Predicting folding pathways between RNA conformational structures guided by RNA stacks

Background: Accurately predicting low energy barrier folding pathways between conformational secondary structures of an RNA molecule can provide valuable information for understanding its catalytic and regulatory functions. Most existing heuristic algorithms guide the construction of folding pathways by free energies of intermediate structures in the next move during the folding. However due to the size and ruggedness of RNA energy landscape, energy-guided search can become trapped in local optima. Results: In this paper, we propose an algorithm that guides the construction of folding pathways through the formation and destruction of RNA stacks. Guiding the construction of folding pathways by coarse grained movements of RNA stacks can help reduce the search space and make it easier to jump out of local optima. RNAEAPath is able to find lower energy barrier folding pathways between secondary structures of conformational switches and outperforms the existing heuristic algorithms in most test cases. Conclusions: RNAEAPath provides an alternate approach for predicting low-barrier folding pathways between RNA conformational secondary structures. The source code of RNAEAPath and the test data sets are available at http://genome.ucf.edu/RNAEAPath

Structural analysis of aligned RNAs

The knowledge about classes of non-coding RNAs (ncRNAs) is growing very fast and it is mainly the structure which is the common characteristic property shared by members of the same class. For correct characterization of such classes it is therefore of great importance to analyse the structural features in great detail. In this manuscript I present RNAlishapes which combines various secondary structure analysis methods, such as suboptimal folding and shape abstraction, with a comparative approach known as RNA alignment folding. RNAlishapes makes use of an extended thermodynamic model and covariance scoring, which allows to reward covariation of paired bases. Applying the algorithm to a set of bacterial trp-operon leaders using shape abstraction it was able to identify the two alternating conformations of this attenuator. Besides providing in-depth analysis methods for aligned RNAs, the tool also shows a fairly well prediction accuracy. Therefore, RNAlishapes provides the community with a powerful tool for structural analysis of classes of RNAs and is also a reasonable method for consensus structure prediction based on sequence alignments. RNAlishapes is available for online use and download at

Computing folding pathways between RNA secondary structures

Given an RNA sequence and two designated secondary structures A, B, we describe a new algorithm that computes a nearly optimal folding pathway from A to B. The algorithm, RNAtabupath, employs a tabu semi-greedy heuristic, known to be an effective search strategy in combinatorial optimization. Folding pathways, sometimes called routes or trajectories, are computed by RNAtabupath in a fraction of the time required by the barriers program of Vienna RNA Package. We benchmark RNAtabupath with other algorithms to compute low energy folding pathways between experimentally known structures of several conformational switches. The RNApathfinder web server, source code for algorithms to compute and analyze pathways and supplementary data are available at http://bioinformatics.bc.edu/clotelab/RNApathfinder

Functional nucleic acids as substrate for information processing

Information processing applications driven by self-assembly and conformation dynamics of nucleic acids are possible. These underlying paradigms (self-assembly and conformation dynamics) are essential for natural information processors as illustrated by proteins. A key advantage in utilising nucleic acids as information processors is the availability of computational tools to support the design process. This provides us with a platform to develop an integrated environment in which an orchestration of molecular building blocks can be realised. Strict arbitrary control over the design of these computational nucleic acids is not feasible. The microphysical behaviour of these molecular materials must be taken into consideration during the design phase. This thesis investigated, to what extent the construction of molecular building blocks for a particular purpose is possible with the support of a software environment. In this work we developed a computational protocol that functions on a multi-molecular level, which enable us to directly incorporate the dynamic characteristics of nucleic acids molecules. To allow the implementation of this computational protocol, we developed a designer that able to solve the nucleic acids inverse prediction problem, not only in the multi-stable states level, but also include the interactions among molecules that occur in each meta-stable state. The realisation of our computational protocol are evaluated by generating computational nucleic acids units that resembles synthetic RNA devices that have been successfully implemented in the laboratory. Furthermore, we demonstrated the feasibility of the protocol to design various types of computational units. The accuracy and diversity of the generated candidates are significantly better than the best candidates produced by conventional designers. With the computational protocol, the design of nucleic acid information processor using a network of interconnecting nucleic acids is now feasible

