Protein-nucleic acid recognition: statistical analysis of atomic interactions and influence of DNA structure

Recent advances in carbon quantum dots for gene delivery: A comprehensive review

Gene therapy is a revolutionary technology in healthcare that provides novel therapeutic options and has immense potential in addressing genetic illnesses, malignancies, and viral infections. Nevertheless, other obstacles still need to be addressed regarding safety, ethical implications, and technological enhancement. Nanotechnology and gene therapy fields have shown significant promise in transforming medical treatments by improving accuracy, effectiveness, and personalization. This review assesses the possible uses of gene therapy, its obstacles, and future research areas, specifically emphasizing the creative combination of gene therapy and nanotechnology. Nanotechnology is essential for gene delivery as it allows for the development of nano-scale carriers, such as carbon quantum dots (CQDs), which may effectively transport therapeutic genes into specific cells. CQDs exhibit distinctive physicochemical characteristics such as small size, excellent stability, and minimal toxicity, which render them highly favorable for gene therapy applications. The objective of this study is to review and describe the current advancements in the utilization of CQDs for gene delivery. Additionally, it intends to assess existing research, explore novel applications, and identify future opportunities and obstacles. This study offers a thorough summary of the current state and future possibilities of using CQDs for gene delivery. Combining recent research findings highlights the potential of CQDs to revolutionize gene therapy and its delivery methods.

qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids

Protein sequence-based predictors of nucleic acid (NA)-binding include methods that predict NA-binding proteins and NA-binding residues. The residue-level tools produce more details but suffer high computational cost since they must predict every amino acid in the input sequence and rely on multiple sequence alignments. We propose an alternative approach that predicts content (fraction) of the NA-binding residues, offering more information than the protein-level prediction and much shorter runtime than the residue-level tools. Our first-of-its-kind content predictor, qNABpredict, relies on a small, rationally designed and fast-to-compute feature set that represents relevant characteristics extracted from the input sequence and a well-parametrized support vector regression model. We provide two versions of qNABpredict, a taxonomy-agnostic model that can be used for proteins of unknown taxonomic origin and more accurate taxonomy-aware models that are tailored to specific taxonomic kingdoms: archaea, bacteria, eukaryota, and viruses. Empirical tests on a low-similarity test dataset show that qNABpredict is 100 times faster and generates statistically more accurate content predictions when compared to the content extracted from results produced by the residue-level predictors. We also show that qNABpredict's content predictions can be used to improve results generated by the residue-level predictors. We release qNABpredict as a convenient webserver and source code at http://biomine.cs.vcu.edu/servers/qNABpredict/. This new tool should be particularly useful to predict details of protein-NA interactions for large protein families and proteomes.

Biologically relevant small variations of intra-cellular pH can have significant effect on stability of protein-DNA complexes, including the nucleosome

Stability of a protein-ligand complex may be sensitive to pH of its environment. Here we explore, computationally, stability of a set of protein-nucleic acid complexes using fundamental thermodynamic linkage relationship. The nucleosome, as well as an essentially random selection of 20 protein complexes with DNA or RNA, are included in the analysis. An increase in intra-cellular/intra-nuclear pH destabilizes most complexes, including the nucleosome. We propose to quantify the effect by ΔΔG^0.3-the change in the binding free energy due to pH increase of 0.3 units, corresponding to doubling of the H ⁺ activity; variations of pH of this amplitude can occur in living cells, including in the course of the cell cycle, and in cancer cells relative to normal ones. We suggest, based on relevant experimental findings, a threshold of biological significance of 1 2 k B T ( ∼ 0.3 k c a l / m o l ) for changes of stability of chromatin-related protein-DNA complexes: a change in the binding affinity above the threshold may have biological consequences. We find that for 70% of the examined complexes, Δ Δ G 0.3 > 1 2 k B T (for 10%, ΔΔG^0.3 is between 3 and 4 k _B T). Thus, small but relevant variations of intra-nuclear pH of 0.3 may have biological consequences for many protein-nucleic acid complexes. The binding affinity between the histone octamer and its DNA, which directly affects the DNA accessibility in the nucleosome, is predicted to be highly sensitive to intra-nuclear pH. A variation of 0.3 units results in ΔΔG^0.3 ∼ 10k _B T ( ∼ 6 k c a l / m o l ) ; for spontaneous unwrapping of 20 bp long entry/exit fragments of the nucleosomal DNA, ΔΔG^0.3 = 2.2k _B T; partial disassembly of the nucleosome into the tetrasome is characterized by ΔΔG^0.3 = 5.2k _B T. The predicted pH -induced modulations of the nucleosome stability are significant enough to suggest that they may have consequences relevant to the biological function of the nucleosome. Accessibility of the nucleosomal DNA is predicted to positively correlate with pH variations during the cell cycle; an increase in intra-cellular pH seen in cancer cells is predicted to lead to a more accessible nucleosomal DNA; a drop in pH associated with apoptosis is predicted to make nucleosomal DNA less accessible. We speculate that processes that depend on accessibility to the DNA in the nucleosomes, such as transcription or DNA replication, might become upregulated due to relatively small, but nevertheless realistic increases of intra-nuclear pH.

Insights into DNA solvation found in protein-DNA structures

The proteins that bind double-helical DNA present various microenvironments that sense and/or induce signals in the genetic material. The high-resolution structures of protein-DNA complexes reveal the nature of both the microenvironments and the conformational responses in DNA and protein. Complex networks of interactions within the structures somehow tie the protein and DNA together and induce the observed spatial forms. Here we show how the cumulative buildup of amino acid atoms around the sugars, phosphates, and bases in different protein-DNA complexes produces a binding cloud around the double helix and how different types of atoms fill that cloud. Rather than focusing on the principles of molecular binding and recognition suggested by the arrangements of amino acids and nucleotides in the macromolecular complexes, we consider the proteins in contact with DNA as organized solvents. We describe differences in the mix of atoms that come in closest contact with DNA, subtle sequence-dependent features in the microenvironment of the sugar-phosphate backbone, a direct link between the localized buildup of ionic species and the electrostatic potential surfaces of the DNA bases, and sites of atomic buildup above and below the basepair planes that transmit the unique features of the base environments along the chain backbone. The inferences about solvation that can be drawn from the survey provide new stimuli for improvement of nucleic acid force fields and fresh ideas for exploration of the properties of DNA in solution.

