protein-RNA Docking Research Articles

Utilization of the EpiMed Coronabank Chemical Collection to identify potential SARS-CoV-2 antivirals: in silico studies targeting the nsp14 ExoN domain and PLpro naphthalene binding site

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome encodes 29 proteins including four structural, 16 nonstructural (nsps), and nine accessory proteins (https://epimedlab.org/sars-cov-2-proteome/). Many of these proteins contain potential targetable sites for the development of antivirals. Despite the widespread use of vaccinations, the emergence of variants necessitates the investigation of new therapeutics and antivirals. Here, the EpiMed Coronabank Chemical Collection (https://epimedlab.org/crl/) was utilized to investigate potential antivirals against the nsp14 exoribonuclease (ExoN) domain. Molecular docking was performed to evaluate the binding characteristics of our chemical library against the nsp14 ExoN site. Based on the initial screen, trisjuglone, ararobinol, corilagin, and naphthofluorescein were identified as potential lead compounds. Molecular dynamics (MD) simulations were subsequently performed, with the results highlighting the stability of the lead compounds in the nsp14 ExoN site. Protein-RNA docking revealed the potential for the lead compounds to disrupt the interaction with RNA when bound to the ExoN site. Moreover, hypericin, cyanidin-3-O-glucoside, and rutin were previously identified as lead compounds targeting the papain-like protease (PLpro) naphthalene binding site. Through performing MD simulations, the stability and interactions of lead compounds with PLpro were further examined. Overall, given the critical role of the exonuclease activity of nsp14 in ensuring viral fidelity and the multifunctional role of PLpro in viral pathobiology and replication, these nsps represent important targets for antiviral drug development. Our databases can be utilized for in silico studies, such as the ones performed here, and this approach can be applied to other potentially druggable SARS-CoV-2 protein targets.

Nucleolin’s Role in the Biogenesis of Pri‐miRNA 16 by the Microprocessor Complex: An In silico Approach

MicroRNAs (miRNAs) are short, non‐coding strands of RNA found in eukaryotes. They play a role in gene silencing through inhibiting translation or degrading mRNA. Primary miRNA (pri‐miRNA) processing is the initial step in producing a mature microRNA molecule. The microprocessor complex (MP) proteins, DROSHA and DGCR8, cleaves the pri‐miRNA molecule to a shorter hairpin structure. This cleaved RNA hairpin, precursor miRNA, is sent to the cytoplasm for further processing into the mature miRNA. Previous research has uncovered that the expression and cellular localization of the stress‐responsive protein, nucleolin (NCL) affects the processing of several miRNAs, including microRNA 16 (miR16). Nucleolin is an RNA binding protein and is primarily located in the nucleolus but can also be localized in various cellular compartments. When NCL is present in the nucleus, the MP complex works more efficiently in cleaving pri‐miR 16‐1 to its pre‐miRNA form resulting in an upregulation of miR 16‐1. It has been proposed that nucleolin binds to pri‐miR 16‐1 and stabilizes its confirmation for cleavage by the MP complex. However, the mechanistic and structural details on how nucleolin interacts with pri‐miRNA 16‐1 or the MP are still unclear. It is also unknown if NCL is similarly involved in miR 16‐2 biogenesis. NCL and miRNA 16 are also intricately involved in the regulation of the expression of bcl2, an anti‐apoptotic and oncogenic protein. When NCL is in the cytoplasm, it stabilizes the 3’ untranslated region of bcl2 mRNA leading to an increased expression of the protein. Unlike nucleolin, miR 16 represses the translation of bcl2 mRNA. At high levels of miR 16 when NCL is localized to the nucleus, bcl2 protein expression is low. When cellular stress occurs, NCL returns to the cytoplasm to stabilize bcl2 and this also results in less efficiency in pri‐miR 16 processing, resulting in an overall increase in bcl2 protein. It is evident that NCL balances the levels of bcl2 and miR16 through its cellular localization. Since the molecular mechanism underlying NCL’s role in the MP‐based processing of pri‐miR16‐1 remains largely unknown, in this study, we used computational approaches including structural alignments, protein‐protein, and protein‐RNA docking analyses to determine how pri‐miR 16‐1, nucleolin, and the MP complex interact. In addition, we have investigated if pri‐miR 16‐2 interacts with the NCL and the MP complex in a similar fashion. We show in our predictive models that the interaction interface of pri‐miR 16‐1 and pri‐miR 16‐2 with nucleolin and the MP complex nucleolin is distinct. For pri‐miR 16‐1, the interaction interface involves NCL and DGCR8 B and C, but in the case of pri‐miR 16‐2, it involves NCL and DROSHA. Additionally, in the presence of nucleolin, the hydrogen bond interactions are reduced between pri‐miR 16‐2 and the MP complex. Although the number of interactions between the different components of the MP complex is the same in both scenarios, the specific residues that form the interaction interface differ, suggesting distinct mechanisms. Since the levels of miR16 expression can lead to apoptosis of cancerous cells and prevent cell proliferation, the results of our study provide important clues in understanding how the NCL‐miR‐MP complex can be targeted in drug design for cancer therapy.

