNA Sliding Clamp

The β subunit of E. coli DNA polymererase III is a DNA sliding clamp associated with increasing the processivity of DNA synthesis. In its free form, it is a circular homodimer structure that can accomodate double-stranded DNA in a nonspecific manner. An open state of the clamp must be accessible before loading the DNA. The opening mechanism is still a matter of debate, as is the effect of bound DNA on opening/closing kinetics. We use a combination of atomistic, coarse-grained, and enhanced sampling strategies in both explicit and implicit solvents to identify opening events in the sliding clamp. Such simulations of large nucleic acid and their complexes are becoming available and are being driven by improvements in force fields and the creation of faster computers. Different models support alternative opening mechanisms, either through an in-plane or out-of-plane opening event. We further note some of the current limitations, despite advances, in modeling these highly charged systems with implicit solvent.

NLDock: a Fast Nucleic Acid-Ligand Docking Algorithm for Modeling RNA/DNA-Ligand Complexes

Nucleic acid-ligand interactions play an important role in numerous cellular processes such as gene function expression and regulation. Therefore, nucleic acids such as RNAs have become more and more important drug targets, where the structural determination of nucleic acid-ligand complexes is pivotal for understanding their functions and thus developing therapeutic interventions. Molecular docking has been a useful computational tool in predicting the complex structure between molecules. However, although a number of docking algorithms have been developed for protein-ligand interactions, only a few docking programs were presented for nucleic acid-ligand interactions. Here, we have developed a fast nucleic acid-ligand docking algorithm, named NLDock, by implementing our intrinsic scoring function ITScoreNL for nucleic acid-ligand interactions into a modified version of the MDock program. NLDock was extensively evaluated on four test sets and compared with five other state-of-the-art docking algorithms including AutoDock, DOCK 6, rDock, GOLD, and Glide. It was shown that our NLDock algorithm obtained a significantly better performance than the other docking programs in binding mode predictions and achieved the success rates of 73%, 36%, and 32% on the largest test set of 77 complexes for local rigid-, local flexible-, and global flexible-ligand docking, respectively. In addition, our NLDock approach is also computationally efficient and consumed an average of as short as 0.97 and 2.08 min for a local flexible-ligand docking job and a global flexible-ligand docking job, respectively. These results suggest the good performance of our NLDock in both docking accuracy and computational efficiency.

ITScore-NL: An Iterative Knowledge-Based Scoring Function for Nucleic Acid-Ligand Interactions

Nucleic acid-ligand complexes underlie numerous cellular processes, such as gene function expression and regulation, in which their three-dimensional structures are important to understand their functions and thus to develop therapeutic interventions. Given the high cost and technical difficulties in experimental methods, computational methods such as molecular docking have been actively used to investigate nucleic acid-ligand interactions in which an accurate scoring function is crucial. However, because of the limited number of experimental nucleic acid-ligand binding data and structures, the scoring function development for nucleic acid-ligand interactions falls far behind that for protein-protein and protein-ligand interactions. Here, based on our statistical mechanics-based iterative approach, we have developed an iterative knowledge-based scoring function for nucleic acid-ligand interactions, named as ITScore-NL, by explicitly including stacking and electrostatic potentials. Our ITScore-NL scoring function was extensively evaluated for its ability in the binding mode and binding affinity predictions on three diverse test sets and compared with state-of-the-art scoring functions. Overall, ITScore-NL obtained significantly better performance than the other 12 scoring functions and predicted near-native poses with rmsd ≤ 1.5 Å for 71.43% of the cases when the top three binding modes were considered and a good correlation of R = 0.64 in binding affinity prediction on the large test set of 77 nucleic acid-ligand complexes. These results suggested the accuracy of ITScore-NL and the necessity of explicitly including stacking and electrostatic potentials.

In silico molecular docking in DNA aptamer development

Aptamers are single-stranded DNA or RNA oligonucleotides generated by SELEX that exhibit binding affinity and specificity against a wide variety of target molecules. Compared to RNA aptamers, DNA aptamers are much more stable and therefore are widely adopted in a number of applications especially in diagnostics. The tediousness and rigor associated with certain steps of the SELEX intensify the efforts to adopt in silico molecular docking approaches together with in vitro SELEX procedures in developing DNA aptamers. Inspired by these endeavors, we carry out an overview of the in silico molecular docking approaches in DNA aptamer generation, by detailing the stepwise procedures as well as shedding some light on the various softwares used. The in silico maturation strategy and the limitations of the in silico approaches are also underscored.

Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models

MOTIVATION

Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB).

