Bioinformatics and Computational Biology

Motions of the IgG structure are evaluated using normal mode analysis of an elastic network model to detect hinges, the dominance of low frequency modes, and the most important internal motions. One question we seek to answer is whether or not IgG hinge motions facilitate antigen binding. We also evaluate the protein crystal and packing effects on the experimental temperature factors and disorder predictions. We find that the effects of the protein environment on the crystallographic temperature factors may be misleading for evaluating specific functional motions of IgG. The extent of motion of the antigen binding domains is computed to show their large spatial sampling. We conclude that the IgG structure is specifically designed to facilitate large excursions of the antigen binding domains. Normal modes are shown as capable of com- putationally evaluating the hinge motions and the spatial sampling by the structure. The antigen binding loops and the major hinge appear to behave similarly to the rest of the structure when we consider the dominance of the low frequency modes and the extent of internal motion. The full IgG structure has a lower spectral dimension than individual Fab domains, pointing to more efficient information transfer through the antibody than through each domain. This supports the claim that the IgG structure is specifically constructed to facilitate antigen binding by coupling motion of the antigen binding loops with the large scale hinge motions.

Predicting protein motions is important for bridging the gap between protein structure and function. With growing numbers of structures of the same, or closely related proteins becoming available, it is now possible to understand more about the intrinsic dynamics of a protein with principal component analysis (PCA) of the motions apparent within ensembles of experimental structures. In this paper, we compare the motions extracted from experimental ensembles of 50 different proteins with the modes of motion predicted by several types of coarse-grained elastic network models (ENMs) which additionally take into account more details of either the protein geometry or the amino acid specificity. We further compare the structural variations in the experimental ensembles with the motions sampled in molecular dynamics (MD) simulations for a smaller subset of 17 proteins with available trajectories. We find that the correlations between the motions extracted from MD trajectories and experimental structure ensembles are slightly different than for the ENMs, possibly reflecting potential sampling biases. We find that there are small gains in the predictive power of the ENMs in reproducing motions present in either experimental or MD ensembles by accounting for the protein geometry rather than the amino acid specificity of the interactions.

We propose a novel method of calculation of free energy for coarse grained models of proteins by combining our newly developed multibody potentials with entropies computed from elastic network models of proteins. Multi-body potentials have been of much interest recently because they take into account three dimensional interactions related to residue packing and capture the cooperativity of these interactions in protein structures. Combining four-body non-sequential, four-body sequential and pairwise short range potentials with optimized weights for each term, our coarse-grained potential improved recognition of native structure among misfolded decoys, outperforming all other contact potentials for CASP8 decoy sets and performance comparable to the fully atomic empirical DFIRE potentials. By combing statistical contact potentials with entropies from elastic network models of the same structures we can compute free energy changes and improve coarse-grained modeling of protein structure and dynamics. The consideration of protein flexibility and dynamics should improve protein structure prediction and refinement of computational models. This work is the first to combine coarse-grained multibody potentials with an entropic model that takes into account contributions of the entire structure, investigating native-like decoy selection.

The dynamics of proteins are important for understanding their functions. In recent years, the simple coarse-grained Gaussian Network Model (GNM) has been fairly successful in interpreting crystallographic B-factors. However, the model clearly ignores the contribution of the rigid body motions and the effect of crystal packing. The model cannot explain the fact that the same protein may have significantly different B-factors under different crystal packing conditions. In this work, we propose a new Gaussian network model, called vGNM, which takes into account both the contribution of the rigid body motions and the effect of crystal packing, by allowing the amplitude of the internal modes to be variables. It hypothesizes that the effect of crystal packing should cause some modes to be amplified, and others to become less feasible. In doing so, vGNM is able to resolve the apparent discrepancy in experimental B-factors among structures of the same protein but with different crystal packing conditions, which GNM cannot explain. With a small number of parameters, vGNM is able to reproduce experimental B-factors for a large set of proteins with significantly better correlations (having a mean value of 0.81 as compared to 0.59 by GNM). The results of applying vGNM also show that the rigid body motions account for nearly 60% of the total fluctuations, in good agreement with previous findings.

