Blind tests of RNA-protein binding affinity prediction

Computational generation and characterization of IsdA-binding aptamers with single-molecule FRET analysis

Staphylococcus aureus is a major foodborne bacterial pathogen. Early detection of S. aureus is crucial to prevent infections and ensure food quality. The iron-regulated surface determinant protein A (IsdA) of S. aureus is a unique surface protein necessary for sourcing vital iron from host cells for the survival and colonization of the bacteria. The function, structure, and location of the IsdA protein make it an important protein for biosensing applications relating to the pathogen. Here, we report an in-silico approach to develop and validate high-affinity binding aptamers for the IsdA protein detection using custom-designed in-silico tools and single-molecule Fluorescence Resonance Energy Transfer (smFRET) measurements. We utilized in-silico oligonucleotide screening methods and metadynamics-based methods to generate 10 aptamer candidates and characterized them based on the Dissociation Free Energy (DFE) of the IsdA-aptamer complexes. Three of the aptamer candidates were shortlisted for smFRET experimental analysis of binding properties. Limits of detection in the low picomolar range were observed for the aptamers, and the results correlated well with the DFE calculations, indicating the potential of the in-silico approach to support aptamer discovery. This study showcases a computational SELEX method in combination with single-molecule binding studies deciphering effective aptamers against S. aureus IsdA, protein. The established approach demonstrates the ability to expedite aptamer discovery that has the potential to cut costs and predict binding efficacy. The application can be extended to designing aptamers for various protein targets, enhancing molecular recognition, and facilitating the development of high-affinity aptamers for multiple uses.

Emerging roles of RNA binding proteins in intervertebral disc degeneration and osteoarthritis

The etiology of intervertebral disc degeneration (IDD) and osteoarthritis (OA) is complex and multifactorial. Both predisposing genes and environmental factors are involved in the pathogenesis of IDD and OA. Moreover, epigenetic modifications affect the development of IDD and OA. Dysregulated phenotypes of nucleus pulposus (NP) cells and OA chondrocytes, including apoptosis, extracellular matrix disruption, inflammation, and angiogenesis, are involved at all developmental stages of IDD and OA. RNA binding proteins (RBPs) have recently been recognized as essential post-transcriptional regulators of gene expression. RBPs are implicated in many cellular processes, such as proliferation, differentiation, and apoptosis. Recently, several RBPs have been reported to be associated with the pathogenesis of IDD and OA. This review briefly summarizes the current knowledge on the RNA-regulatory networks controlled by RBPs and their potential roles in the pathogenesis of IDD and OA. These initial findings support the idea that specific modulation of RBPs represents a promising approach for managing IDD and OA.

Web tools support predicting protein-nucleic acid complexes stability with affinity changes

Numerous biological processes, such as transcription, replication, and translation, rely on protein-nucleic acid interactions (PNIs). Demonstrating the binding stability of protein-nucleic acid complexes is vital to deciphering the code for PNIs. Numerous web-based tools have been developed to attach importance to protein-nucleic acid stability, facilitating the prediction of PNIs characteristics rapidly. However, the data and tools are dispersed and lack comprehensive integration to understand the stability of PNIs better. In this review, we first summarize existing databases for evaluating the stability of protein-nucleic acid binding. Then, we compare and evaluate the pros and cons of web tools for forecasting the interaction energies of protein-nucleic acid complexes. Finally, we discuss the application of combining models and capabilities of PNIs. We may hope these web-based tools will facilitate the discovery of recognition mechanisms for protein-nucleic acid binding stability. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

