Cavities in protein-DNA and protein-RNA interfaces

Probing pharmaceutically important amino acids L-isoleucine and L-tyrosine Solubilities: Unraveling the solvation thermodynamics in diverse mixed solvent systems

The study specifically investigates the solubilities of L-isoleucine and L-tyrosine in water-mixed solvent systems (DMF, DMSO, and ACN), exploring the behaviour of amino acids in complex environments. The experimental methods prioritize meticulous solvent purification to ensure reliable results. The work explores solubility data, uncovering temperature-dependent trends and intricate interactions influencing solubility in the chosen mixed solvent systems. The study emphasizes the impact of thermodynamic properties, solvent-solvent interactions, and amino acid structure on solubility patterns. The broader implications highlight the relevance of understanding amino acid behaviour in diverse solvent environments, offering potential applications in cosmetics and pharmaceutical industries. The distinct solubility patterns contribute valuable insights, enhancing on the understanding of the solution stability and interactions of L-isoleucine and L-tyrosine in different solvent systems. In conclusion, work suggests the enhanced utilization of L-isoleucine and L-tyrosine in various industries, driven by a profound understanding of their solubility in mixed solvent systems. The research expands our knowledge of amino acid behaviour, paving the way for advancements in industries relying on protein-based products and technologies.

Dynamic N6 -methyladenosine RNA modification regulates peanut resistance to bacterial wilt

N6 -methyladenosine (m6 A) is the most abundant mRNA modification in eukaryotes and is an important regulator of gene expression as well as many other critical biological processes. However, the characteristics and functions of m6 A in peanut (Arachis hypogea L.) resistance to bacterial wilt (BW) remain unknown. Here, we analyzed the dynamic of m6 A during infection of resistant (H108) and susceptible (H107) peanut accessions with Ralstonia solanacearum (R. solanacearum), the causative agent of BW. Throughout the transcriptome, we identified 'URUAY' as a highly conserved motif for m6 A in peanut. The majority of differential m6 A located within the 3' untranslated region (UTR) of the transcript, with fewer in the exons. Integrative analysis of RNA-Seq and m6 A methylomes suggests the correlation between m6 A and gene expression in peanut R. solanacearum infection, and functional analysis reveals that m6 A-associated genes were related to plant-pathogen interaction. Our experimental analysis suggests that AhALKBH15 is an m6 A demethylase in peanut, leading to decreased m6 A levels and upregulation of the resistance gene AhCQ2G6Y. The upregulation of AhCQ2G6Y expression appears to promote BW resistance in the H108 accession.

Ribonucleoprotein particles: advances and challenges in computational methods

RNA-binding proteins (RBPs) interact with RNA to form Ribonucleoprotein Particles (RNPs). The interaction between RBPs and their RNA partners are traditionally thought to be mediated by highly conserved RNA-binding domains (RBDs). Recently, high-throughput studies led to the discovery of hundreds of novel proteins and domains, of which many do not follow the classical definition of RNA-binding. Despite technological innovations, experimental screenings are currently limited to the detection of specific types of RNPs, underscoring the importance of computational methods for predicting novel RBPs and RNA interacting residues and interfaces. Here, we discuss major challenges in computational prediction of RBPs and RBDs and outline new strategies to circumvent current limitations of experimental techniques.

Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces

The increasing number of protein structures with uncharacterized function necessitates the development of in silico prediction methods for functional annotations on proteins. In this chapter, different kinds of computational approaches are briefly introduced to predict DNA-binding residues on surface of DNA-binding proteins, and the merits and limitations of these methods are mainly discussed. This chapter focuses on the structure-based approaches and mainly discusses the framework of machine learning methods in application to DNA-binding prediction task.

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.

Improved Accuracy in RNA-Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics Simulation into the Scoring Function

