Role of Electrostatics in Protein-RNA Binding: The Global vs the Local Energy Landscape

Functionality of IAV packaging signals depends on site-specific charges within the viral nucleoprotein

The coordinated packaging of the segmented genome of the influenza A virus (IAV) into virions is an essential step of the viral life cycle. This process is controlled by the interaction of packaging signals present in all eight viral RNA (vRNA) segments and the viral nucleoprotein (NP), which binds vRNA via a positively charged binding groove. However, mechanistic models of how the packaging signals and NP work together to coordinate genome packaging are missing. Here, we studied genome packaging in influenza A/SC35M virus mutants that carry mutated packaging signals as well as specific amino acid substitutions at the highly conserved lysine (K) residues 184 and 229 in the RNA-binding groove of NP. Because these lysines are acetylated and thus neutrally charged in infected host cells, we replaced them with glutamine to mimic the acetylated, neutrally charged state or arginine to mimic the non-acetylated, positively charged state. Our analysis shows that the coordinated packaging of eight vRNAs is influenced by (i) the charge state of the replacing amino acid and (ii) its location within the RNA-binding groove. Accordingly, we propose that lysine acetylation induces different charge states within the RNA-binding groove of NP, thereby supporting the activity of specific packaging signals during coordinated genome packaging.

IMPORTANCE

Influenza A viruses (IAVs) have a segmented viral RNA (vRNA) genome encapsidated by multiple copies of the viral nucleoprotein (NP) and organized into eight distinct viral ribonucleoprotein complexes. Although genome segmentation contributes significantly to viral evolution and adaptation, it requires a highly sophisticated genome-packaging mechanism. How eight distinct genome complexes are incorporated into the virion is poorly understood, but previous research suggests an essential role for both vRNA packaging signals and highly conserved NP amino acids. By demonstrating that the packaging process is controlled by charge-dependent interactions of highly conserved lysine residues in NP and vRNA packaging signals, our study provides new insights into the sophisticated packaging mechanism of IAVs.

Electrostatic complementarity at the interface drives transient protein-protein interactions

Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes' stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of [Formula: see text] 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

Structural basis of mRNA binding by the human FERRY Rab5 effector complex

The pentameric FERRY Rab5 effector complex is a molecular link between mRNA and early endosomes in mRNA intracellular distribution. Here, we determine the cryo-EM structure of human FERRY. It reveals a unique clamp-like architecture that bears no resemblance to any known structure of Rab effectors. A combination of functional and mutational studies reveals that while the Fy-2 C-terminal coiled-coil acts as binding region for Fy-1/3 and Rab5, both coiled-coils and Fy-5 concur to bind mRNA. Mutations causing truncations of Fy-2 in patients with neurological disorders impair Rab5 binding or FERRY complex assembly. Thus, Fy-2 serves as a binding hub connecting all five complex subunits and mediating the binding to mRNA and early endosomes via Rab5. Our study provides mechanistic insights into long-distance mRNA transport and demonstrates that the particular architecture of FERRY is closely linked to a previously undescribed mode of RNA binding, involving coiled-coil domains.

Spliceosomal SL1 RNA binding to U1-70K: the role of the extended RRM

The RNA recognition motif (RRM) occurs widely in RNA-binding proteins, but does not always by itself support full binding. For example, it is known that binding of SL1 RNA to the protein U1-70K in the U1 spliceosomal particle is reduced when a region flanking the RRM is truncated. How the RRM flanking regions that together with the RRM make up an ‘extended RRM’ (eRRM) contribute to complex stability and structural organization is unknown. We study the U1-70K eRRM bound to SL1 RNA by thermal dissociation and laser temperature jump kinetics; long-time molecular dynamics simulations interpret the experiments with atomistic resolution. Truncation of the helix flanking the RRM on its N-terminal side, ‘N-helix,’ strongly reduces overall binding, which is further weakened under higher salt and temperature conditions. Truncating the disordered region flanking the RRM on the C-terminal side, ‘C-IDR’, affects the local binding site. Surprisingly, all-atom simulations show that protein truncation enhances base stacking interactions in the binding site and leaves the overall number of hydrogen bonds intact. Instead, the flanking regions of the eRRM act in a distributed fashion via collective interactions with the RNA when external stresses such as temperature or high salt mimicking osmotic imbalance are applied.