RESULTS

Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/mprdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MARTINI-Based Protein-DNA Coarse-Grained HADDOCKing

Modeling biomolecular assemblies is an important field in computational structural biology. The inherent complexity of their energy landscape and the computational cost associated with modeling large and complex assemblies are major drawbacks for integrative modeling approaches. The so-called coarse-graining approaches, which reduce the degrees of freedom of the system by grouping several atoms into larger “pseudo-atoms,” have been shown to alleviate some of those limitations, facilitating the identification of the global energy minima assumed to correspond to the native state of the complex, while making the calculations more efficient. Here, we describe and assess the implementation of the MARTINI force field for DNA into HADDOCK, our integrative modeling platform. We combine it with our previous implementation for protein-protein coarse-grained docking, enabling coarse-grained modeling of protein-nucleic acid complexes. The system is modeled using MARTINI topologies and interaction parameters during the rigid body docking and semi-flexible refinement stages of HADDOCK, and the resulting models are then converted back to atomistic resolution by an atom-to-bead distance restraints-guided protocol. We first demonstrate the performance of this protocol using 44 complexes from the protein-DNA docking benchmark, which shows an overall ~6-fold speed increase and maintains similar accuracy as compared to standard atomistic calculations. As a proof of concept, we then model the interaction between the PRC1 and the nucleosome (a former CAPRI target in round 31), using the same information available at the time the target was offered, and compare all-atom and coarse-grained models.

Pre- and post-docking sampling of conformational changes using ClustENM and HADDOCK for protein-protein and protein-DNA systems

Incorporating the dynamic nature of biomolecules in the modeling of their complexes is a challenge, especially when the extent and direction of the conformational changes taking place upon binding is unknown. Estimating whether the binding of a biomolecule to its partner(s) occurs in a conformational state accessible to its unbound form (“conformational selection”) and/or the binding process induces conformational changes (“induced‐fit”) is another challenge. We propose here a method combining conformational sampling using ClustENM—an elastic network‐based modeling procedure—with docking using HADDOCK, in a framework that incorporates conformational selection and induced‐fit effects upon binding. The extent of the applied deformation is estimated from its energetical costs, inspired from mechanical tensile testing on materials. We applied our pre‐ and post‐docking sampling of conformational changes to the flexible multidomain protein‐protein docking benchmark and a subset of the protein‐DNA docking benchmark. Our ClustENM‐HADDOCK approach produced acceptable to medium quality models in 7/11 and 5/6 cases for the protein‐protein and protein‐DNA complexes, respectively. The conformational selection (sampling prior to docking) has the highest impact on the quality of the docked models for the protein‐protein complexes. The induced‐fit stage of the pipeline (post‐sampling), however, improved the quality of the final models for the protein‐DNA complexes. Compared to previously described strategies to handle conformational changes, ClustENM‐HADDOCK performs better than two‐body docking in protein‐protein cases but worse than a flexible multidomain docking approach. However, it does show a better or similar performance compared to previous protein‐DNA docking approaches, which makes it a suitable alternative.

Molecular Modelling of the Ni(II)-Responsive Synechocystis PCC 6803 Transcriptional Regulator InrS in the Metal Bound Form

InrS (internal nickel-responsive sensor) is a transcriptional regulator found in cyanobacteria that represses the transcription of the nickel exporter NrsD in the apo form and de-represses expression of the exporter upon Ni(II) binding. Although a crystal structure of apo-InrS from Synechocystis PCC 6803 has been reported, no structure of the protein with metal ions bound is available. Here we report the results of a computational study aimed to reconstruct the metal binding site by taking advantage of recent X-ray absorption spectroscopy (XAS) data and to envisage the structural rearrangements occurring upon Ni(II) binding. The modelled Ni(II) binding site shows a square planar geometry consistent with experimental data. The structural details of the conformational changes occurring upon metal binding are also discussed in the framework of trying to rationalize the different affinity of the apo- and holo-forms of the protein for DNA.

Molecular Modeling Applied to Nucleic Acid-Based Molecule Development

Molecular modeling by means of docking and molecular dynamics (MD) has become an integral part of early drug discovery projects, enabling the screening and enrichment of large libraries of small molecules. In the past decades, special emphasis was drawn to nucleic acid (NA)-based molecules in the fields of therapy, diagnosis, and drug delivery. Research has increased dramatically with the advent of the SELEX (systematic evolution of ligands by exponential enrichment) technique, which results in single-stranded DNA or RNA sequences that bind with high affinity and specificity to their targets. Herein, we discuss the role and contribution of docking and MD to the development and optimization of new nucleic acid-based molecules. This review focuses on the different approaches currently available for molecular modeling applied to NA interaction with proteins. We discuss topics ranging from structure prediction to docking and MD, highlighting their main advantages and limitations and the influence of flexibility on their calculations.