Effects of External Perturbations on Protein Systems: A Microscopic View

Protein folding can be viewed as the origami engineering of biology resulting from the long process of evolution. Even decades after its recognition, research efforts worldwide focus on demystifying molecular factors that underlie protein structure-function relationships; this is particularly relevant in the era of proteopathic disease. A complex co-occurrence of different physicochemical factors such as temperature, pressure, solvent, cosolvent, macromolecular crowding, confinement, and mutations that represent realistic biological environments are known to modulate the folding process and protein stability in unique ways. In the current review, we have contextually summarized the substantial efforts in unveiling individual effects of these perturbative factors, with major attention toward bottom-up approaches. Moreover, we briefly present some of the biotechnological applications of the insights derived from these studies over various applications including pharmaceuticals, biofuels, cryopreservation, and novel materials. Finally, we conclude by summarizing the challenges in studying the combined effects of multifactorial perturbations in protein folding and refer to complementary advances in experiment and computational techniques that lend insights to the emergent challenges.

Alignment of major-groove hydrogen bond arrays uncovers shared information between different DNA sequences that bind the same protein

Protein-DNA binding is of a great interest due to its importance in many biological processes. Previous studies have presented many factors responsible for the recognition and specificity, but understanding the minimal informational requirements for proteins that bind to multiple DNA-sites is still an understudied area of bioinformatics. Here we focus on the hydrogen bonds displayed by the target DNA in the major groove that take part in protein-binding. We show that analyses focused on the base pair identity may overlook key hydrogen bonds. We have developed an algorithm that converts a nucleotide sequence into an array of hydrogen bond donors and acceptors and methyl groups. It then aligns these non-covalent interaction arrays to identify what information is being maintained among multiple DNA sequences. For three different DNA-binding proteins, Lactose repressor, controller protein and λ-CI repressor, we uncovered the minimal pattern of hydrogen bonds that are common amongst all the binding sequences. Notably in the three proteins, key interacting hydrogen bonds are maintained despite nucleobase mutations in the corresponding binding sites. We believe this work will be useful for developing new DNA binding proteins and shed new light on evolutionary relationships.

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.

Structure- and Interaction-Based Design of Anti-SARS-CoV-2 Aptamers

Aptamer selection against novel infections is a complicated and time-consuming approach. Synergy can be achieved by using computational methods together with experimental procedures. This study aims to develop a reliable methodology for a rational aptamer in silico et vitro design. The new approach combines multiple steps: (1) Molecular design, based on screening in a DNA aptamer library and directed mutagenesis to fit the protein tertiary structure; (2) 3D molecular modeling of the target; (3) Molecular docking of an aptamer with the protein; (4) Molecular dynamics (MD) simulations of the complexes; (5) Quantum-mechanical (QM) evaluation of the interactions between aptamer and target with further analysis; (6) Experimental verification at each cycle for structure and binding affinity by using small-angle X-ray scattering, cytometry, and fluorescence polarization. By using a new iterative design procedure, structure- and interaction-based drug design (SIBDD), a highly specific aptamer to the receptor-binding domain of the SARS-CoV-2 spike protein, was developed and validated. The SIBDD approach enhances speed of the high-affinity aptamers development from scratch, using a target protein structure. The method could be used to improve existing aptamers for stronger binding. This approach brings to an advanced level the development of novel affinity probes, functional nucleic acids. It offers a blueprint for the straightforward design of targeting molecules for new pathogen agents and emerging variants.

Principles of Amino Acid and Nucleotide Revealed by Binding Affinities between Homogeneous Oligopeptides and Single-stranded DNA Molecule s

We have determined the binding strengths between nucleotides of adenine, thymine, guanine and cytosine in homogeneous single stranded DNAs and homo-octapeptides consisting of 20 common amino acids. We use a bead-based fluorescence assay for these measurements in which octapeptides are immobilized on the bead surface and ssDNAs are in solutions. The results provide a molecular basis for analyzing selectivity, specificity and polymorphisms of amino-acid-nucleotide interactions. Comparative analyses of the distribution of the binding energies reveal unique binding strengths patterns assignable to each pair of DNA nucleotide and amino acid originating from the chemical structures. Pronounced favorable (such as Arg-G , etc.) and unfavorable (such as Ile-T , etc.) binding interactions can be identified in selected groups of amino acid and nucleotide pairs that could provide basis to elucidate energetics of amino-acid-nucleotide interactions. Such interaction selectivity, specificity and polymorphism manifest the contributions from DNA backbone, DNA bases, as well as main chain and side chain of the amino acids.

LISTERIN E3 Ubiquitin Ligase and Ribosome-Associated Quality Control (RQC) Mechanism

According to cellular demands, ribosomes synthesize and maintain the desired pool of proteins inside the cell. However, sometimes due to defects in ribosomal machinery and faulty mRNAs, these nascent polypeptides are constantly under threat to become non-functional. In such conditions, cells acquire the help of ribosome-associated quality control mechanisms (RQC) to eliminate such aberrant nascent proteins. The primary regulator of RQC is RING domain containing LISTERIN E3 ubiquitin ligase, which is associated with ribosomes and alleviates non-stop proteins-associated stress in cells. Mouse RING finger protein E3 ubiquitin ligase LISTERIN is crucial for embryonic development, and a loss in its function causes neurodegeneration. LISTERIN is overexpressed in the mouse brain and spinal cord regions, and its perturbed functions generate neurological and motor deficits, but the mechanism of the same is unclear. Overall, LISTERIN is crucial for brain health and brain development. The present article systematically describes the detailed nature, molecular functions, and cellular physiological characterization of LISTERIN E3 ubiquitin ligase. Improve comprehension of LISTERIN's neurological roles may uncover pathways linked with neurodegeneration, which in turn might elucidate a promising novel therapeutic intervention against human neurodegenerative diseases.

