Pathological conformations of disease mutant Ryanodine Receptors revealed by cryo-EM.

Ryanodine Receptors (RyRs) are massive channels that release Ca2+ from the endoplasmic and sarcoplasmic reticulum. Hundreds of mutations are linked to malignant hyperthermia (MH), myopathies, and arrhythmias. Here, we explore the first MH mutation identified in humans by providing cryo-EM snapshots of the pig homolog, R615C, showing that it affects an interface between three solenoid regions. We also show the impact of apo-calmodulin (apoCaM) and how it can induce opening by bending of the bridging solenoid, mediated by its N-terminal lobe. For R615C RyR1, apoCaM binding abolishes a pathological 'intermediate' conformation, distributing the population to a mixture of open and closed channels, both different from the structure without apoCaM. Comparisons show that the mutation primarily affects the closed state, inducing partial movements linked to channel activation. This shows that disease mutations can cause distinct pathological conformations of the RyR and facilitate channel opening by disrupting interactions between different solenoid regions.

Discovery of a hidden transient state in all bromodomain families.

Bromodomains (BDs) are small protein modules that interact with acetylated marks in histones. These posttranslational modifications are pivotal to regulate gene expression, making BDs promising targets to treat several diseases. While the general structure of BDs is well known, their dynamical features and their interplay with other macromolecules are poorly understood, hampering the rational design of potent and selective inhibitors. Here, we combine extensive molecular dynamics simulations, Markov state modeling, and available structural data to reveal a transiently formed state that is conserved across all BD families. It involves the breaking of two backbone hydrogen bonds that anchor the ZA-loop with the αA helix, opening a cryptic pocket that partially occludes the one associated to histone binding. By analyzing more than 1,900 experimental structures, we unveil just two adopting the hidden state, explaining why it has been previously unnoticed and providing direct structural evidence for its existence. Our results suggest that this state is an allosteric regulatory switch for BDs, potentially related to a recently unveiled BD-DNA-binding mode.

DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.

Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network-based protein secondary structure method (DNSS1) and successfully advanced the prediction accuracy beyond 80%. In this work, we developed multiple advanced deep learning architectures (DNSS2) to further improve secondary structure prediction. The major improvements over the DNSS1 method include (a) designing and integrating six advanced one-dimensional deep convolutional/recurrent/residual/memory/fractal/inception networks to predict 3-state and 8-state secondary structure, and (b) using more sensitive profile features inferred from Hidden Markov model (HMM) and multiple sequence alignment (MSA). Most of the deep learning architectures are novel for protein secondary structure prediction. DNSS2 was systematically benchmarked on independent test data sets with eight state-of-art tools and consistently ranked as one of the best methods. Particularly, DNSS2 was tested on the protein targets of 2018 CASP13 experiment and achieved the Q3 score of 81.62%, SOV score of 72.19%, and Q8 score of 73.28%. DNSS2 is freely available at: https://github.com/multicom-toolbox/DNSS2.

Hup-Type Hydrogenases of Purple Bacteria: Homology Modeling and Computational Assessment of Biotechnological Potential.

Three-dimensional structures of six closely related hydrogenases from purple bacteria were modeled by combining the template-based and ab initio modeling approach. The results led to the conclusion that there should be a 4Fe3S cluster in the structure of these enzymes. Thus, these hydrogenases could draw interest for exploring their oxygen tolerance and practical applicability in hydrogen fuel cells. Analysis of the 4Fe3S cluster's microenvironment showed intragroup heterogeneity. A possible function of the C-terminal part of the small subunit in membrane binding is discussed. Comparison of the built models with existing hydrogenases of the same subgroup (membrane-bound oxygen-tolerant hydrogenases) was carried out. Analysis of intramolecular interactions in the large subunits showed statistically reliable differences in the number of hydrophobic interactions and ionic interactions. Molecular tunnels were mapped in the models and compared with structures from the PDB. Protein-protein docking showed that these enzymes could exchange electrons in an oligomeric state, which is important for oxygen-tolerant hydrogenases. Molecular docking with model electrode compounds showed mostly the same results as with hydrogenases from E. coli, H. marinus, R. eutropha, and S. enterica; some interesting results were shown in case of HupSL from Rba. sphaeroides and Rvi. gelatinosus.

The DNA-binding protein HTa from Thermoplasma acidophilum is an archaeal histone analog.