EMQIT: a machine learning approach for energy based PWM matrix quality improvement

Background

Transcription factor binding affinities to DNA play a key role for the gene regulation. Learning the specificity of the mechanisms of binding TFs to DNA is important both to experimentalists and theoreticians. With the development of high-throughput methods such as, e.g., ChiP-seq the need to provide unbiased models of binding events has been made apparent. We present EMQIT a modification to the approach introduced by Alamanova et al. and later implemented as 3DTF server. We observed that tuning of Boltzmann factor weights, used for conversion of calculated energies to nucleotide probabilities, has a significant impact on the quality of the associated PWM matrix.

Results

Consequently, we proposed to use receiver operator characteristics curves and the 10-fold cross-validation to learn best weights using experimentally verified data from TRANSFAC database. We applied our method to data available for various TFs. We verified the efficiency of detecting TF binding sites by the 3DTF matrices improved with our technique using experimental data from the TRANSFAC database. The comparison showed a significant similarity and comparable performance between the improved and the experimental matrices (TRANSFAC). Improved 3DTF matrices achieved significantly higher AUC values than the original 3DTF matrices (at least by 0.1) and, at the same time, detected notably more experimentally verified TFBSs.

Conclusions

The resulting new improved PWM matrices for analyzed factors show similarity to TRANSFAC matrices. Matrices had comparable predictive capabilities. Moreover, improved PWMs achieve better results than matrices downloaded from 3DTF server. Presented approach is general and applicable to any energy-based matrices.

EMQIT is available online at http://biosolvers.polsl.pl:3838/emqit.

Reviewers

This article was reviewed by Oliviero Carugo, Marek Kimmel and István Simon.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-017-0189-y) contains supplementary material, which is available to authorized users.

Characterization of Protein Tyrosine Phosphatase 1B Inhibition by Chlorogenic Acid and Cichoric Acid

Protein tyrosine phosphatase 1B (PTP1B) is a known regulator of the insulin and leptin signaling pathways and is an active target for the design of inhibitors for the treatment of type II diabetes and obesity. Recently, cichoric acid (CHA) and chlorogenic acid (CGA) were predicted by docking methods to be allosteric inhibitors that bind distal to the active site. However, using a combination of steady-state inhibition kinetics, solution nuclear magnetic resonance experiments, and molecular dynamics simulations, we show that CHA is a competitive inhibitor that binds in the active site of PTP1B. CGA, while a noncompetitive inhibitor, binds in the second aryl phosphate binding site, rather than the predicted benzfuran binding pocket. The molecular dynamics simulations of the apo enzyme and cysteine-phosphoryl intermediate states with and without bound CGA suggest CGA binding inhibits PTP1B by altering hydrogen bonding patterns at the active site. This study provides a mechanistic understanding of the allosteric inhibition of PTP1B.

Docking Methodologies and Recent Advances

Docking, a molecular modelling method, has wide applications in identification and optimization in modern drug discovery. This chapter addresses the recent advances in the docking methodologies like fragment docking, covalent docking, inverse docking, post processing, hybrid techniques, homology modeling etc. and its protocol like searching and scoring functions. Advances in scoring functions for e.g. consensus scoring, quantum mechanics methods, clustering and entropy based methods, fingerprinting, etc. are used to overcome the limitations of the commonly used force-field, empirical and knowledge based scoring functions. It will cover crucial necessities and different algorithms of docking and scoring. Further different aspects like protein flexibility, ligand sampling and flexibility, and the performance of scoring function will be discussed.

Evolution of Macromolecular Docking Techniques: The Case Study of Nickel and Iron Metabolism in Pathogenic Bacteria

The interaction between macromolecules is a fundamental aspect of most biological processes. The computational techniques used to study protein-protein and protein-nucleic acid interactions have evolved in the last few years because of the development of new algorithms that allow the a priori incorporation, in the docking process, of experimentally derived information, together with the possibility of accounting for the flexibility of the interacting molecules. Here we review the results and the evolution of the techniques used to study the interaction between metallo-proteins and DNA operators, all involved in the nickel and iron metabolism of pathogenic bacteria, focusing in particular on Helicobacter pylori (Hp). In the first part of the article we discuss the methods used to calculate the structure of complexes of proteins involved in the activation of the nickel-dependent enzyme urease. In the second part of the article, we concentrate on two applications of protein-DNA docking conducted on the transcription factors HpFur (ferric uptake regulator) and HpNikR (nickel regulator). In both cases we discuss the technical expedients used to take into account the conformational variability of the multi-domain proteins involved in the calculations.

