Structural features of protein-nucleic acid recognition sites

Intrinsic disorder and salt-dependent conformational changes of the N-terminal region of TFIP11 splicing factor

Tuftelin Interacting Protein 11 (TFIP11) was identified as a critical human spliceosome assembly regulator, interacting with multiple proteins and localising in membrane-less organelles. However, a lack of structural information on TFIP11 limits the rationalisation of its biological role. TFIP11 is predicted as an intrinsically disordered protein (IDP), and more specifically concerning its N-terminal (N-TER) region. IDPs lack a defined tertiary structure, existing as a dynamic conformational ensemble, favouring protein-protein and protein-RNA interactions. IDPs are involved in liquid-liquid phase separation (LLPS), driving the formation of subnuclear compartments. Combining disorder prediction, molecular dynamics, and spectroscopy methods, this contribution shows the first evidence TFIP11 N-TER is a polyampholytic IDP, exhibiting a structural duality with the coexistence of ordered and disordered assemblies, depending on the ionic strength. Increasing the salt concentration enhances the protein conformational flexibility, presenting a more globule-like shape, and a fuzzier unstructured arrangement that could favour LLPS and protein-RNA interaction. The most charged and hydrophilic regions are the most impacted, including the G-Patch domain essential to TFIP11 function. This study gives a better understanding of the salt-dependent conformational behaviour of the N-TER TFIP11, supporting the hypothesis of the formation of different types of protein assembly, in line with its multiple biological roles.

Investigating the Interactions of the Cucumber Mosaic Virus 2b Protein with the Viral 1a Replicase Component and the Cellular RNA Silencing Factor Argonaute 1

The cucumber mosaic virus (CMV) 2b protein is a suppressor of plant defenses and a pathogenicity determinant. Amongst the 2b protein's host targets is the RNA silencing factor Argonaute 1 (AGO1), which it binds to and inhibits. In Arabidopsis thaliana, if 2b-induced inhibition of AGO1 is too efficient, it induces reinforcement of antiviral silencing by AGO2 and triggers increased resistance against aphids, CMV's insect vectors. These effects would be deleterious to CMV replication and transmission, respectively, but are moderated by the CMV 1a protein, which sequesters sufficient 2b protein molecules into P-bodies to prevent excessive inhibition of AGO1. Mutant 2b protein variants were generated, and red and green fluorescent protein fusions were used to investigate subcellular colocalization with AGO1 and the 1a protein. The effects of mutations on complex formation with the 1a protein and AGO1 were investigated using bimolecular fluorescence complementation and co-immunoprecipitation assays. Although we found that residues 56-60 influenced the 2b protein's interactions with the 1a protein and AGO1, it appears unlikely that any single residue or sequence domain is solely responsible. In silico predictions of intrinsic disorder within the 2b protein secondary structure were supported by circular dichroism (CD) but not by nuclear magnetic resonance (NMR) spectroscopy. Intrinsic disorder provides a plausible model to explain the 2b protein's ability to interact with AGO1, the 1a protein, and other factors. However, the reasons for the conflicting conclusions provided by CD and NMR must first be resolved.

PNAbind: Structure-based prediction of protein-nucleic acid binding using graph neural networks

The recognition and binding of nucleic acids (NAs) by proteins depends upon complementary chemical, electrostatic and geometric properties of the protein-NA binding interface. Structural models of protein-NA complexes provide insights into these properties but are scarce relative to models of unbound proteins. We present a deep learning approach for predicting protein-NA binding given the apo structure of a protein (PNAbind). Our method utilizes graph neural networks to encode spatial distributions of physicochemical and geometric properties of the protein molecular surface that are predictive of NA binding. Using global physicochemical encodings, our models predict the overall binding function of a protein and can discriminate between specificity for DNA or RNA binding. We show that such predictions made on protein structures modeled with AlphaFold2 can be used to gain mechanistic understanding of chemical and structural features that determine NA recognition. Using local encodings, our models predict the location of NA binding sites at the level of individual binding residues. Binding site predictions were validated against benchmark datasets, achieving AUROC scores in the range of 0.92-0.95. We applied our models to the HIV-1 restriction factor APOBEC3G and show that our predictions are consistent with experimental RNA binding data.

Single-Molecule X-ray Scattering Used to Visualize the Conformation Distribution of Biological Molecules via Single-Object Scattering Sampling

Biological macromolecules, the fundamental building blocks of life, exhibit dynamic structures in their natural environment. Traditional structure determination techniques often oversimplify these multifarious conformational spectra by capturing only ensemble- and time-averaged molecular structures. Addressing this gap, in this work, we extend the application of the single-object scattering sampling (SOSS) method to diverse biological molecules, including RNAs and proteins. Our approach, referred to as "Bio-SOSS", leverages ultrashort X-ray pulses to capture instantaneous structures. In Bio-SOSS, we employ two gold nanoparticles (AuNPs) as labels, which provide strong contrast in the X-ray scattering signal, to ensure precise distance determinations between labeled sites. We generated hypothetical Bio-SOSS images for RNAs, proteins, and an RNA-protein complex, each labeled with two AuNPs at specified positions. Subsequently, to validate the accuracy of Bio-SOSS, we extracted distances between these nanoparticle labels from the images and compared them with the actual values used to generate the Bio-SOSS images. Specifically, for a representative RNA (1KXK), the standard deviation in distance discrepancies between molecular dynamics snapshots and Bio-SOSS retrievals was found to be optimally around 0.2 Å, typically within 1 Å under practical experimental conditions at state-of-the-art X-ray free-electron laser facilities. Furthermore, we conducted an in-depth analysis of how various experimental factors, such as AuNP size, X-ray properties, and detector geometry, influence the accuracy of Bio-SOSS. This comprehensive investigation highlights the practicality and potential of Bio-SOSS in accurately capturing the diverse conformation spectrum of biological macromolecules, paving the way for deeper insights into their dynamic natures.

Three-Dimensional Modeling of CpG DNA Binding with Matrix Lumican Shows Leucine-Rich Repeat Motif Involvement as in TLR9-CpG DNA Interactions

Lumican is an extracellular matrix proteoglycan known to regulate toll-like receptor (TLR) signaling in innate immune cells. In experimental settings, lumican suppresses TLR9 signaling by binding to and sequestering its synthetic ligand, CpG-DNA, in non-signal permissive endosomes. However, the molecular details of lumican interactions with CpG-DNA are obscure. Here, the 3-D structure of the 22 base-long CpG-DNA (CpG ODN_2395) bound to lumican or TLR9 were modeled using homology modeling and docking methods. Some of the TLR9-CpG ODN_2395 features predicted by our model are consistent with the previously reported TLR9-CpG DNA crystal structure, substantiating our current analysis. Our modeling indicated a smaller buried surface area for lumican-CpG ODN_2395 (1803 Å2) compared to that of TLR9-CpG ODN_2395 (2094 Å2), implying a potentially lower binding strength for lumican and CpG-DNA than TLR9 and CpG-DNA. The docking analysis identified 32 amino acids in lumican LRR1-11 interacting with CpG ODN_2395, primarily through hydrogen bonding, salt-bridges, and hydrophobic interactions. Our study provides molecular insights into lumican and CpG-DNA interactions that may lead to molecular targets for modulating TLR9-mediated inflammation and autoimmunity.

