Protein-nucleic Acid Interactions Research Articles

The stability of PCV2 virus-like particles from mammalian cells and challenges for biotechnological applications

Porcine circovirus type 2 (PCV2) is a highly damaging pathogen for pig farming, causing significant economic losses. Despite the availability of vaccines based on different technologies, the virus steadily infects the world's pig population. In this context, virus-like particles (VLPs) constitute appealing alternatives for vaccine development as they lack the viral genome but present intact external surfaces.Using PCV2 VLPs expressed and purified from Expi293F cells, we demonstrate the potential to generate high-purity VLPs with excellent antigenic properties through biochemical, biophysical, and immunological characterization. Using different techniques, we also determined the melting temperature of these VLPs at nearly 55 °C.Furthermore, we conducted multiscale simulations of whole VLPs combined with multiple sequence analyses to provide a new perspective into the stability determinants. Computational results support our findings and underscore the importance of protein-nucleic acid interactions in stabilizing the VLP structure. Moreover, we spotted an unforeseen correlation between amino acid conservation, solvent exposure, and flexibility, revealing a link to viral assembly and immune evasion. These novel insights are crucial to guide the development of stabilized VLP for new vaccine prototypes to respond to the emergence of new PCV2 genotypes.

Single-molecule dynamic structural biology with vertically arranged DNA on a fluorescence microscope.

The intricate interplay between DNA and proteins is key for biological functions such as DNA replication, transcription and repair. Dynamic nanoscale observations of DNA structural features are necessary for understanding these interactions. Here we introduce graphene energy transfer with vertical nucleic acids (GETvNA), a method to investigate DNA-protein interactions that exploits the vertical orientation adopted by double-stranded DNA on graphene. This approach enables the dynamic study of DNA conformational changes via energy transfer from a probe dye to graphene, achieving spatial resolution down to the Ångström scale at subsecond temporal resolution. We measured DNA bending induced by adenine tracts, bulges, abasic sites and the binding of endonuclease IV. In addition, we observed the translocation of the O6-alkylguanine DNA alkyltransferase on DNA, reaching single base-pair resolution and detecting preferential binding to adenine tracts. This method promises widespread use for dynamical studies of nucleic acids and nucleic acid-protein interactions with resolution so far reserved for traditional structural biology techniques.

Recent Advances in Mass Spectrometry-based Protein Interactome Studies

The foundation of all biological processes is the network of diverse and dynamic protein interactions with other molecules in cells known as the interactome. Understanding the interactome is crucial for elucidating molecular mechanisms but has been a longstanding challenge. Recent developments in mass spectrometry (MS)-based techniques, including affinity purification, proximity labeling, cross-linking, and co-fractionation mass spectrometry (MS), have significantly enhanced our abilities to study the interactome. They do so by identifying and quantifying protein interactions, yielding profound insights into protein organizations and functions. This review summarizes recent advances in MS-based interactomics, focusing on the development of techniques that capture protein-protein, protein-metabolite, and protein-nucleic acid interactions. Additionally, we discuss how integrated MS-based approaches have been applied to diverse biological samples, focusing on significant discoveries that have leveraged our understanding of cellular functions. Finally, we highlight state-of-the-art bioinformatic approaches for predictions of interactome and complex modeling, as well as strategies for combining experimental interactome data with computation methods, thereby enhancing the ability of MS-based techniques to identify protein interactomes. Indeed, advances in MS technologies and their integrations with computational biology provide new directions and avenues for interactome research, leveraging new insights into mechanisms that govern the molecular architecture of living cells and, thereby, our comprehension of biological processes.

Opening and closing of a cryptic pocket in VP35 toggles it between two different RNA-binding modes.

Cryptic pockets are of growing interest as potential drug targets, particularly to control protein-nucleic acid interactions that often occur via flat surfaces. However, it remains unclear whether cryptic pockets contribute to protein function or if they are merely happenstantial features that can easily be evolved away to achieve drug resistance. Here, we explore whether a cryptic pocket in the Interferon Inhibitory Domain (IID) of viral protein 35 (VP35) of Zaire ebolavirus aids its ability to bind double-stranded RNA (dsRNA). We use simulations and experiments to study the relationship between cryptic pocket opening and dsRNA binding of the IIDs of two other filoviruses, Reston and Marburg. These homologs have nearly identical structures but block different interferon pathways due to different affinities for blunt ends and backbone of the dsRNA. Simulations and thiol-labeling experiments demonstrate that the homologs have varying probabilities of pocket opening. Subsequent dsRNA-binding assays suggest that closed conformations preferentially bind dsRNA blunt ends while open conformations prefer binding the backbone. Point mutations that modulate pocket opening proteins further confirm this preference. These results demonstrate the open cryptic pocket has a function, suggesting cryptic pockets are under selective pressure and may be difficult to evolve away to achieve drug resistance.

