Understanding the recognition mechanism of protein-RNA complexes using energy based approach

Protein-RNA interactions perform diverse functions within the cell. Understanding the recognition mechanism of protein-RNA complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-RNA complexes. The new approach considers the repulsive interactions as well as the effect of distance between the atoms in protein and RNA in terms of interaction energy, which are not considered in traditional distance based methods to identify the binding sites. We found that the positively charged, polar and aromatic residues are important for binding. These residues influence to form electrostatic, hydrogen bonding and stacking interactions. Our observation has been verified with the experimental binding specificity of protein-RNA complexes and found good agreement with experiments. Further, the propensities of residues/nucleotides in the binding sites of proteins/RNA and their atomic contributions have been derived. Based on these results we have proposed a novel mechanism for the recognition of protein-RNA complexes: the charged and polar residues in proteins initiate recognition with RNA by making electrostatic and hydrogen bonding interactions between them; the aromatic side chains tend to form aromatic-aromatic interactions and the hydrophobic residues aid to stabilize the complex.     

Segmental,Domain‐Selective Perdeuteration and Small‐Angle Neutron Scattering for Structural Analysis of Multi‐Domain Proteins

Multi‐domain proteins play critical roles in fine‐tuning essential processes in cellular signaling and gene regulation. Typically, multiple globular domains that are connected by flexible linkers undergo dynamic rearrangements upon binding to protein, DNA or RNA ligands. RNA binding proteins (RBPs) represent an important class of multi‐domain proteins, which regulate gene expression by recognizing linear or structured RNA sequence motifs. Here, we employ segmental perdeuteration of the three RNA recognition motif (RRM) domains in the RBP TIA‐1 using Sortase A mediated protein ligation. We show that domain‐selective perdeuteration combined with contrast‐matched small‐angle neutron scattering (SANS), SAXS and computational modeling provides valuable information to precisely define relative domain arrangements. The approach is generally applicable to study conformational arrangements of individual domains in multi‐domain proteins and changes induced by ligand binding.     

Segmental,Domain‐Selective Perdeuteration and Small‐Angle Neutron Scattering for Structural Analysis of Multi‐Domain Proteins

Multi‐domain proteins play critical roles in fine‐tuning essential processes in cellular signaling and gene regulation. Typically, multiple globular domains that are connected by flexible linkers undergo dynamic rearrangements upon binding to protein, DNA or RNA ligands. RNA binding proteins (RBPs) represent an important class of multi‐domain proteins, which regulate gene expression by recognizing linear or structured RNA sequence motifs. Here, we employ segmental perdeuteration of the three RNA recognition motif (RRM) domains in the RBP TIA‐1 using Sortase A mediated protein ligation. We show that domain‐selective perdeuteration combined with contrast‐matched small‐angle neutron scattering (SANS), SAXS and computational modeling provides valuable information to precisely define relative domain arrangements. The approach is generally applicable to study conformational arrangements of individual domains in multi‐domain proteins and changes induced by ligand binding.     

Bidirectional Regulation of mRNA Translation in Mammalian Cells by Using PUF Domains

The regulation of gene expression is crucial in diverse areas of biological science, engineering, and medicine. A genetically encoded system based on the RNA binding domain of the Pumilio and FBF (PUF) proteins was developed for the bidirectional regulation (i.e., either upregulation or downregulation) of the translation of a target mRNA. PUF domains serve as designable scaffolds for the recognition of specific RNA elements and the specificity can be easily altered to target any 8‐nucleotide RNA sequence. The expression of a reporter could be varied by over 17‐fold when using PUF‐based activators and repressors. The specificity of the method was established by using wild‐type and mutant PUF domains. Furthermore, this method could be used to activate the translation of target mRNA downstream of PUF binding sites in a light‐dependent manner. Such specific bidirectional control of mRNA translation could be particularly useful in the fields of synthetic biology, developmental biology, and metabolic engineering.     