RNA-Protein Interactome at the Hepatitis E Virus Internal Ribosome Entry Site

Multiple processes exist in a cell to ensure continuous production of essential proteins either through cap-dependent or cap-independent translation processes. Viruses depend on the host translation machinery for viral protein synthesis. Therefore, viruses have evolved clever strategies to use the host translation machinery. Earlier studies have shown that genotype 1 hepatitis E virus (g1-HEV) uses both cap-dependent and cap-independent translation machineries for its translation and proliferation. Cap-independent translation in g1-HEV is driven by an 87-nucleotide-long RNA element that acts as a noncanonical, internal ribosome entry site-like (IRESl) element. Here, we have identified the RNA-protein interactome of the HEV IRESl element and characterized the functional significance of some of its components. Our study identifies the association of HEV IRESl with several host ribosomal proteins, demonstrates indispensable roles of ribosomal protein RPL5 and DHX9 (RNA helicase A) in mediating HEV IRESl activity, and establishes the latter as a bona fide internal translation initiation site. IMPORTANCE Protein synthesis is a fundamental process for survival and proliferation of all living organisms. The majority of cellular proteins are produced through cap-dependent translation. Cells also use a variety of cap-independent translation processes to synthesize essential proteins during stress. Viruses depend on the host cell translation machinery to synthesize their own proteins. Hepatitis E virus (HEV) is a major cause of hepatitis worldwide and has a capped positive-strand RNA genome. Viral nonstructural and structural proteins are synthesized through a cap-dependent translation process. An earlier study from our laboratory reported the presence of a fourth open reading frame (ORF) in genotype 1 HEV, which produces the ORF4 protein using a cap-independent internal ribosome entry site-like (IRESl) element. In the current study, we identified the host proteins that associate with the HEV-IRESl RNA and generated the RNA-protein interactome. Through a variety of experimental approaches, our data prove that HEV-IRESl is a bona fide internal translation initiation site.

Isolation of 5' regulatory region of COLD1 gene and its functional characterization through transient expression analysis in tobacco and sugarcane

Chilling Tolerant Divergence 1 (COLD1) gene consists of Golgi pH Receptor (GPHR) as well as Abscisic Acid-linked G Protein-Coupled Receptor (ABA_GPCR), which are the major transmembrane proteins in plants. This gene expression has been found to be differentially regulated, under various stress conditions, in wild Saccharum-related genera, Erianthus arundinaceus, compared to commercial sugarcane variety. In this study, Rapid Amplification of Genomic Ends (RAGE) technique was employed to isolate the 5' upstream region of COLD1 gene to gain knowledge about the underlying stress regulatory mechanism. The current study established the cis-acting elements, main promoter regions, and Transcriptional Start Site (TSS) present within the isolated 5' upstream region (Cold1P) of COLD1, with the help of specific bioinformatics techniques. Phylogenetic analysis results revealed that the isolated Cold1P promoter is closely related to the species, Sorghum bicolor. Cold1P promoter-GUS gene construct was generated in pCAMBIA 1305.1 vector that displayed a constitutive expression of the GUS reporter gene in both monocot as well as dicot plants. The histochemical GUS assay outcomes confirmed that Cold1P can drive expression in both monocot as well as dicot plants. Cold1P's activities under several abiotic stresses such as cold, heat, salt, and drought, revealed its differential expression profile in commercial sugarcane variety. The highest activity of the GUS gene was found after 24 h of cold stress, driven by the isolated Cold1P promoter. The outcomes from GUS fluorimetric assay correlated with that of the GUS expression findings. This is the first report on Cold1P isolated from the species, E. arundinaceus.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03650-8.

FRET-Based Single-Molecule Detection of Pathogen Protein IsdA Using Computationally Selected Aptamers

Iron-regulated surface determinant protein A (IsdA) is a key surface protein found in the foodborne bacteria─Staphylococcus aureus (S. aureus)─which is known to be critical for bacterial survival and colonization. S. aureus is pathogenic and has been linked to foodborne diseases; thus, early detection is critical to prevent diseases caused by this bacterium. Despite IsdA being a specific marker for S. aureus and several detection methods have been developed for sensitive detection of this bacteria such as cell culture, nucleic acids amplification, and other colorimetric and electrochemical methods, the detection of S. aureus through IsdA is underdeveloped. Here, by combining computational generation of target-guided aptamers and fluorescence resonance energy transfer (FRET)-based single-molecule analysis, we presented a widely applicable and robust detection method for IsdA. Three different RNA aptamers specific to the IsdA protein were identified and their ability to switch a FRET construct to a high-FRET state in the presence of protein was verified. The presented approach demonstrated the detection of IsdA down to picomolar levels (×10^-12 M, equivalent to ∼1.1 femtomoles IsdA) with a dynamic range extending to ∼40 nM. The FRET-based single-molecule technique that we reported here is capable of detecting the foodborne pathogen protein IsdA with high sensitivity and specificity and has a broader application in the food industry and aptamer-based sensing field by enabling quantitative detection of a wide range of pathogen proteins.