CHIMERA_NA: A Customizable Mutagenesis Tool for Structural Manipulations in Nucleic Acids and Their Complexes.

Studying the structure and dynamics of nucleic acids and their complexes is crucial for understanding fundamental biological processes and developing therapeutic interventions. However, the limited availability of experimentally characterized nucleic acid structures poses a challenge for exploring their properties comprehensively. To address this, we developed a customizable mutagenesis tool, CHIMERA_NA, to manipulate nucleic acid structures and their complexes. Utilizing the user-friendly CHIMERA_NA, researchers can perform mutations in nucleic acid structures, enabling the exploration of diverse structural configurations and dynamic behaviors. The tool offers the flexibility to generate all possible combinations of mutations or specific user-defined mutations based on research requirements. CHIMERA_NA leverages the capabilities of UCSF Chimera software, a widely used platform for molecular structure analysis, to facilitate the generation of mutations in nucleic acids. Our tool modifies the reference structure of nucleic acids or their complexes to generate initial coordinates of mutated structures/complexes within seconds for further computational exploration. This capability allows users to extend their investigations beyond structural repositories, enabling the study of DNA/RNA drug recognition, nucleic acid-protein interactions, and the intrinsic structural and dynamic properties of nucleic acids. By providing a user-friendly and customizable approach to nucleic acid mutagenesis, CHIMERA_NA contributes to advancing our understanding of nucleic acid biology and facilitating drug discovery efforts targeting nucleic acid-based mechanisms. CHIMERA_NA is freely available in the Supporting Information of this article.

Multivalent Protein-Nucleic Acid Interactions Probed by Composition-Gradient Multiangle Light Scattering.

Many RNA-binding proteins, such as TDP-43 or CELF1, interact multivalently with nucleic acid repetitive elements. The molecular stoichiometry of protein to nucleic acid is often difficult to assess, particularly by standard electrophoretic mobility shift assays (EMSAs). Here, we investigate the use of composition-gradient multiangle light scattering (CG-MALS) for quantifying binding affinity and stoichiometry for two RNA-binding proteins with their nucleic acid partners of varied sequence and length: TDP43's N-terminal RNA recognition motifs with both TG/GU-repeat ssDNA and ssRNA, respectively, and CELF1's two N-terminal RNA recognition motifs with (TG/UGUU/GU) repeats and an experimentally defined cognate GU-rich element (GRE). Our CG-MALS data derived from each of these interactions is consistent with expected ranges of binding affinity and stoichiometry for proteins binding to shorter nucleic acid repeats. Furthermore, we conclude that CG-MALS can be an excellent method for obtaining quantitative estimates even for high (>2) protein-nucleic acid stoichiometric ratios.

PNBACE: an ensemble algorithm to predict the effects of mutations on protein-nucleic acid binding affinity

BackgroundMutations occurring in nucleic acids or proteins may affect the binding affinities of protein-nucleic acid interactions. Although many efforts have been devoted to the impact of protein mutations, few computational studies have addressed the effect of nucleic acid mutations and explored whether the identical methodology could be applied to the prediction of binding affinity changes caused by these two mutation types.ResultsHere, we developed a generalized algorithm named PNBACE for both DNA and protein mutations. We first demonstrated that DNA mutations could induce varying degrees of changes in binding affinity from multiple perspectives. We then designed a group of energy-based topological features based on different energy networks, which were combined with our previous partition-based energy features to construct individual prediction models through feature selections. Furthermore, we created an ensemble model by integrating the outputs of individual models using a differential evolution algorithm. In addition to predicting the impact of single-point mutations, PNBACE could predict the influence of multiple-point mutations and identify mutations significantly reducing binding affinities. Extensive comparisons indicated that PNBACE largely performed better than existing methods on both regression and classification tasks.ConclusionsPNBACE is an effective method for estimating the binding affinity changes of protein-nucleic acid complexes induced by DNA or protein mutations, therefore improving our understanding of the interactions between proteins and DNA/RNA.