蛋白质-RNA相互作用界面预测与设计

蛋白质-RNA之间的相互作用是蛋白质在细胞里面行使功能的重要方式之一. 结构生物学家利用实验手段可以得到蛋白质-RNA复合物的三维结构, 通过原子水平的晶体结构来解释蛋白质与RNA的识别过程. 但实验取得蛋白质-RNA的复合物结构非常困难, 耗钱、耗时, 同时受限于其相互作用强度. 因而利用理论的方法对蛋白质-RNA相互作用界面进行预测与设计在生物医学研究中十分重要. 本文主要综述了近期蛋白质-RNA相互作用界面预测与设计方面的进展, 包括以下几个方面: (1) 蛋白质-RNA分子对接算法以及对接前后存在的构象变化的处理; (2) 蛋白质-RNA 识别机制的研究; (3) 基于蛋白质-RNA 相互作用界面的分子设计. 蛋白质-RNA分子对接算法逐步完善将有助于我们对大量未知功能的蛋白质与RNA进行功能注释, 而基于生物大分子相互作用界面的分子设计将在药物设计领域中有广阔的应用前景.     

Molecular recognition properties of FN3 monobodies that bind the Src SH3 domain

We have constructed a phage-displayed library based on the human fibronectin tenth type III domain (FN3) scaffold by randomizing residues in its FG and BC loops. Screening against the SH3 domain of human c-Src yielded six different clones. Five of these contained proline-rich sequences in their FG loop that resembled class I (i.e., +xxPxxP) peptide ligands for the Src SH3 domain. The sixth clone lacked the proline-rich sequence and showed particularly high binding specificity to the Src SH3 domain among various SH3 domains tested. Competitive binding, loop replacement, and NMR perturbation experiments were conducted to analyze the recognition properties of selected binders. The strongest binder was able to pull down full-length c-Src from murine fibroblast cell extracts, further demonstrating the potential of this scaffold for use as an antibody mimetic.     

Controlling protein activity with ligand-regulated RNA aptamers

Controlling the activity of a protein is necessary for defining its function in vivo. RNA aptamers are capable of inhibiting proteins with high affinity and specificity, but this effect is not readily reversible. We describe a general method for discovering aptamers that bind and inhibit their target protein, but addition of a specific small molecule disrupts the protein-RNA complex. A SELEX protocol was used to raise RNA aptamers to the DNA repair enzyme, formamidopyrimidine glycosylase (Fpg), and neomycin was employed in each round to dissociate Fpg-bound RNAs. We identified an RNA molecule able to completely inhibit Fpg at 100 nM concentration. Importantly, Fpg activity is recovered by the addition of neomycin. We envision these ligand-regulated aptamers (LIRAs) as valuable tools in the study of biological phenomena in which the timing of molecular events is critical.     

Prediction of small molecule binding property of protein domains with Bayesian classifiers based on Markov chains

Accurate computational methods that can help to predict biological function of a protein from its sequence are of great interest to research biologists and pharmaceutical companies. One approach to assume the function of proteins is to predict the interactions between proteins and other molecules. In this work, we propose a machine learning method that uses a primary sequence of a domain to predict its propensity for interaction with small molecules. By curating the Pfam database with respect to the small molecule binding ability of its component domains, we have constructed a dataset of small molecule binding and non-binding domains. This dataset was then used as training set to learn a Bayesian classifier, which should distinguish members of each class. The domain sequences of both classes are modelled with Markov chains. In a Jack-knife test, our classification procedure achieved the predictive accuracies of 77.2% and 66.7% for binding and non-binding classes respectively. We demonstrate the applicability of our classifier by using it to identify previously unknown small molecule binding domains. Our predictions are available as supplementary material and can provide very useful information to drug discovery specialists. Given the ubiquitous and essential role small molecules play in biological processes, our method is important for identifying pharmaceutically relevant components of complete proteomes. The software is available from the author upon request.     

Targeting metastable coiled-coil domains by computational design

Approximately 30% of eukaryotic genomes are predicted to encode partially unfolded proteins. Many of these unstructured domains contact multiple partners in short-lived interactions critical for cellular homeostasis. Understanding the functional implications of these transient binding events is a current challenge that could be addressed with designed peptide inhibitors. Most current protein design methodologies, however, target only structurally well-defined, stable structures. To address this limitation, we implemented a computational design strategy that alternates between a fixed backbone sequence search for binding specificity and structural optimization of the designed interfaces. We applied this method to create specific peptide inhibitors of the C-terminal metastable coiled-coil domain of the essential yeast septin Cdc12p. Specific binding of the designed sequences was demonstrated by circular dichroism and equilibrium ultracentrifugation. Our results validate computational methods to design specific peptide ligands to protein domains lacking intrinsic structural stability and set the stage for functional analysis of Cdc12p coiled coil function in vivo.     