Exploring the Nature of Hydrogen Bonding between RNA and Proteins: A Comprehensive Analysis of RNA : Protein Complexes

A nonredundant dataset of ∼300 high (up to 2.5 Å) resolution X-ray structures of RNA:protein complexes were analyzed for hydrogen bonds between amino-acid residues and canonical ribonucleotides (rNs). The identified 17100 contacts were classified based on the identity (rA, rC, rG or rU) and interacting fragment (base, sugar, or ribose) of the rN, the nature (polar or nonpolar) and interacting moiety (main chain or side chain) of the amino-acid residue, as well as the rN and amino-acid atoms participating in the hydrogen bonding. 80 possible hydrogen-bonding combinations (4 (rNs) X 20 (amino acids)) involve a wide variety of RNA and protein types and are present in multiple occurrences in almost all PDB files. Comparison with the analogously-selected DNA:protein complexes reveals that the absence of 2'-OH group in DNA mainly accounts for the differences in DNA:protein and RNA:protein hydrogen bonding. Search for intrinsically-stable base:amino acid pairs containing single or multiple hydrogen bonds reveals 37 unique pairs, which may act as well-defined RNA:protein interaction motifs. Overall, our work collectively analyzes the largest set of nucleic acid-protein hydrogen bonds to date, and therefore highlights several trends that may help frame structural rules governing the physiochemical characteristics of RNA:protein recognition.

DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method

It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding proteins provide critical clues about protein function and drug discovery. However, the current prediction methods of DNA binding sites have relatively poor accuracy. Using 3D coordinates and the atom-type of surface protein atom as the input, we trained and tested a deep learning model to predict how likely a voxel on the protein surface is to be a DNA-binding site. Based on three different evaluation datasets, the results show that our model not only outperforms several previous methods on two commonly used datasets, but also demonstrates its robust performance to be consistent among the three datasets. The visualized prediction outcomes show that the binding sites are also mostly located in correct regions. We successfully built a deep learning model to predict the DNA binding sites on target proteins. It demonstrates that 3D protein structures plus atom-type information on protein surfaces can be used to predict the potential binding sites on a protein. This approach should be further extended to develop the binding sites of other important biological molecules.

A purified inactivated vaccine derived from Vero cell-adapted zika virus elicits protection in mice

The Zika virus (ZIKV) outbreak in 2015-2016 raised public health concerns and created a pressing need for vaccine development. However, no vaccine has been developed and most of the ones under development use a single serotype of ZIKV. In this study, we established a Vero cell-adapted ZIKV strain (GMZ-002) and developed a purified inactivated virus (PIV) vaccine. GMZ-002 presented significantly increased productivity in Vero cells, and IFNAR1-blocked C57BL/6 mice administered two doses of the PIV were fully protected against lethal challenge. Vaccine efficacy was illustrated by the high level of serum neutralizing antibodies and strong innate immune response, along with an absence of detectable viremia in vaccinated mice. Furthermore, anti-sera neutralized both African and Asian genetic lineages of the virus in vitro. Our results suggest that GMZ-002 PIV elicited robust and persistent protective immunity, and therefore represents a promising vaccine candidate for ZIKV.

Bacterial Growth Inhibition Screen (BGIS) identifies a loss-of-function mutant of the DEK oncogene, indicating DNA modulating activities of DEK in chromatin

The DEK oncoprotein regulates cellular chromatin function via a number of protein-protein interactions. However, the biological relevance of its unique pseudo-SAP/SAP-box domain, which transmits DNA modulating activities in vitro, remains largely speculative. As hypothesis-driven mutations failed to yield DNA-binding null (DBN) mutants, we combined random mutagenesis with the Bacterial Growth Inhibition Screen (BGIS) to overcome this bottleneck. Re-expression of a DEK-DBN mutant in newly established human DEK knockout cells failed to reduce the increase in nuclear size as compared to wild type, indicating roles for DEK-DNA interactions in cellular chromatin organization. Our results extend the functional roles of DEK in metazoan chromatin and highlight the predictive ability of recombinant protein toxicity in E. coli for unbiased studies of eukaryotic DNA modulating protein domains.

Anatomy of noncovalent interactions between the nucleobases or ribose and π-containing amino acids in RNA-protein complexes

A set of >300 nonredundant high-resolution RNA–protein complexes were rigorously searched for π-contacts between an amino acid side chain (W, H, F, Y, R, E and D) and an RNA nucleobase (denoted π–π interaction) or ribose moiety (denoted sugar–π). The resulting dataset of >1500 RNA–protein π-contacts were visually inspected and classified based on the interaction type, and amino acids and RNA components involved. More than 80% of structures searched contained at least one RNA–protein π-interaction, with π–π contacts making up 59% of the identified interactions. RNA–protein π–π and sugar–π contacts exhibit a range in the RNA and protein components involved, relative monomer orientations and quantum mechanically predicted binding energies. Interestingly, π–π and sugar–π interactions occur more frequently with RNA (4.8 contacts/structure) than DNA (2.6). Moreover, the maximum stability is greater for RNA–protein contacts than DNA–protein interactions. In addition to highlighting distinct differences between RNA and DNA–protein binding, this work has generated the largest dataset of RNA–protein π-interactions to date, thereby underscoring that RNA–protein π-contacts are ubiquitous in nature, and key to the stability and function of RNA–protein complexes.

How human serum albumin-selective DNA aptamer binds to bovine and canine serum albumins

Serum albumin (SA) is the most abundant carrier protein in blood. SA carries a diverse range of nutrients, drugs, and metal ions. It has wide clinical and biochemical applications. Human serum albumin (HSA) can be used as a biomarker for kidney and liver diseases. Aptasensor is one of potential HSA detection methods. HSA-specific aptamer was selected for HSA detection. In animals, bovine serum albumin (BSA) and canine serum albumins (CSA) share high sequence similarities to HSA. Thus, it is interesting to explore the possibility of using HSA-selective aptamer for BSA and CSA aptasensor. In this study, molecular dynamics (MD) simulations were initially employed to investigate the binding of aptamer to BSA and CSA in comparison to HSA. Like HSA, both BSA and CSA can bind aptamer, but different binding affinities are observed. BSA shows the tighter binding to aptamer than CSA. Domain III is found to be the aptamer-binding domain although no specific aptamer conformation is captured. However, in all cases, the aptamer utilizes the 3'-end to attach on an albumin surface. Both nucleobases and phosphate backbones on a DNA aptamer are important for albumin-aptamer complexation. Our results imply the possibility of using HSA-specific aptamer for BSA detection due to tighter binding observed, but may be less effective in CSA. However, the test in actual complicated condition must be further studied.