Histones are a principal constituent of chromatin in eukaryotes and fundamental to our understanding of eukaryotic gene regulation. In archaea, histones are widespread but not universal: several lineages have lost histone genes. What prompted or facilitated these losses and how archaea without histones organize their chromatin remains largely unknown. Here, we elucidate primary chromatin architecture in an archaeon without histones, Thermoplasma acidophilum, which harbors a HU family protein (HTa) that protects part of the genome from micrococcal nuclease digestion. Charting HTa-based chromatin architecture in vitro, in vivo and in an HTa-expressing E. coli strain, we present evidence that HTa is an archaeal histone analog. HTa preferentially binds to GC-rich sequences, exhibits invariant positioning throughout the growth cycle, and shows archaeal histone-like oligomerization behavior. Our results suggest that HTa, a DNA-binding protein of bacterial origin, has converged onto an architectural role filled by histones in other archaea.

A Short-Term Assessment of Nascent HIV-1 Transmission Clusters Among Newly Diagnosed Individuals Using Envelope Sequence-Based Phylogenetic Analyses.

The identification of transmission clusters (TCs) of HIV-1 using phylogenetic analyses can provide insights into viral transmission network and help improve prevention strategies. We compared the use of partial HIV-1 envelope fragment of 1,070 bp with its loop 3 (108 bp) to determine its utility in inferring HIV-1 transmission clustering. Serum samples of recently (n = 106) and chronically (n = 156) HIV-1-infected patients with status confirmed were sequenced. HIV-1 envelope nucleotide-based phylogenetic analyses were used to infer HIV-1 TCs. Those were constructed using ClusterPickerGUI_1.2.3 considering a pairwise genetic distance of ≤10% threshold. Logistic regression analyses were used to examine the relationship between the demographic factors that were likely associated with HIV-1 clustering. Ninety-eight distinct consensus envelope sequences were subjected to phylogenetic analyses. Using a partial envelope fragment sequence, 42 sequences were grouped into 15 distinct small TCs while the V3 loop reproduces 10 clusters. The agreement between the partial envelope and the V3 loop fragments was significantly moderate with a Cohen's kappa (κ) coefficient of 0.59, p < .00001. The mean age (<38.8 years) and HIV-1 B subtype are two factors identified that were significantly associated with HIV-1 transmission clustering in the cohort, odds ratio (OR) = 0.25, 95% confidence interval (CI, 0.04-0.66), p = .002 and OR: 0.17, 95% CI (0.10-0.61), p = .011, respectively. The present study confirms that a partial fragment of the HIV-1 envelope sequence is a better predictor of transmission clustering. However, the loop 3 segment may be useful in screening purposes and may be more amenable to integration in surveillance programs.

Development of an Efficient and Cost-Effective Enzymatic Process for Production of (R)-[3,5-bis(trifluoromethyl)phenyl] Ethanol Using Carbonyl Reductase Derived from Leifsonia sp. S749.

(R)-[3,5-bis(trifluoromethyl) phenyl] ethanol [(R)-3,5-BTPE] is a crucial chiral intermediate for the synthesis of the NK-1 receptor antagonists aprepitant, rolapitant and fosaprepitant. The carbonyl reductase KR01 from Leifsonia sp. S749, discovered by protein sequence alignment, could convert 3',5'-bis(trifluoromethyl) acetophenone (3,5-BTAP) into (R)-3,5-BTPE with excellent activity and enantioselectivity. In order to enhance the conversion efficiency at high substrate concentrations, the reaction conditions were optimized by response surface analysis. The results showed that 600 g/L 3,5-BTAP was bioreduced to (R)-3,5-BTPE (> 99.9% enantiomeric excess) by the recombinant Escherichia coli/pET-28a (+)-KR01 whole cells, with a 98.3% conversion and 59 g/L/h productivity under the optimized reaction conditions. In addition, the recombinant E. coli cells could be repeatedly used up to seven times in the reaction mixture containing 90% isopropanol (IPA). This is the highest substrate loading and productivity for the bioreduction of 3,5-BTAP by carbonyl reductase ever reported, and this method represents an efficient and cost-effective process for production of (R)-3,5-BTPE.

Specific enzyme functionalities of Fusarium oxysporum compared to host plants.

