Protein-nucleic Acid Complexes Research Articles

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.

Computational insights into intrinsically disordered regions in protein-nucleic acid complexes

We study transitions in intrinsically disordered regions (IDRs) upon complex formation, utilizing X-ray-solved structural dataset of protein-DNA and protein-RNA complexes, along with their available unbound protein forms. The identified IDRs are categorized into three classes: Disordered-to-Ordered (D-O), Disordered-to-Partial Ordered (D-PO) and Disordered-to-Disordered (D-D) after comparing them in unbound and complex forms. In the D-O class, IDRs form secondary structures like coils, helices, and strands upon binding to nucleic acids. Though a majority of these IDRs are present at the surface of the complexes, a significant number of IDRs are also observed at the interfaces and are involved in polar interactions. The hydrogen bonds made by the interface IDRs (B_IDRs) with phosphates and bases of nucleic acids are comparatively more than those formed with sugars. B_IDRs form more H-bonds with the ribose in protein-RNA than with the deoxyribose in protein-DNA. Among the B_IDRs, Arg and Lys prefer to interact with the major and minor grooves of DNA and RNA, respectively. Ser, however, prefers the minor groove in both the nucleic acids. Interestingly, we report 61 and 48 IDRs in 31 protein-DNA and 22 protein-RNA complexes, respectively, suggesting nucleic acid binding to proteins may also result in ordered-to-disordered transitions.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review.

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein-nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Data-driven regularization lowers the size barrier of cryo-EM structure determination

Macromolecular structure determination by electron cryo-microscopy (cryo-EM) is limited by the alignment of noisy images of individual particles. Because smaller particles have weaker signals, alignment errors impose size limitations on its applicability. Here, we explore how image alignment is improved by the application of deep learning to exploit prior knowledge about biological macromolecular structures that would otherwise be difficult to express mathematically. We train a denoising convolutional neural network on pairs of half-set reconstructions from the electron microscopy data bank (EMDB) and use this denoiser as an alternative to a commonly used smoothness prior. We demonstrate that this approach, which we call Blush regularization, yields better reconstructions than do existing algorithms, in particular for data with low signal-to-noise ratios. The reconstruction of a protein–nucleic acid complex with a molecular weight of 40 kDa, which was previously intractable, illustrates that denoising neural networks will expand the applicability of cryo-EM structure determination for a wide range of biological macromolecules.

Functionalized graphene-oxide grids enable high-resolution cryo-EM structures of the SNF2h-nucleosome complex without crosslinking

Single-particle cryo-EM is widely used to determine enzyme-nucleosome complex structures. However, cryo-EM sample preparation remains challenging and inconsistent due to complex denaturation at the air-water interface (AWI). Here, to address this issue, we develop graphene-oxide-coated EM grids functionalized with either single-stranded DNA (ssDNA) or thiol-poly(acrylic acid-co-styrene) (TAASTY) co-polymer. These grids protect complexes between the chromatin remodeler SNF2h and nucleosomes from the AWI and facilitate collection of high-quality micrographs of intact SNF2h-nucleosome complexes in the absence of crosslinking. The data yields maps ranging from 2.3 to 3 Å in resolution. 3D variability analysis reveals nucleotide-state linked conformational changes in SNF2h bound to a nucleosome. In addition, the analysis provides structural evidence for asymmetric coordination between two SNF2h protomers acting on the same nucleosome. We envision these grids will enable similar detailed structural analyses for other enzyme-nucleosome complexes and possibly other protein-nucleic acid complexes in general.

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/.

Thorough Assessment of Machine Learning Techniques for Predicting Protein-Nucleic Acid Binding Hot Spots

Background: Proteins and nucleic acids are vital biomolecules that contribute significantly to biological life. The precise and efficient identification of hot spots at protein-nucleic acid interfaces is crucial for guiding drug development, advancing protein engineering, and exploring the underlying molecular recognition mechanisms. As experimental methods like alanine scanning mutagenesis prove to be time-consuming and expensive, a growing number of machine learning techniques are being employed to predict hot spots. However, the existing approach is distinguished by a lack of uniform standards, a scarcity of data, and a wide range of attributes. Currently, there is no comprehensive overview or evaluation of this field. As a result, providing a full overview and review is extremely helpful. Methods: In this study, we present an overview of cutting-edge machine learning approaches utilized for hot spot prediction in protein-nucleic acid complexes. Additionally, we outline the feature categories currently in use, derived from relevant biological data sources, and assess conventional feature selection methods based on 600 extracted features. Simultaneously, we create two new benchmark datasets, PDHS87 and PRHS48, and develop distinct binary classification models based on these datasets to evaluate the advantages and disadvantages of various machine-learning techniques. Results: Prediction of protein-nucleic acid interaction hotspots is a challenging task. The study demonstrates that structural neighborhood features play a crucial role in identifying hot spots. The prediction performance can be improved by choosing effective feature selection methods and machine learning methods. Among the existing prediction methods, XGBPRH has the best performance. Conclusion: It is crucial to continue studying hot spot theories, discover new and effective features, add accurate experimental data, and utilize DNA/RNA information. Semi-supervised learning, transfer learning, and ensemble learning can optimize predictive ability. Combining computational docking with machine learning methods can potentially further improve predictive performance.