Alterations in Glucose Metabolism Due to Decreased Expression of Heterogeneous Nuclear Ribonucleoprotein M in Pancreatic Ductal Adenocarcinoma

Simple Summary

Pancreatic cancer has one of the worst prognoses when compared to those of other cancer subtypes. One of the reasons is the resistance of this tumor to the hypovascular environment (an environment with low blood flow and low supply of oxygen and nutrients (especially glucose)). However, the detailed mechanism remains elusive. Recently, it has been reported that heterogeneous ribonuclear protein M (HNRNPM) is a splicing factor associated with malignant tumors. Thus, in this study, we investigated the expression and effects of HNRNPM in pancreatic ductal adenocarcinoma (PDA). We revealed that HNRNPM was highly expressed in pancreatic tissues but expression decreased in PDA tissues. Furthermore, we found that knockdown of HNRNPM protein expression under low-glucose conditions altered glucose metabolism and prolonged cell survival by suppressing glucose consumption. These results suggest that reduced expression of HNRNPM in PDAs may be involved in adaptation to a hypovascular environment, and that therapeutic agents for this target may lead to improved prognosis for pancreatic cancer.

Abstract

The prognosis of pancreatic cancer is considerably worse than that of other cancers, as early detection of pancreatic cancer is difficult and due to its hypovascular environment, which involves low blood flow and a low supply of oxygen and nutrients. Moreover, pancreatic cancer demonstrates a mechanism that allows it to survive in a hypovascular environment. However, the detailed mechanism remains elusive. Recently, it has been reported that heterogeneous ribonuclear protein M (HNRNPM) is a splicing factor associated with malignant tumors. Thus, in this study, we investigated the expression and effects of HNRNPM in pancreatic ductal adenocarcinoma (PDA). We observed that HNRNPM expression, which is highly expressed in pancreatic tissues, was reduced in PDA tissues. Additionally, knockdown of HNRNPM under low-glucose conditions that mimic a hypovascular environment was shown to alter glucose metabolism and prolong cell survival by suppressing glucose consumption. These results suggest that the decreased expression of HNRNPM in PDA may be involved in its adaptation to a hypovascular environment.

Protein Side-Chain-DNA Contacts Probed by Fast Magic-Angle Spinning NMR

Protein–nucleic acid interactions are essential in a variety of biological events ranging from the replication of genomic DNA to the synthesis of proteins. Noncovalent interactions guide such molecular recognition events, and protons are often at the center of them, particularly due to their capability of forming hydrogen bonds to the nucleic acid phosphate groups. Fast magic-angle spinning experiments (100 kHz) reduce the proton NMR line width in solid-state NMR of fully protonated protein–DNA complexes to such an extent that resolved proton signals from side-chains coordinating the DNA can be detected. We describe a set of NMR experiments focusing on the detection of protein side-chains from lysine, arginine, and aromatic amino acids and discuss the conclusions that can be obtained on their role in DNA coordination. We studied the 39 kDa enzyme of the archaeal pRN1 primase complexed with DNA and characterize protein–DNA contacts in the presence and absence of bound ATP molecules.

Asparagine and Glutamine Side-Chains and Ladders in HET-s(218-289) Amyloid Fibrils Studied by Fast Magic-Angle Spinning NMR

Asparagine and glutamine side-chains can form hydrogen-bonded ladders which contribute significantly to the stability of amyloid fibrils. We show, using the example of HET-s(218–289) fibrils, that the primary amide side-chain proton resonances can be detected in cross-polarization based solid-state NMR spectra at fast magic-angle spinning (MAS). J-coupling based experiments offer the possibility to distinguish them from backbone amide groups if the spin-echo lifetimes are long enough, which turned out to be the case for the glutamine side-chains, but not for the asparagine side-chains forming asparagine ladders. We explore the sensitivity of NMR observables to asparagine ladder formation. One of the two possible asparagine ladders in HET-s(218–289), the one comprising N226 and N262, is assigned by proton-detected 3D experiments at fast MAS and significant de-shielding of one of the NH₂ proton resonances indicative of hydrogen-bond formation is observed. Small rotating-frame ¹⁵N relaxation-rate constants point to rigidified asparagine side-chains in this ladder. The proton resonances are homogeneously broadened which could indicate chemical exchange, but is presently not fully understood. The second asparagine ladder (N243 and N279) in contrast remains more flexible.

A quantum chemical view of the interaction of RNA nucleobases and base pairs with the side chains of polar amino acids

Hydrogen bonding between amino acids and nucleobases is important for RNA-protein recognition. As a first step toward understanding the physicochemical features of these contacts, the present work employs density functional theory calculations to critically analyze the intrinsic structures and strength of all theoretically possible model hydrogen-bonded complexes involving RNA nucleobase edges and polar amino acid side chains. Our geometry optimizations uncover a number of unique complexes that involve variable hydrogen-bonding characteristics, including conventional donor-acceptor interactions, bifurcated interactions and single hydrogen-bonded contacts. Further, significant strength of these complexes in the gas phase (-27 kJ mol^-1 to -226 kJ mol^-1) and solvent phase (-19 kJ mol^-1 to -78 kJ mol^-1) points toward the ability of associated contacts to provide stability to RNA-protein complexes. More importantly, for the first time, our study uncovers the features of complexes involving protonated nucleobases, as well as those involving the weakly polar cysteine side chain, and thereby highlights their potential importance in biological processes that involve RNA-protein interactions. Additional analysis on select base pair-amino acid complexes uncovers the ability of amino acid side chain to simultaneously interact with both nucleobases of the base pair, and highlights the greater strength of such interactions compared to base-amino acid interactions. Overall, our analysis provides a basic physicochemical framework for understanding the molecular basis of nucleic acid-protein interactions. Further, our quantum chemical data can be used to design better algorithms for automated search of these contacts at the RNA-protein interface.Communicated by Ramaswamy H. Sarma.