The genus Fusarium contains some of the most studied and important species of plant pathogens that economically affect world agriculture and horticulture. Fusarium spp. are ubiquitous fungi widely distributed in soil, plants as well as in different organic substrates and are also considered as opportunistic human pathogens. The identification of specific enzymes essential to the metabolism of these fungi is expected to provide molecular targets to control the diseases they induce to their hosts. Through applications of traditional techniques of sequence homology comparison by similarity search and Markov modeling, this report describes the characterization of enzymatic functionalities associated to protein targets that could be considered for the control of root rots induced by Fusarium oxysporum. From the analysis of 318 F. graminearum enzymes, we retrieved 30 enzymes that are specific of F. oxysporum compared to 15 species of host plants. By comparing these 30 specific enzymes of F. oxysporum with the genome of Arabidopsis thaliana, Brassica rapa, Glycine max, Jatropha curcas and Ricinus communis, we found 7 key specific enzymes whose inhibition is expected to affect significantly the development of the fungus and 5 specific enzymes that were considered here to be secondary because they are inserted in pathways with alternative routes.

Assessment of the Clinical Relevance of BRCA2 Missense Variants by Functional and Computational Approaches.

Many variants of uncertain significance (VUS) have been identified in BRCA2 through clinical genetic testing. VUS pose a significant clinical challenge because the contribution of these variants to cancer risk has not been determined. We conducted a comprehensive assessment of VUS in the BRCA2 C-terminal DNA binding domain (DBD) by using a validated functional assay of BRCA2 homologous recombination (HR) DNA-repair activity and defined a classifier of variant pathogenicity. Among 139 variants evaluated, 54 had ?99% probability of pathogenicity, and 73 had ?95% probability of neutrality. Functional assay results were compared with predictions of variant pathogenicity from the Align-GVGD protein-sequence-based prediction algorithm, which has been used for variant classification. Relative to the HR assay, Align-GVGD significantly (p < 0.05) over-predicted pathogenic variants. We subsequently combined functional and Align-GVGD prediction results in a Bayesian hierarchical model (VarCall) to estimate the overall probability of pathogenicity for each VUS. In addition, to predict the effects of all other BRCA2 DBD variants and to prioritize variants for functional studies, we used the endoPhenotype-Optimized Sequence Ensemble (ePOSE) algorithm to train classifiers for BRCA2 variants by using data from the HR functional assay. Together, the results show that systematic functional assays in combination with in silico predictors of pathogenicity provide robust tools for clinical annotation of BRCA2 VUS.

The HMMER Web Server for Protein Sequence Similarity Search.

Protein sequence similarity search is one of the most commonly used bioinformatics methods for identifying evolutionarily related proteins. In general, sequences that are evolutionarily related share some degree of similarity, and sequence-search algorithms use this principle to identify homologs. The requirement for a fast and sensitive sequence search method led to the development of the HMMER software, which in the latest version (v3.1) uses a combination of sophisticated acceleration heuristics and mathematical and computational optimizations to enable the use of profile hidden Markov models (HMMs) for sequence analysis. The HMMER Web server provides a common platform by linking the HMMER algorithms to databases, thereby enabling the search for homologs, as well as providing sequence and functional annotation by linking external databases. This unit describes three basic protocols and two alternate protocols that explain how to use the HMMER Web server using various input formats and user defined parameters. © 2017 by John Wiley & Sons, Inc.

Hybrid approach to structure modeling of the histamine H3 receptor: Multi-level assessment as a tool for model verification.

The crucial role of G-protein coupled receptors and the significant achievements associated with a better understanding of the spatial structure of known receptors in this family encouraged us to undertake a study on the histamine H3 receptor, whose crystal structure is still unresolved. The latest literature data and availability of different software enabled us to build homology models of higher accuracy than previously published ones. The new models are expected to be closer to crystal structures; and therefore, they are much more helpful in the design of potential ligands. In this article, we describe the generation of homology models with the use of diverse tools and a hybrid assessment. Our study incorporates a hybrid assessment connecting knowledge-based scoring algorithms with a two-step ligand-based docking procedure. Knowledge-based scoring employs probability theory for global energy minimum determination based on information about native amino acid conformation from a dataset of experimentally determined protein structures. For a two-step docking procedure two programs were applied: GOLD was used in the first step and Glide in the second. Hybrid approaches offer advantages by combining various theoretical methods in one modeling algorithm. The biggest advantage of hybrid methods is their intrinsic ability to self-update and self-refine when additional structural data are acquired. Moreover, the diversity of computational methods and structural data used in hybrid approaches for structure prediction limit inaccuracies resulting from theoretical approximations or fuzziness of experimental data. The results of docking to the new H3 receptor model allowed us to analyze ligand-receptor interactions for reference compounds.