Studying Macromolecular Interactions of Cellular Machines by the Combined Use of Analytical Ultracentrifugation, Light Scattering, and Fluorescence Spectroscopy Methods.

Cellular machines formed by the interaction and assembly of macromolecules are essential in many processes of the living cell. These assemblies involve homo- and hetero-associations, including protein-protein, protein-DNA, protein-RNA, and protein-polysaccharide associations, most of which are reversible. This chapter describes the use of analytical ultracentrifugation, light scattering, and fluorescence-based methods, well-established biophysical techniques, to characterize interactions leading to the formation of macromolecular complexes and their modulation in response to specific or unspecific factors. We also illustrate, with several examples taken from studies on bacterial processes, the advantages of the combined use of subsets of these techniques as orthogonal analytical methods to analyze protein oligomerization and polymerization, interactions with ligands, hetero-associations involving membrane proteins, and protein-nucleic acid complexes.

Single molecule technique unveils the role of electrostatic interactions in ssDNA-gp32 molecular complex stability.

The exploration of single-strand DNA-binding protein (SSB)-ssDNA interactions and their crucial roles in essential biological processes lagged behind other types of protein-nucleic acid interactions, such as protein-dsDNA and protein-RNA interactions. The ssDNA binding protein gene product 32 (gp32) of the T4 bacteriophage is a central integrating component of the replication complex that must continuously bind to and unbind from transiently exposed template strands during the DNA synthesis. To gain deeper insights into the electrostatic conditions influencing the stability of the ssDNA-gp32 molecular complex, like the salt concentration or some metal ions proven to specifically bind to gp32, we employed a method that performs rapid measurements of the DNA-protein stability using an α-Hemolysin (α-HL) protein nanopore. We indirectly probed the stability of a protein-nucleic acid complex by monitoring the dissociation process between the gp32 protein and the ssDNA molecular complex in single-molecular electrophysiology experiments, but also through fluorescence spectroscopy techniques. We have shown that the complex is more stable in 0.5 M KCl solution than in 2 M KCl solution and that the presence of Zn2+ ions further increases this stability for any salt used in the present study. This method can be applied to other nucleic acid-protein molecular complexes, as well as for an accurate determination of the drug-protein carrier stability.

Accurate prediction of protein-nucleic acid complexes using RoseTTAFoldNA.

Protein-RNA and protein-DNA complexes play critical roles in biology. Despite considerable recent advances in protein structure prediction, the prediction of the structures of protein-nucleic acid complexes without homology to known complexes is a largely unsolved problem. Here we extend the RoseTTAFold machine learning protein-structure-prediction approach to additionally predict nucleic acid and protein-nucleic acid complexes. We develop a single trained network, RoseTTAFoldNA, that rapidly produces three-dimensional structure models with confidence estimates for protein-DNA and protein-RNA complexes. Here we show that confident predictions have considerably higher accuracy than current state-of-the-art methods. RoseTTAFoldNA should be broadly useful for modeling the structure of naturally occurring protein-nucleic acid complexes, and for designing sequence-specific RNA and DNA-binding proteins.

Pif1 Helicase Mediates Remodeling of Protein-Nucleic Acid Complexes by Promoting Dissociation of Sub1 from G-Quadruplex DNA and Cdc13 from G-Rich Single-Stranded DNA.