Chemical crosslinking extends and complements UV crosslinking in analysis of RNA/DNA nucleic acid-protein interaction sites by mass spectrometry.

UV (ultra-violet) crosslinking with mass spectrometry (XL-MS) has been established for identifying RNA-and DNA-binding proteins along with their domains and amino acids involved. Here, we explore chemical XL-MS for RNA-protein, DNA-protein, and nucleotide-protein complexes in vitro and in vivo . We introduce a specialized nucleotide-protein-crosslink search engine, NuXL, for robust and fast identification of such crosslinks at amino acid resolution. Chemical XL-MS complements UV XL-MS by generating different crosslink species, increasing crosslinked protein yields in vivo almost four-fold and thus it expands the structural information accessible via XL-MS. Our workflow facilitates integrative structural modelling of nucleic acid-protein complexes and adds spatial information to the described RNA-binding properties of enzymes, for which crosslinking sites are often observed close to their cofactor-binding domains. In vivo UV and chemical XL-MS data from E. coli cells analysed by NuXL establish a comprehensive nucleic acid-protein crosslink inventory with crosslink sites at amino acid level for more than 1500 proteins. Our new workflow combined with the dedicated NuXL search engine identified RNA crosslinks that cover most RNA-binding proteins, with DNA and RNA crosslinks detected in transcriptional repressors and activators.

Deciphering the Thermal Stability of Bacteriophage MS2-Derived Virus-like Particle and Its Engineered Variant.

RNA bacteriophage MS2-derived virus-like particles (VLPs) have been widely used in biomedical research as model systems to study virus assembly, structure-function relationships, vaccine development, and drug delivery. Considering the diverse utility of these VLPs, a systemic engineering approach has been utilized to generate smaller particles with optimal serum stability and tissue penetrance. Additionally, it is crucial to demonstrate the overall stability of these mini MS2 VLPs, ensuring cargo protection until they reach their target cell/organ. However, no detailed analysis of the thermal stability and heat-induced disassembly of MS2 VLPs has yet been attempted. In this work, we investigated the thermal stability of both wild-type (WT) MS2 VLP and its "mini" variant containing S37P mutation (mini MS2 VLP). The mini MS2 VLP exhibits a higher capsid melting temperature (Tm) when compared to its WT MS2 VLP counterpart, possibly attributed to its smaller interdimer angle. Our study presents that the thermal unfolding of MS2 VLPs follows a sequential process involving particle destabilization, nucleic acid exposure/melting, and disassembly of VLP. This observation underscores the disruption of cooperative intersubunit interactions and protein-nucleic acid interactions, shedding light on the mechanism of heat-induced VLP disassembly.

Oligonucleotide-Based Photoaffinity Probes: Chemical Tools and Applications for Protein Labeling.

A variety of proteins interact with DNA and RNA, including polymerases, histones, ribosomes, transcription factors, and repair enzymes. However, the transient non-covalent nature of these interactions poses challenges for analysis. Introducing a covalent bond between proteins and DNA via photochemical activation of a photosensitive functional group introduced onto nucleic acids offers a means to stabilize these often weak interactions without significantly altering the binding interface. Consequently, photoactivatable oligonucleotides are powerful tools for investigating nucleic acid-protein interactions involved in numerous biological and pathological processes. In this review, we provide a comprehensive overview of the chemical tools developed so far and the different strategies used for incorporating the most commonly used photoreactive reagents into oligonucleotide probes or nucleic acids. Furthermore, we illustrate their application with several examples including protein binding site mapping, identification of protein binding partners, and in cell studies.

Measuring Interactions Between Proteins and Small Molecules or Nucleic Acids.