Small-molecule inhibitors of the TLR3/dsRNA complex

The protein-RNA interface has been regarded as "undruggable" despite its importance in many biological processes. The toll-like receptor 3 (TLR3)/double-stranded RNA (dsRNA) complex provides an exciting target for a number of infectious diseases and cancers. We describe the development of a series of small-molecule probes that were shown to be competitive inhibitors of dsRNA binding to TLR3 with high affinity and specificity. In a multitude of assays, compound 4a was profiled as a potent antagonist to TLR3 signaling and also repressed the expression of downstream signaling pathways mediated by the TLR3/dsRNA complex, including TNF-α and IL-1β.     

Prediction of salt and mutational effects on the association rate of U1A protein and U1 small nuclear RNA stem/loop II

We have developed a computational approach for predicting protein-protein association rates (Alsallaq and Zhou, Structure 2007, 15, 215). Here we expand the range of applicability of this approach to protein-RNA binding and report the first results for protein-RNA binding rates predicted from atomistic modeling. The system studied is the U1A protein and stem/loop II of the U1 small nuclear RNA. Experimentally it was observed that the binding rate is significantly reduced by increasing salt concentration while the dissociation changes little with salt concentration, and charges distant from the binding site make marginal contribution to the binding rate. These observations are rationalized. Moreover, predicted effects of salt and charge mutations are found to be in quantitative agreement with experimental results.     

Scoring function based approach for locating binding sites and understanding recognition mechanism of protein-DNA complexes

Protein-DNA recognition plays an essential role in the regulation of gene expression. Understanding the recognition mechanism of protein-DNA complexes is a challenging task in molecular and computational biology. In this work, a scoring function based approach has been developed for identifying the binding sites and delineating the important residues for binding in protein-DNA complexes. This approach considers both the repulsive interactions and the effect of distance between atoms in protein and DNA. The results showed that positively charged, polar, and aromatic residues are important for binding. These residues influence the formation of electrostatic, hydrogen bonding, and stacking interactions. Our observation has been verified with experimental binding specificity of protein-DNA complexes and found to be in good agreement with experiments. The comparison of protein-RNA and protein-DNA complexes reveals that the contribution of phosphate atoms in DNA is twice as large as in protein-RNA complexes. Furthermore, we observed that the positively charged, polar, and aromatic residues serve as hotspot residues in protein-RNA complexes, whereas other residues also altered the binding specificity in protein-DNA complexes. Based on the results obtained in the present study and related reports, a plausible mechanism has been proposed for the recognition of protein-DNA complexes.     

Simultaneous prediction of binding free energy and specificity for PDZ domain–peptide interactions

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain–peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with Rosetta. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several Rosetta protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain–peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain–peptide interactions.     

Do collective atomic fluctuations account for cooperative effects? Molecular dynamics studies of the U1A-RNA complex

A complete understanding of gene expression relies on a comprehensive understanding of the protein-RNA recognition process. However, the study of protein-RNA recognition is complicated by many factors that contribute to both binding affinity and specificity, including structure, energetics, dynamical motions, and cooperative interactions. Several recent studies have suggested that energetic coupling between residues contributes to formation of the complex between the U1A protein and stem loop 2 of U1 snRNA as a consequence of a cooperative network of interactions. We have performed molecular dynamics simulations on the U1A-RNA complex, including explicit water and counterions, and analyzed the results based on the calculated positional cross-correlations of atomic fluctuations. The results indicate that cross-correlations calculated on a per residue basis agree well with the observed inter-residue cooperativity and predict that the networks identified to date may also be coupled into an extensive hyper-network that reflects the intrinsic rigidity of the RNA recognition motif. In addition, we report a comparison of the MD calculated correlations with the results of a positional covariance analysis based on the sequences of 330 RNA recognition motifs, including U1A. The calculated inter-residue cross-correlations agree very well with the results of the sites exhibiting positional covariance. Collectively, these results strongly support the hypothesis that collective fluctuations contribute to cooperativity and the corresponding observed thermodynamic coupling. Predictions of additional sites in U1A that may be involved in cooperative networks are advanced.     