Promoter activation by CII, a potent transcriptional activator from bacteriophage 186

The lysogeny promoting protein CII from bacteriophage 186 is a potent transcriptional activator, capable of mediating at least a 400-fold increase in transcription over basal activity. Despite being functionally similar to its counterpart in phage λ, it shows no homology at the level of protein sequence and does not belong to any known family of transcriptional activators. It also has the unusual property of binding DNA half-sites that are separated by 20 base pairs, center to center. Here we investigate the structural and functional properties of CII using a combination of genetics, in vitro assays, and mutational analysis. We find that 186 CII possesses two functional domains, with an independent activation epitope in each. 186 CII owes its potent activity to activation mechanisms that are dependent on both the σ(70) and α C-terminal domain (αCTD) components of RNA polymerase, contacting different functional domains. We also present evidence that like λ CII, 186 CII is proteolytically degraded in vivo, but unlike λ CII, 186 CII proteolysis results in a specific, transcriptionally inactive, degradation product with altered self-association properties.

Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine

Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined.

Structure of bacterial transcription factor SpoIIID and evidence for a novel mode of DNA binding

SpoIIID is evolutionarily conserved in endospore-forming bacteria, and it activates or represses many genes during sporulation of Bacillus subtilis. An SpoIIID monomer binds DNA with high affinity and moderate sequence specificity. In addition to a predicted helix-turn-helix motif, SpoIIID has a C-terminal basic region that contributes to DNA binding. The nuclear magnetic resonance (NMR) solution structure of SpoIIID in complex with DNA revealed that SpoIIID does indeed have a helix-turn-helix domain and that it has a novel C-terminal helical extension. Residues in both of these regions interact with DNA, based on the NMR data and on the effects on DNA binding in vitro of SpoIIID with single-alanine substitutions. These data, as well as sequence conservation in SpoIIID binding sites, were used for information-driven docking to model the SpoIIID-DNA complex. The modeling resulted in a single cluster of models in which the recognition helix of the helix-turn-helix domain interacts with the major groove of DNA, as expected. Interestingly, the C-terminal extension, which includes two helices connected by a kink, interacts with the adjacent minor groove of DNA in the models. This predicted novel mode of binding is proposed to explain how a monomer of SpoIIID achieves high-affinity DNA binding. Since SpoIIID is conserved only in endospore-forming bacteria, which include important pathogenic Bacilli and Clostridia, whose ability to sporulate contributes to their environmental persistence, the interaction of the C-terminal extension of SpoIIID with DNA is a potential target for development of sporulation inhibitors.

Model of a DNA-protein complex of the architectural monomeric protein MC1 from Euryarchaea

In Archaea the two major modes of DNA packaging are wrapping by histone proteins or bending by architectural non-histone proteins. To supplement our knowledge about the binding mode of the different DNA-bending proteins observed across the three domains of life, we present here the first model of a complex in which the monomeric Methanogen Chromosomal protein 1 (MC1) from Euryarchaea binds to the concave side of a strongly bent DNA. In laboratory growth conditions MC1 is the most abundant architectural protein present in Methanosarcina thermophila CHTI55. Like most proteins that strongly bend DNA, MC1 is known to bind in the minor groove. Interaction areas for MC1 and DNA were mapped by Nuclear Magnetic Resonance (NMR) data. The polarity of protein binding was determined using paramagnetic probes attached to the DNA. The first structural model of the DNA-MC1 complex we propose here was obtained by two complementary docking approaches and is in good agreement with the experimental data previously provided by electron microscopy and biochemistry. Residues essential to DNA-binding and -bending were highlighted and confirmed by site-directed mutagenesis. It was found that the Arg25 side-chain was essential to neutralize the negative charge of two phosphates that come very close in response to a dramatic curvature of the DNA.