3D modeling of CpG DNA binding with matrix lumican shows leucine-rich repeat motif involvement as in TLR9-CpG DNA interactions

Lumican is an extracellular matrix proteoglycan, known to regulate toll-like receptor (TLR) signaling in innate immune cells. In experimental settings, lumican suppresses TLR9 signaling by binding to, and sequestering its synthetic ligand, CpG-DNA, in non-signal permissive endosomes. However, the molecular details of lumican interactions with CpG-DNA are obscure. Here, the 3-D structure of the 22 base-long CpG-DNA (CpG ODN_2395) bound to lumican or TLR9 were modeled using homology modeling and docking methods. Some of the TLR9-CpG ODN_2395 features predicted by our model are consistent with the previously reported TLR9-CpG DNA crystal structure, substantiating our current analysis. Our modeling indicated a smaller buried surface area for lumican-CpG ODN_2395 (1803 Å2) compared to that of TLR9-CpG ODN_2395 (2094 Å2), implying a potentially lower binding strength for lumican and CpG-DNA than TLR9 and CpG-DNA. The docking analysis identified 32 amino acids in lumican LRR1-11 interacting with CpG ODN_2395, primarily through hydrogen bonding, salt-bridges and hydrophobic interactions. Our study provides molecular insights into lumican and CpG-DNA interactions that may lead to molecular targets for modulating TLR9 mediated inflammation and autoimmunity.

ProDFace: A web-tool for the dissection of protein-DNA interfaces

Protein-DNA interactions play a crucial role in gene expression and regulation. Identifying the DNA binding surface of proteins has long been a challenge–in comparison to protein-protein interactions, limited progress has been made in the development of efficient DNA binding site prediction and protein-DNA docking methods. Here we present ProDFace, a web tool that characterizes the binding region of a protein-DNA complex based on amino acid propensity, hydrogen bond (HB) donor capacity (number of solvent accessible HB donor groups), sequence conservation at the interface core and rim region, and geometry. The program takes as input the structure of a protein-DNA complex in PDB (Protein Data Bank) format, and outputs various physicochemical and geometric parameters of the interface, as well as conservation of the interface residues in the protein component. Values are provided for the whole interface, and after dissecting it into core and rim regions. Details of water mediated HBs between protein and DNA, potential HB donor groups present at the binding surface of protein, and conserved interface residues are also provided as downloadable text files. These parameters can be useful in evaluating and validating protein-DNA docking solutions, structures derived from simulation as well as solutions from the available prediction tools, and facilitate the development of more efficient prediction methods. The web-tool is freely available at structbioinfo.iitj.ac.in/resources/bioinfo/pd_interface.

A deep learning-based method for the prediction of DNA interacting residues in a protein

DNA-protein interaction is one of the most crucial interactions in the biological system, which decides the fate of many processes such as transcription, regulation and splicing of genes. In this study, we trained our models on a training dataset of 646 DNA-binding proteins having 15 636 DNA interacting and 298 503 non-interacting residues. Our trained models were evaluated on an independent dataset of 46 DNA-binding proteins having 965 DNA interacting and 9911 non-interacting residues. All proteins in the independent dataset have less than 30% of sequence similarity with proteins in the training dataset. A wide range of traditional machine learning and deep learning (1D-CNN) techniques-based models have been developed using binary, physicochemical properties and Position-Specific Scoring Matrix (PSSM)/evolutionary profiles. In the case of machine learning technique, eXtreme Gradient Boosting-based model achieved a maximum area under the receiver operating characteristics (AUROC) curve of 0.77 on the independent dataset using PSSM profile. Deep learning-based model achieved the highest AUROC of 0.79 on the independent dataset using a combination of all three profiles. We evaluated the performance of existing methods on the independent dataset and observed that our proposed method outperformed all the existing methods. In order to facilitate scientific community, we developed standalone software and web server, which are accessible from https://webs.iiitd.edu.in/raghava/dbpred.

Chemical Protection and Premodification-Binding Interference for the Identification of Phosphate and Base-Specific Contacts in Protein-DNA Complexes

The specificity and strength of protein-DNA complexes rely on tight interactions between side- and main chain atoms of amino acid residues and phosphates, sugars, and base-specific groups. Various (in-gel) footprinting methods (for more information, see Chapter 11 ) allow the identification of the global-binding region but do not provide details on the contribution to complex formation of individual sequence-specific constituents of the DNA-binding site. Here, we describe how various chemicals can be used to randomly and sparingly modify specific bases or phosphates and allow the identification of those residues that are specifically protected against modification upon protein binding (protection studies) or interfere with complex formation when modified or removed prior to protein binding (premodification-binding interference). Each one of these complementary approaches has its advantages and shortcomings and results have to be interpreted with caution, having in mind the precise chemistry of the modification. However, used in combination, these methods provide an accurate and high-resolution image of the protein-DNA contacts.

Role of salt-bridging interactions in recognition of viral RNA by arginine-rich peptides

Interactions between RNA molecules and proteins are critical to many cellular processes and are implicated in various diseases. The RNA-peptide complexes are good model systems to probe the recognition mechanism of RNA by proteins. In this work, we report studies on the binding-unbinding process of a helical peptide from a viral RNA element using nonequilibrium molecular dynamics simulations. We explored the existence of various dissociation pathways with distinct free-energy profiles that reveal metastable states and distinct barriers to peptide dissociation. We also report the free-energy differences for each of the four pathways to be 96.47 ± 12.63, 96.1 ± 10.95, 91.83 ± 9.81, and 92 ± 11.32 kcal/mol. Based on the free-energy analysis, we further propose the preferred pathway and the mechanism of peptide dissociation. The preferred pathway is characterized by the formation of sequential hydrogen-bonding and salt-bridging interactions between several key arginine amino acids and the viral RNA nucleotides. Specifically, we identified one arginine amino acid (R8) of the peptide to play a significant role in the recognition mechanism of the peptide by the viral RNA molecule.

Revealing atomic-scale molecular diffusion of a plant-transcription factor WRKY domain protein along DNA

In transcription factors’ search for target genes, one-dimensional diffusion of the protein along DNA is essential. Experimentally, it remains challenging to resolve the individual diffusional steps of protein on DNA. Here, we report mainly all-atom equilibrium simulations of a WRKY domain protein in association with and diffusion along DNA. We demonstrate a complete stepping cycle of the protein for one base pair on DNA within microseconds, along with stochastic motions. Processive protein diffusions on DNA have been further sampled in a coarse-grained model. We have also found preferential DNA-strand association of the domain protein, which becomes most prominent at specific DNA binding, and it can be common for small-domain proteins to balance movements on the DNA with the sequence recognition.

