Evolutionary Triplet Models of Structured RNA

The reconstruction and synthesis of ancestral RNAs is a feasible goal for paleogenetics. This will require new bioinformatics methods, including a robust statistical framework for reconstructing histories of substitutions, indels and structural changes. We describe a “transducer composition” algorithm for extending pairwise probabilistic models of RNA structural evolution to models of multiple sequences related by a phylogenetic tree. This algorithm draws on formal models of computational linguistics as well as the 1985 protosequence algorithm of David Sankoff. The output of the composition algorithm is a multiple-sequence stochastic context-free grammar. We describe dynamic programming algorithms, which are robust to null cycles and empty bifurcations, for parsing this grammar. Example applications include structural alignment of non-coding RNAs, propagation of structural information from an experimentally-characterized sequence to its homologs, and inference of the ancestral structure of a set of diverged RNAs. We implemented the above algorithms for a simple model of pairwise RNA structural evolution; in particular, the algorithms for maximum likelihood (ML) alignment of three known RNA structures and a known phylogeny and inference of the common ancestral structure. We compared this ML algorithm to a variety of related, but simpler, techniques, including ML alignment algorithms for simpler models that omitted various aspects of the full model and also a posterior-decoding alignment algorithm for one of the simpler models. In our tests, incorporation of basepair structure was the most important factor for accurate alignment inference; appropriate use of posterior-decoding was next; and fine details of the model were least important. Posterior-decoding heuristics can be substantially faster than exact phylogenetic inference, so this motivates the use of sum-over-pairs heuristics where possible (and approximate sum-over-pairs). For more exact probabilistic inference, we discuss the use of transducer composition for ML (or MCMC) inference on phylogenies, including possible ways to make the core operations tractable.     

Sequence-dependent inhibition of cGAS and TLR9 DNA sensing by 2′-O-methyl gapmer oligonucleotides

Oligonucleotide-based therapeutics have the capacity to engage with nucleic acid immune sensors to activate or block their response, but a detailed understanding of these immunomodulatory effects is currently lacking. We recently showed that 2′-O-methyl (2′OMe) gapmer antisense oligonucleotides (ASOs) exhibited sequence-dependent inhibition of sensing by the RNA sensor Toll-Like Receptor (TLR) 7. Here we discovered that 2′OMe ASOs can also display sequence-dependent inhibitory effects on two major sensors of DNA, namely cyclic GMP-AMP synthase (cGAS) and TLR9. Through a screen of 80 2′OMe ASOs and sequence mutants, we characterized key features within the 20-mer ASOs regulating cGAS and TLR9 inhibition, and identified a highly potent cGAS inhibitor. Importantly, we show that the features of ASOs inhibiting TLR9 differ from those inhibiting cGAS, with only a few sequences inhibiting both pathways. Together with our previous studies, our work reveals a complex pattern of immunomodulation where 95% of the ASOs tested inhibited at least one of TLR7, TLR9 or cGAS by ≥30%, which may confound interpretation of their in vivo functions. Our studies constitute the broadest analysis of the immunomodulatory effect of 2′OMe ASOs on nucleic acid sensing to date and will support refinement of their therapeutic development.     

Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments

Background  

While the pairwise alignments produced by sequence similarity searches are a powerful tool for identifying homologous proteins - proteins that share a common ancestor and a similar structure; pairwise sequence alignments often fail to represent accurately the structural alignments inferred from three-dimensional coordinates. Since sequence alignment algorithms produce optimal alignments, the best structural alignments must reflect suboptimal sequence alignment scores. Thus, we have examined a range of suboptimal sequence alignments and a range of scoring parameters to understand better which sequence alignments are likely to be more structurally accurate.     

A probabilistic model of local sequence alignment that simplifies statistical significance estimation

Sequence database searches require accurate estimation of the statistical significance of scores. Optimal local sequence alignment scores follow Gumbel distributions, but determining an important parameter of the distribution (λ) requires time-consuming computational simulation. Moreover, optimal alignment scores are less powerful than probabilistic scores that integrate over alignment uncertainty (“Forward” scores), but the expected distribution of Forward scores remains unknown. Here, I conjecture that both expected score distributions have simple, predictable forms when full probabilistic modeling methods are used. For a probabilistic model of local sequence alignment, optimal alignment bit scores (“Viterbi” scores) are Gumbel-distributed with constant λ=log 2, and the high scoring tail of Forward scores is exponential with the same constant λ. Simulation studies support these conjectures over a wide range of profile/sequence comparisons, using 9,318 profile-hidden Markov models from the Pfam database. This enables efficient and accurate determination of expectation values (E-values) for both Viterbi and Forward scores for probabilistic local alignments.     

Improving accuracy of multiple sequence alignment algorithms based on alignment of neighboring residues

While most of the recent improvements in multiple sequence alignment accuracy are due to better use of vertical information, which include the incorporation of consistency-based pairwise alignments and the use of profile alignments, we observe that it is possible to further improve accuracy by taking into account alignment of neighboring residues when aligning two residues, thus making better use of horizontal information. By modifying existing multiple alignment algorithms to make use of horizontal information, we show that this strategy is able to consistently improve over existing algorithms on a few sets of benchmark alignments that are commonly used to measure alignment accuracy, and the average improvements in accuracy can be as much as 1–3% on protein sequence alignment and 5–10% on DNA/RNA sequence alignment. Unlike previous algorithms, consistent average improvements can be obtained across all identity levels.     