Pif1 is a molecular motor enzyme that is conserved from yeast to mammals. It translocates on ssDNA with a directional bias (5' → 3') and unwinds duplexes using the energy obtained from ATP hydrolysis. Pif1 is involved in dsDNA break repair, resolution of G-quadruplex (G4) structures, negative regulation of telomeres, and Okazaki fragment maturation. An important property of this helicase is to exert force and disrupt protein-DNA complexes, which may otherwise serve as barriers to various cellular pathways. Previously, Pif1 was reported to displace streptavidin from biotinylated DNA, Rap1 from telomeric DNA, and telomerase from DNA ends. Here, we have investigated the ability of S. cerevisiae Pif1 helicase to disrupt protein barriers from G4 and telomeric sites. Yeast chromatin-associated transcription coactivator Sub1 was characterized as a G4 binding protein. We found evidence for a physical interaction between Pif1 helicase and Sub1 protein. Here, we demonstrate that Pif1 is capable of catalyzing the disruption of Sub1-bound G4 structures in an ATP-dependent manner. We also investigated Pif1-mediated removal of yeast telomere-capping protein Cdc13 from DNA ends. Cdc13 exhibits a high-affinity interaction with an 11-mer derived from the yeast telomere sequence. Our results show that Pif1 uses its translocase activity to enhance the dissociation of this telomere-specific protein from its binding site. The rate of dissociation increased with an increase in the helicase loading site length. Additionally, we examined the biochemical mechanism for Pif1-catalyzed protein displacement by mutating the sequence of the telomeric 11-mer on the 5'-end and the 3'-end. The results support a model whereby Pif1 disrupts Cdc13 from the ssDNA in steps.

Atomic force microscopy-based approaches for single-molecule investigation of nucleic acid-protein complexes.

The interaction of nucleic acids with proteins plays an important role in many fundamental biological processes in living cells, including replication, transcription, and translation. Therefore, understanding nucleic acid-protein interaction is of high relevance in many areas of biology, medicine and technology. During almost four decades of its existence atomic force microscopy (AFM) accumulated a significant experience in investigation of biological molecules at a single-molecule level. AFM has become a powerful tool of molecular biology and biophysics providing unique information about properties, structure, and functioning of biomolecules. Despite a great variety of nucleic acid-protein systems under AFM investigations, there are a number of typical approaches for such studies. This review is devoted to the analysis of the typical AFM-based approaches of investigation of DNA (RNA)-protein complexes with a major focus on transcription studies. The basic strategies of AFM analysis of nucleic acid-protein complexes including investigation of the products of DNA-protein reactions and real-time dynamics of DNA-protein interaction are categorized and described by the example of the most relevant research studies. The described approaches and protocols have many universal features and, therefore, are applicable for future AFM studies of various nucleic acid-protein systems.

Biologically relevant small variations of intra-cellular pH can have significant effect on stability of protein-DNA complexes, including the nucleosome.

Stability of a protein-ligand complex may be sensitive to pH of its environment. Here we explore, computationally, stability of a set of protein-nucleic acid complexes using fundamental thermodynamic linkage relationship. The nucleosome, as well as an essentially random selection of 20 protein complexes with DNA or RNA, are included in the analysis. An increase in intra-cellular/intra-nuclear pH destabilizes most complexes, including the nucleosome. We propose to quantify the effect by ΔΔG0.3-the change in the binding free energy due to pH increase of 0.3 units, corresponding to doubling of the H + activity; variations of pH of this amplitude can occur in living cells, including in the course of the cell cycle, and in cancer cells relative to normal ones. We suggest, based on relevant experimental findings, a threshold of biological significance of for changes of stability of chromatin-related protein-DNA complexes: a change in the binding affinity above the threshold may have biological consequences. We find that for 70% of the examined complexes, (for 10%, ΔΔG0.3 is between 3 and 4 k B T). Thus, small but relevant variations of intra-nuclear pH of 0.3 may have biological consequences for many protein-nucleic acid complexes. The binding affinity between the histone octamer and its DNA, which directly affects the DNA accessibility in the nucleosome, is predicted to be highly sensitive to intra-nuclear pH. A variation of 0.3 units results in ΔΔG0.3 ∼ 10k B T ; for spontaneous unwrapping of 20 bp long entry/exit fragments of the nucleosomal DNA, ΔΔG0.3 = 2.2k B T; partial disassembly of the nucleosome into the tetrasome is characterized by ΔΔG0.3 = 5.2k B T. The predicted pH -induced modulations of the nucleosome stability are significant enough to suggest that they may have consequences relevant to the biological function of the nucleosome. Accessibility of the nucleosomal DNA is predicted to positively correlate with pH variations during the cell cycle; an increase in intra-cellular pH seen in cancer cells is predicted to lead to a more accessible nucleosomal DNA; a drop in pH associated with apoptosis is predicted to make nucleosomal DNA less accessible. We speculate that processes that depend on accessibility to the DNA in the nucleosomes, such as transcription or DNA replication, might become upregulated due to relatively small, but nevertheless realistic increases of intra-nuclear pH.

A conserved oligomerization domain in the disordered linker of coronavirus nucleocapsid proteins.