Transcription factor (TF) target search on genome is highly essential for gene expression and regulation. High-resolution determination of TF diffusion along DNA remains technically challenging. Here, we constructed a TF model system using the plant WRKY domain protein in complex with DNA from crystallography and demonstrated microsecond diffusion dynamics of WRKY on DNA by employing all-atom molecular-dynamics (MD) simulations. Notably, we found that WRKY preferentially binds to one strand of DNA with significant energetic bias compared with the other, or nonpreferred strand. The preferential DNA-strand binding becomes most prominent in the static process, from nonspecific to specific DNA binding, but less distinct during diffusive movements of the domain protein on the DNA. Remarkably, without employing acceleration forces or bias, we captured a complete one-base-pair stepping cycle of the protein tracking along major groove of DNA with a homogeneous poly-adenosine sequence, as individual hydrogen bonds break and reform at the protein–DNA binding interface. Further DNA-groove tracking motions of the protein forward or backward, with occasional sliding as well as strand crossing to minor groove of DNA, were also captured. The processive diffusion of WRKY along DNA has been further sampled via coarse-grained MD simulations. The study thus provides structural dynamics details on diffusion of a small TF domain protein, suggests how the protein approaches a specific recognition site on DNA, and supports further high-precision experimental detection. The stochastic movements revealed in the TF diffusion also provide general clues about how other protein walkers step and slide along DNA.

New insights into protein-DNA binding specificity from hydrogen bond based comparative study

Knowledge of protein-DNA binding specificity has important implications in understanding DNA metabolism, transcriptional regulation and developing therapeutic drugs. Previous studies demonstrated hydrogen bonds between amino acid side chains and DNA bases play major roles in specific protein-DNA interactions. In this paper, we investigated the roles of individual DNA strands and protein secondary structure types in specific protein-DNA recognition based on side chain-base hydrogen bonds. By comparing the contribution of each DNA strand to the overall binding specificity between DNA-binding proteins with different degrees of binding specificity, we found that highly specific DNA-binding proteins show balanced hydrogen bonding with each of the two DNA strands while multi-specific DNA binding proteins are generally biased towards one strand. Protein-base pair hydrogen bonds, in which both bases of a base pair are involved in forming hydrogen bonds with amino acid side chains, are more prevalent in the highly specific protein-DNA complexes than those in the multi-specific group. Amino acids involved in side chain-base hydrogen bonds favor strand and coil secondary structure types in highly specific DNA-binding proteins while multi-specific DNA-binding proteins prefer helices.

Structural Insights Into How Proteoglycans Determine Chemokine-CXCR1/CXCR2 Interactions: Progress and Challenges

Proteoglycans (PGs), present in diverse environments, such as the cell membrane surface, extracellular milieu, and intracellular granules, are fundamental to life. Sulfated glycosaminoglycans (GAGs) are covalently attached to the core protein of proteoglycans. PGs are complex structures, and are diverse in terms of amino acid sequence, size, shape, and in the nature and number of attached GAG chains, and this diversity is further compounded by the phenomenal diversity in GAG structures. Chemokines play vital roles in human pathophysiology, from combating infection and cancer to leukocyte trafficking, immune surveillance, and neurobiology. Chemokines mediate their function by activating receptors that belong to the GPCR class, and receptor interactions are regulated by how, when, and where chemokines bind GAGs. GAGs fine-tune chemokine function by regulating monomer/dimer levels and chemotactic/haptotactic gradients, which are also coupled to how they are presented to their receptors. Despite their small size and similar structures, chemokines show a range of GAG-binding geometries, affinities, and specificities, indicating that chemokines have evolved to exploit the repertoire of chemical and structural features of GAGs. In this review, we summarize the current status of research on how GAG interactions regulate ELR-chemokine activation of CXCR1 and CXCR2 receptors, and discuss knowledge gaps that must be overcome to establish causal relationships governing the impact of GAG interactions on chemokine function in human health and disease.

Multiple protein-DNA interfaces unravelled by evolutionary information, physico-chemical and geometrical properties

Interactions between proteins and nucleic acids are at the heart of many essential biological processes. Despite increasing structural information about how these interactions may take place, our understanding of the usage made of protein surfaces by nucleic acids is still very limited. This is in part due to the inherent complexity associated to protein surface deformability and evolution. In this work, we present a method that contributes to decipher such complexity by predicting protein-DNA interfaces and characterizing their properties. It relies on three biologically and physically meaningful descriptors, namely evolutionary conservation, physico-chemical properties and surface geometry. We carefully assessed its performance on several hundreds of protein structures and compared it to several machine-learning state-of-the-art methods. Our approach achieves a higher sensitivity compared to the other methods, with a similar precision. Importantly, we show that it is able to unravel ‘hidden’ binding sites by applying it to unbound protein structures and to proteins binding to DNA via multiple sites and in different conformations. It is also applicable to the detection of RNA-binding sites, without significant loss of performance. This confirms that DNA and RNA-binding sites share similar properties. Our method is implemented as a fully automated tool, JETDNA2, freely accessible at: http://www.lcqb.upmc.fr/JET2DNA. We also provide a new dataset of 187 protein-DNA complex structures, along with a subset of 82 associated unbound structures. The set represents the largest body of high-resolution crystallographic structures of protein-DNA complexes, use biological protein assemblies as DNA-binding units, and covers all major types of protein-DNA interactions. It is available at: http://www.lcqb.upmc.fr/PDNAbenchmarks.

Protein-DNA interactions are essential to living organisms and their impairment is associated to many diseases. For these reasons, they have become increasingly important therapeutic targets. Experimental structure determination has revealed different binding motifs and modes, associated to different functions. Yet, the available structural data gives us only a glimpse of the multiplicity and complexity of protein surface usage by DNA. In this work, we use a three-layer model to describe and predict DNA-binding sites at protein surfaces. Given a protein, we consider the way its residues are conserved through evolution, their physico-chemical properties and geometrical shapes to decrypt its surface. We are able to detect a large portion of interacting residues with good precision, even when they are ‘hidden’ by conformational changes. We highlight cases where one protein binds DNA via distinct regions to perform different functions. We are able to uncover the alternative binding sites and relate their properties with their specific roles. Our work can help guiding mutagenesis experiments and the development of new drugs specifically targeting one site while limiting possible side effects.

A structure-based model for the prediction of protein-RNA binding affinity

Protein-RNA recognition is highly affinity-driven and regulates a wide array of cellular functions. In this study, we have curated a binding affinity data set of 40 protein-RNA complexes, for which at least one unbound partner is available in the docking benchmark. The data set covers a wide affinity range of eight orders of magnitude as well as four different structural classes. On average, we find the complexes with single-stranded RNA have the highest affinity, whereas the complexes with the duplex RNA have the lowest. Nevertheless, free energy gain upon binding is the highest for the complexes with ribosomal proteins and the lowest for the complexes with tRNA with an average of -5.7 cal/mol/Å2 in the entire data set. We train regression models to predict the binding affinity from the structural and physicochemical parameters of protein-RNA interfaces. The best fit model with the lowest maximum error is provided with three interface parameters: relative hydrophobicity, conformational change upon binding and relative hydration pattern. This model has been used for predicting the binding affinity on a test data set, generated using mutated structures of yeast aspartyl-tRNA synthetase, for which experimentally determined ΔG values of 40 mutations are available. The predicted ΔGempirical values highly correlate with the experimental observations. The data set provided in this study should be useful for further development of the binding affinity prediction methods. Moreover, the model developed in this study enhances our understanding on the structural basis of protein-RNA binding affinity and provides a platform to engineer protein-RNA interfaces with desired affinity.