DbClustal: rapid and reliable global multiple alignments of protein sequences detected by database searches

DbClustal addresses the important problem of the automatic multiple alignment of the top scoring full-length sequences detected by a database homology search. By combining the advantages of both local and global alignment algorithms into a single system, DbClustal is able to provide accurate global alignments of highly divergent, complex sequence sets. Local alignment information is incorporated into a ClustalW global alignment in the form of a list of anchor points between pairs of sequences. The method is demonstrated using anchors supplied by the Blast post-processing program, Ballast. The rapidity and reliability of DbClustal have been demonstrated using the recently annotated Pyrococcus abyssi proteome where the number of alignments with totally misaligned sequences was reduced from 20% to <2%. A web site has been implemented proposing BlastP database searches with automatic alignment of the top hits by DbClustal.     

Improved alignment quality by combining evolutionary information, predicted secondary structure and self-organizing maps

Background  

Protein sequence alignment is one of the basic tools in bioinformatics. Correct alignments are required for a range of tasks including the derivation of phylogenetic trees and protein structure prediction. Numerous studies have shown that the incorporation of predicted secondary structure information into alignment algorithms improves their performance. Secondary structure predictors have to be trained on a set of somewhat arbitrarily defined states (e.g. helix, strand, coil), and it has been shown that the choice of these states has some effect on alignment quality. However, it is not unlikely that prediction of other structural features also could provide an improvement. In this study we use an unsupervised clustering method, the self-organizing map, to assign sequence profile windows to "structural states" and assess their use in sequence alignment.     

The STI1‐domain is a flexible alpha‐helical fold with a hydrophobic groove

STI1‐domains are present in a variety of co‐chaperone proteins and are required for the transfer of hydrophobic clients in various cellular processes. The domains were first identified in the yeast Sti1 protein where they were referred to as DP1 and DP2. Based on hidden Markov model searches, this domain had previously been found in other proteins including the mammalian co‐chaperone SGTA, the DNA damage response protein Rad23, and the chloroplast import protein Tic40. Here, we refine the domain definition and carry out structure‐based sequence alignment of STI1‐domains showing conservation of five amphipathic helices. Upon examinations of these identified domains, we identify a preceding helix 0 and unifying sequence properties, determine new molecular models, and recognize that STI1‐domains nearly always occur in pairs. The similarity at the sequence, structure, and molecular levels likely supports a unified functional role.     

Measuring global credibility with application to local sequence alignment

Computational biology is replete with high-dimensional (high-D) discrete prediction and inference problems, including sequence alignment, RNA structure prediction, phylogenetic inference, motif finding, prediction of pathways, and model selection problems in statistical genetics. Even though prediction and inference in these settings are uncertain, little attention has been focused on the development of global measures of uncertainty. Regardless of the procedure employed to produce a prediction, when a procedure delivers a single answer, that answer is a point estimate selected from the solution ensemble, the set of all possible solutions. For high-D discrete space, these ensembles are immense, and thus there is considerable uncertainty. We recommend the use of Bayesian credibility limits to describe this uncertainty, where a (1−α)%, 0≤α≤1, credibility limit is the minimum Hamming distance radius of a hyper-sphere containing (1−α)% of the posterior distribution. Because sequence alignment is arguably the most extensively used procedure in computational biology, we employ it here to make these general concepts more concrete. The maximum similarity estimator (i.e., the alignment that maximizes the likelihood) and the centroid estimator (i.e., the alignment that minimizes the mean Hamming distance from the posterior weighted ensemble of alignments) are used to demonstrate the application of Bayesian credibility limits to alignment estimators. Application of Bayesian credibility limits to the alignment of 20 human/rodent orthologous sequence pairs and 125 orthologous sequence pairs from six Shewanella species shows that credibility limits of the alignments of promoter sequences of these species vary widely, and that centroid alignments dependably have tighter credibility limits than traditional maximum similarity alignments.     

In vitro selection and characterization of a highly efficient Zn(II)-dependent RNA-cleaving deoxyribozyme

A group of highly efficient Zn(II)-dependent RNA-cleaving deoxyribozymes has been obtained through in vitro selection. They share a common motif with the ‘8–17’ deoxyribozyme isolated under different conditions, including different design of the random pool and metal ion cofactor. We found that this commonly selected motif can efficiently cleave both RNA and DNA/RNA chimeric substrates. It can cleave any substrate containing rNG (where rN is any ribonucleotide base and G can be either ribo- or deoxyribo-G). The pH profile and reaction products of this deoxyribozyme are similar to those reported for hammerhead ribozyme. This deoxyribozyme has higher activity in the presence of transition metal ions compared to alkaline earth metal ions. At saturating concentrations of Zn²⁺, the cleavage rate is 1.35 min^–1 at pH 6.0; based on pH profile this rate is estimated to be at least ~30 times faster at pH 7.5, where most assays of Mg²⁺-dependent DNA and RNA enzymes are carried out. This work represents a comprehensive characterization of a nucleic acid-based endonuclease that prefers transition metal ions to alkaline earth metal ions. The results demonstrate that nucleic acid enzymes are capable of binding transition metal ions such as Zn²⁺ with high affinity, and the resulting enzymes are more efficient at RNA cleavage than most Mg²⁺-dependent nucleic acid enzymes under similar conditions.