P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking.

The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy. In this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3-20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK's decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein-RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html. www.rnabinding.com/P3DOCK/P3DOCK.html. Supplementary data are available at Bioinformatics online.

Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models.

Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB). Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions. http://huanglab.phys.hust.edu.cn/mprdock/. Supplementary data are available at Bioinformatics online.

Protein-RNA Docking Using ICM.

Protein-RNA interactions play an important role in many biological processes. Computational methods such as docking have been developed to complement existing biophysical and structural biology techniques. Computational prediction of protein-RNA complex structures includes two steps: generating candidate structures from the individual protein and RNA parts and scoring the generated poses to pick out the correct one. In this work, we considered three recently developed data sets of protein-RNA complexes to evaluate and improve the performance of the FFT-based rigid-body docking algorithm implemented in the ICM package. An electrostatic term describing interactions between negatively charged phosphate groups and positively charged protein residues was added to the energy function used during the docking step to take into account the greater role that electrostatic interactions play in protein-RNA complexes. Next, the docking results were used to optimize a scoring function including van der Waals, electrostatic, and solvation terms. This optimization yielded a much smaller weight for the solvation term indicating that solvation energy may be less important for the scoring of protein-RNA structures. Rescoring of the generated poses with the new scoring function led to much higher success rates, while pose clustering by contact fingerprints produced further improvements, achieving a success rate of 0.66 for the top 100 structures.

Abstract 476: A human scFv as a tool to understand the biogenesis of a subset of oncogenic microRNAs

Abstract Trans-acting factors such as RNA-binding proteins (RBPs) and microRNAs (miRNA) have recently been recognized as prime regulators of gene expression during tumorigenesis. Nucleolin (NCL) is the most abundant nucleolar RNA-binding protein, controlling critical cellular processes such as chromatin remodeling, ribosome biogenesis and miRNA biogenesis. In the cytoplasm, NCL regulates gene expression post-transcriptionally by controlling mRNA stability and/or translation via ARE- and miRNA-mediated pathways. In contrast, NCL at the cell surface plays pivotal roles in cancer progression and metastasis in many types of tumors. Therefore, surface-NCL is an appealing target for cancer therapy. Earlier our group described 4LB5, a human-derived single chain fragment variable (scFv) that specifically targets surface-NCL exerting cytotoxic effects, both in vitro and in vivo. We have identified the central RNA-binding domains (RBD1-4) of NCL as 4LB5-interacting sites. 4LB5-mediated blocking of NCL-RBDs significantly impairs the biogenesis of a subset of oncogenic miRNAs, such as miR-21, -221 and -222, and their binding to the microprocessor complex. The structural fold of all the 4 RBDs is highly conserved, but there are significant differences in their sequences. RBD1-2 have been well studied in their mechanism and capability to form high-affinity RNA-binding complexes, but RBD 3-4 have not been explored to the same extent. We hypothesize that consecutive or selective combinations of RBDs in NCL might have distinct roles in RNA biology. NCL RBD1-2 domains are well documented in the literature for their high affinity for mRNAs and rRNAs. However, RNA-specificity on NCL-RBDs has yet to be elucidated. In this study, we used protein-antibody and protein-RNA docking analyses to identify potential differential binding affinities between multiple NCL modules (RDB1-2, RBD3-4 and RBD1-4) and either 4LB5 or miRNAs. In silico results were also evaluated by multiple biochemical assays (ELISA, Surface Plasmon Resonance, Pull-Down and Electrophoretic Mobility Shift Assays) using NCL Modules, 4LB5 and RNA molecules. Finally, competition assays were performed to assess ability of our scFv to impair miRNA binding to NCL. Our results indicate the importance of the NCL region targeted by 4LB5 in microRNA maturation. Functional studies will help us to better elucidate the biologic role of NCL RBDs, and their involvement in cancer pathogenesis. Citation Format: Valerio Embrione, Michael Scarpati, Zachary D. Uzzel, Rahimah Ahmad, Fortuna A. De Sury, Tyler J. Sheetz, Vincenzo Coppola, Shaneen M. Singh, Anjana D. Saxena, Carlo M. Croce, Dario Palmieri. A human scFv as a tool to understand the biogenesis of a subset of oncogenic microRNAs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 476.

Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes.

Molecular docking provides a computationally efficient way to predict the atomic structural details of protein–RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein–RNA docking, but their prediction performance for protein–RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein–RNA systems with different solvent models and interior dielectric constants (εin). For predicting the binding affinities, the predictions given by MM/GBSA based on the minimized structures in explicit solvent and the GBGBn1 model with εin = 2 yielded the highest correlation with the experimental data. Moreover, the MM/GBSA calculations based on the minimized structures in implicit solvent and the GBGBn1 model distinguished the near-native binding structures within the top 10 decoys for 117 out of the 148 protein–RNA systems (79.1%). This performance is better than all docking scoring functions studied here. Therefore, the MM/GBSA rescoring is an efficient way to improve the prediction capability of scoring functions for protein–RNA systems.

Single Molecule Translation Imaging of Local Protein Synthesis and RNA Docking Reveals the Regulation of Site Specific Axon Remodeling In Vivo

Single Molecule Translation Imaging of Local Protein Synthesis and RNA Docking Reveals the Regulation of Site Specific Axon Remodeling In Vivo

A combinatorial scoring function for protein-RNA docking.

Protein-RNA docking is still an open question. One of the main challenges is to develop an effective scoring function that can discriminate near-native structures from the incorrect ones. To solve the problem, we have constructed a knowledge-based residue-nucleotide pairwise potential with secondary structure information considered for nonribosomal protein-RNA docking. Here we developed a weighted combined scoring function RpveScore that consists of the pairwise potential and six physics-based energy terms. The weights were optimized using the multiple linear regression method by fitting the scoring function to L_rmsd for the bound docking decoys from Benchmark II. The scoring functions were tested on 35 unbound docking cases. The results show that the scoring function RpveScore including all terms performs best. Also RpveScore was compared with the statistical mechanics-based method derived potential ITScore-PR, and the united atom-based statistical potentials QUASI-RNP and DARS-RNP. The success rate of RpveScore is 71.6% for the top 1000 structures and the number of cases where a near-native structure is ranked in top 30 is 25 out of 35 cases. For 32 systems (91.4%), RpveScore can find the binding mode in top 5 that has no lower than 50% native interface residues on protein and nucleotides on RNA. Additionally, it was found that the long-range electrostatic attractive energy plays an important role in distinguishing near-native structures from the incorrect ones. This work can be helpful for the development of protein-RNA docking methods and for the understanding of protein-RNA interactions. RpveScore program is available to the public at http://life.bjut.edu.cn/kxyj/kycg/2017116/14845362285362368_1.html Proteins 2017; 85:741-752. © 2016 Wiley Periodicals, Inc.

Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches.

MicroRNA (miRNA) and Argonaute (AGO) protein together form the RNA-induced silencing complex (RISC) that plays an essential role in the regulation of gene expression. Elucidating the underlying mechanism of AGO-miRNA recognition is thus of great importance not only for the in-depth understanding of miRNA function but also for inspiring new drugs targeting miRNAs. In this chapter we introduce a combined computational approach of molecular dynamics (MD) simulations, Markov state models (MSMs), and protein-RNA docking to investigate AGO-miRNA recognition. Constructed from MD simulations, MSMs can elucidate the conformational dynamics of AGO at biologically relevant timescales. Protein-RNA docking can then efficiently identify the AGO conformations that are geometrically accessible to miRNA. Using our recent work on human AGO2 as an example, we explain the rationale and the workflow of our method in details. This combined approach holds great promise to complement experiments in unraveling the mechanisms of molecular recognition between large, flexible, and complex biomolecules.