Unraveling the Role of Charge Patterning in the Micellar Structure of Sequence-Defined Amphiphilic Peptoid Oligomers by Molecular Dynamics Simulations

Electrostatic interactions play a significant role in regulating biological systems and have received increasing attention due to their usefulness in designing advanced stimulus-responsive materials. Polypeptoids are highly tunable N-substituted peptidomimetic polymers that lack backbone hydrogen bonding and chirality. Therefore, polypeptoids are suitable systems to study the effect of noncovalent interactions of substituents without complications of backbone intramolecular and intermolecular hydrogen bonding. In this study, all-atom molecular dynamics (MD) simulations were performed on micelles formed by a series of sequence-defined ionic polypeptoid block copolymers consisting of a hydrophobic segment and a hydrophilic segment in an aqueous solution. By combining the results from MD simulations and experimental small-angle neutron scattering data, further insights were gained into the internal structure of the formed polypeptoid micelles, which is not always directly accessible from experiments. In addition, information was gained into the physics of the noncovalent interactions responsible for the self-assembly of weakly charged polypeptoids in an aqueous solution. While the aggregation number is governed by electrostatic repulsion of the negatively charged carboxylate (COO^–) substituents on the polypeptoid chain within the micelle, MD simulations indicate that the position of the charge on singly charged chains mediates the shape of the micelle through the charge–dipole interactions between the COO^– substituent and the surrounding water. Therefore, the polypeptoid micelles formed from the single-charged series offer the possibility for tailorable micelle shapes. In contrast, the polypeptoid micelles formed from the triple-charged series are characterized by more pronounced electrostatic repulsion that competes with more significant charge–sodium interactions, making it difficult to predict the shape of the micelles. This work has helped further develop design principles for the shape and structure of self-assembled micelles by controlling the position of charged moieties on the backbone of polypeptoid block copolymers.

rBPDL:Predicting RNA-Binding Proteins Using Deep Learning

RNA-binding protein (RBP) is a powerful and wide-ranging regulator that plays an important role in cell development, differentiation, metabolism, health and disease. The prediction of RBPs provides valuable guidance for biologists. Although experimental methods have made great progress in predicting RBP, they are time-consuming and not flexible. Therefore, we developed a network model, rBPDL, by combining a convolutional neural network and long short-term memory for multilabel classification of RBPs. Moreover, to achieve better prediction results, we used a voting algorithm for ensemble learning of the model. We compared rBPDL with state-of-the-art methods and found that rBPDL significantly improved identification performance for the RBP68 dataset, with a macro-Area Under Curve (AUC), micro-AUC, and weighted AUC of 0.936, 0.962, and 0.946, respectively. Furthermore, through AUC statistical analysis of the RBP domain, we analyzed the performance of rBPDL and found that the RBP identification performance in the same domain was similar. In addition, we analyzed the performance preferences and physicochemical properties of the binding protein amino acids and explored the characteristics that affect the binding by using the RBP86 dataset.

Development of a structure-analysis pipeline using multiple-solvent crystal structures of barrier-to-autointegration factor

The multiple-solvent crystal structure (MSCS) approach uses high concentrations of organic solvents to characterize the interactions and effects of solvents on proteins. Here, the method has been further developed and an MSCS data-handling pipeline is presented that uses the Detection of Related Solvent Positions (DRoP) program to improve data quality. DRoP is used to selectively model conserved water molecules, so that an advanced stage of structural refinement is reached quickly. This allows the placement of organic molecules more accurately and convergence on high-quality maps and structures. This pipeline was applied to the chromatin-associated protein barrier-to-autointegration factor (BAF), resulting in structural models with better than average statistics. DRoP and Phenix Structure Comparison were used to characterize the data sets and to identify a binding site that overlaps with the interaction site of BAF with emerin. The conserved water-mediated networks identified by DRoP suggested a mechanism by which water molecules are used to drive the binding of DNA. Normalized and differential B-factor analysis is shown to be a valuable tool to characterize the effects of specific solvents on defined regions of BAF. Specific solvents are identified that cause stabilization of functionally important regions of the protein. This work presents tools and a standardized approach for the analysis and comprehension of MSCS data sets.