RNA-protein interactions play fundamental roles in many biological processes. To understand these interactions, it is necessary to know the three-dimensional structures of RNA-protein complexes. However, determining the tertiary structure of these complexes is often difficult, suggesting that an accurate rigid body docking for RNA-protein complexes is needed. In general, the rigid body docking process is divided into two steps: generating candidate structures from the individual RNA and protein structures and then narrowing down the candidates. In this study, we focus on the former problem to improve the prediction accuracy in RNA-protein docking. Our method is based on the integration of physicochemical information about RNA into ZDOCK, which is known as one of the most successful computer programs for protein-protein docking. Because recent studies showed the current force field for molecular dynamics simulation of protein and nucleic acids is quite accurate, we modeled the physicochemical information about RNA by force fields such as AMBER and CHARMM. A comprehensive benchmark of RNA-protein docking, using three recently developed data sets, reveals the remarkable prediction accuracy of the proposed method compared with existing programs for docking: the highest success rate is 34.7% for the predicted structure of the RNA-protein complex with the best score and 79.2% for 3,600 predicted ones. Three full atomistic force fields for RNA (AMBER94, AMBER99, and CHARMM22) produced almost the same accurate result, which showed current force fields for nucleic acids are quite accurate. In addition, we found that the electrostatic interaction and the representation of shape complementary between protein and RNA plays the important roles for accurate prediction of the native structures of RNA-protein complexes.

Identification of single-stranded and double-stranded DNA binding proteins based on protein structure

Background

Protein-DNA interactions are essential for many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. DNA binding proteins can be classified into double-stranded DNA binding proteins (DSBs) and single-stranded DNA binding proteins (SSBs), and they take part in different biological functions. DSBs usually act as transcriptional factors to regulate the genes' expressions, while SSBs usually play roles in DNA replication, recombination, and repair, etc. Understanding the binding specificity of a DNA binding protein is helpful for the research of protein functions.

Results

In this paper, we investigated the differences between DSBs and SSBs on surface tunnels as well as the OB-fold domain information. We detected the largest clefts on the protein surfaces, to obtain several features to be used for distinguishing the potential interfaces between SSBs and DSBs, and compared its structure with each of the six OB-fold protein templates, and use the maximal alignment score TM-score as the OB-fold feature of the protein, based on which, we constructed the support vector machine (SVM) classification model to automatically distinguish these two kinds of proteins, with prediction accuracy of 87%,83% and 83% for HOLO-set, APO-set and Mixed-set respectively.

Conclusions

We found that they have different ranges of tunnel lengths and tunnel curvatures; moreover, the alignment results with OB-fold templates have also found to be the discriminative feature of SSBs and DSBs. Experimental results on 10-fold cross validation indicate that the new feature set are effective to describe DNA binding proteins. The evaluation results on both bound (DNA-bound) and non-bound (DNA-free) proteins have shown the satisfactory performance of our method.

Hydration of protein-RNA recognition sites

We investigate the role of water molecules in 89 protein–RNA complexes taken from the Protein Data Bank. Those with tRNA and single-stranded RNA are less hydrated than with duplex or ribosomal proteins. Protein–RNA interfaces are hydrated less than protein–DNA interfaces, but more than protein–protein interfaces. Majority of the waters at protein–RNA interfaces makes multiple H-bonds; however, a fraction do not make any. Those making H-bonds have preferences for the polar groups of RNA than its partner protein. The spatial distribution of waters makes interfaces with ribosomal proteins and single-stranded RNA relatively ‘dry’ than interfaces with tRNA and duplex RNA. In contrast to protein–DNA interfaces, mainly due to the presence of the 2′OH, the ribose in protein–RNA interfaces is hydrated more than the phosphate or the bases. The minor groove in protein–RNA interfaces is hydrated more than the major groove, while in protein–DNA interfaces it is reverse. The strands make the highest number of water-mediated H-bonds per unit interface area followed by the helices and the non-regular structures. The preserved waters at protein–RNA interfaces make higher number of H-bonds than the other waters. Preserved waters contribute toward the affinity in protein–RNA recognition and should be carefully treated while engineering protein–RNA interfaces.

Dangling ends perturb the stability of RNA duplexes responsive to surrounding conditions

Unpaired terminal nucleotides (dangling ends) occur in various biologically important RNA structures. We studied the thermal stability of RNA duplexes with dangling ends under conditions that mimic those in cells. Dangling ends of one or two nucleotides stabilized a duplex up to approximately 2.7 kcal mol(-1) in the absence of cosolutes. RNA duplexes with dangling purine nucleotides were more stable than those with pyrimidine nucleotides. Interestingly, in the presence of various cosolutes, RNA duplexes with purine dangling ends were significantly destabilized, although those with pyrimidine dangling ends were destabilized slightly. For example, in 30 wt % poly(ethylene glycol), stabilization resulting from adenine dangling ends was reduced by 1.4 kcal mol(-1) . Our quantitative analyses also showed that the number of water molecules bound to the dangling ends in an aqueous solution was independent of the nucleotide type but dependent on the stability of the dangling-end region. It has been considered that dangling ends stabilize helices; however, our results suggest that the stabilization is responsive to the surrounding conditions.