A non-redundant protein-RNA docking benchmark version 2.0.

We present an updated version of the protein-RNA docking benchmark, which we first published four years back. The non-redundant protein-RNA docking benchmark version 2.0 consists of 126 test cases, a threefold increase in number compared to its previous version. The present version consists of 21 unbound-unbound cases, of which, in 12 cases, the unbound RNAs are taken from another complex. It also consists of 95 unbound-bound cases where only the protein is available in the unbound state. Besides, we introduce 10 new bound-unbound cases where only the RNA is found in the unbound state. Based on the degree of conformational change of the interface residues upon complex formation the benchmark is classified into 72 rigid-body cases, 25 semiflexible cases and 19 full flexible cases. It also covers a wide range of conformational flexibility including small side chain movement to large domain swapping in protein structures as well as flipping and restacking in RNA bases. This benchmark should provide the docking community with more test cases for evaluating rigid-body as well as flexible docking algorithms. Besides, it will also facilitate the development of new algorithms that require large number of training set. The protein-RNA docking benchmark version 2.0 can be freely downloaded from http://www.csb.iitkgp.ernet.in/applications/PRDBv2. Proteins 2017; 85:256-267. © 2016 Wiley Periodicals, Inc.

Structural and energy determinants in protein-RNA docking.

Deciphering the structural and energetic determinants of protein-RNA interactions harbors the potential to understand key cell processes at molecular level, such as gene expression and regulation. With this purpose, computational methods like docking aim to complement current biophysical and structural biology efforts. However, the few reported docking algorithms for protein-RNA interactions show limited predictive success rates, mainly due to incomplete sampling of the conformational space of both the protein and the RNA molecules, as well as to the difficulties of the scoring function in identifying the correct docking models. Here, we have tested the predictive value of a variety of knowledge-based and energetic scoring functions on a recently published protein-RNA docking benchmark and developed a scoring function able to efficiently discriminate docking decoys. We first performed docking calculations with the bound conformation, which allowed us to analyze the problem in optimal conditions. We found that geometry-based terms and electrostatics were the most important scoring terms, while binding propensities and desolvation were much less relevant for the scoring of protein-RNA models. This is in contrast with what we observed for protein-protein docking. The results also showed an interesting dependence of the predictive rates on the flexibility of the protein molecule, which arises from the observed higher positive charge of flexible interfaces and provides hints for future development of more efficient protein-RNA docking methods.

Binding Site Identification and Flexible Docking of Single Stranded RNA to Proteins Using a Fragment-Based Approach.

Protein-RNA docking is hampered by the high flexibility of RNA, and particularly single-stranded RNA (ssRNA). Yet, ssRNA regions typically carry the specificity of protein recognition. The lack of methodology for modeling such regions limits the accuracy of current protein-RNA docking methods. We developed a fragment-based approach to model protein-bound ssRNA, based on the structure of the protein and the sequence of the RNA, without any prior knowledge of the RNA binding site or the RNA structure. The conformational diversity of each fragment is sampled by an exhaustive RNA fragment library that was created from all the existing experimental structures of protein-ssRNA complexes. A systematic and detailed analysis of fragment-based ssRNA docking was performed which constitutes a proof-of-principle for the fragment-based approach. The method was tested on two 8-homo-nucleotide ssRNA-protein complexes and was able to identify the binding site on the protein within 10 Å. Moreover, a structure of each bound ssRNA could be generated in close agreement with the crystal structure with a mean deviation of ~1.5 Å except for a terminal nucleotide. This is the first time a bound ssRNA could be modeled from sequence with high precision.

Computational Prediction of RNA-Binding Proteins and Binding Sites.

Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%–8% of all proteins are RNA-binding proteins (RBPs). Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein–RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein–RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions.

Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement.

Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems.

Protein-RNA complexes and efficient automatic docking: expanding RosettaDock possibilities.