Diversity, versatility and complexity of bacterial gene regulation mechanisms: opportunities and drawbacks for applications in synthetic biology

Gene expression occurs in two essential steps: transcription and translation. In bacteria, the two processes are tightly coupled in time and space, and highly regulated. Tight regulation of gene expression is crucial. It limits wasteful consumption of resources and energy, prevents accumulation of potentially growth inhibiting reaction intermediates, and sustains the fitness and potential virulence of the organism in a fluctuating, competitive and frequently stressful environment. Since the onset of studies on regulation of enzyme synthesis, numerous distinct regulatory mechanisms modulating transcription and/or translation have been discovered. Mostly, various regulatory mechanisms operating at different levels in the flow of genetic information are used in combination to control and modulate the expression of a single gene or operon. Here, we provide an extensive overview of the very diverse and versatile bacterial gene regulatory mechanisms with major emphasis on their combined occurrence, intricate intertwinement and versatility. Furthermore, we discuss the potential of well-characterized basal expression and regulatory elements in synthetic biology applications, where they may ensure orthogonal, predictable and tunable expression of (heterologous) target genes and pathways, aiming at a minimal burden for the host.

Structural changes in DNA-binding proteins on complexation

Characterization and prediction of the DNA-biding regions in proteins are essential for our understanding of how proteins recognize/bind DNA. We analyze the unbound (U) and the bound (B) forms of proteins from the protein–DNA docking benchmark that contains 66 binary protein–DNA complexes along with their unbound counterparts. Proteins binding DNA undergo greater structural changes on complexation (in particular, those in the enzyme category) than those involved in protein–protein interactions (PPI). While interface atoms involved in PPI exhibit an increase in their solvent-accessible surface area (ASA) in the bound form in the majority of the cases compared to the unbound interface, protein–DNA interactions indicate increase and decrease in equal measure. In 25% structures, the U form has missing residues which are located in the interface in the B form. The missing atoms contribute more toward the buried surface area compared to other interface atoms. Lys, Gly and Arg are prominent in disordered segments that get ordered in the interface on complexation. In going from U to B, there may be an increase in coil and helical content at the expense of turns and strands. Consideration of flexibility cannot distinguish the interface residues from the surface residues in the U form.

Dissecting macromolecular recognition sites in ribosome: implication to its self-assembly

Interactions between macromolecules play a crucial role in ribosome assembly that follows a highly coordinated process involving RNA folding and binding of ribosomal proteins (r-proteins). Although extensive studies have been carried out to understand macromolecular interactions in ribosomes, most of them are confined to either large or small ribosomal-subunit of few species. A comparative analysis of macromolecular interactions across different domains is still missing. We have analyzed the structural and physicochemical properties of protein-protein (PP), protein-RNA (PR) and RNA-RNA (RR) interfaces in small and large subunits of ribosomes, as well as in between the two subunits. Additionally, we have also developed Random Forest (RF) classifier to catalog the r-proteins. We find significant differences as well as similarities in macromolecular recognition sites between ribosomal assemblies of prokaryotes and eukaryotes. PR interfaces are substantially larger and have more ionic interactions than PP and RR interfaces in both prokaryotes and eukaryotes. PP, PR and RR interfaces in eukaryotes are well packed compared to those in prokaryotes. However, the packing density between the large and the small subunit interfaces in the entire assembly is strikingly low in both prokaryotes and eukaryotes, indicating the periodic association and dissociation of the two subunits during the translation. The structural and physicochemical properties of PR interfaces are used to predict the r-proteins in the assembly pathway into early, intermediate and late binders using RF classifier with an accuracy of 80%. The results provide new insights into the classification of r-proteins in the assembly pathway.

Determinants of Oligonucleotide Selectivity of APOBEC3B

APOBEC3B (A3B) is a prominent source of mutation in many cancers. To date, it has been difficult to capture the native protein-DNA interactions that confer A3B's substrate specificity by crystallography due to the highly dynamic nature of wild-type A3B active site. We use computational tools to restore a recent crystal structure of a DNA-bound A3B C-terminal domain mutant construct to its wild type sequence, and run molecular dynamics simulations to study its substrate recognition mechanisms. Analysis of these simulations reveal dynamics of the native A3Bctd-oligonucleotide interactions, including the experimentally inaccessible loop 1-oligonucleotide interactions. A second series of simulations in which the target cytosine nucleotide was computationally mutated from a deoxyribose to a ribose show a change in sugar ring pucker, leading to a rearrangement of the binding site and revealing a potential intermediate in the binding pathway. Finally, apo simulations of A3B, starting from the DNA-bound open state, experience a rapid and consistent closure of the binding site, reaching conformations incompatible with substrate binding. This study reveals a more realistic and dynamic view of the wild type A3B binding site and provides novel insights for structure-guided design efforts for A3B.

Lysines and Arginines play non-redundant roles in mediating chemokine-glycosaminoglycan interactions

Glycosaminoglycans (GAGs) bind a large array of proteins and mediate fundamental and diverse roles in human physiology. Ion pair interactions between protein lysines/arginines and GAG sulfates/carboxylates mediate binding. Neutrophil-activating chemokines (NAC) are GAG-binding proteins, and their sequences reveal high selectivity for lysines over arginines indicating they are functionally not equivalent. NAC binding to GAGs impacts gradient formation, receptor functions, and endothelial activation, which together regulate different components of neutrophil migration. We characterized the consequence of mutating lysine to arginine in NAC CXCL8, a well-characterized GAG-binding protein. We chose three lysines — two highly conserved lysines (K20 and K64) and a CXCL8-specific lysine (K67). Interestingly, the double K64R/K20R and K64R/K67R mutants are highly impaired in recruiting neutrophils in a mouse model. Further, both the mutants bind GAG heparin with higher affinity but show similar receptor activity. NMR and MD studies indicate that the structures are essentially identical to the WT, but the mutations alter the network of intramolecular ion pair interactions. These observations collectively indicate that the reduced in vivo recruitment is due to altered GAG interactions, higher GAG binding affinity can be detrimental, and specificity of lysines fine-tunes in vivo GAG interactions and function.

Quantifying Interactions of Nucleobase Atoms with Model Compounds for the Peptide Backbone and Glutamine and Asparagine Side Chains in Water

Alkylureas display hydrocarbon and amide groups, the primary functional groups of proteins. To obtain the thermodynamic information that is needed to analyze interactions of amides and proteins with nucleobases and nucleic acids, we quantify preferential interactions of alkylureas with nucleobases differing in the amount and composition of water-accessible surface area (ASA) by solubility assays. Using an established additive ASA-based analysis, we interpret these thermodynamic results to determine interactions of each alkylurea with five types of nucleobase unified atoms (carbonyl sp²O, amino sp³N, ring sp²N, methyl sp³C, and ring sp²C). All alkylureas interact favorably with nucleobase sp²C and sp³C atoms; these interactions become more favorable with an increasing level of alkylation of urea. Interactions with nucleobase sp²O are most favorable for urea, less favorable for methylurea and ethylurea, and unfavorable for dialkylated ureas. Contributions to overall alkylurea-nucleobase interactions from interactions with each nucleobase atom type are proportional to the ASA of that atom type with proportionality constant (interaction strength) α, as observed previously for urea. Trends in α-values for interactions of alkylureas with nucleobase atom types parallel those for corresponding amide compound atom types, offset because nucleobase α-values are more favorable. Comparisons between ethylated and methylated ureas show interactions of amide compound sp³C with nucleobase sp²C, sp³C, sp²N, and sp³N atoms are favorable while amide sp³C-nucleobase sp²O interactions are unfavorable. Strongly favorable interactions of urea with nucleobase sp²O but weakly favorable interactions with nucleobase sp³N indicate that amide sp²N-nucleobase sp²O and nucleobase sp³N-amide sp²O hydrogen bonding (NH···O═C) interactions are favorable while amide sp²N-nucleobase sp³N interactions are unfavorable. These favorable amide-nucleobase hydrogen bonding interactions are prevalent in specific protein-nucleotide complexes.

