Covariance analysis of RNA recognition motifs identifies functionally linked amino acids

A 3-dimensional trimeric β-barrel model for Chlamydia MOMP contains conserved and novel elements of Gram-negative bacterial porins

Chlamydia trachomatis is the most prevalent cause of bacterial sexually transmitted diseases and the leading cause of preventable blindness worldwide. Global control of Chlamydia will best be achieved with a vaccine, a primary target for which is the major outer membrane protein, MOMP, which comprises ~60% of the outer membrane protein mass of this bacterium. In the absence of experimental structural information on MOMP, three previously published topology models presumed a16-stranded barrel architecture. Here, we use the latest β-barrel prediction algorithms, previous 2D topology modeling results, and comparative modeling methodology to build a 3D model based on the 16-stranded, trimeric assumption. We find that while a 3D MOMP model captures many structural hallmarks of a trimeric 16-stranded β-barrel porin, and is consistent with most of the experimental evidence for MOMP, MOMP residues 320-334 cannot be modeled as β-strands that span the entire membrane, as is consistently observed in published 16-stranded β-barrel crystal structures. Given the ambiguous results for β-strand delineation found in this study, recent publications of membrane β-barrel structures breaking with the canonical rule for an even number of β-strands, findings of β-barrels with strand-exchanged oligomeric conformations, and alternate folds dependent upon the lifecycle of the bacterium, we suggest that although the MOMP porin structure incorporates canonical 16-stranded conformations, it may have novel oligomeric or dynamic structural changes accounting for the discrepancies observed.

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.

A study of collective atomic fluctuations and cooperativity in the U1A-RNA complex based on molecular dynamics simulations

Cooperative interactions play an important role in recognition and binding in macromolecular systems. In this study, we find that cross-correlated atomic fluctuations can be used to identify cooperative networks in a protein-RNA system. The dynamics of the RRM-containing protein U1A-stem loop 2 RNA complex have been calculated theoretically from a 10 ns molecular dynamics (MD) simulation. The simulation was analyzed by calculating the covariance matrix of all atomic fluctuations. These matrix elements are then presented in the form of a two-dimensional grid, which displays fluctuations on a per residue basis. The results indicate the presence of strong, selective cross-correlated fluctuations throughout the RRM in U1A-RNA. The atomic fluctuations correspond well with previous biophysical studies in which a multiplicity of cooperative networks have been reported and indicate that the various networks identified in separate individual experiments are fluctuationally correlated into a hyper-network encompassing most of the RRM. The calculated results also correspond well with independent results from a statistical covariance analysis of 330 aligned RRM sequences. This method has significant implications as a predictive tool regarding cooperativity in the protein-nucleic acid recognition process.

High-resolution structural validation of the computational redesign of human U1A protein

Achieving atomic-level resolution in the computational design of a protein structure remains a challenging problem despite recent progress. Rigorous experimental tests are needed to improve protein design algorithms, yet studies of the structure and dynamics of computationally designed proteins are very few. The NMR structure and backbone dynamics of a redesigned protein of 96 amino acids are compared here with the design target, human U1A protein. We demonstrate that the redesigned protein reproduces the target structure to within the uncertainty of the NMR coordinates, even as 65 out of 96 amino acids were simultaneously changed by purely computational methods. The dynamics of the backbone of the redesigned protein also mirror those of human U1A, suggesting that the protein design algorithm captures the shape of the potential energy landscape in addition to the local energy minimum.