Characteristics of 29 novel atypical solute carriers of major facilitator superfamily type: evolutionary conservation, predicted structure and neuronal co-expression.

Solute carriers (SLCs) are vital as they are responsible for a major part of the molecular transport over lipid bilayers. At present, there are 430 identified SLCs, of which 28 are called atypical SLCs of major facilitator superfamily (MFS) type. These are MFSD1, 2A, 2B, 3, 4A, 4B, 5, 6, 6 L, 7, 8, 9, 10, 11, 12, 13A, 14A and 14B; SV2A, SV2B and SV2C; SVOP and SVOPL; SPNS1, SPNS2 and SPNS3; and UNC93A and UNC93B1. We studied their fundamental properties, and we also included CLN3, an atypical SLC not yet belonging to any protein family (Pfam) clan, because its involvement in the same neuronal degenerative disorders as MFSD8. With phylogenetic analyses and bioinformatic sequence comparisons, the proteins were divided into 15 families, denoted atypical MFS transporter families (AMTF1-15). Hidden Markov models were used to identify orthologues from human to Drosophila melanogaster and Caenorhabditis elegans Topology predictions revealed 12 transmembrane segments (for all except CLN3), corresponding to the common MFS structure. With single-cell RNA sequencing and in situ proximity ligation assay on brain cells, co-expressions of several atypical SLCs were identified. Finally, the transcription levels of all genes were analysed in the hypothalamic N25/2 cell line after complete amino acid starvation, showing altered expression levels for several atypical SLCs.

A computational assessment of the predicted structures of Human Macrophage Migration Inhibitory Factor 1 orthologs in parasites and its affinity to human CD74 receptor.

The human macrophage migration inhibitory factor 1 (Hu-MIF-1) is a protein involved in the inflammatory and immunology response to parasite infection. In the present study, the existence of Hu-MIF-1 from parasites have been explored by mining WormBase. A total of 35 helminths were found to have Hu-MIF-1 homologs, including some parasites of importance for public health. Physicochemical, structural, and biological properties of Hu-MIF-1 were compared with its orthologs in parasites showing that most of these are secretory proteins, with positive net charge and presence of the Cys-Xaa-Xaa-Cys motif that is critical for its oxidoreductase activity. The inhibitor-binding site present in Hu-MIF-1 is well conserved among parasite MIFs suggesting that Hu-MIF inhibitors may target orthologs in pathogens. The binding of Hu-MIF-1 to its cognate receptor CD74 was predicted by computer-assisted docking, and it resulted to be very similar to the predicted complexes formed by parasite MIFs and human CD74. More than 1 plausible conformation of MIFs in the extracellular loops of CD74 may be possible as demonstrated by the different predicted conformations of MIF orthologs in complex with CD74. Parasite MIFs in complex with CD74 resulted with some charged residues oriented to CD74, which was not observed in the Hu-MIF-1/CD74 complex. Our findings predict the binding mode of Hu-MIF-1 and orthologs with CD74, which can assist in the design of novel MIF inhibitors. Whether the parasite MIFs function specifically subvert host immune responses to suit the parasite is an open question that needs to be further investigated. Future research should lead to a better understanding of parasite MIF action in the parasite biology.

Assessment of sequence homology and immunologic cross-reactivity between tree shrew (Tupaia belangeri) and human IL-21.

Many studies have indicated that the expression of interleukin-21 (IL-21) is associated with the pathogenesis of certain liver diseases. However, in alternative animal models of liver diseases, it remains unknown whether the tree shrew could be utilized to analyze the relationship between IL-21 and liver diseases. Here, the phylogenetic tree, sequence alignment and protein structure model of tree shrew and human IL-21 were analyzed using bioinformatics software. A pEGFP-N3/tsIL-21 eukaryotic expression vector of tree shrew IL-21 (tsIL-21) was constructed, and IL-21 expression by the vector-transfected Huh7 cells was evaluated using the newly established quantitative real-time PCR and immunologic protocols for assessing human IL-21. The cytokine profiles were also evaluated in tree shrew spleen lymphocytes induced by recombinant human IL-21 or concanavalin A. It was found that the coding sequence (CDS) of tsIL-21 amplified from spleen lymphocytes belonged to the predicted sequence. The tsIL-21 was closely clustered with primate IL-21 rather than rodent IL-21, and it had an alignment of 83.33% with the human IL-21 nucleotide sequence and 69.93% with the amino acid sequence. The profiles of secondary structure, hydrophobicity and surface charge of tsIL-21 were also similar with those of human IL-21. The tsIL-21 expressed by the vector-transfected Huh7 cells could be identified by their different sources of antibodies against human IL-21, which were all dose-dependent. Recombinant human IL-21 could induce the change of the cytokine profiles of tree shrew spleen lymphocytes, which showed a higher expression of IL-10 and IFN-γ rather than IL-2, IL-4, IL-17, TNF-a and IL-21 during the five-day stimulation. These results indicate that tsIL-21 has a high degree of homology, structural similarity and immunological cross-reactivity with human IL-21 and also confirm the accuracy of this predicted tsIL-21CDS. The protocols utilized in this study will lead to the experimental feasibility of further IL-21-related studies in vivo.