A licensing step links AID to transcription elongation for mutagenesis in B cells

Activation-induced deaminase (AID) mutates the immunoglobulin (Ig) genes to initiate somatic hypermutation (SHM) and class switch recombination (CSR) in B cells, thus underpinning antibody responses. AID mutates a few hundred other loci, but most AID-occupied genes are spared. The mechanisms underlying productive deamination versus non-productive AID targeting are unclear. Here we show that three clustered arginine residues define a functional AID domain required for SHM, CSR, and off-target activity in B cells without affecting AID deaminase activity or Escherichia coli mutagenesis. Both wt AID and mutants with single amino acid replacements in this domain broadly associate with Spt5 and chromatin and occupy the promoter of AID target genes. However, mutant AID fails to occupy the corresponding gene bodies and loses association with transcription elongation factors. Thus AID mutagenic activity is determined not by locus occupancy but by a licensing mechanism, which couples AID to transcription elongation.

Activation-induced deaminase (AID) is important for inducing desirable mutations at the B cell receptor genes for effective antibody responses. Here the authors show that three key arginine residues of AID link AID-chromatin association with transcription elongation to license AID for specific mutagenesis in B cells.

Analysis of Evolutionarily Independent Protein-RNA Complexes Yields a Criterion to Evaluate the Relevance of Prebiotic Scenarios

A central difficulty facing study of the origin of life on Earth is evaluating the relevance of different proposed prebiotic scenarios. Perhaps the most established feature of the origin of life was the progression through an RNA World, a prebiotic stage dominated by functional RNA. We use the appearance of proteins in the RNA World to understand the prebiotic milieu and develop a criterion to evaluate proposed synthetic scenarios. Current consensus suggests that the earliest amino acids of the genetic code were anionic or small hydrophobic or polar amino acids. However, the ability to interact with the RNA World would have been a crucial feature of early proteins. To determine which amino acids would be important for the RNA World, we analyze non-biological protein-aptamer complexes in which the RNA or DNA is the result of in vitro evolution. This approach avoids confounding effects of biological context and evolutionary history. We use bioinformatic analysis and molecular dynamics simulations to characterize these complexes. We find that positively charged and aromatic amino acids are over-represented whereas small hydrophobic amino acids are under-represented. Binding enthalpy is found to be primarily electrostatic, with positively charged amino acids contributing cooperatively to binding enthalpy. Arginine dominates all modes of interaction at the interface. These results suggest that proposed prebiotic syntheses must be compatible with cationic amino acids, particularly arginine or a biophysically similar amino acid, in order to be relevant to the invention of protein by the RNA World.

The influence of like-charge attraction on the structure and dynamics of ionic liquids: NMR chemical shifts, quadrupole coupling constants, rotational correlation times and failure of Stokes–Einstein–Debye

The formation of clusters of like-charge influences the structure and dynamics of ionic liquids.

A structural dissection of protein–RNA interactions based on different RNA base areas of interfaces

Protein–RNA interactions are very common cellular processes, but the mechanisms of interactions are not fully understood, mainly due to the complicated RNA structures. By the elaborate investigation on RNA structures of protein–RNA complexes, it was firstly found in this paper that RNAs in these complexes could be clearly classified into three classes (high, medium and low) based on the different levels of P_base (the percentage of base area buried in the RNA interface). In view of the three RNA classes, more detailed analyses on protein–RNA interactions were comprehensively performed from various aspects, including interface area, structure, composition and interaction force, so as to achieve a deeper understanding of the recognition specificity for the three classes of protein–RNA interactions. According to our classification strategy, the three complex classes have significant differences in terms of almost all properties. Complexes in the high class have short and extended RNA structures and behave like protein–ssDNA interactions. Their hydrogen bonds and hydrophobic interactions are strong. For complexes in low class, their RNA structures are mainly double-stranded, like protein–dsDNA interactions, and electrostatic interactions frequently occur. The complexes in medium class have the longest RNA chains and largest average interface area. Meanwhile, they do not show any preference for the interaction force. On average, in terms of composition, secondary structures and intermolecular physicochemical properties, significant feature preferences can be observed in high and low complexes, but no highly specific features are found for medium complexes. We found that our proposed P_base is an important parameter which can be used as a new determinant to distinguish protein–RNA complexes. For high and low complexes, we can more easily understand the specificity of the recognition process from the interface features than for medium complexes. In the future, medium complexes should be our research focus to further structurally analyze from more feature aspects. Overall, this study may contribute to further understanding of the mechanism of protein–RNA interactions on a more detailed level.

Qualitative and quantitative measurements of the influence of structure and composition of RNA interfaces on protein–RNA interactions.

The identification of FANCD2 DNA binding domains reveals nuclear localization sequences

Fanconi anemia (FA) is a recessive genetic disorder characterized by congenital abnormalities, progressive bone-marrow failure, and cancer susceptibility. The FA pathway consists of at least 21 FANC genes (FANCA-FANCV), and the encoded protein products interact in a common cellular pathway to gain resistance against DNA interstrand crosslinks. After DNA damage, FANCD2 is monoubiquitinated and accumulates on chromatin. FANCD2 plays a central role in the FA pathway, using yet unidentified DNA binding regions. By using synthetic peptide mapping and DNA binding screen by electromobility shift assays, we found that FANCD2 bears two major DNA binding domains predominantly consisting of evolutionary conserved lysine residues. Furthermore, one domain at the N-terminus of FANCD2 bears also nuclear localization sequences for the protein. Mutations in the bifunctional DNA binding/NLS domain lead to a reduction in FANCD2 monoubiquitination and increase in mitomycin C sensitivity. Such phenotypes are not fully rescued by fusion with an heterologous NLS, which enable separation of DNA binding and nuclear import functions within this domain that are necessary for FANCD2 functions. Collectively, our results enlighten the importance of DNA binding and NLS residues in FANCD2 to activate an efficient FA pathway.