Structural predictions of protein-DNA binding: MELD-DNA

Structural, regulatory and enzymatic proteins interact with DNA to maintain a healthy and functional genome. Yet, our structural understanding of how proteins interact with DNA is limited. We present MELD-DNA, a novel computational approach to predict the structures of protein-DNA complexes. The method combines molecular dynamics simulations with general knowledge or experimental information through Bayesian inference. The physical model is sensitive to sequence-dependent properties and conformational changes required for binding, while information accelerates sampling of bound conformations. MELD-DNA can: (i) sample multiple binding modes; (ii) identify the preferred binding mode from the ensembles; and (iii) provide qualitative binding preferences between DNA sequences. We first assess performance on a dataset of 15 protein-DNA complexes and compare it with state-of-the-art methodologies. Furthermore, for three selected complexes, we show sequence dependence effects of binding in MELD predictions. We expect that the results presented herein, together with the freely available software, will impact structural biology (by complementing DNA structural databases) and molecular recognition (by bringing new insights into aspects governing protein-DNA interactions).

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

UPF1 mutants with intact ATPase but deficient helicase activities promote efficient nonsense-mediated mRNA decay

The conserved RNA helicase UPF1 coordinates nonsense-mediated mRNA decay (NMD) by engaging with mRNAs, RNA decay machinery and the terminating ribosome. UPF1 ATPase activity is implicated in mRNA target discrimination and completion of decay, but the mechanisms through which UPF1 enzymatic activities such as helicase, translocase, RNP remodeling, and ATPase-stimulated dissociation influence NMD remain poorly defined. Using high-throughput biochemical assays to quantify UPF1 enzymatic activities, we show that UPF1 is only moderately processive (<200 nt) in physiological contexts and undergoes ATPase-stimulated dissociation from RNA. We combine an in silico screen with these assays to identify and characterize known and novel UPF1 mutants with altered helicase, ATPase, and RNA binding properties. We find that UPF1 mutants with substantially impaired processivity (E797R, G619K/A546H), faster (G619K) or slower (K547P, E797R, G619K/A546H) unwinding rates, and/or reduced mechanochemical coupling (i.e. the ability to harness ATP hydrolysis for work; K547P, R549S, G619K, G619K/A546H) can still support efficient NMD of well-characterized targets in human cells. These data are consistent with a central role for UPF1 ATPase activity in driving cycles of RNA binding and dissociation to ensure accurate NMD target selection.

Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses

Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.

A Mathematical Analysis of HDV Genotypes: From Molecules to Cells

Hepatitis D virus (HDV) is classified according to eight genotypes. The various genotypes are included in the HDVdb database, where each HDV sequence is specified by its genotype. In this contribution, a mathematical analysis is performed on RNA sequences in HDVdb. The RNA folding predicted structures of the Genbank HDV genome sequences in HDVdb are classified according to their coarse-grain tree-graph representation. The analysis allows discarding in a simple and efficient way the vast majority of the sequences that exhibit a rod-like structure, which is important for the virus replication, to attempt to discover other biological functions by structure consideration. After the filtering, there remain only a small number of sequences that can be checked for their additional stem-loops besides the main one that is known to be responsible for virus replication. It is found that a few sequences contain an additional stem-loop that is responsible for RNA editing or other possible functions. These few sequences are grouped into two main classes, one that is well-known experimentally belonging to genotype 3 for patients from South America associated with RNA editing, and the other that is not known at present belonging to genotype 7 for patients from Cameroon. The possibility that another function besides virus replication reminiscent of the editing mechanism in HDV genotype 3 exists in HDV genotype 7 has not been explored before and is predicted by eigenvalue analysis. Finally, when comparing native and shuffled sequences, it is shown that HDV sequences belonging to all genotypes are accentuated in their mutational robustness and thermodynamic stability as compared to other viruses that were subjected to such an analysis.