Protein-RNA complexes provide a wide range of essential functions in the cell. Their atomic experimental structure solving, despite essential to the understanding of these functions, is often difficult and expensive. Docking approaches that have been developed for proteins are often challenging to adapt for RNA because of its inherent flexibility and the structural data available being relatively scarce. In this study we adapted the RosettaDock protocol for protein-RNA complexes both at the nucleotide and atomic levels. Using a genetic algorithm-based strategy, and a non-redundant protein-RNA dataset, we derived a RosettaDock scoring scheme able not only to discriminate but also score efficiently docking decoys. The approach proved to be both efficient and robust for generating and identifying suitable structures when applied to two protein-RNA docking benchmarks in both bound and unbound settings. It also compares well to existing strategies. This is the first approach that currently offers a multi-level optimized scoring approach integrated in a full docking suite, leading the way to adaptive fully flexible strategies.

The dataset for protein–RNA binding affinity

We have developed a non-redundant protein-RNA binding benchmark dataset derived from the available protein-RNA structures in the Protein Database Bank. It consists of 73 complexes with measured binding affinity. The experimental conditions (pH and temperature) for binding affinity measurements are also listed in our dataset. This binding affinity dataset can be used to compare and develop protein-RNA scoring functions. The predicted binding free energy of the 73 complexes from three available scoring functions for protein-RNA docking has a low correlation with the binding Gibbs free energy calculated from Kd.

A nonredundant structure dataset for benchmarking protein‐RNA computational docking

Protein-RNA interactions play an important role in many biological processes. The ability to predict the molecular structures of protein-RNA complexes from docking would be valuable for understanding the underlying chemical mechanisms. We have developed a novel nonredundant benchmark dataset for protein-RNA docking and scoring. The diverse dataset of 72 targets consists of 52 unbound-unbound test complexes, and 20 unbound-bound test complexes. Here, unbound-unbound complexes refer to cases in which both binding partners of the cocrystallized complex are either in apo form or in a conformation taken from a different protein-RNA complex, whereas unbound-bound complexes are cases in which only one of the two binding partners has another experimentally determined conformation. The dataset is classified into three categories according to the interface root mean square deviation and the percentage of native contacts in the unbound structures: 49 easy, 16 medium, and 7 difficult targets. The bound and unbound cases of the benchmark dataset are expected to benefit the development and improvement of docking and scoring algorithms for the docking community. All the easy-to-view structures are freely available to the public at http://zoulab.dalton.missouri.edu/RNAbenchmark/.

A protein–RNA docking benchmark (I): Nonredundant cases

We have developed a nonredundant protein-RNA docking benchmark dataset, which is derived from the available bound and unbound structures in the Protein Data Bank involving polypeptide and nucleic acid chains. It consists of nine unbound-unbound cases where both the protein and the RNA are available in the free form. The other 36 cases are of unbound-bound type where only the protein is available in the free form. The conformational change upon complex formation is calculated by a distance matrix alignment method, and based on that, complexes are classified into rigid, semi-flexible, and full flexible. Although in the rigid body category, no significant conformational change accompanies complex formation, the fully flexible test cases show large domain movements, RNA base flips, etc. The benchmark covers four major groups of RNA, namely, t-RNA, ribosomal RNA, duplex RNA, and single-stranded RNA. We find that RNA is generally more flexible than the protein in the complexes, and the interface region is as flexible as the molecule as a whole. The structural diversity of the complexes in the benchmark set should provide a common ground for the development and comparison of the protein-RNA docking methods. The benchmark can be freely downloaded from the internet.

A protein‐RNA docking benchmark (II): Extended set from experimental and homology modeling data

We present here an extended protein-RNA docking benchmark composed of 71 test cases in which the coordinates of the interacting protein and RNA molecules are available from experimental structures, plus an additional set of 35 cases in which at least one of the interacting subunits is modeled by homology. All cases in the experimental set have available unbound protein structure, and include five cases with available unbound RNA structure, four cases with a pseudo-unbound RNA structure, and 62 cases with the bound RNA form. The additional set of modeling cases comprises five unbound-model, eight model-unbound, 19 model-bound, and three model-model protein-RNA cases. The benchmark covers all major functional categories and contains cases with different degrees of difficulty for docking, as far as protein and RNA flexibility is concerned. The main objective of this benchmark is to foster the development of protein-RNA docking algorithms and to contribute to the better understanding and prediction of protein-RNA interactions. The benchmark is freely available at http://life.bsc.es/pid/protein-rna-benchmark.