Hidden Markov Models for Protein Domain Homology Identification and Analysis.

Protein domain identification and analysis are cornerstones of modern proteomics. The tools available to protein domain researchers avail a variety of approaches to understanding large protein domain families. Hidden Markov Models (HMM) form the basis for identifying and categorizing evolutionarily linked protein domains. Here I describe the use of HMM models for predicting and identifying Src Homology 2 (SH2) domains within the proteome.

A post-decoding re-ranking algorithm for predicting interacting residues in proteins with hidden Markov models incorporating long-distance information.

Protein-protein interactions play a central role in the biological processes of cells. Accurate prediction of the interacting residues in protein-protein interactions enhances understanding of the interaction mechanisms and enables in silico mutagenesis, which can help facilitate drug design and deepen our understanding of the inner workings of cells. Correlations have been found among interacting residues as a result of selection pressure to retain the interaction during evolution. In previous work, incorporation of such correlations in the interaction profile hidden Markov models with a special decoding algorithm (ETB-Viterbi) has led to improvement in prediction accuracy. In this work, we first demonstrated the sub-optimality of the ETB-Viterbi algorithm, and then reformulated the optimality of decoding paths to include correlations between interacting residues. To identify optimal decoding paths, we propose a post-decoding re-ranking algorithm based on a genetic algorithm with simulated annealing and show that the new method gains an increase of near 14% in prediction accuracy over the ETB-Viterbi algorithm.

Assessment of the PrPc Amino-Terminal Domain in Prion Species Barriers.

Chronic wasting disease (CWD) in cervids and bovine spongiform encephalopathy (BSE) in cattle are prion diseases that are caused by the same protein-misfolding mechanism, but they appear to pose different risks to humans. We are interested in understanding the differences between the species barriers of CWD and BSE. We used real-time, quaking-induced conversion (RT-QuIC) to model the central molecular event in prion disease, the templated misfolding of the normal prion protein, PrPc, to a pathogenic, amyloid isoform, scrapie prion protein, PrPSc We examined the role of the PrPc amino-terminal domain (N-terminal domain [NTD], amino acids [aa] 23 to 90) in cross-species conversion by comparing the conversion efficiency of various prion seeds in either full-length (aa 23 to 231) or truncated (aa 90 to 231) PrPc We demonstrate that the presence of white-tailed deer and bovine NTDs hindered seeded conversion of PrPc, but human and bank vole NTDs did the opposite. Additionally, full-length human and bank vole PrPcs were more likely to be converted to amyloid by CWD prions than were their truncated forms. A chimera with replacement of the human NTD by the bovine NTD resembled human PrPc The requirement for an NTD, but not for the specific human sequence, suggests that the NTD interacts with other regions of the human PrPc to increase promiscuity. These data contribute to the evidence that, in addition to primary sequence, prion species barriers are controlled by interactions of the substrate NTD with the rest of the substrate PrPc molecule. IMPORTANCE: We demonstrate that the amino-terminal domain of the normal prion protein, PrPc, hinders seeded conversion of bovine and white-tailed deer PrPcs to the prion forms, but it facilitates conversion of the human and bank vole PrPcs to the prion forms. Additionally, we demonstrate that the amino-terminal domain of human and bank vole PrPcs requires interaction with the rest of the molecule to facilitate conversion by CWD prions. These data suggest that interactions of the amino-terminal domain with the rest of the PrPc molecule play an important role in the susceptibility of humans to CWD prions.

Assessment of transfer methods for comparative genomics of regulatory networks in bacteria.