On the use of knowledge-based potentials for the evaluation of models of protein-protein, protein-DNA, and protein-RNA interactions

Proteins are the bricks and mortar of cells, playing structural and functional roles. In order to perform their function, they interact with each other as well as with other biomolecules such as DNA or RNA. Therefore, to fathom the function of a protein, we require knowing its partners and the atomic details of its interactions (i.e., the structure of the complex). However, the amount of protein interactions with an experimentally determined three-dimensional structure is scarce. Therefore, computational techniques such as homology modeling are foremost to fill this gap. Protein interactions can be modeled using as templates the interactions of homologous proteins, if the structure of the complex is known, or using docking methods. In both approaches, the estimation of the quality of models is essential. There are several ways to address this problem. In this review, we focus on the use of knowledge-based potentials for the analysis of protein interactions. We describe the procedure to derive statistical potentials and split them into different energetic terms that can be used for different purposes. We extensively discuss the fields where knowledge-based potentials have been successfully applied to (1) model protein-protein, protein-DNA, and protein-RNA interactions and (2) predict binding sites (in the protein and in the DNA). Moreover, we provide ready-to-use resources for docking and benchmarking protein interactions.

Predicting protein-DNA interactions by full search computational docking

Protein-DNA interactions are essential for many biological processes. X-ray crystallography can provide high-resolution structures, but protein-DNA complexes are difficult to crystallize and typically contain only small DNA fragments. Thus, there is a need for computational methods that can provide useful predictions to give insights into mechanisms and guide the design of new experiments. We used the program DOT, which performs an exhaustive, rigid-body search between two macromolecules, to investigate four diverse protein-DNA interactions. Here, we compare our computational results with subsequent experimental data on related systems. In all cases, the experimental data strongly supported our structural hypotheses from the docking calculations: a mechanism for weak, nonsequence-specific DNA binding by a transcription factor, a large DNA-binding footprint on the surface of the DNA-repair enzyme uracil-DNA glycosylase (UNG), viral and host DNA-binding sites on the catalytic domain of HIV integrase, and a three-DNA-contact model of the linker histone bound to the nucleosome. In the case of UNG, the experimental design was based on the DNA-binding surface found by docking, rather than the much smaller surface observed in the crystallographic structure. These comparisons demonstrate that the DOT electrostatic energy gives a good representation of the distinctive electrostatic properties of DNA and DNA-binding proteins. The large, favourably ranked clusters resulting from the dockings identify active sites, map out large DNA-binding sites, and reveal multiple DNA contacts with a protein. Thus, computational docking can not only help to identify protein-DNA interactions in the absence of a crystal structure, but also expand structural understanding beyond known crystallographic structures.

Elastic Network Models of Nucleic Acids Flexibility

Elastic network models (ENMs) are a useful tool for describing large scale motions in protein systems. While they are well validated in the context of proteins, relatively little is known about their applicability to nucleic acids, whose different architecture does not necessarily warrant comparable performance. In this study we thoroughly evaluate and optimize the efficiency of popular ENMs for capturing RNA and DNA flexibility. We also introduce two alternative models in which the strength of elastic connections at a coarse-grained level is governed by distance distribution at atomic resolution. For each of the considered ENMs we report the optimal length of spring connections as well as the scaling of elastic force constants that provides the best agreement of vibrational frequencies with normal modes based on atomic force field. In order to determine the absolute values of force constants we introduce a novel method based on the overlap of pseudoinverse of Hessian matrices.

Solvated protein-DNA docking using HADDOCK

Interfacial water molecules play an important role in many aspects of protein-DNA specificity and recognition. Yet they have been mostly neglected in the computational modeling of these complexes. We present here a solvated docking protocol that allows explicit inclusion of water molecules in the docking of protein-DNA complexes and demonstrate its feasibility on a benchmark of 30 high-resolution protein-DNA complexes containing crystallographically-determined water molecules at their interfaces. Our protocol is capable of reproducing the solvation pattern at the interface and recovers hydrogen-bonded water-mediated contacts in many of the benchmark cases. Solvated docking leads to an overall improvement in the quality of the generated protein-DNA models for cases with limited conformational change of the partners upon complex formation. The applicability of this approach is demonstrated on real cases by docking a representative set of 6 complexes using unbound protein coordinates, model-built DNA and knowledge-based restraints. As HADDOCK supports the inclusion of a variety of NMR restraints, solvated docking is also applicable for NMR-based structure calculations of protein-DNA complexes.