Molecular dynamics simulations of DNA-DNA and DNA-protein interactions

The all-atom molecular dynamics method can characterize the molecular-level interactions in DNA and DNA-protein systems with unprecedented resolution. Recent advances in computational technologies have allowed the method to reveal the unbiased behavior of such systems at the microseconds time scale, whereas enhanced sampling approaches have matured enough to characterize the interaction free energy with quantitative precision. Here, we describe recent progress toward increasing the realism of such simulations by refining the accuracy of the molecular dynamics force field, and we highlight recent application of the method to systems of outstanding biological interest.

Protein electrostatics: From computational and structural analysis to discovery of functional fingerprints and biotechnological design

Computationally driven engineering of proteins aims to allow them to withstand an extended range of conditions and to mediate modified or novel functions. Therefore, it is crucial to the biotechnological industry, to biomedicine and to afford new challenges in environmental sciences, such as biocatalysis for green chemistry and bioremediation. In order to achieve these goals, it is important to clarify molecular mechanisms underlying proteins stability and modulating their interactions. So far, much attention has been given to hydrophobic and polar packing interactions and stability of the protein core. In contrast, the role of electrostatics and, in particular, of surface interactions has received less attention. However, electrostatics plays a pivotal role along the whole life cycle of a protein, since early folding steps to maturation, and it is involved in the regulation of protein localization and interactions with other cellular or artificial molecules. Short- and long-range electrostatic interactions, together with other forces, provide essential guidance cues in molecular and macromolecular assembly. We report here on methods for computing protein electrostatics and for individual or comparative analysis able to sort proteins by electrostatic similarity. Then, we provide examples of electrostatic analysis and fingerprints in natural protein evolution and in biotechnological design, in fields as diverse as biocatalysis, antibody and nanobody engineering, drug design and delivery, molecular virology, nanotechnology and regenerative medicine.

RNA-Binding Domains of Heterologous Viral Proteins Substituted for Basic Residues in the RSV Gag NC Domain Restore Specific Packaging of Genomic RNA

The Rous sarcoma virus Gag polyprotein transiently traffics through the nucleus, which is required for efficient incorporation of the viral genomic RNA (gRNA) into virus particles. Packaging of gRNA is mediated by two zinc knuckles and basic residues located in the nucleocapsid (NC) domain in Gag. To further examine the role of basic residues located downstream of the zinc knuckles in gRNA encapsidation, we used a gain-of-function approach. We replaced a basic residue cluster essential for gRNA packaging with heterologous basic residue motif (BR) with RNA-binding activity from either the HIV-1 Rev protein (Rev BR) or the HSV ICP27 protein (ICP27 BR). Compared to wild-type Gag, the mutant ICP27 BR and Rev BR Gag proteins were much more strongly localized to the nucleus and released significantly lower levels of virus particles. Surprisingly, both the ICP27 BR and Rev BR mutants packaged normal levels of gRNA per virus particle when examined in the context of a proviral vector, yet both mutants were noninfectious. These results support the hypothesis that basic residues located in the C-terminal region of NC are required for selective gRNA packaging, potentially by binding non-specifically to RNA via electrostatic interactions.

Modeling Electrostatic Force in Protein-Protein Recognition

Electrostatic interactions are important for understanding molecular interactions, since they are long-range interactions and can guide binding partners to their correct binding positions. To investigate the role of electrostatic forces in molecular recognition, we calculated electrostatic forces between binding partners separated at various distances. The investigation was done on a large set of 275 protein complexes using recently developed DelPhiForce tool and in parallel, evaluating the total electrostatic force via electrostatic association energy. To accomplish the goal, we developed a method to find an appropriate direction to move one chain of protein complex away from its bound position and then calculate the corresponding electrostatic force as a function of separation distance. It is demonstrated that at large distances between the partners, the electrostatic force (magnitude and direction) is consistent among the protocols used and the main factors contributing to it are the net charge of the partners and their interfaces. However, at short distances, where partners form specific pair-wise interactions or de-solvation penalty becomes significant, the outcome depends on the precise balance of these factors. Based on the electrostatic force profile (force as a function of distance), we group the cases into four distinctive categories, among which the most intriguing is the case termed "soft landing." In this case, the electrostatic force at large distances is favorable assisting the partners to come together, while at short distance it opposes binding, and thus slows down the approach of the partners toward their physical binding.