Protein-RNA interactions are central to essential cellular processes such as protein synthesis and regulation of gene expression and play roles in human infectious and genetic diseases. Reliable identification of protein-RNA interfaces is critical for understanding the structural bases and functional implications of such interactions and for developing effective approaches to rational drug design. Sequence-based computational methods offer a viable, cost-effective way to identify putative RNA-binding residues in RNA-binding proteins. Here we report two novel approaches: (i) HomPRIP, a sequence homology-based method for predicting RNA-binding sites in proteins; (ii) RNABindRPlus, a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA-binding proteins. Although highly reliable, HomPRIP cannot make predictions for the unaligned parts of query proteins and its coverage is limited by the availability of close sequence homologs of the query protein with experimentally determined RNA-binding sites. RNABindRPlus overcomes these limitations. We compared the performance of HomPRIP and RNABindRPlus with that of several state-of-the-art predictors on two test sets, RB44 and RB111. On a subset of proteins for which homologs with experimentally determined interfaces could be reliably identified, HomPRIP outperformed all other methods achieving an MCC of 0.63 on RB44 and 0.83 on RB111. RNABindRPlus was able to predict RNA-binding residues of all proteins in both test sets, achieving an MCC of 0.55 and 0.37, respectively, and outperforming all other methods, including those that make use of structure-derived features of proteins. More importantly, RNABindRPlus outperforms all other methods for any choice of tradeoff between precision and recall. An important advantage of both HomPRIP and RNABindRPlus is that they rely on readily available sequence and sequence-derived features of RNA-binding proteins. A webserver implementation of both methods is freely available at http://einstein.cs.iastate.edu/RNABindRPlus/.

Obligate biotrophic pathogens of plants must circumvent or counteract defenses to guarantee accommodation inside the host. To do so, they secrete a variety of effectors that regulate host immunity and facilitate the establishment of pathogen feeding structures called haustoria. The barley powdery mildew fungus Blumeria graminis f. sp. hordeiproduces a large number of proteins predicted to be secreted from haustoria. Fifty of these Blumeria effector candidates (BEC) were screened by host-induced gene silencing (HIGS), and eight were identified that contribute to infection. One shows similarity to β-1,3 glucosyltransferases, one to metallo-proteases, and two to microbial secreted ribonucleases; the remainder have no similarity to proteins of known function. Transcript abundance of all eight BEC increases dramatically in the early stages of infection and establishment of haustoria, consistent with a role in that process. Complementation analysis using silencing-insensitive synthetic cDNAs demonstrated that the ribonuclease-like BEC 1011 and 1054 are bona fide effectors that function within the plant cell. BEC1011 specifically interferes with pathogen-induced host cell death. Both are part of a gene superfamily unique to the powdery mildew fungi. Structural modeling was consistent, with BEC1054 adopting a ribonuclease-like fold, a scaffold not previously associated with effector function.

During cotranslational protein targeting by the signal recognition particle (SRP), information about signal sequence binding in the SRP's M domain must be effectively communicated to its GTPase domain to turn on its interaction with the SRP receptor (SR) and thus deliver the cargo proteins to the membrane. A universally conserved “fingerloop” lines the signal sequence–binding groove of SRP; the precise role of this fingerloop in protein targeting has remained elusive. In this study, we show that the fingerloop plays important roles in SRP function by helping to induce the SRP into a more active conformation that facilitates multiple steps in the pathway, including efficient recruitment of SR, GTPase activation in the SRP•SR complex, and most significantly, the unloading of cargo onto the target membrane. On the basis of these results and recent structural work, we propose that the fingerloop is the first structural element to detect signal sequence binding; this information is relayed to the linker connecting the SRP's M and G domains and thus activates the SRP and SR for carrying out downstream steps in the pathway.

Background

RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.

Results

We propose RPISeq, a family of classifiers for predicting R NA-p rotein i nteractions using only seq uence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens.

Conclusions

Our experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.

Using genomic technologies, it is now possible to address research hypotheses in the context of entire developmental or biochemical pathways, gene networks, and chromosomal location of relevant genes and their inferred evolutionary history. Through a range of platforms, researchers can survey an entire transcriptome under a variety of experimental and field conditions. Interpretation of such data has led to new insights and revealed previously undescribed phenomena. In the area of plant-pathogen interactions, transcript profiling has provided unparalleled perception into the mechanisms underlying gene-for-gene resistance and basal defense, host vs nonhost resistance, biotrophy vs necrotrophy, and pathogenicity of vascular vs nonvascular pathogens, among many others. In this way, genomic technologies have facilitated a system-wide approach to unifying themes and unique features in the interactions of hosts and pathogens.