Molecular interactions within the halophilic, thermophilic, and mesophilic prokaryotic ribosomal complexes: clues to environmental adaptation

Using the available crystal structures of 50S ribosomal subunits from three prokaryotic species: Escherichia coli (mesophilic), Thermus thermophilus (thermophilic), and Haloarcula marismortui (halophilic), we have analyzed different structural features of ribosomal RNAs (rRNAs), proteins, and of their interfaces. We have correlated these structural features with the environmental adaptation strategies of the corresponding species. While dense intra-rRNA packing is observed in thermophilic, loose intra-rRNA packing is observed in halophilic (both compared to mesophilic). Interestingly, protein-rRNA interfaces of both the extremophiles are densely packed compared to that of the mesophilic. The intersubunit bridge regions are almost devoid of cavities, probably ensuring the proper formation of each bridge (by not allowing any loosely packed region nearby). During rRNA binding, the ribosomal proteins experience some structural transitions. Here, we have analyzed the intrinsically disordered and ordered regions of the ribosomal proteins, which are subjected to such transitions. The intrinsically disordered and disorder-to-order transition sites of the thermophilic and mesophilic ribosomal proteins are simultaneously (i) highly conserved and (ii) slowly evolving compared to rest of the protein structure. Although high conservation is observed at such sites of halophilic ribosomal proteins, but slow rate of evolution is absent. Such differences between thermophilic, mesophilic, and halophilic can be explained from their environmental adaptation strategy. Interestingly, a universal biophysical principle evident by a linear relationship between the free energy of interface formation, interface area, and structural changes of r-proteins during assembly is always maintained, irrespective of the environmental conditions.

Bioinformatic analysis of the protein/DNA interface

To investigate the principles driving recognition between proteins and DNA, we analyzed more than thousand crystal structures of protein/DNA complexes. We classified protein and DNA conformations by structural alphabets, protein blocks [de Brevern, Etchebest and Hazout (2000) (Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks. Prots. Struct. Funct. Genet., 41:271-287)] and dinucleotide conformers [Svozil, Kalina, Omelka and Schneider (2008) (DNA conformations and their sequence preferences. Nucleic Acids Res., 36:3690-3706)], respectively. Assembling the mutually interacting protein blocks and dinucleotide conformers into 'interaction matrices' revealed their correlations and conformer preferences at the interface relative to their occurrence outside the interface. The analyzed data demonstrated important differences between complexes of various types of proteins such as transcription factors and nucleases, distinct interaction patterns for the DNA minor groove relative to the major groove and phosphate and importance of water-mediated contacts. Water molecules mediate proportionally the largest number of contacts in the minor groove and form the largest proportion of contacts in complexes of transcription factors. The generally known induction of A-DNA forms by complexation was more accurately attributed to A-like and intermediate A/B conformers rare in naked DNA molecules.

Analysis of base-pairing probabilities of RNA molecules involved in protein-RNA interactions

MOTIVATION

Understanding the details of protein-RNA interactions is important to reveal the functions of both the RNAs and the proteins. In these interactions, the secondary structures of the RNAs play an important role. Because RNA secondary structures in protein-RNA complexes are variable, considering the ensemble of RNA secondary structures is a useful approach. In particular, recent studies have supported the idea that, in the analysis of RNA secondary structures, the base-pairing probabilities (BPPs) of RNAs (i.e. the probabilities of forming a base pair in the ensemble of RNA secondary structures) provide richer and more robust information about the structures than a single RNA secondary structure, for example, the minimum free energy structure or a snapshot of structures in the Protein Data Bank. However, there has been no investigation of the BPPs in protein-RNA interactions.

RESULTS

In this study, we analyzed BPPs of RNA molecules involved in known protein-RNA complexes in the Protein Data Bank. Our analysis suggests that, in the tertiary structures, the BPPs (which are computed using only sequence information) for unpaired nucleotides with intermolecular hydrogen bonds (hbonds) to amino acids were significantly lower than those for unpaired nucleotides without hbonds. On the other hand, no difference was found between the BPPs for paired nucleotides with and without intermolecular hbonds. Those findings were commonly supported by three probabilistic models, which provide the ensemble of RNA secondary structures, including the McCaskill model based on Turner's free energy of secondary structures.