Combining NMR and small angle X-ray and neutron scattering in the structural analysis of a ternary protein-RNA complex

Many processes in the regulation of gene expression and signaling involve the formation of protein complexes involving multi-domain proteins. Individual domains that mediate protein-protein and protein-nucleic acid interactions are typically connected by flexible linkers, which contribute to conformational dynamics and enable the formation of complexes with distinct binding partners. Solution techniques are therefore required for structural analysis and to characterize potential conformational dynamics. Nuclear magnetic resonance spectroscopy (NMR) provides such information but often only sparse data are obtained with increasing molecular weight of the complexes. It is therefore beneficial to combine NMR data with additional structural restraints from complementary solution techniques. Small angle X-ray/neutron scattering (SAXS/SANS) data can be efficiently combined with NMR-derived information, either for validation or by providing additional restraints for structural analysis. Here, we show that the combination of SAXS and SANS data can help to refine structural models obtained from data-driven docking using HADDOCK based on sparse NMR data. The approach is demonstrated with the ternary protein-protein-RNA complex involving two RNA recognition motif (RRM) domains of Sex-lethal, the N-terminal cold shock domain of Upstream-to-N-Ras, and msl-2 mRNA. Based on chemical shift perturbations we have mapped protein-protein and protein-RNA interfaces and complemented this NMR-derived information with SAXS data, as well as SANS measurements on subunit-selectively deuterated samples of the ternary complex. Our results show that, while the use of SAXS data is beneficial, the additional combination with contrast variation in SANS data resolves remaining ambiguities and improves the docking based on chemical shift perturbations of the ternary protein-RNA complex.

ACA-specific RNA sequence recognition is acquired via the loop 2 region of MazF mRNA interferase

MazF is an mRNA interferase that cleaves mRNAs at a specific RNA sequence. MazF from E. coli (MazF-ec) cleaves RNA at A^CA. To date, a large number of MazF homologs that cleave RNA at specific three- to seven-base sequences have been identified from bacteria to archaea. MazF-ec forms a dimer, in which the interface between the two subunits is known to be the RNA substrate-binding site. Here, we investigated the role of the two loops in MazF-ec, which are closely associated with the interface of the MazF-ec dimer. We examined whether exchanging the loop regions of MazF-ec with those from other MazF homologs, such as MazF from Myxococcus xanthus (MazF-mx) and MazF from Mycobacterium tuberculosis (MazF-mt3), affects RNA cleavage specificity. We found that exchanging loop 2 of MazF-ec with loop 2 regions from either MazF-mx or MazF-mt3 created a new cleavage sequence at (A/U)(A/U)AA^C in addition to the original cleavage site, A^CA, whereas exchanging loop 1 did not alter cleavage specificity. Intriguingly, exchange of loop 2 with 8 or 12 consecutive Gly residues also resulted in a new RNA cleavage site at (A/U)(A/U)AA^C. The present study suggests a method for expanding the RNA cleavage repertoire of mRNA interferases, which is crucial for potential use in the regulation of specific gene expression and for biotechnological applications.

MTMDAT-HADDOCK: high-throughput, protein complex structure modeling based on limited proteolysis and mass spectrometry

BACKGROUND

MTMDAT is a program designed to facilitate analysis of mass spectrometry data of proteins and biomolecular complexes that are probed structurally by limited proteolysis. This approach can provide information about stable fragments of multidomain proteins, yield tertiary and quaternary structure data, and help determine the origin of stability changes at the amino acid residue level. Here, we introduce a pipeline between MTMDAT and HADDOCK, that facilitates protein-protein complex structure probing in a high-throughput and highly automated fashion.

RESULTS

A new feature of MTMDAT allows for the direct identification of residues that are involved in complex formation by comparing the mass spectra of bound and unbound proteins after proteolysis. If 3D structures of the unbound components are available, this data can be used to define restraints for data-driven docking to calculate a model of the complex. We describe here a new implementation of MTMDAT, which includes a pipeline to the data-driven docking program HADDOCK, thus streamlining the entire procedure. This addition, together with usability improvements in MTMDAT, enables high-throughput modeling of protein complexes from mass spectrometry data. The algorithm has been validated by using the protein-protein interaction between the ubiquitin-binding domain of proteasome component Rpn13 and ubiquitin. The resulting structural model, based on restraints extracted by MTMDAT from limited proteolysis and modeled by HADDOCK, was compared to the published NMR structure, which relied on twelve unambiguous intermolecular NOE interactions. The MTMDAT-HADDOCK structure was of similar quality to structures generated using only chemical shift perturbation data derived by NMR titration experiments.

CONCLUSIONS

The new MTMDAT-HADDOCK pipeline enables direct high-throughput modeling of protein complexes from mass spectrometry data. MTMDAT-HADDOCK can be downloaded from http://www.ifm.liu.se/chemistry/molbiotech/maria_sunnerhagens_group/mtmdat/together with the manual and example files. The program is free for academic/non-commercial purposes.