Interactions between proteins and small molecules or nucleic acids play a pivotal role in numerous biological processes critical for human health and are fundamental for advancing our understanding of biological systems. Proteins are the workhorses of the cell, executing various functions ranging from catalyzing biochemical reactions to transmitting signals within the body. Small molecules, including drugs and metabolites, can modulate protein activity, thereby impacting cellular processes and disease pathways. Similarly, nucleic acids, such as DNA and RNA, regulate protein synthesis and function through intricate interactions. Understanding these interactions is crucial for drug discovery and development and can shed light on gene regulation, transcriptional control, and RNA processing, providing insights into genetic diseases and developmental disorders. Moreover, studying protein-small molecule and protein-nucleic acid interactions enhances our comprehension of fundamental biological mechanisms. A wide array of methods to study these interactions range in cost, sensitivity, materials usage, throughput, and complexity. Notably in the last decade, new techniques have been developed that enhance our understanding of these interactions. In this review, we aim to summarize the new state-of-the-art methods for detecting interactions between proteins and small molecules or nucleic acids, as well as discuss older methods that still hold value today. © 2024 Wiley Periodicals LLC.

Small molecule telomerase inhibitors are also potent inhibitors of telomeric C-strand synthesis.

Telomere replication is essential for continued proliferation of human cells, such as stem cells and cancer cells. Telomerase lengthens the telomeric G-strand, while C-strand replication is accomplished by CST-polymerase α-primase (CST-PP). Replication of both strands is inhibited by formation of G-quadruplex (GQ) structures in the G-rich single-stranded DNA. TMPyP4 and pyridostatin (PDS), which stabilize GQ structures in both DNA and RNA, inhibit telomerase in vitro, and in human cells they cause telomere shortening that has been attributed to telomerase inhibition. Here, we show that TMPyP4 and PDS also inhibit C-strand synthesis by stabilizing DNA secondary structures and thereby preventing CST-PP from binding to telomeric DNA. We also show that these small molecules inhibit CST-PP binding to a DNA sequence containing no consecutive guanine residues, which is unlikely to form GQs. Thus, while these "telomerase inhibitors" indeed inhibit telomerase, they are also robust inhibitors of telomeric C-strand synthesis. Furthermore, given their binding to GQ RNA and their limited specificity for GQ structures, they may disrupt many other protein-nucleic acid interactions in human cells.

From Protein Structures to Functional Biomimetics

AbstractThe development of complex molecular scaffolds with defined folding properties represents a central challenge in chemical research. Proteins are natural scaffolds defined by a hierarchy of structural complexity and have evolved to manifest unique functional characteristics; for example, molecular recognition capabilities that facilitate the binding of target molecules with high affinity and selectivity. Utilizing these features, proteins have been used as a starting point for the design of synthetic foldamers and enhanced biocatalysts, as well as bioactive reagents in drug discovery. In this account, we describe the strategies used in our group to stabilize protein folds, ranging from the constraint of bioactive peptide conformations to chemical protein engineering. We discuss the evolution of peptides into peptidomimetics to inhibit protein–protein and protein–nucleic acid interactions, and the selective chemical modification of proteins to enhance their properties for biotechnological applications. The reported peptide- and proteomimetic structures cover a broad range of molecular sizes and they highlight the importance of structure stabilization for the design of functional biomimetics.1 Introduction2 Constraining the Conformation of Peptides3 Peptide-Based Covalent Protein Modifiers4 Chemical Protein Engineering5 Conclusions

Accurate structure prediction of biomolecular interactions with AlphaFold 3

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2–6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein–ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein–nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody–antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.

PPI3D: a web server for searching, analyzing and modeling protein-protein, protein-peptide and protein-nucleic acid interactions.

Structure-resolved protein interactions with other proteins, peptides and nucleic acids are key for understanding molecular mechanisms. The PPI3D web server enables researchers to query preprocessed and clustered structural data, analyze the results and make homology-based inferences for protein interactions. PPI3D offers three interaction exploration modes: (i) all interactions for proteins homologous to the query, (ii) interactions between two proteins or their homologsand (iii) interactions within a specific PDB entry. The server allows interactive analysis of the identified interactions in both summarized and detailed manner. This includes protein annotations, structures, the interface residues and the corresponding contact surface areas. In addition, users can make inferences about residues at the interaction interface for the query protein(s) from the sequence alignments and homology models. The weekly updated PPI3D database includes all the interaction interfaces and binding sites from PDB, clustered based on both protein sequence and structural similarity, yielding non-redundant datasets without loss of alternative interaction modes. Consequently, the PPI3D users avoid being flooded with redundant information, a typical situation for intensely studied proteins. Furthermore, PPI3D provides a possibility to download user-defined sets of interaction interfaces and analyze them locally. The PPI3D web server is available at https://bioinformatics.lt/ppi3d.