Prediction of DNA-Binding Residues in Local Segments of Protein Sequences with Fuzzy Cognitive Maps

While protein-DNA interactions are crucial for a wide range of cellular functions, only a small fraction of these interactions was annotated to date. One solution to close this annotation gap is to employ computational methods that accurately predict protein-DNA interactions from widely available protein sequences. We present and empirically test first-of-its-kind predictor of DNA-binding residues in local segments of protein sequences that relies on the Fuzzy Cognitive Map (FCM) model. The FCM model uses information about putative solvent accessibility, evolutionary conservation, and relative propensities of amino acid to interact with DNA to generate putative DNA-binding residues. Empirical tests on a benchmark dataset reveal that the FCM model secures AUC = 0.72 and outperforms recently released hybridNAP predictor and several popular machine learning methods including Support Vector Machines, Naïve Bayes, and k-Nearest Neighbor. The improvements in the predictive performance result from an intrinsic feature of FCMs that incorporate relations between the input features, besides the relations between the inputs and output that are modelled by other algorithms. We also empirically demonstrate that use of a short sliding window results in further improvements in the predictive quality. The funDNApred webserver that implements the FCM predictor is available at http://biomine.cs.vcu.edu/servers/funDNApred/.

SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences

Motivation

Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein–protein interactions, helps protein–protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use.

Results

We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein–protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins.

Availability and implementation

SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/.

Supplementary information

Supplementary data are available at Bioinformatics online.

A solid-state NMR tool box for the investigation of ATP-fueled protein engines

Motor proteins are involved in a variety of cellular processes. Their main purpose is to convert the chemical energy released during adenosine triphosphate (ATP) hydrolysis into mechanical work. In this review, solid-state Nuclear Magnetic Resonance (NMR) approaches are discussed allowing studies of structures, conformational events and dynamic features of motor proteins during a variety of enzymatic reactions. Solid-state NMR benefits from straightforward sample preparation based on sedimentation of the proteins directly into the Magic-Angle Spinning (MAS) rotor. Protein resonance assignment is the crucial and often time-limiting step in interpreting the wealth of information encoded in the NMR spectra. Herein, potentials, challenges and limitations in resonance assignment for large motor proteins are presented, focussing on both biochemical and spectroscopic approaches. This work highlights NMR tools available to study the action of the motor domain and its coupling to functional processes, as well as to identify protein-nucleotide interactions during events such as DNA replication. Arrested protein states of reaction coordinates such as ATP hydrolysis can be trapped for NMR studies by using stable, non-hydrolysable ATP analogues that mimic the physiological relevant states as accurately as possible. Recent advances in solid-state NMR techniques ranging from Dynamic Nuclear Polarization (DNP), ³¹P-based heteronuclear correlation experiments, ¹H-detected spectra at fast MAS frequencies >100 kHz to paramagnetic NMR are summarized and their applications to the bacterial DnaB helicase from Helicobacter pylori are discussed.

Structural Patterns and Stabilities of Hydrogen-Bonded Pairs Involving Ribonucleotide Bases and Arginine, Glutamic Acid, or Glutamine Residues of Proteins from Quantum Mechanical Calculations

Ribonucleotide:protein interactions play crucial roles in a number of biological processes. Unlike the RNA:protein interface where van der Waals contacts are prevalent, the recognition of a single ribonucleotide such as ATP by a protein occurs predominantly through hydrogen-bonding interactions. As a first step toward understanding the role of hydrogen bonding in ribonucleotide:protein recognition, the present work employs density functional theory to provide a detailed quantum-mechanical analysis of the structural and energetic characteristics of 18 unique hydrogen-bonded pairs involving the nucleobase/nucleoside moiety of four canonical ribonucleotides and the side chains of three polar amino-acid residues (arginine, glutamine, and glutamic acid) of proteins. In addition, we model five new pairs that are till now not observed in crystallographically identified ribonucleotide:protein complexes but may be identified in complexes crystallized in the future. We critically examine the characteristics of each pair in its ribonucleotide:protein crystal structure occurrence and (gas phase and water phase) optimized intrinsic structure. We further evaluated the interaction energy of each pair and characterized the associated hydrogen bonds using a number of quantum mechanics-based relationships including natural bond orbital analysis, quantum theory atoms in molecules analysis, Iogansen relationships, Nikolaienko-Bulavin-Hovorun relationships, and noncovalent interaction-reduced density gradient analysis. Our analyses reveal rich variability in hydrogen bonds in the crystallographic as well as intrinsic structure of each pair, which includes conventional O/N-H···N/O and C-H···O hydrogen bonds as well as donor/acceptor-bifurcated hydrogen bonds. Further, we identify five combinations of nucleobase and amino acid moieties; each of which exhibits at least two alternate (i.e., multimodal) structures that interact through the same nucleobase edge. In fact, one such pair exhibits four multimodal structures; one of which possesses unconventional "amino-acceptor" hydrogen bonding with comparable (-9.4 kcal mol^-1) strength to the corresponding conventional (i.e., amino:donor) structure (-9.2 kcal mol^-1). This points to the importance of amino-acceptor hydrogen bonds in RNA:protein interactions and suggests that such interactions must be considered in the future while studying the dynamics in the context of molecular recognition. Overall, our study provides preliminary insights into the intrinsic features of ribonucleotide:amino acid interactions, which may help frame a clearer picture of the molecular basis of RNA:protein recognition and further appreciate the role of such contacts in biology.

The origin of chromosomal histones in a 30S ribosomal protein

Histones are genes that regulate chromatin structure. They are present in both eukaryotes and archaea, and form nucleosomes with DNA, but their exact evolutionary origins have hitherto remained a mystery. A longstanding hypothesis is that they have precursors in ribosomal proteins with whom they share much in common in terms of size and chemistry. By examining the proteome of the Asgard archaeon, Lokiarchaeum, the most conserved of all the histones, H4, is found to plausibly be homologous with one of its 30S ribosomal proteins, RPS6. This is based on both sequence identity and statistical analysis. The N-terminal tail, containing key sites involved in post-translational modifications, is notably present in the precursor gene. Although other archaeal groups possess similar homologs, these are not as close to H4 as the one found in Lokiarchaeum. The other core histones, H2A, H2B and H3, appear to have also evolved from the same ribosomal protein. Parts of H4 are also similar to another ribosomal protein, namely RPS15, suggesting that the ancestral precursor could have resembled both. Eukaryotic histones, in addition, appear to have an independent origin to that of their archaeal counterparts that evolved from similar, but still different, 30S subunit proteins, some of which are much more like histones in terms of their physical structure. The nucleosome may, therefore, be not only of archaeal but also of ribosomal origin.