A toolkit and benchmark study for FRET-restrained high-precision structural modeling

We present a comprehensive toolkit for Förster resonance energy transfer (FRET)-restrained modeling of biomolecules and their complexes for quantitative applications in structural biology. A dramatic improvement in the precision of FRET-derived structures is achieved by explicitly considering spatial distributions of dye positions, which greatly reduces uncertainties due to flexible dye linkers. The precision and confidence levels of the models are calculated by rigorous error estimation. The accuracy of this approach is demonstrated by docking a DNA primer-template to HIV-1 reverse transcriptase. The derived model agrees with the known X-ray structure with an r.m.s. deviation of 0.5 Å. Furthermore, we introduce FRET-guided 'screening' of a large structural ensemble created by molecular dynamics simulations. We used this hybrid approach to determine the formerly unknown configuration of the flexible single-strand template overhang.

A nonredundant structure dataset for benchmarking protein-RNA computational docking

Protein-RNA interactions play an important role in many biological processes. The ability to predict the molecular structures of protein-RNA complexes from docking would be valuable for understanding the underlying chemical mechanisms. We have developed a novel nonredundant benchmark dataset for protein-RNA docking and scoring. The diverse dataset of 72 targets consists of 52 unbound-unbound test complexes, and 20 unbound-bound test complexes. Here, unbound-unbound complexes refer to cases in which both binding partners of the cocrystallized complex are either in apo form or in a conformation taken from a different protein-RNA complex, whereas unbound-bound complexes are cases in which only one of the two binding partners has another experimentally determined conformation. The dataset is classified into three categories according to the interface root mean square deviation and the percentage of native contacts in the unbound structures: 49 easy, 16 medium, and 7 difficult targets. The bound and unbound cases of the benchmark dataset are expected to benefit the development and improvement of docking and scoring algorithms for the docking community. All the easy-to-view structures are freely available to the public at http://zoulab.dalton.missouri.edu/RNAbenchmark/.

Protein-DNA docking with a coarse-grained force field

Background

Protein-DNA interactions are important for many cellular processes, however structural knowledge for a large fraction of known and putative complexes is still lacking. Computational docking methods aim at the prediction of complex architecture given detailed structures of its constituents. They are becoming an increasingly important tool in the field of macromolecular assemblies, complementing particularly demanding protein-nucleic acids X ray crystallography and providing means for the refinement and integration of low resolution data coming from rapidly advancing methods such as cryoelectron microscopy.

Results

We present a new coarse-grained force field suitable for protein-DNA docking. The force field is an extension of previously developed parameter sets for protein-RNA and protein-protein interactions. The docking is based on potential energy minimization in translational and orientational degrees of freedom of the binding partners. It allows for fast and efficient systematic search for native-like complex geometry without any prior knowledge regarding binding site location.

Conclusions

We find that the force field gives very good results for bound docking. The quality of predictions in the case of unbound docking varies, depending on the level of structural deviation from bound geometries. We analyze the role of specific protein-DNA interactions on force field performance, both with respect to complex structure prediction, and the reproduction of experimental binding affinities. We find that such direct, specific interactions only partially contribute to protein-DNA recognition, indicating an important role of shape complementarity and sequence-dependent DNA internal energy, in line with the concept of indirect protein-DNA readout mechanism.

TFinDit: transcription factor-DNA interaction data depository

Background

One of the crucial steps in regulation of gene expression is the binding of transcription factor(s) to specific DNA sequences. Knowledge of the binding affinity and specificity at a structural level between transcription factors and their target sites has important implications in our understanding of the mechanism of gene regulation. Due to their unique functions and binding specificity, there is a need for a transcription factor-specific, structure-based database and corresponding web service to facilitate structural bioinformatics studies of transcription factor-DNA interactions, such as development of knowledge-based interaction potential, transcription factor-DNA docking, binding induced conformational changes, and the thermodynamics of protein-DNA interactions.

Description

TFinDit is a relational database and a web search tool for studying transcription factor-DNA interactions. The database contains annotated transcription factor-DNA complex structures and related data, such as unbound protein structures, thermodynamic data, and binding sequences for the corresponding transcription factors in the complex structures. TFinDit also provides a user-friendly interface and allows users to either query individual entries or generate datasets through culling the database based on one or more search criteria.

Conclusions

TFinDit is a specialized structural database with annotated transcription factor-DNA complex structures and other preprocessed data. We believe that this database/web service can facilitate the development and testing of TF-DNA interaction potentials and TF-DNA docking algorithms, and the study of protein-DNA recognition mechanisms.

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

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.