A comparison of structural and evolutionary attributes of Escherichia coli and Thermus thermophilus small ribosomal subunits: signatures of thermal adaptation

Here we compare the structural and evolutionary attributes of Thermus thermophilus and Escherichia coli small ribosomal subunits (SSU). Our results indicate that with few exceptions, thermophilic 16S ribosomal RNA (16S rRNA) is densely packed compared to that of mesophilic at most of the analogous spatial regions. In addition, we have located species-specific cavity clusters (SSCCs) in both species. E. coli SSCCs are numerous and larger compared to T. thermophilus SSCCs, which again indicates densely packed thermophilic 16S rRNA. Thermophilic ribosomal proteins (r-proteins) have longer disordered regions than their mesophilic homologs and they experience larger disorder-to-order transitions during SSU-assembly. This is reflected in the predicted higher conformational changes of thermophilic r-proteins compared to their mesophilic homologs during SSU-assembly. This high conformational change of thermophilic r-proteins may help them to associate with the 16S ribosomal RNA with high complementary interfaces, larger interface areas, and denser molecular contacts, compared to those of mesophilic. Thus, thermophilic protein-rRNA interfaces are tightly associated with 16S rRNA than their mesophilic homologs. Densely packed 16S rRNA interior and tight protein-rRNA binding of T. thermophilus (compared to those of E. coli) are likely the signatures of its thermal adaptation. We have found a linear correlation between the free energy of protein-RNA interface formation, interface size, and square of conformational changes, which is followed in both prokaryotic and eukaryotic SSU. Disorder is associated with high protein-RNA interface polarity. We have found an evolutionary tendency to maintain high polarity (thereby disorder) at protein-rRNA interfaces, than that at rest of the protein structures. However, some proteins exhibit exceptions to this general trend.

Dissecting the protein-RNA interface: the role of protein surface shapes and RNA secondary structures in protein-RNA recognition

Protein-RNA interactions are essential for many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. Here, we analyzed the protein surface shape (dented, intermediate or protruded) and the RNA base pairing properties (paired or unpaired nucleotides) at the interfaces of 91 protein-RNA complexes derived from the Protein Data Bank. Dented protein surfaces prefer unpaired nucleotides to paired ones at the interface, and hydrogen bonds frequently occur between the protein backbone and RNA bases. In contrast, protruded protein surfaces do not show such a preference, rather, electrostatic interactions initiate the formation of hydrogen bonds between positively charged amino acids and RNA phosphate groups. Interestingly, in many protein-RNA complexes that interact via an RNA loop, an aspartic acid is favored at the interface. Moreover, in most of these complexes, nucleotide bases in the RNA loop are flipped out and form hydrogen bonds with the protein, which suggests that aspartic acid is important for RNA loop recognition through a base-flipping process. This study provides fundamental insights into the role of the shape of the protein surface and RNA secondary structures in mediating protein-RNA interactions.

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces

Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design.

Modeling of the water network at protein-RNA interfaces

Water plays an important role in the mediation of biomolecular interactions. Thus, accurate prediction and evaluation of water-mediated interactions is an important element in the computational design of interfaces involving proteins, RNA, and DNA. Here, we use an algorithm (WATGEN) to predict the locations of interfacial water molecules for a data set of 224 protein-RNA interfaces. The accuracy of the prediction is validated against water molecules present in the X-ray structures of 105 of these complexes. The complexity of the water networks is deconvoluted through definition of the characteristics of each water molecule based on its bridging properties between the protein and RNA and on its depth in the interface with respect to the bulk solvent. This approach has the potential for scoring the water network for incorporation into the computational design of protein-RNA complexes.

Novel modulation factor quantifies the role of water molecules in protein interactions

Water molecules decrease the potential of mean force of a hydrogen bond (H-bond), as well as modulate (de)solvation forces, but exactly how much has not been easy to determine. Crystallographic water molecules provide snapshots of optimal solutions for the role of solvent in protein interactions, information that is often ignored by implicit solvent models. Motivated by high-resolution crystal structures, we describe a simple quantitative approach to explicitly incorporate the role of molecular water in protein interactions. Applications to protein-DNA interactions show that the accuracy of binding free-energy estimates improves significantly if a distinction is made between H-bonds that are desolvated (or only contact crystal waters), solvated by mobile waters trapped at the binding interface, or partially solvated through connections to bulk water. These different environments are modeled by a unique "water" scaling factor that decreases or increases the strength of hydrogen bonds depending on whether water contacts the acceptor or donor atoms or the bond is fully desolvated, respectively. Our empirical energies are fully consistent with mobile water molecules having a strong polarization effect in direct intermolecular interactions.