Nucleotide Binding Modes in a Motor Protein Revealed by 31 P- and 1 H-Detected MAS Solid-State NMR Spectroscopy

Protein-nucleic acid interactions play important roles not only in energy-providing reactions, such as ATP hydrolysis, but also in reading, extending, packaging, or repairing genomes. Although they can often be analyzed in detail with X-ray crystallography, complementary methods are needed to visualize them in complexes, which are not crystalline. Here, we show how solid-state NMR spectroscopy can detect and classify protein-nucleic interactions through site-specific 1 H- and 31 P-detected spectroscopic methods. The sensitivity of 1 H chemical-shift values on noncovalent interactions involved in these molecular recognition processes is exploited allowing us to probe directly the chemical bonding state, an information, which is not directly accessible from an X-ray structure. We show that these methods can characterize interactions in easy-to-prepare sediments of the 708 kDa dodecameric DnaB helicase in complex with ADP:AlF4- :DNA, and this despite the very challenging size of the complex.

Drought Stress Causes Specific Changes to the Spliceosome and Stress Granule Components

The spliceosome processes RNAs from a pre-RNA state to a mature mRNA thereby influencing RNA availability for translation, localization, and turnover. It consists of complex structures containing RNA-binding proteins (RBPs) essential for post-transcriptional gene expression control. Here we investigate the dynamic modifications of spliceosomal RBPs under stress and in particular drought stress. We do so by mRNA interactome capture in Arabidopsis thaliana using label free quantitation. This approach identified 44 proteins associated with the spliceosome and further 32 proteins associated with stress granules. We noted a high enrichment in the motifs RDRR and RSRSRS that are characteristic of RNA interacting proteins. Identification of splicing factors reflect direct and/or indirect stress induced splicing events that have a direct effect on transcriptome and proteome changes under stress. Furthermore, detection of stress granule components is consistent with transcriptional arrest. Identification of drought induced stress granule components is critical in determining common abiotic stress-induced foci that can have biotechnological applications. This study may therefore open ways to modify plant stress responses at a systems level through the modification of key spliceosome components.

Scoring Functions for Protein-RNA Complex Structure Prediction: Advances, Applications, and Future Directions

Protein-RNA interaction is among the most essential of biological events in living cells, being involved in protein synthesizing, RNA processing and transport, DNA transcription, and regulation of gene expression, and many other critical bio-molecular activities. A thorough understanding of this interaction is of paramount importance in fundamental study of a variety of vital cellular processes and therapeutic application for remedy of a broad range of diseases. Experimental high-resolution 3D structure determination is the primary source of knowledge for protein-RNA complexes. However, due to technical limitations, the existing techniques for experimental structure determination couldn't match the demand from fast growing interest in academia and industry. This problem necessitates the alternative high-throughput computational method for protein-RNA complex structure prediction. Similar to the in silico methods used for protein-protein and protein-DNA interactions, a reliable prediction of protein-RNA complex structure requires a scoring function with commensurate discriminatory power. Derived from determined structures and purposed to predict the to-be-determined structures, the scoring function is not only a predictive tool but also a gauge of our knowledge of protein-RNA interaction. In this review, we present an overview of the status of existing scoring functions and the scientific principle behind their constructions as well as their strengths and limitations. Finally, we will discuss about future directions of the scoring function development for protein-RNA structure prediction.

Changes in the Arabidopsis RNA-binding proteome reveal novel stress response mechanisms

BACKGROUND

RNA-binding proteins (RBPs) are increasingly recognized as regulatory component of post-transcriptional gene expression. RBPs interact with mRNAs via RNA-binding domains and these interactions affect RNA availability for translation, RNA stability and turn-over thus affecting both RNA and protein expression essential for developmental and stimulus specific responses. Here we investigate the effect of severe drought stress on the RNA-binding proteome to gain insights into the mechanisms that govern drought stress responses at the systems level.

RESULTS

Label-free mass spectrometry enabled the identification 567 proteins of which 150 significantly responded to the drought-induced treatment. A gene ontology analysis revealed enrichment in the "RNA binding" and "RNA processing" categories as well as biological processes such as "response to abscisic acid" and "response to water deprivation". Importantly, a large number of the stress responsive proteins have not previously been identified as RBPs and include proteins in carbohydrate metabolism and in the glycolytic and citric acid pathways in particular. This suggests that RBPs have hitherto unknown roles in processes that govern metabolic changes during stress responses. Furthermore, a comparative analysis of RBP domain architectures shows both, plant specific and common domain architectures between plants and animals. The latter could be an indication that RBPs are part of an ancient stress response.

CONCLUSION

This study establishes mRNA interactome capture technique as an approach to study stress signal responses implicated in environmental changes. Our findings denote RBP changes in the proteome as critical components in plant adaptation to changing environments and in particular drought stress protein-dependent changes in RNA metabolism.

Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents

Conventional modeling techniques to model macromolecular solvation and its effect on binding in the framework of Poisson-Boltzmann based implicit solvent models make use of a geometrically defined surface to depict the separation of macromolecular interior (low dielectric constant) from the solvent phase (high dielectric constant). Though this simplification saves time and computational resources without significantly compromising the accuracy of free energy calculations, it bypasses some of the key physio-chemical properties of the solute-solvent interface, e.g., the altered flexibility of water molecules and that of side chains at the interface, which results in dielectric properties different from both bulk water and macromolecular interior, respectively. Here we present a Gaussian-based smooth dielectric model, an inhomogeneous dielectric distribution model that mimics the effect of macromolecular flexibility and captures the altered properties of surface bound water molecules. Thus, the model delivers a smooth transition of dielectric properties from the macromolecular interior to the solvent phase, eliminating any unphysical surface separating the two phases. Using various examples of macromolecular binding, we demonstrate its utility and illustrate the comparison with the conventional 2-dielectric model. We also showcase some additional abilities of this model, viz. to account for the effect of electrolytes in the solution and to render the distribution profile of water across a lipid membrane.