Computational investigation of proton transfer, pKa shifts and pH-optimum of protein-DNA and protein-RNA complexes

Protein-nucleic acid interactions play a crucial role in many biological processes. This work investigates the changes of pKa values and protonation states of ionizable groups (including nucleic acid bases) that may occur at protein-nucleic acid binding. Taking advantage of the recently developed pKa calculation tool DelphiPka, we utilize the large protein-nucleic acid interaction database (NPIDB database) to model pKa shifts caused by binding. It has been found that the protein's interfacial basic residues experience favorable electrostatic interactions while the protein acidic residues undergo proton uptake to reduce the energy cost upon the binding. This is in contrast with observations made for protein-protein complexes. In terms of DNA/RNA, both base groups and phosphate groups of nucleotides are found to participate in binding. Some DNA/RNA bases undergo pKa shifts at complex formation, with the binding process tending to suppress charged states of nucleic acid bases. In addition, a weak correlation is found between the pH-optimum of protein-DNA/RNA binding free energy and the pH-optimum of protein folding free energy. Overall, the pH-dependence of protein-nucleic acid binding is not predicted to be as significant as that of protein-protein association. Proteins 2017; 85:282-295. © 2016 Wiley Periodicals, Inc.

Dihedral angle preferences of DNA and RNA binding amino acid residues in proteins

A protein can interact with DNA or RNA molecules to perform various cellular processes. Identifying or analyzing DNA/RNA binding site amino acid residues is important to understand molecular recognition process. It is quite possible to accurately model DNA/RNA binding amino acid residues in experimental protein-DNA/RNA complex by using the electron density map whereas, locating/modeling the binding site amino acid residues in the predicted three dimensional structures of DNA/RNA binding proteins is still a difficult task. Considering the above facts, in the present work, we have carried out a comprehensive analysis of dihedral angle preferences of DNA and RNA binding site amino acid residues by using a classical Ramachandran map. We have computed backbone dihedral angles of non-DNA/RNA binding residues and used as control dataset to make a comparative study. The dihedral angle preference of DNA and RNA binding site residues of twenty amino acid type is presented. Our analysis clearly revealed that the dihedral angles (φ, ψ) of DNA/RNA binding amino acid residues prefer to occupy (-89° to -60°, -59° to -30°) bins. The results presented in this paper will help to model/locate DNA/RNA binding amino acid residues with better accuracy.

Controlling the kinetic and thermodynamic stability of cationic clusters by the addition of molecules or counterions

We discuss the stability of cationic clusters when adding molecules or counterions, and predict their occurrence in gas phase experiments.

Investigating the effect of charged amino acids on DNA conformation in EcoRI-DNA complex: a molecular dynamics simulation study

Sequence-specific binding of proteins to DNA is essential for almost all the cellular processes like transcription, translation, replication, etc. One among the various mechanisms that has been identified so far that contributes to the specificity in protein-DNA interaction is the DNA conformational change. Electrostatic neutralization of the phosphate groups by the positively charged amino acid residues in proteins is thought to bring about such conformational changes in DNA. Here, we employ molecular dynamics simulations to examine the effect of charge on amino acids Lys113, Arg145, and Asp91 which are attached to the scissile phosphate on the conformation of DNA in EcoRI-DNA complex. The results indicate that the charge of these amino acids is essential for maintaining the local conformation of DNA in the EcoRI-bound form. Interestingly, we observe that the positively charged amino acids Lys113 and Arg145 have a long-range influence on the DNA conformation, whereas the negatively charged amino acid Asp91 has only a localized effect on the DNA conformation. The charge on the amino acids also alters the collective dynamics of EcoRI. Collectively, the results shed light on the diversity of the effect of charges on DNA conformation as well as on protein dynamics.

CD72 negatively regulates B lymphocyte responses to the lupus-related endogenous toll-like receptor 7 ligand Sm/RNP

Akatsu and colleagues show that CD72 specifically recognizes Sm/RNP, a lupus-related self-antigen and an endogenous TLR7 ligand, and inhibits B cell responses to Sm/RNP. In mice, CD72 prevents production of anti-Sm/RNP antibodies crucial for lupus development.

Toll-like receptor 7 (TLR7) plays an essential role in development of systemic lupus erythematosus by co-stimulating B cells reactive to the endogenous TLR7 ligand Sm/ribonucleoprotein (RNP), a crucial lupus self-antigen. However, how the TLR7-mediated autoimmune response is regulated is not yet known. In this study, we demonstrate that CD72, an inhibitory B cell co-receptor known to prevent development of lupus, recognizes Sm/RNP at the extracellular C-type lectin-like domain (CTLD) and specifically inhibits B cell response to Sm/RNP. Moreover, the CTLD of CD72^c, a lupus-susceptible allele, binds to Sm/RNP less strongly than that of lupus-resistant CD72^a. Reduced binding of CD72^c is supported by x-ray crystallographic analysis that reveals a considerable alteration in charge at the putative ligand-binding site. Thus, CD72 appears to specifically inhibit B cell response to the endogenous TLR7 ligand Sm/RNP through CTLD-mediated recognition of Sm/RNP, thereby preventing production of anti-Sm/RNP antibody crucial for development of lupus.

Changes in conformational dynamics of basic side chains upon protein-DNA association

Basic side chains play major roles in recognition of nucleic acids by proteins. However, dynamic properties of these positively charged side chains are not well understood. In this work, we studied changes in conformational dynamics of basic side chains upon protein–DNA association for the zinc-finger protein Egr-1. By nuclear magnetic resonance (NMR) spectroscopy, we characterized the dynamics of all side-chain cationic groups in the free protein and in the complex with target DNA. Our NMR order parameters indicate that the arginine guanidino groups interacting with DNA bases are strongly immobilized, forming rigid interfaces. Despite the strong short-range electrostatic interactions, the majority of the basic side chains interacting with the DNA phosphates exhibited high mobility, forming dynamic interfaces. In particular, the lysine side-chain amino groups exhibited only small changes in the order parameters upon DNA-binding. We found a similar trend in the molecular dynamics (MD) simulations for the free Egr-1 and the Egr-1–DNA complex. Using the MD trajectories, we also analyzed side-chain conformational entropy. The interfacial arginine side chains exhibited substantial entropic loss upon binding to DNA, whereas the interfacial lysine side chains showed relatively small changes in conformational entropy. These data illustrate different dynamic characteristics of the interfacial arginine and lysine side chains.

G-LoSA: An efficient computational tool for local structure-centric biological studies and drug design

Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G-LoSA. G-LoSA aligns protein local structures in a sequence order independent way and provides a GA-score, a chemical feature-based and size-independent structure similarity score. Our benchmark validation shows the robust performance of G-LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure-centric comparative biology studies. In particular, G-LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G-LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer-aided drug design. We hope that G-LoSA can be a useful computational method for exploring interesting biological problems through large-scale comparison of protein local structures and facilitating drug discovery research and development. G-LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/.

DOMMINO 2.0: integrating structurally resolved protein-, RNA-, and DNA-mediated macromolecular interactions

Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction's mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein-protein interactions or protein-DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1,040,000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43,000 RNA-mediated interactions, and ∼12,000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N- termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary interaction and interaction interface. Database URL: http://dommino.org.