Structural models of protein-DNA complexes based on interface prediction and docking

Protein-DNA interactions are the physical basis of gene expression and DNA modification. Structural models that reveal these interactions are essential for their understanding. As only a limited number of structures for protein-DNA complexes have been determined by experimental methods, computation methods provide a potential way to fill the need. We have developed the DISPLAR method to predict DNA binding sites on proteins. Predicted binding sites have been used to assist the building of structural models by docking, either by guiding the docking or by selecting near-native candidates from the docked poses. Here we applied the DISPLAR method to predict the DNA binding sites for 20 DNA-binding proteins, which have had their DNA binding sites characterized by NMR chemical shift perturbation. For two of these proteins, the structures of their complexes with DNA have also been determined. With the help of the DISPLAR predictions, we built structural models for these two complexes. Evaluations of both the DNA binding sites for 20 proteins and the structural models of the two protein-DNA complexes against experimental results demonstrate the significant promise of our model-building approach.

On docking, scoring and assessing protein-DNA complexes in a rigid-body framework

We consider the identification of interacting protein-nucleic acid partners using the rigid body docking method FTdock, which is systematic and exhaustive in the exploration of docking conformations. The accuracy of rigid body docking methods is tested using known protein-DNA complexes for which the docked and undocked structures are both available. Additional tests with large decoy sets probe the efficacy of two published statistically derived scoring functions that contain a huge number of parameters. In contrast, we demonstrate that state-of-the-art machine learning techniques can enormously reduce the number of parameters required, thereby identifying the relevant docking features using a miniscule fraction of the number of parameters in the prior works. The present machine learning study considers a 300 dimensional vector (dependent on only 15 parameters), termed the Chemical Context Profile (CCP), where each dimension reflects a specific type of protein amino acid-nucleic acid base interaction. The CCP is designed to capture the chemical complementarities of the interface and is well suited for machine learning techniques. Our objective function is the Chemical Context Discrepancy (CCD), which is defined as the angle between the native system's CCP vector and the decoy's vector and which serves as a substitute for the more commonly used root mean squared deviation (RMSD). We demonstrate that the CCP provides a useful scoring function when certain dimensions are properly weighted. Finally, we explore how the amino acids on a protein's surface can help guide DNA binding, first through long-range interactions, followed by direct contacts, according to specific preferences for either the major or minor grooves of the DNA.

Databases and bioinformatics tools for the study of DNA repair

DNA is continuously exposed to many different damaging agents such as environmental chemicals, UV light, ionizing radiation, and reactive cellular metabolites. DNA lesions can result in different phenotypical consequences ranging from a number of diseases, including cancer, to cellular malfunction, cell death, or aging. To counteract the deleterious effects of DNA damage, cells have developed various repair systems, including biochemical pathways responsible for the removal of single-strand lesions such as base excision repair (BER) and nucleotide excision repair (NER) or specialized polymerases temporarily taking over lesion-arrested DNA polymerases during the S phase in translesion synthesis (TLS). There are also other mechanisms of DNA repair such as homologous recombination repair (HRR), nonhomologous end-joining repair (NHEJ), or DNA damage response system (DDR). This paper reviews bioinformatics resources specialized in disseminating information about DNA repair pathways, proteins involved in repair mechanisms, damaging agents, and DNA lesions.

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.

ParaDock: a flexible non-specific DNA--rigid protein docking algorithm

Accurate prediction of protein–DNA complexes could provide an important stepping stone towards a thorough comprehension of vital intracellular processes. Few attempts were made to tackle this issue, focusing on binding patch prediction, protein function classification and distance constraints-based docking. We introduce ParaDock: a novel ab initio protein–DNA docking algorithm. ParaDock combines short DNA fragments, which have been rigidly docked to the protein based on geometric complementarity, to create bent planar DNA molecules of arbitrary sequence. Our algorithm was tested on the bound and unbound targets of a protein–DNA benchmark comprised of 47 complexes. With neither addressing protein flexibility, nor applying any refinement procedure, CAPRI acceptable solutions were obtained among the 10 top ranked hypotheses in 83% of the bound complexes, and 70% of the unbound. Without requiring prior knowledge of DNA length and sequence, and within <2 h per target on a standard 2.0 GHz single processor CPU, ParaDock offers a fast ab initio docking solution.

Nuclear magnetic resonance analysis of protein-DNA interactions

Recent methodological and instrumental advances in solution-state nuclear magnetic resonance have opened up the way to investigating challenging problems in structural biology such as large macromolecular complexes. This review focuses on the experimental strategies currently employed to solve structures of protein-DNA complexes and to analyse their dynamics. It highlights how these approaches can help in understanding detailed molecular mechanisms of target recognition.