Structural Insights into the Osteopontin-Aptamer Complex by Molecular Dynamics Simulations

Osteopontin is an intrinsically disordered protein involved in tissue remodeling. As a biomarker for pathological hypertrophy and fibrosis, the protein is targeted by an RNA aptamer. In this work, we model the interactions between osteopontin and its aptamer, including mono- (Na⁺) and divalent (Mg²⁺) cations. The molecular dynamics simulations suggest that the presence of divalent cations forces the N-terminus of osteopontin to bind the shell of divalent cations adsorbed over the surface of its RNA aptamer, the latter exposing a high negative charge density. The osteopontin plasticity as a function of the local concentration of Mg is discussed in the frame of the proposed strategies for osteopontin targeting as biomarker and in theranostic.

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.

Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains

Proteins interact with a variety of molecules including proteins and nucleic acids. We review a comprehensive collection of over 50 studies that analyze and/or predict these interactions. While majority of these studies address either solely protein–DNA or protein–RNA binding, only a few have a wider scope that covers both protein–protein and protein–nucleic acid binding. Our analysis reveals that binding residues are typically characterized with three hallmarks: relative solvent accessibility (RSA), evolutionary conservation and propensity of amino acids (AAs) for binding. Motivated by drawbacks of the prior studies, we perform a large-scale analysis to quantify and contrast the three hallmarks for residues that bind DNA-, RNA-, protein- and (for the first time) multi-ligand-binding residues that interact with DNA and proteins, and with RNA and proteins. Results generated on a well-annotated data set of over 23 000 proteins show that conservation of binding residues is higher for nucleic acid- than protein-binding residues. Multi-ligand-binding residues are more conserved and have higher RSA than single-ligand-binding residues. We empirically show that each hallmark discriminates between binding and nonbinding residues, even predicted RSA, and that combining them improves discriminatory power for each of the five types of interactions. Linear scoring functions that combine these hallmarks offer good predictive performance of residue-level propensity for binding and provide intuitive interpretation of predictions. Better understanding of these residue-level interactions will facilitate development of methods that accurately predict binding in the exponentially growing databases of protein sequences.

Supramolecular β-Sheets Stabilized Protein Nanocarriers for Drug Delivery and Gene Transfection

Suckerin proteins, recently discovered in the sucker ring teeth of squids, represent a family of promising structural biomacromolecules that can form supramolecular networks stabilized by nanoconfined β-sheets. Exploiting this feature as well as their specific amino acid composition, we demonstrate that artificial suckerin-19 (S-19) can be engineered into nanocarriers for efficient drug delivery and gene transfection in vitro and in vivo. First, we demonstrate that S-19 self-assembles into β-sheet stabilized nanoparticles with controlled particle sizes of 100-200 nm that are able to encapsulate hydrophobic drugs for pH-dependent release in vitro, and that can effectively inhibit tumor growth in vivo. We also show that S-19 can complex and stabilize plasmid DNA, with the complexes stabilized by hydrophobic interactions of the β-sheet domains as opposed to electrostatic interactions commonly achieved with cationic polymers, thus lowering cytotoxicity. The elevated Histidine content of S-19 appears critical to trigger endosomal escape by the proton sponge effect, thereby ensuring efficient gene transfection both in vitro and in vivo. Our study demonstrates that S-19 represents a promising functional protein nanocarrier that could be used for various drug and gene delivery applications.

Direct Comparison of Amino Acid and Salt Interactions with Double-Stranded and Single-Stranded DNA from Explicit-Solvent Molecular Dynamics Simulations

Given the ubiquitous nature of protein-DNA interactions, it is important to understand the interaction thermodynamics of individual amino acid side chains for DNA. One way to assess these preferences is to perform molecular dynamics (MD) simulations. Here we report MD simulations of 20 amino acid side chain analogs interacting simultaneously with both a 70-base-pair double-stranded DNA and with a 70-nucleotide single-stranded DNA. The relative preferences of the amino acid side chains for dsDNA and ssDNA match well with values deduced from crystallographic analyses of protein-DNA complexes. The estimated apparent free energies of interaction for ssDNA, on the other hand, correlate well with previous simulation values reported for interactions with isolated nucleobases, and with experimental values reported for interactions with guanosine. Comparisons of the interactions with dsDNA and ssDNA indicate that, with the exception of the positively charged side chains, all types of amino acid side chain interact more favorably with ssDNA, with intercalation of aromatic and aliphatic side chains being especially notable. Analysis of the data on a base-by-base basis indicates that positively charged side chains, as well as sodium ions, preferentially bind to cytosine in ssDNA, and that negatively charged side chains, and chloride ions, preferentially bind to guanine in ssDNA. These latter observations provide a novel explanation for the lower salt dependence of DNA duplex stability in GC-rich sequences relative to AT-rich sequences.

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.

Noncovalent Interactions in Specific Recognition Motifs of Protein-DNA Complexes

In view of the importance of protein-DNA interactions in biological processes, we extracted from the Protein Data Bank several one-to-one complexes of amino acids with nucleotides that matched certain geometric and energetic specificity criteria and investigated them using quantum chemistry methods. The CCSD(T)/CBS interaction energies were used as a benchmark to compare the performance of the MP2.5, MP2-F12, DFT-D3, and PM6-D3H4 methods. All methods yielded good agreement with the reference values, with declining accuracy from MP2.5 to PM6-D3H4. Regardless of the site of interaction, the minima found after full optimization in implicit solvent with high dielectric constant were close to the structures experimentally detected in protein-DNA complexes. According to DFT-SAPT analysis, the nature of noncovalent interactions strongly depends on the type of amino acid. The negatively charged sugar-phosphate backbone of DNA heavily influences the strength of interactions and must be included in the computational model, especially in the case of interactions with charged amino acids.