Assembly of SARS-CoV-2 nucleocapsid protein with nucleic acid.

The viral genome of SARS-CoV-2 is packaged by the nucleocapsid (N-)protein into ribonucleoprotein particles (RNPs), 38±10 of which are contained in each virion. Their architecture has remained unclear due to the pleomorphism of RNPs, the high flexibility of N-protein intrinsically disordered regions, and highly multivalent interactions between viral RNA and N-protein binding sites in both N-terminal (NTD) and C-terminal domain (CTD). Here we explore critical interaction motifs of RNPs by applying a combination of biophysical techniques to ancestral andmutant proteins binding different nucleic acids in an in vitro assay for RNP formation, and by examining nucleocapsidprotein variants in a viral assembly assay. We find that nucleic acid-bound N-protein dimers oligomerize via a recently described protein-protein interface presented by a transient helix in its long disordered linker region between NTD and CTD. The resulting hexameric complexes are stabilized by multivalent protein-nucleic acid interactions that establish crosslinks between dimeric subunits. Assemblies are stabilized by the dimeric CTD of N-protein offering more than one binding site for stem-loop RNA. Our study suggests a model for RNP assembly where N-protein scaffolding at high density on viral RNA is followed by cooperative multimerization through protein-protein interactions in the disordered linker.

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses

BackgroundThe RNA-Recognition motif (RRM) is a protein domain that binds single-stranded RNA (ssRNA) and is present in as much as 2% of the human genome. Despite this important role in biology, RRM-ssRNA interactions are very challenging to study on the structural level because of the remarkable flexibility of ssRNA. In the absence of atomic-level experimental data, the only method able to predict the 3D structure of protein-ssRNA complexes with any degree of accuracy is ssRNA’TTRACT, an ssRNA fragment-based docking approach using ATTRACT. However, since ATTRACT parameters are not ssRNA-specific and were determined in 2010, there is substantial opportunity for enhancement.ResultsHere we present HIPPO, a composite RRM-ssRNA scoring potential derived analytically from contact frequencies in near-native versus non-native docking models. HIPPO consists of a consensus of four distinct potentials, each extracted from a distinct reference pool of protein-trinucleotide docking decoys. To score a docking pose with one potential, for each pair of RNA–protein coarse-grained bead types, each contact is awarded or penalised according to the relative frequencies of this contact distance range among the correct and incorrect poses of the reference pool. Validated on a fragment-based docking benchmark of 57 experimentally solved RRM-ssRNA complexes, HIPPO achieved a threefold or higher enrichment for half of the fragments, versus only a quarter with the ATTRACT scoring function. In particular, HIPPO drastically improved the chance of very high enrichment (12-fold or higher), a scenario where the incremental modelling of entire ssRNA chains from fragments becomes viable. However, for the latter result, more research is needed to make it directly practically applicable. Regardless, our approach already improves upon the state of the art in RRM-ssRNA modelling and is in principle extendable to other types of protein-nucleic acid interactions.

RNA modulates hnRNPA1A amyloid formation mediated by biomolecular condensates

Several RNA binding proteins involved in membraneless organelles can form pathological amyloids associated with neurodegenerative diseases, but the mechanisms of how this aggregation is modulated remain elusive. Here we investigate how heterotypic protein–RNA interactions modulate the condensation and the liquid to amyloid transition of hnRNPA1A, a protein involved in amyothropic lateral sclerosis. In the absence of RNA, formation of condensates promotes hnRNPA1A aggregation and fibrils are localized at the interface of the condensates. Addition of RNA modulates the soluble to amyloid transition of hnRNPA1A according to different pathways depending on RNA/protein stoichiometry. At low RNA concentrations, RNA promotes both condensation and amyloid formation, and the catalytic effect of RNA adds to the role of the interface between the dense and dilute phases. At higher RNA concentrations, condensation is suppressed according to re-entrant phase behaviour but formation of hnRNPA1A amyloids is observed over longer incubation times. Our findings show how heterotypic nucleic acid–protein interactions affect the kinetics and molecular pathways of amyloid formation.

DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting KD and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface: http://14.139.174.41:8080/.