Human splicing factor SPF45 (RBM17) confers broad multidrug resistance to anticancer drugs when overexpressed--a phenotype partially reversed by selective estrogen receptor modulators

The splicing factor SPF45 (RBM17) is frequently overexpressed in many solid tumors, and stable expression in HeLa cells confers resistance to doxorubicin and vincristine. In this study, we characterized stable transfectants of A2780 ovarian carcinoma cells. In a 3-day cytotoxicity assay, human SPF45 overexpression conferred 3- to 21-fold resistance to carboplatin, vinorelbine, doxorubicin, etoposide, mitoxantrone, and vincristine. In addition, resistance to gemcitabine and pemetrexed was observed at the highest drug concentrations tested. Knockdown of SPF45 in parental A2780 cells using a hammerhead ribozyme sensitized A2780 cells to etoposide by approximately 5-fold relative to a catalytically inactive ribozyme control and untransfected cells, suggesting a role for SPF45 in intrinsic resistance to some drugs. A2780-SPF45 cells accumulated similar levels of doxorubicin as vector-transfected and parental A2780 cells, indicating that drug resistance is not due to differences in drug accumulation. Efforts to identify small molecules that could block SPF45-mediated drug resistance revealed that the selective estrogen receptor (ER) modulators tamoxifen and LY117018 (a raloxifene analogue) partially reversed SPF45-mediated drug resistance to mitoxantrone in A2780-SPF45 cells from 21-fold to 8- and 5-fold, respectively, but did not significantly affect the mitoxantrone sensitivity of vector control cells. Quantitative PCR showed that ERbeta but not ERalpha was expressed in A2780 transfectants. Coimmunoprecipitation experiments suggest that SPF45 and ERbeta physically interact in vivo. Thus, SPF45-mediated drug resistance in A2780 cells may result in part from effects of SPF45 on the transcription or alternate splicing of ERbeta-regulated genes.

Systematic variation of amino acid substitutions for stringent assessment of pairwise covariation

During protein evolution, amino acids change due to a combination of functional constraints and genetic drift. Proteins frequently contain pairs of amino acids that appear to change together (covariation). Analysis of covariation from naturally occurring sets of orthologs cannot distinguish between residue pairs retained by functional requirements of the protein and those pairs existing due to changes along a common evolutionary path. Here, we have separated the two types of covariation by independently recombining every naturally occurring amino acid variant within a set of 15 subtilisin orthologs. Our analysis shows that in this family of subtilisin orthologs, almost all possible pairwise combinations of amino acids can coexist. This suggests that amino acid covariation found in the subtilisin orthologs is almost entirely due to common ancestral origin of the changes rather than functional constraints. We conclude that naturally occurring sequence diversity can be used to identify positions that can vary independently without destroying protein function.

Sequence analysis of an artificial family of RNA-binding peptides

Diverse peptide sequences recognizing the lambda boxB RNA hairpin were previously isolated from a library encoding the 22-residue lambda N peptide with random amino acids at positions 13-22 using mRNA display. We have statistically analyzed amino acid distributions in 65 unique sequences from rounds 11 and 12 of this selection and evaluated the resulting structural and functional predictions by alanine-scanning mutagenesis and circular dichroism spectrometry. This artificial sequence family has a consensus structure that continues the bent alpha helix of lambda N up to position 17 when bound to lambda boxB. A charge pair (E(14)R(15)) and hydrophobic patch (A(21)L(22) or V(21)L(22)) have important functional roles in this context. Notably, amino acid covariance reveals six specific pairs of random region positions with >95% significant linkage and strong overall helical (i+1, i+3, and i+4) couplings. The covariance analysis suggests that (1) the sequence context of every residue in each insert has been optimized, (2) selected sequences are local optima on a rugged fitness landscape, and (3) it is possible to detect more subtle structural features with artificial protein sequence families than natural homologs. Our results provide a framework for investigating the structures of in vitro selected proteins by functional minimization, reselection, and covariance analysis.

Intramolecular interactions at protein surfaces and their impact on protein function

Protein surfaces play a key role in the biological function of proteins. Consequently, structural features of protein surfaces are the basis for predicting function from structure. A well-established principle of binding by proteins is that ligands must compete with water and other small molecules to form interactions with protein surfaces. A less obvious issue, and the emphasis of this article, is that ligands must also compete with interactions among residues at protein surfaces. Results from structural surveys, a variety of experimental studies and computations suggest that intramolecular interactions are present at protein surfaces and that the energetics of these interactions can change when proteins bind to other molecules.