BACKGROUND: Comparative genomics can leverage the vast amount of available genomic sequences to reconstruct and analyze transcriptional regulatory networks in Bacteria, but the efficacy of this approach hinges on the ability to transfer regulatory network information from reference species to the genomes under analysis. Several methods have been proposed to transfer regulatory information between bacterial species, but the paucity and distributed nature of experimental information on bacterial transcriptional networks have prevented their systematic evaluation. RESULTS: We report the compilation of a large catalog of transcription factor-binding sites across Bacteria and its use to systematically benchmark proposed transfer methods across pairs of bacterial species. We evaluate motif- and accuracy-based metrics to assess the results of regulatory network transfer and we identify the precision-recall area-under-the-curve as the best metric for this purpose due to the large class-imbalanced nature of the problem. Methods assuming conservation of the transcription factor-binding motif (motif-based) are shown to substantially outperform those assuming conservation of regulon composition (network-based), even though their efficiency can decrease sharply with increasing phylogenetic distance. Variations of the basic motif-based transfer method do not yield significant improvements in transfer accuracy. Our results indicate that detection of a large enough number of regulated orthologs is critical for network-based transfer methods, but that relaxing orthology requirements does not improve results. Using the transcriptional regulators LexA and Fur as case examples, we also show how DNA-binding domain sequence similarity can yield confounding results as an indicator of transfer efficiency for motif-based methods. CONCLUSIONS: Counter to standard practice, our evaluation of metrics to assess the efficiency of methods for regulatory network information transfer reveals that the area under precision-recall (PR) curves is a more precise and informative metric than that of receiver-operating-characteristic (ROC) curves, confirming similar findings in other class-imbalanced settings. Our systematic assessment of transfer methods reveals that simple approaches to both motif- and network-based transfer of regulatory information provide equal or better results than more elaborate methods. We also show that there are not effective predictors of transfer efficacy, substantiating the long-standing practice of manual curation in comparative genomics analyses.

PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins.

MOTIVATION: The PRED-TMBB method is based on Hidden Markov Models and is capable of predicting the topology of beta-barrel outer membrane proteins and discriminate them from water-soluble ones. Here, we present an updated version of the method, PRED-TMBB2, with several newly developed features that improve its performance. The inclusion of a properly defined end state allows for better modeling of the beta-barrel domain, while different emission probabilities for the adjacent residues in strands are used to incorporate knowledge concerning the asymmetric amino acid distribution occurring there. Furthermore, the training was performed using newly developed algorithms in order to optimize the labels of the training sequences. Moreover, the method is retrained on a larger, non-redundant dataset which includes recently solved structures, and a newly developed decoding method was added to the already available options. Finally, the method now allows the incorporation of evolutionary information in the form of multiple sequence alignments. RESULTS: The results of a strict cross-validation procedure show that PRED-TMBB2 with homology information performs significantly better compared to other available prediction methods. It yields 76% in correct topology predictions and outperforms the best available predictor by 7%, with an overall SOV of 0.9. Regarding detection of beta-barrel proteins, PRED-TMBB2, using just the query sequence as input, achieves an MCC value of 0.92, outperforming even predictors designed for this task and are much slower. AVAILABILITY AND IMPLEMENTATION: The method, along with all datasets used, is freely available for academic users at http://www.compgen.org/tools/PRED-TMBB2 CONTACT: pbagos@compgen.org.

aPPRove: An HMM-Based Method for Accurate Prediction of RNA-Pentatricopeptide Repeat Protein Binding Events.

Pentatricopeptide repeat containing proteins (PPRs) bind to RNA transcripts originating from mitochondria and plastids. There are two classes of PPR proteins. The [Formula: see text] class contains tandem [Formula: see text]-type motif sequences, and the [Formula: see text] class contains alternating [Formula: see text], [Formula: see text] and [Formula: see text] type sequences. In this paper, we describe a novel tool that predicts PPR-RNA interaction; specifically, our method, which we call aPPRove, determines where and how a [Formula: see text]-class PPR protein will bind to RNA when given a PPR and one or more RNA transcripts by using a combinatorial binding code for site specificity proposed by Barkan et al. Our results demonstrate that aPPRove successfully locates how and where a PPR protein belonging to the [Formula: see text] class can bind to RNA. For each binding event it outputs the binding site, the amino-acid-nucleotide interaction, and its statistical significance. Furthermore, we show that our method can be used to predict binding events for [Formula: see text]-class proteins using a known edit site and the statistical significance of aligning the PPR protein to that site. In particular, we use our method to make a conjecture regarding an interaction between CLB19 and the second intronic region of ycf3. The aPPRove web server can be found at www.cs.colostate.edu/~approve.