Mapping invisible epitopes by NMR spectroscopy

Defining discontinuous antigenic epitopes remains a substantial challenge, as exemplified by the case of lipid transfer polyproteins, which are common pollen allergens. Hydrogen/deuterium exchange monitored by NMR can be used to map epitopes onto folded protein surfaces, but only if the complex rapidly dissociates. Modifying the standard NMR-exchange measurement to detect substoichiometric complexes overcomes this time scale limitation and provides new insights into recognition of lipid transfer polyprotein by antibodies. In the future, this new and exciting development should see broad application to a range of tight macromolecular interactions.

The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding

The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.

Long non-coding RNA ZFAS1 promotes colorectal cancer tumorigenesis and development through DDX21-POLR1B regulatory axis

Increasing evidence supports long non-coding RNA-ZFAS1 as master protein regulators involved in a variety of human cancers. However, the molecular mechanism is not fully understood in colorectal cancer (CRC) and remains to be elucidated. Here, we uncovered a previously unreported mechanism linking RNA helicase DDX21 regulated by lncRNA ZFAS1 in control of POLR1B expression in CRC initiation and progression. Specifically, ZFAS1 exerted its oncogenic functions and was significantly up-regulated accompanied by elevated DDX21, POLR1B expression in CRC cells and tissues, which further closely associated with poor clinical outcomes. Notably, ZFAS1 knockdown dramatically suppressed CRC cell proliferation, invasion, migration, and increased cell apoptosis, which were contrary to the effect caused by ZFAS1 up-regulation. We further revealed that the inhibitory effect caused by ZFAS1 knockdown could be reversed by DDX21 overexpression in vitro and in vivo. Mechanistically, our research found that ZFAS1 could directly recruit DDX21 protein by harboring the specific motif (AAGA or CAGA). Finally, POLR1B was identified as the downstream target of DDX21 regulated by ZFAS1, which was also up-regulated in CRC cells and tissues and closely related to poor prognosis. The unrecognized ZFAS1/DDX21/POLR1B signaling regulation axis may provide new biomarkers and targets for CRC treatment and prognostic evaluation.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Single nucleotide polymorphisms affect RNA-protein interactions at a distance through modulation of RNA secondary structures

Single nucleotide polymorphisms are widely associated with disease, but the ways in which they cause altered phenotypes are often unclear, especially when they appear in non-coding regions. One way in which non-coding polymorphisms could cause disease is by affecting crucial RNA-protein interactions. While it is clear that changing a protein binding motif will alter protein binding, it has been shown that single nucleotide polymorphisms can affect RNA secondary structure, and here we show that single nucleotide polymorphisms can affect RNA-protein interactions from outside binding motifs through altered RNA secondary structure. By using a modified version of the Vienna Package and PAR-CLIP data for HuR (ELAVL1) in humans we characterize the genome-wide effect of single nucleotide polymorphisms on HuR binding and show that they can have a many-fold effect on the affinity of HuR binding to RNA transcripts from tens of bases away. We also find some evidence that the effect of single nucleotide polymorphisms on protein binding might be under selection, with the non-reference alleles tending to make it harder for a protein to bind.

On the Upper Bounds of the Real-Valued Predictions

Predictions are fundamental in science as they allow to test and falsify theories. Predictions are ubiquitous in bioinformatics and also help when no first principles are available. Predictions can be distinguished between classifications (when we associate a label to a given input) or regression (when a real value is assigned). Different scores are used to assess the performance of regression predictors; the most widely adopted include the mean square error, the Pearson correlation (ρ), and the coefficient of determination (or R2). The common conception related to the last 2 indices is that the theoretical upper bound is 1; however, their upper bounds depend both on the experimental uncertainty and the distribution of target variables. A narrow distribution of the target variable may induce a low upper bound. The knowledge of the theoretical upper bounds also has 2 practical applications: (1) comparing different predictors tested on different data sets may lead to wrong ranking and (2) performances higher than the theoretical upper bounds indicate overtraining and improper usage of the learning data sets. Here, we derive the upper bound for the coefficient of determination showing that it is lower than that of the square of the Pearson correlation. We provide analytical equations for both indices that can be used to evaluate the upper bound of the predictions when the experimental uncertainty and the target distribution are available. Our considerations are general and applicable to all regression predictors.