Limitations of Protein Structure Prediction Algorithms in Therapeutic Protein Development

The three-dimensional protein structure is pivotal in comprehending biological phenomena. It directly governs protein function and hence aids in drug discovery. The development of protein prediction algorithms, such as AlphaFold2, ESMFold, and trRosetta, has given much hope in expediting protein-based therapeutic discovery. Though no study has reported a conclusive application of these algorithms, the efforts continue with much optimism. We intended to test the application of these algorithms in rank-ordering therapeutic proteins for their instability during the pre-translational modification stages, as may be predicted according to the confidence of the structure predicted by these algorithms. The selected molecules were based on a harmonized category of licensed therapeutic proteins; out of the 204 licensed products, 188 that were not conjugated were chosen for analysis, resulting in a lack of correlation between the confidence scores and structural or protein properties. It is crucial to note here that the predictive accuracy of these algorithms is contingent upon the presence of the known structure of the protein in the accessible database. Consequently, our conclusion emphasizes that these algorithms primarily replicate information derived from existing structures. While our findings caution against relying on these algorithms for drug discovery purposes, we acknowledge the need for a nuanced interpretation. Considering their limitations and recognizing that their utility may be constrained to scenarios where known structures are available is important. Hence, caution is advised when applying these algorithms to characterize various attributes of therapeutic proteins without the support of adequate structural information. It is worth noting that the two main algorithms, AlfphaFold2 and ESMFold, also showed a 72% correlation in their scores, pointing to similar limitations. While much progress has been made in computational sciences, the Levinthal paradox remains unsolved

Maximum expected accuracy structural neighbors of an RNA secondary structure

International audienceBACKGROUND: Since RNA molecules regulate genes and control alternative splicing by allostery, it is important to develop algorithms to predict RNA conformational switches. Some tools, such as paRNAss, RNAshapes and RNAbor, can be used to predict potential conformational switches; nevertheless, no existent tool can detect general (i.e., not family specific) entire riboswitches (both aptamer and expression platform) with accuracy. Thus, the development of additional algorithms to detect conformational switches seems important, especially since the difference in free energy between the two metastable secondary structures may be as large as 15-20 kcal/mol. It has recently emerged that RNA secondary structure can be more accurately predicted by computing the maximum expected accuracy (MEA) structure, rather than the minimum free energy (MFE) structure. RESULTS: Given an arbitrary RNA secondary structure S₀ for an RNA nucleotide sequence a = a₁,..., a(n), we say that another secondary structure S of a is a k-neighbor of S₀, if the base pair distance between S₀ and S is k. In this paper, we prove that the Boltzmann probability of all k-neighbors of the minimum free energy structure S₀ can be approximated with accuracy ε and confidence 1 - p, simultaneously for all 0 ≤ k N(ε,p,K)=Φ⁻¹(p/2K)²/4ε², where Φ(z) is the cumulative distribution function (CDF) for the standard normal distribution. We go on to describe the algorithm RNAborMEA, which for an arbitrary initial structure S₀ and for all values 0 ≤ k < K, computes the secondary structure MEA(k), having maximum expected accuracy over all k-neighbors of S₀. Computation time is O(n³ * K²), and memory requirements are O(n² * K). We analyze a sample TPP riboswitch, and apply our algorithm to the class of purine riboswitches. CONCLUSIONS: The approximation of RNAbor by sampling, with rigorous bound on accuracy, together with the computation of maximum expected accuracy k-neighbors by RNAborMEA, provide additional tools toward conformational switch detection. Results from RNAborMEA are quite distinct from other tools, such as RNAbor, RNAshapes and paRNAss, hence may provide orthogonal information when looking for suboptimal structures or conformational switches. Source code for RNAborMEA can be downloaded from http://sourceforge.net/projects/rnabormea/ or http://bioinformatics.bc.edu/clotelab/RNAborMEA/