Predicting protein-protein interactions using graph invariants and a neural network

The PDZ domain of proteins mediates a protein-protein interaction by recognizing the hydrophobic C-terminal tail of the target protein. One of the challenges put forth by the DREAM (Discussions on Reverse Engineering Assessment and Methods) 2009 Challenge consists of predicting a position weight matrix (PWM) that describes the specificity profile of five PDZ domains to their target peptides. We consider the primary structures of each of the five PDZ domains as a numerical sequence derived from graph-theoretic models of each of the individual amino acids in the protein sequence. Using available PDZ domain databases to obtain known targets, the graph-theoretic based numerical sequences are then used to train a neural network to recognize their targets. Given the challenge sequences, the target probabilities are computed and a corresponding position weight matrix is derived. In this work we present our method. The results of our method placed second in the DREAM 2009 challenge.     

Predictive, structure-based model of amino acid recognition by nonribosomal peptide synthetase adenylation domains

BACKGROUND: Nonribosomal peptide synthetases (NRPSs) are large modular proteins that selectively bind, activate and condense amino acids in an ordered manner. Substrate recognition and activation occurs by reaction with ATP within the adenylation (A) domain of each module. Recently, the crystal structure of the A domain from the gramicidin synthetase (GrsA) with L-phenylalanine and adenosine monophosphate bound has been determined. RESULTS: Critical residues in all known NRPS A domains have been identified that align with eight binding-pocket residues in the GrsA A domain and define sets of remarkably conserved recognition templates. Phylogenetic relationships among these sets and the likely specificity determinants for polar and nonpolar amino acids were determined in light of extensive published biochemical data for these enzymes. The binding specificity of greater than 80% of the known NRPS A domains has been correlated with more than 30 amino acid substrates. CONCLUSIONS: The analysis presented allows the specificity of A domains of unknown function (e.g. from polymerase chain reaction amplification or genome sequencing) to be predicted. Furthermore, it provides a rational framework for altering of A domain specificity by site-directed mutagenesis, which has significant potential for engineering the biosynthesis of novel natural products.     

A flexible docking procedure for the exploration of peptide binding selectivity to known structures and homology models of PDZ domains

PDZ domains are important scaffolding modules that typically bind to the C-termini of their interaction partners. Several structures of such complexes have been solved, revealing a conserved binding site in the PDZ domain and an extended conformation of the bound peptide. A compendium of information regarding PDZ complexes demonstrates that dissimilar C-terminal peptides bind to the same PDZ domain, and different PDZ domains can bind the same peptides. A detailed understanding of the PDZ-peptide recognition is needed to elucidate this complexity. To this end, we have designed a family of docking protocols for PDZ domains (termed PDZ-DocScheme) that is based on simulated annealing molecular dynamics and rotamer optimization, and is applicable to the docking of long peptides (20-40 rotatable bonds) to both known PDZ structures and to the more complicated problem of homology models of these domains. The resulting protocol reproduces the structures of PDZ complexes with peptides 4-8 amino acids long within 1-2 A from the experimental structure when the docking is performed to the original structure. If the structure of the target PDZ domain is an apo structure or a homology model, the docking protocol yields structures within 3 A in 9 out of 12 test cases. The automated docking procedure PDZ-DocScheme can serve in the generation of a structural context for validation of PDZ domain specificity from mutagenesis and ligand binding data.     

PPM-Dom: A novel method for domain position prediction

Domains are the structural basis of the physiological functions of proteins, and the prediction of which is an advantageous process on the study of protein structure and function. This article proposes a new complete automatic prediction method, PPM-Dom (Domain Position Prediction Method), for predicting the particular positions of domains in a target protein via its atomic coordinate. The presented method integrates complex networks, community division, and fuzzy mean operator (FMO). The whole sequences are divided into potential domain regions by the complex network and community division, and FMO allows the final determination for the domain position. This method will suffice to predict regions that will form a domain structure and those that are unstructured based on completely new atomic coordinate information of the query sequence, and be able to separate different domains in the same query sequence from each other. On evaluating the performance using an independent testing dataset, PPM-Dom reached 91.41% for prediction accuracy, 96.12% for sensitivity and 92.86% for specificity. The tool bag of PPM-Dom is freely available at http://cic.scu.edu.cn/bioinformatics/PPMDom.zip.