Intrinsic Relative Stabilities of the Neutral Tautomers of Arginine Side-Chain Models

The specific protonation state of amino acids is crucial for the physicochemical properties of proteins and their biological functions. These protonation states influence, for instance, properties related to hydrogen bonding, solubility, and folding. pKa calculations for proteins are, therefore, important and require, in principle, a specification of the most stable protonated and deprotonated forms of each titratable group. This is complicated by the existence of multiple tautomers, like the five neutral tautomers of the guanidine moiety in arginine. In this study, the compounds N-methyl-guanidine and N-ethyl-guanidine were used to model the charged and all neutral protonation states of the arginine side chain. The relative stabilities of all five neutral tautomers were investigated systematically for the first time, using quantum-mechanical calculations. These relative stabilities were obtained in vacuo, water and chloroform, by combining the quantum-mechanical calculations with a continuum solvation model. The water model was used to represent arginines exposed to an aqueous solution, whereas the chloroform model has a polarity representative of a protein core or a membrane. This allowed determining the relative pKa's associated with each neutral tautomer in these environments. A key result is that significant differences in stability are found between the neutral tautomers, in both water and chloroform. Some tautomers are consistently found to be the most stable. These findings will be helpful to refine pKa calculations in proteins.

Physicochemical Properties of Ion Pairs of Biological Macromolecules

Ion pairs (also known as salt bridges) of electrostatically interacting cationic and anionic moieties are important for proteins and nucleic acids to perform their function. Although numerous three-dimensional structures show ion pairs at functionally important sites of biological macromolecules and their complexes, the physicochemical properties of the ion pairs are not well understood. Crystal structures typically show a single state for each ion pair. However, recent studies have revealed the dynamic nature of the ion pairs of the biological macromolecules. Biomolecular ion pairs undergo dynamic transitions between distinct states in which the charged moieties are either in direct contact or separated by water. This dynamic behavior is reasonable in light of the fundamental concepts that were established for small ions over the last century. In this review, we introduce the physicochemical concepts relevant to the ion pairs and provide an overview of the recent advancement in biophysical research on the ion pairs of biological macromolecules.

C-Terminal Domain of Integrase Binds between the Two Active Sites

HIV integrase (HIV-IN), one of three HIV enzymes, is a target for the treatment of AIDS, but the full biological assembly has been difficult to characterize, hampering inhibitor design. The recent crystallographic structures of integrase from prototype foamy virus (PFV-IN) with bound DNA were a breakthrough, revealing how viral DNA organizes two integrase dimers into a tetramer that has the two active sites appropriately spaced for insertion of the viral DNA into host DNA. The organization of domains within each PFV-IN protein chain, however, varies significantly from that found in HIV-IN structures. With the goal of identifying shared structural characteristics, the interactions among components of the PFV-IN and HIV-IN assemblies were investigated with the macromolecular docking program DOT. DOT performs an exhaustive, rigid-body search between two macromolecules. Computational docking reproduced the crystallographic interactions of the PFV-IN catalytic and N-terminal domains with viral DNA and found similar viral DNA interactions for HIV-IN. Computational docking did not reproduce the crystallographic interactions of the PFV-IN C-terminal domain (CTD). Instead, two symmetry-related positions were found for the PFV-IN CTD that indicate formation of a CTD dimer between the two active sites. Our predicted CTD dimer is consistent with cross-linking studies showing interactions of the CTD with viral DNA that appear to be blocked in the PFV-IN structures. The CTD dimer can insert two arginine-rich loops between the two bound vDNA molecules and the host DNA, a region that is unoccupied in the PFV-IN crystallographic structures. The positive potential from these two loops would alleviate the large negative potential created by the close proximity of two viral vDNA ends, helping to bring together the two active sites and assisting host DNA binding. This study demonstrates the ability of computational docking to evaluate complex crystallographic assemblies, identify interactions that are influenced by the crystal environment, and provide plausible alternatives.

In Silico Characterization and Analysis of RTBP1 and NgTRF1 Protein Through MD Simulation and Molecular Docking: A Comparative Study

Gaining access to sequence and structure information of telomere-binding proteins helps in understanding the essential biological processes involve in conserved sequence-specific interaction between DNA and the proteins. Rice telomere-binding protein (RTBP1) and Nicotiana glutinosa telomere repeat binding factor (NgTRF1) are helix-turn-helix motif type of proteins that plays role in telomeric DNA protection and length regulation. Both the proteins share same type of domain, but till now there is very less communication on the in silico studies of these complete proteins. Here we intend to do a comparative study between two proteins through modeling of the complete proteins, physiochemical characterization, MD simulation and DNA-protein docking. I-TASSER and CLC protein work bench was performed to find out the protein 3D structure as well as the different parameters to characterize the proteins. MD simulation was completed by GROMOS forcefield of GROMACS for 10 ns of time stretch. The simulated 3D structures were docked with template DNA (3D DNA modeled through 3D-DART) of TTTAGGG conserved sequence motif using HADDOCK Web server. By digging up all the facts about the proteins, it was revealed that around 120 amino acids in the tail part were showing a good sequence similarity between the proteins. Molecular modeling, sequence characterization and secondary structure prediction also indicate the similarity between the protein's structure and sequence. The result of MD simulation highlights on the RMSD, RMSF, Rg, PCA and energy plots which also conveys the similar type of motional behavior between them. The best complex formation for both the proteins in docking result also indicates for the first interaction site which is mainly the helix3 region of the DNA-binding domain. The overall computational analysis reveals that RTBP1 and NgTRF1 proteins display good amount of similarity in their physicochemical properties, structure, dynamics and binding mode.

The Inner Membrane Protein PilG Interacts with DNA and the Secretin PilQ in Transformation

Expression of type IV pili (Tfp), filamentous appendages emanating from the bacterial surface, is indispensable for efficient neisserial transformation. Tfp pass through the secretin pore consisting of the membrane protein PilQ. PilG is a polytopic membrane protein, conserved in Gram-positive and Gram-negative bacteria, that is required for the biogenesis of neisserial Tfp. PilG null mutants are devoid of pili and non-competent for transformation. Here, recombinant full-length, truncated and mutated variants of meningococcal PilG were overexpressed, purified and characterized. We report that meningococcal PilG directly binds DNA in vitro, detected by both an electromobility shift analysis and a solid phase overlay assay. PilG DNA binding activity was independent of the presence of the consensus DNA uptake sequence. PilG-mediated DNA binding affinity was mapped to the N-terminus and was inactivated by mutation of residues 43 to 45. Notably, reduced meningococcal transformation of DNA in vivo was observed when PilG residues 43 to 45 were substituted by alanine in situ, defining a biologically significant DNA binding domain. N-terminal PilG also interacted with the N-terminal region of PilQ, which previously was shown to bind DNA. Collectively, these data suggest that PilG and PilQ in concert bind DNA during Tfp-mediated transformation.

A Non-Invasive NMR Method Based on Histidine Imidazoles to Analyze the pH-Modulation of Protein-Nucleic Acid Interfaces

A useful (2) J(N-H) coupling-based NMR spectroscopic approach is proposed to unveil, at the molecular level, the contribution of the imidazole groups of histidines from RNA/DNA-binding proteins on the modulation of binding to nucleic acids by pH. Such protonation/deprotonation events have been monitored on the single His96 located at the second RNA/DNA recognition motif (RRM2) of T-cell intracellular antigen-1 (TIA-1) protein. The pKa values of the His96 ionizable groups were substantially higher in the complexes with short U-rich RNA and T-rich DNA oligonucleotides than those of the isolated TIA-1 RRM2. Herein, the methodology applied to determine changes in pKa of histidine side chains upon DNA/RNA binding, gives valuable information to understand the pH effect on multidomain DNA/RNA-binding proteins that shuttle among different cellular compartments.