Computational approaches to macromolecular interactions in the cell

Structural modeling of a cell is an evolving strategic direction in computational structural biology. It takes advantage of new powerful modeling techniques, deeper understanding of fundamental principles of molecular structure and assembly, and rapid growth of the amount of structural data generated by experimental techniques. Key modeling approaches to principal types of macromolecular assemblies in a cell already exist. The main challenge, along with the further development of these modeling approaches, is putting them together in a consistent, unified whole cell model. This opinion piece addresses the fundamental aspects of modeling macromolecular assemblies in a cell, and the state-of-the-art in modeling of the principal types of such assemblies.

Crimean-Congo hemorrhagic fever virus nucleocapsid protein harbors distinct RNA-binding sites in the stalk and head domains

Crimean-Congo hemorrhagic fever virus (CCHFV) is a tick-borne Nairovirus that causes severe hemorrhagic fever with a mortality rate of up to 30% in certain outbreaks worldwide. The virus has wide endemic distribution. There is no effective antiviral therapeutic or FDA approved vaccine for this zoonotic viral illness. The multifunctional CCHFV nucleocapsid protein (N protein) plays a crucial role in the establishment of viral infection and is an important structural component of the virion. Here we show that CCHFV N protein has a distant RNA-binding site in the stalk domain that specifically recognizes the vRNA panhandle, formed by the base pairing of complementary nucleotides at the 5' and 3' termini of the vRNA genome. Using multiple approaches, including filter-bonding analysis, GFP reporter assay, and biolayer interferometry we observed an N protein-panhandle interaction both in vitro and in vivo The purified WT CCHFV N protein and the stalk domain also recognize the vRNA panhandle of hazara virus, another Nairovirus in the family Bunyaviridae, demonstrating the genus-specific nature of N protein-panhandle interaction. Another RNA-binding site was identified at the head domain of CCHFV N protein that nonspecifically recognizes the single strand RNA (ssRNA) of viral or nonviral origin. Expression of CCHFV N protein stalk domain active in panhandle binding, dramatically inhibited the hazara virus replication in cell culture, illustrating the role of N protein-panhandle interaction in Nairovirus replication. Our findings reveal the stalk domain of N protein as a potential target in therapeutic interventions to manage CCHFV disease.

A structural dissection of protein–RNA interactions based on different RNA base areas of interfaces

Protein–RNA interactions are very common cellular processes, but the mechanisms of interactions are not fully understood, mainly due to the complicated RNA structures. By the elaborate investigation on RNA structures of protein–RNA complexes, it was firstly found in this paper that RNAs in these complexes could be clearly classified into three classes (high, medium and low) based on the different levels of P_base (the percentage of base area buried in the RNA interface). In view of the three RNA classes, more detailed analyses on protein–RNA interactions were comprehensively performed from various aspects, including interface area, structure, composition and interaction force, so as to achieve a deeper understanding of the recognition specificity for the three classes of protein–RNA interactions. According to our classification strategy, the three complex classes have significant differences in terms of almost all properties. Complexes in the high class have short and extended RNA structures and behave like protein–ssDNA interactions. Their hydrogen bonds and hydrophobic interactions are strong. For complexes in low class, their RNA structures are mainly double-stranded, like protein–dsDNA interactions, and electrostatic interactions frequently occur. The complexes in medium class have the longest RNA chains and largest average interface area. Meanwhile, they do not show any preference for the interaction force. On average, in terms of composition, secondary structures and intermolecular physicochemical properties, significant feature preferences can be observed in high and low complexes, but no highly specific features are found for medium complexes. We found that our proposed P_base is an important parameter which can be used as a new determinant to distinguish protein–RNA complexes. For high and low complexes, we can more easily understand the specificity of the recognition process from the interface features than for medium complexes. In the future, medium complexes should be our research focus to further structurally analyze from more feature aspects. Overall, this study may contribute to further understanding of the mechanism of protein–RNA interactions on a more detailed level.

Qualitative and quantitative measurements of the influence of structure and composition of RNA interfaces on protein–RNA interactions.