Phylogenetic and Chemical Probing Information as Soft Constraints in RNA Secondary Structure Prediction.

Extrinsic, experimental information can be incorporated into thermodynamics-based RNA folding algorithms in the form of pseudo-energies. Evolutionary conservation of RNA secondary structure elements is detectable in alignments of phylogenetically related sequences and provides evidence for the presence of certain base pairs that can also be converted into pseudo-energy contributions. We show that the centroid base pairs computed from a consensus folding model such as RNAalifold result in a substantial improvement of the prediction accuracy for single sequences. Evidence for specific base pairs turns out to be more informative than a position-wise profile for the conservation of the pairing status. A comparison with chemical probing data, furthermore, strongly suggests that phylogenetic base pairing data are more informative than position-specific data on (un)pairedness as obtained from chemical probing experiments. In this context we demonstrate, in addition, that the conversion of signal from probing data into pseudo-energies is possible using thermodynamic structure predictions as a reference instead of known RNA structures.

Modified Nucleotides and RNA Structure Prediction.

Nucleotide modifications are occurrent in all types of RNA and play an important role in RNA structure formation and stability. Modified bases not only possess the ability to shift the RNA structure ensemble towards desired functional confirmations. By changes in the base pairing partner preference, they may even enlarge or reduce the conformational space, i.e., the number and types of structures the RNA molecule can adopt. However, most methods to predict RNA secondary structure do not provide the means to include the effect of modifications on the result. With the help of a heavily modified transfer RNA (tRNA) molecule, this chapter demonstrates how to include the effect of different base modifications into secondary structure prediction using the ViennaRNA Package. The constructive approach demonstrated here allows for the calculation of minimum free energy structure and suboptimal structures at different levels of modified base support. In particular we, show how to incorporate the isomerization of uridine to pseudouridine ( Ψ ) and the reduction of uridine to dihydrouridine (D).

Exploring Structural Insights of Aß42 and α-Synuclein Monomers and Heterodimer: A Comparative Study Using Implicit and Explicit Solvent Simulations.

Protein misfolding, aggregation, and fibril formation play a central role in the development of severe neurological disorders, including Alzheimer's and Parkinson's diseases. The structural stability of mature fibrils in these diseases is of great importance, as organisms struggle to effectively eliminate amyloid plaques. To address this issue, it is crucial to investigate the early stages of fibril formation when monomers aggregate into small, toxic, and soluble oligomers. However, these structures are inherently disordered, making them challenging to study through experimental approaches. Recently, it has been shown experimentally that amyloid-ß 42 (Aß42) and α-synuclein (α-Syn) can coassemble. This has motivated us to investigate the interaction between their monomers as a first step toward exploring the possibility of forming heterodimeric complexes. In particular, our study involves the utilization of various Amber and CHARMM force-fields, employing both implicit and explicit solvent models in replica exchange and conventional simulation modes. This comprehensive approach allowed us to assess the strengths and weaknesses of these solvent models and force fields in comparison to experimental and theoretical findings, ensuring the highest level of robustness. Our investigations revealed that Aß42 and α-Syn monomers can indeed form stable heterodimers, and the resulting heterodimeric model exhibits stronger interactions compared to the Aß42 dimer. The binding of α-Syn to Aß42 reduces the propensity of Aß42 to adopt fibril-prone conformations and induces significant changes in its conformational properties. Notably, in AMBER-FB15 and CHARMM36m force fields with the use of explicit solvent, the presence of Aß42 significantly increases the ß-content of α-Syn, consistent with the experiments showing that Aß42 triggers α-Syn aggregation. Our analysis clearly shows that although the use of implicit solvent resulted in too large compactness of monomeric α-Syn, structural properties of monomeric Aß42 and the heterodimer were preserved in explicit-solvent simulations. We anticipate that our study sheds light on the interaction between α-Syn and Aß42 proteins, thus providing the atom-level model required to assess the initial stage of aggregation mechanisms related to Alzheimer's and Parkinson's diseases.

Modified RNAs and predictions with the ViennaRNA Package.

MOTIVATION: In living organisms, many RNA molecules are modified post-transcriptionally. This turns the widely known four-letter RNA alphabet ACGU into a much larger one with currently more than 300 known distinct modified bases. The roles for the majority of modified bases remain uncertain, but many are already well-known for their ability to influence the preferred structures that an RNA may adopt. In fact, tRNAs sometimes require certain modifications to fold into their cloverleaf shaped structure. However, predicting the structure of RNAs with base modifications is still difficult due to the lack of efficient algorithms that can deal with the extended sequence alphabet, as well as missing parameter sets that account for the changes in stability induced by the modified bases. RESULTS: We present an approach to include sparse energy parameter data for modified bases into the ViennaRNA Package. Our method does not require any changes to the underlying efficient algorithms but instead uses a set of plug-in constraints that adapt the predictions in terms of loop evaluation at runtime. These adaptations are efficient in the sense that they are only performed for loops where additional parameters are actually available for. In addition, our approach also facilitates the inclusion of more modified bases as soon as further parameters become available. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are available at https://www.tbi.univie.ac.at/RNA.