The structure of bradyzoite-specific enolase from Toxoplasma gondii reveals insights into its dual cytoplasmic and nuclear functions

In addition to catalyzing a central step in glycolysis, enolase assumes a remarkably diverse set of secondary functions in different organisms, including transcription regulation as documented for the oncogene c-Myc promoter-binding protein 1. The apicomplexan parasite Toxoplasma gondii differentially expresses two nuclear-localized, plant-like enolases: enolase 1 (TgENO1) in the latent bradyzoite cyst stage and enolase 2 (TgENO2) in the rapidly replicative tachyzoite stage. A 2.75 Å resolution crystal structure of bradyzoite enolase 1, the second structure to be reported of a bradyzoite-specific protein in Toxoplasma, captures an open conformational state and reveals that distinctive plant-like insertions are located on surface loops. The enolase 1 structure reveals that a unique residue, Glu164, in catalytic loop 2 may account for the lower activity of this cyst-stage isozyme. Recombinant TgENO1 specifically binds to a TTTTCT DNA motif present in the cyst matrix antigen 1 (TgMAG1) gene promoter as demonstrated by gel retardation. Furthermore, direct physical interactions of both nuclear TgENO1 and TgENO2 with the TgMAG1 gene promoter are demonstrated in vivo using chromatin immunoprecipitation (ChIP) assays. Structural and biochemical studies reveal that T. gondii enolase functions are multifaceted, including the coordination of gene regulation in parasitic stage development. Enolase 1 provides a potential lead in the design of drugs against Toxoplasma brain cysts.

Molecular architecture of protein-RNA recognition sites

The molecular architecture of protein-RNA interfaces are analyzed using a non-redundant dataset of 152 protein-RNA complexes. We find that an average protein-RNA interface is smaller than an average protein-DNA interface but larger than an average protein-protein interface. Among the different classes of protein-RNA complexes, interfaces with tRNA are the largest, while the interfaces with the single-stranded RNA are the smallest. Significantly, RNA contributes more to the interface area than its partner protein. Moreover, unlike protein-protein interfaces where the side chain contributes less to the interface area compared to the main chain, the main chain and side chain contributions flipped in protein-RNA interfaces. We find that the protein surface in contact with the RNA in protein-RNA complexes is better packed than that in contact with the DNA in protein-DNA complexes, but loosely packed than that in contact with the protein in protein-protein complexes. Shape complementarity and electrostatic potential are the two major factors that determine the specificity of the protein-RNA interaction. We find that the H-bond density at the protein-RNA interfaces is similar with that of protein-DNA interfaces but higher than the protein-protein interfaces. Unlike protein-DNA interfaces where the deoxyribose has little role in intermolecular H-bonds, due to the presence of an oxygen atom at the 2' position, the ribose in RNA plays significant role in protein-RNA H-bonds. We find that besides H-bonds, salt bridges and stacking interactions also play significant role in stabilizing protein-nucleic acids interfaces; however, their contribution at the protein-protein interfaces is insignificant.

In silico characterization and analysis of RTBP1 and NgTRF1 protein through MD simulation and molecular docking - A comparative study

Gaining access to sequence and structure information of telomere binding proteins helps in understanding the essential biological processes involve in conserved sequence specific interaction between DNA and the proteins. Rice telomere binding protein (RTBP1) and Nicotiana glutinosa telomere repeat binding factor (NgTRF1) are helix turn helix motif type of proteins that plays role in telomeric DNA protection and length regulation. Both the proteins share same type of domain but till now there is very less communication on the in silico studies of these complete proteins.Here we intend to do a comparative study between two proteins through modeling of the complete proteins, physiochemical characterization, MD simulation and DNA-protein docking. I-TASSER and CLC protein work bench was performed to find out the protein 3D structure as well as the different parameters to characterize the proteins. MD simulation was completed by GROMOS forcefield of GROMACS for 10 ns of time stretch. The simulated 3D structures were docked with template DNA (3D DNA modeled through 3D-DART) of TTTAGGG conserved sequence motif using HADDOCK web server.Digging up all the facts about the proteins it was reveled that around 120 amino acids in the tail part was showing a good sequence similarity between the proteins. Molecular modeling, sequence characterization and secondary structure prediction also indicates the similarity between the protein's structure and sequence. The result of MD simulation highlights on the RMSD, RMSF, Rg, PCA and Energy plots which also conveys the similar type of motional behavior between them. The best complex formation for both the proteins in docking result also indicates for the first interaction site which is mainly the helix3 region of the DNA binding domain. The overall computational analysis reveals that RTBP1 and NgTRF1 proteins display good amount of similarity in their physicochemical properties, structure, dynamics and binding mode.

Can We Execute Stable Microsecond-Scale Atomistic Simulations of Protein-RNA Complexes?

We report over 30 μs of unrestrained molecular dynamics simulations of six protein-RNA complexes in explicit solvent. We utilize the AMBER ff99bsc0χ(OL3) RNA force field combined with the ff99SB protein force field and its more recent ff12SB version with reparametrized side-chain dihedrals. The simulations show variable behavior, ranging from systems that are essentially stable to systems with progressive deviations from the experimental structure, which we could not stabilize anywhere close to the starting experimental structure. For some systems, microsecond-scale simulations are necessary to achieve stabilization after initial sizable structural perturbations. The results show that simulations of protein-RNA complexes are challenging and every system should be treated individually. The simulations are affected by numerous factors, including properties of the starting structures (the initially high force field potential energy, resolution limits, conformational averaging, crystal packing, etc.), force field imbalances, and real flexibility of the studied systems. These factors, and thus the simulation behavior, differ from system to system. The structural stability of simulated systems does not correlate with the size of buried interaction surface or experimentally determined binding affinities but reflects the type of protein-RNA recognition. Protein-RNA interfaces involving shape-specific recognition of RNA are more stable than those relying on sequence-specific RNA recognition. The differences between the protein force fields are considerably smaller than the uncertainties caused by sampling and starting structures. The ff12SB improves description of the tyrosine side-chain group, which eliminates some problems associated with tyrosine dynamics.

Mining the entire Protein DataBank for frequent spatially cohesive amino acid patterns

BACKGROUND

The three-dimensional structure of a protein is an essential aspect of its functionality. Despite the large diversity in protein structures and functionality, it is known that there are common patterns and preferences in the contacts between amino acid residues, or between residues and other biomolecules, such as DNA. The discovery and characterization of these patterns is an important research topic within structural biology as it can give fundamental insight into protein structures and can aid in the prediction of unknown structures.

RESULTS

Here we apply an efficient spatial pattern miner to search for sets of amino acids that occur frequently in close spatial proximity in the protein structures of the Protein DataBank. This allowed us to mine for a new class of amino acid patterns, that we term FreSCOs (Frequent Spatially Cohesive Component sets), which feature synergetic combinations. To demonstrate the relevance of these FreSCOs, they were compared in relation to the thermostability of the protein structure and the interaction preferences of DNA-protein complexes. In both cases, the results matched well with prior investigations using more complex methods on smaller data sets.

CONCLUSIONS

The currently characterized protein structures feature a diverse set of frequent amino acid patterns that can be related to the stability of the protein molecular structure and that are independent from protein function or specific conserved domains.