The nucleocapsid (N-)protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a key role in viral assembly and scaffolding of the viral RNA. It promotes liquid-liquid phase separation (LLPS), forming dense droplets that support the assembly of ribonucleoprotein particles with as-of-yet unknown macromolecular architecture. Combining biophysical experiments, molecular dynamics simulations, and analysis of the mutational landscape, we describe a heretofore unknown oligomerization site that contributes to LLPS, is required for the assembly of higher-order protein-nucleic acid complexes, and is coupled to large-scale conformational changes of N-protein upon nucleic acid binding. The self-association interface is located in a leucine-rich sequence of the intrinsically disordered linker between N-protein folded domains and formed by transient helices assembling into trimeric coiled-coils. Critical residues stabilizing hydrophobic and electrostatic interactions between adjacent helices are highly protected against mutations in viable SARS-CoV-2 genomes, and the oligomerization motif is conserved across related coronaviruses, thus presenting a target for antiviral therapeutics.

Quantitative analyses of interactions between SpoVG and RNA/DNA

The Borrelia burgdorferi SpoVG protein has previously been found to be a DNA- and RNA-binding protein. To aid in the elucidation of ligand motifs, affinities for numerous RNAs, ssDNAs, and dsDNAs were measured and compared. The loci used in the study were spoVG, glpFKD, erpAB, bb0242, flaB, and ospAB, with particular focus on the untranslated 5′ portion of the mRNAs. Performing binding and competition assays yielded that the 5′ end of spoVG mRNA had the highest affinity while the lowest observed affinity was to the 5’ end of flaB mRNA. Mutagenesis studies of spoVG RNA and ssDNA sequences suggested that the formation of SpoVG-nucleic acid complexes are not entirely dependent on either sequence or structure. Additionally, exchanging uracil for thymine in ssDNAs did not affect protein-nucleic acid complex formation.

Flex-LZerD: Protein docking with extremely large conformational changes.

Large flexibility of proteins as they interact is key to their function in many systems, such as with calmodulin, importin, or interleukin receptors. To understand these mechanisms at the molecular level, it is necessary to consider the 3D atomic structures involved. When these have not been determined, protein docking can generate useful structure models. However, traditional docking typically considers flexibility limited to small movements on the order of ∼2 Å root mean square deviation (RMSD), or otherwise to small structures. These methods account for such minor backbone flexibility via conventional normal mode or other sampling, but modeling ordered proteins of arbitrary size which undergo large-scale conformational changes, on the order of 10 Å RMSD and above, requires different treatment to be generally feasible. Here, we present Flex-LZerD, a computational method for assembling such complexes with large-scale conformational changes on the order of 10 Å RMSD and above. Via an initial partial-assembly multidomain docking and an iterative anisotropic normal mode analysis admitting curvilinear motions, we demonstrate the ability to model the assembly of a variety of protein-protein and protein-nucleic acid complexes. Also yielded are full-atom trajectories taking the input flexible protein from its initial conformation to its docked conformation, which could shed light on potential intermediate states. AlphaFold and other current deep learning-based methods predict static structure models, but do not explicitly address flexibility. AlphaFold is further limited to proteins, while here protein-nucleic acid complexes are also considered. Flex-LZerD successfully modeled protein-protein complexes for 5 of 9 unbound test targets. On a further unbound-bound study of 15 protein-protein complexes and 8 protein-nucleic acid complexes, Flex-LZerD likewise successfully modeled 9 and 8, respectively. Flex-LZerD is thus a step toward the explicit treatment of flexibility and multiple states that is the future of structure modeling.

Structure and function of SARS-CoV-2 nucleocapsid protein measured using optical tweezers, confocal fluorescence, and AFM.

The nucleocapsid (N) protein, one of four structural proteins expressed by SARS-CoV-2, primarily functions by binding and compacting the ∼30 kilobase viral RNA genome (>10 μm linear length) to enable packaging into viral particles (<100 nm diameter). We use optical tweezers to isolate a single ssDNA substrate and observe the binding and compacting function of N protein in real time. The compaction is a multistep process, with kinetics consistent with initial diffusion-limited binding of free protein to the substrate followed by a sudden stochastically triggered reorganization of the protein-DNA complex into a compacted form. Experiments utilizing truncated protein variants lacking either the ordered N or C terminal domains (NTD and CTD) and the intrinsically disordered linker demonstrate that initial binding is driven by the NTD while the formation of the compacted DNA structure requires the CTD. We further localize the binding of fluorescently labeled protein along the substrate using confocal imaging, showing N protein able to both uniformly coat the entire length of the substrate non specifically and subsequently form dense protein clusters through interprotein oligomerization. Finally, various DNA and RNA substrates of differing lengths and sequences are incubated with either full length or truncated N protein and imaged using AFM. Measurement of these nucleic acid-protein complexes reveal both the binding activity of N protein and its ability to remodel complex, folded nucleic acid structures. Our results help elucidate the biophysical mechanism through which N protein enables efficient packaging of the long vRNA under physiological conditions.

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.