Synthetic oligonucleotide antigens modified with locked nucleic acids detect disease specific antibodies

New techniques to detect and quantify antibodies to nucleic acids would provide a significant advance over current methods, which often lack specificity. We investigate the potential of novel antigens containing locked nucleic acids (LNAs) as targets for antibodies. Particularly, employing molecular dynamics we predict optimal nucleotide composition for targeting DNA-binding antibodies. As a proof of concept, we address a problem of detecting anti-DNA antibodies that are characteristic of systemic lupus erythematosus, a chronic autoimmune disease with multiple manifestations. We test the best oligonucleotide binders in surface plasmon resonance studies to analyze binding and kinetic aspects of interactions between antigens and target DNA. These DNA and LNA/DNA sequences showed improved binding in enzyme-linked immunosorbent assay using human samples of pediatric lupus patients. Our results suggest that the novel method is a promising tool to create antigens for research and point-of-care monitoring of anti-DNA antibodies.

Implication of Terminal Residues at Protein-Protein and Protein-DNA Interfaces

Terminal residues of protein chains are charged and more flexible than other residues since they are constrained only on one side. Do they play a particular role in protein-protein and protein-DNA interfaces? To answer this question, we considered large sets of non-redundant protein-protein and protein-DNA complexes and analyzed the status of terminal residues and their involvement in interfaces. In protein-protein complexes, we found that more than half of terminal residues (62%) are either modified by attachment of a tag peptide (10%) or have missing coordinates in the analyzed structures (52%). Terminal residues are almost exclusively located at the surface of proteins (94%). Contrary to charged residues, they are not over or under-represented in protein-protein interfaces, but strongly prefer the peripheral region of interfaces when present at the interface (83% of terminal residues). The almost exclusive location of terminal residues at the surface of the proteins or in the rim regions of interfaces explains that experimental methods relying on tail hybridization can be successfully applied without disrupting the complexes under study. Concerning conformational rearrangement in protein-protein complexes, despite their expected flexibility, terminal residues adopt similar locations between the free and bound forms of the docking benchmark. In protein-DNA complexes, N-terminal residues are twice more frequent than C-terminal residues at interfaces. Both N-terminal and C-terminal residues are under-represented in interfaces, in contrast to positively charged residues, which are strongly favored. When located in protein-DNA interfaces, terminal residues prefer the periphery. N-terminal and C-terminal residues thus have particular properties with regard to interfaces, which cannot be reduced to their charged nature.

Principles of protein targeting to the nucleolus

The nucleolus is the hallmark of nuclear compartmentalization and has been shown to exert multiple roles in cellular metabolism besides its main function as the place of rRNA synthesis and assembly of ribosomes. Nucleolar proteins dynamically localize and accumulate in this nuclear compartment relative to the surrounding nucleoplasm. In this study, we have assessed the molecular requirements that are necessary and sufficient for the localization and accumulation of peptides and proteins inside the nucleoli of living cells. The data showed that positively charged peptide entities composed of arginines alone and with an isoelectric point at and above 12.6 are necessary and sufficient for mediating significant nucleolar accumulation. A threshold of 6 arginines is necessary for peptides to accumulate in nucleoli, but already 4 arginines are sufficient when fused within 15 amino acid residues of a nuclear localization signal of a protein. Using a pH sensitive dye, we found that the nucleolar compartment is particularly acidic when compared to the surrounding nucleoplasm and, hence, provides the ideal electrochemical environment to bind poly-arginine containing proteins. In fact, we found that oligo-arginine peptides and GFP fusions bind RNA in vitro. Consistent with RNA being the main binding partner for arginines in the nucleolus, we found that the same principles apply to cells from insects to man, indicating that this mechanism is highly conserved throughout evolution.

Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors

BACKGROUND

RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers.

RESULTS

In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631.

CONCLUSIONS

The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

Topology of RNA-protein nucleobase-amino acid π-π interactions and comparison to analogous DNA-protein π-π contacts

The present work analyzed 120 high-resolution X-ray crystal structures and identified 335 RNA-protein π-interactions (154 nonredundant) between a nucleobase and aromatic (W, H, F, or Y) or acyclic (R, E, or D) π-containing amino acid. Each contact was critically analyzed (including using a visual inspection protocol) to determine the most prevalent composition, structure, and strength of π-interactions at RNA-protein interfaces. These contacts most commonly involve F and U, with U:F interactions comprising one-fifth of the total number of contacts found. Furthermore, the RNA and protein π-systems adopt many different relative orientations, although there is a preference for more parallel (stacked) arrangements. Due to the variation in structure, the strength of the intermolecular forces between the RNA and protein components (as determined from accurate quantum chemical calculations) exhibits a significant range, with most of the contacts providing significant stability to the associated RNA-protein complex (up to -65 kJ mol(-1)). Comparison to the analogous DNA-protein π-interactions emphasizes differences in RNA- and DNA-protein π-interactions at the molecular level, including the greater abundance of RNA contacts and the involvement of different nucleobase/amino acid residues. Overall, our results provide a clearer picture of the molecular basis of nucleic acid-protein binding and underscore the important role of these contacts in biology, including the significant contribution of π-π interactions to the stability of nucleic acid-protein complexes. Nevertheless, more work is still needed in this area in order to further appreciate the properties and roles of RNA nucleobase-amino acid π-interactions in nature.

L-tryptophan and dipeptide derivatives for supercoiled plasmid DNA purification

The present study focus on the preparation of chromatography supports for affinity-based chromatography of supercoiled plasmid purification. Three l-tryptophan based supports are prepared through immobilization on epoxy-activated Sepharose and characterized by HR-MAS NMR. The SPR is employed for a fast screening of l-tryptophan derivatives, as potential ligands for the biorecognition of supercoiled isoform, as well as, to establish the suitable experimental conditions for the chromatography. The results reveal that the overall affinity is high (KD=10(-9) and 10(-8)M) and the conditions tested show that the use of HEPES 100mM enables the separation and purification of supercoiled at T=10°C. The STD-NMR is performed to accomplish the epitope mapping of the 5'-mononucleotides bound to l-tryptophan derivatives supports. The data shows that the interactions between the three supports and the 5'-mononucleotides are mainly hydrophobic and π-π stacking. The chromatography experiments are performed with l-tryptophan support and plasmids pVAX-LacZ and pPH600. The supercoiled isoform separation is achieved at T=10°C by decreasing the concentration of (NH4)2SO4 from 2.7 to 0M in HEPES for pVAX-LacZ and 2.65M to 0M in HEPES for pPH600. Overall, l-tryptophan derivatives can be a promising strategy to purify supercoiled for pharmaceutical applications.

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.