Efficient mapping of RNA-binding residues in RNA-binding proteins using local sequence features of binding site residues in protein-RNA complexes.

Protein-RNA interactions play vital roles in plethora of biological processes such as regulation of gene expression, protein synthesis, mRNA processing and biogenesis. Identification of RNA-binding residues (RBRs) in proteins is essential to understand RNA-mediated protein functioning, to perform site-directed mutagenesis and to develop novel targeted drug therapies. Moreover, the extensive gap between sequence and structural data restricts the identification of binding sites in unsolved structures. However, efficient use of computational methods demanding only sequence to identify binding residues can bridge this huge sequence-structure gap. In this study, we have extensively studied protein-RNA interface in known RNA-binding proteins (RBPs). We find that the interface is highly enriched in basic and polar residues with Gly being the most common interface neighbor. We investigated several amino acid features and developed a method to predict putative RBRs from amino acid sequence. We have implemented balanced random forest (BRF) classifier with local residue features of protein sequences for prediction. With 5-fold cross-validations, the sequence pattern derived dipeptide composition based BRF model (DCP-BRF) resulted in an accuracy of 87.9%, specificity of 88.8%, sensitivity of 82.2%, Mathew's correlation coefficient of 0.60 and AUC of 0.93, performing better than few existing methods. We further validated our prediction model on known human RBPs through RBR prediction and could map ~54% of them. Further, knowledge of binding site preferences obtained from computational predictions combined with experimental validations of potential RNA binding sites can enhance our understanding of protein-RNA interactions. This may serve to accelerate investigations on functional roles of many novel RBPs.

Modular architecture and functional annotation of human RNA-binding proteins containing RNA recognition motif.

RNA-binding proteins (RBPs) are structurally and functionally diverse macromolecules with significant involvement in several post-transcriptional gene regulatory processes and human diseases. RNA recognition motif (RRM) is one of the most abundant RNA-binding domains in human RBPs. The unique modular architecture of each RBP containing RRM is crucial for its diverse target recognition and function. Genome-wide study of these structurally conserved and functionally diverse domains can enhance our understanding of their functional implications. In this study, modular architecture of RRM containing RBPs in human proteome is identified and systematically analysed. We observe that 30% of human RBPs with RNA-binding function contain RRM in single or multiple repeats or with other domains with maximum of six repeats. Zinc-fingers are the most frequently co-occurring domain partner of RRMs. Human RRM containing RBPs mostly belong to RNA metabolism class of proteins and are significantly enriched in two functional pathways including spliceosome and mRNA surveillance. Various human diseases are associated with 18% of the RRM containing RBPs. Single RRM containing RBPs are highly enriched in disorder regions. Gene ontology (GO) molecular functions including poly(A), poly(U) and miRNA binding are highly depleted in RBPs with single RRM, indicating the significance of modular nature of RRMs in specific function. The current study reports all the possible domain architectures of RRM containing human RBPs and their functional enrichment. The idea of domain architecture, and how they confer specificity and new functionalities to RBPs, can help in re-designing of modular RRM containing RBPs with re-engineered function.

Molecular insights into binding dynamics of tandem RNA recognition motifs (tRRMs) of human antigen R (HuR) with mRNA and the effect of point mutations in impaired HuR-mRNA recognition.

Human antigen R (HuR) is a key regulatory protein with prominent roles in RNA metabolism and post-transcriptional gene regulation. Many studies have shown the involvement of HuR in plethora of human diseases, which are often manifestations of impaired HuR-RNA interactions. However, the inherent complexities of highly flexible protein-RNA interactions have limited our understanding of the structural basis of HuR-RNA recognition. In this study, we dissect the underlying molecular mechanism of interaction between N-terminal tandem RNA-recognition motifs (tRRMs) of HuR and mRNA using molecular dynamics simulation. We have also explored the effect of point mutations (T90A, R97A and R136A) of three reported critical residues in HuR-mRNA binding specificity. Our findings show that N-terminal tRRMs exhibit conformational stability upon RNA binding. We further show that R136A and R97A mutants significantly lose their binding affinity owing to the loss of critical interactions with mRNA. This may be attributed to the larger domain rearrangements in the mutant complexes, especially the ß2ß3 loops in both the tRRMs, leading to unfavourable conformations and loss of binding affinity. We have identified critical binding residues in tRRMs of HuR, contributing favourable binding energy in mRNA recognition. This study contributes significantly to understand the molecular mechanism of RNA recognition by tandem RRMs and provides a platform to modulate binding affinities through mutations. This may further guide in future structure-based drug-therapies targeting impaired HuR-RNA interactions.Communicated by Ramaswamy H. Sarma.

A comparative analysis of machine learning classifiers for predicting protein-binding nucleotides in RNA sequences.

RNA-protein interactions play vital roles in driving the cellular machineries. Despite significant involvement in several biological processes, the underlying molecular mechanism of RNA-protein interactions is still elusive. This may be due to the experimental difficulties in solving co-crystallized RNA-protein complexes. Inherent flexibility of RNA molecules to adopt different conformations makes them functionally diverse. Their interactions with protein have implications in RNA disease biology. Thus, study of binding interfaces can provide a mechanistic insight of the molecular functioning and aberrations caused due to altered interactions. Moreover, high-throughput sequencing technologies have generated huge sequence data compared to available structural data of RNA-protein complexes. In such a scenario, efficient computational algorithms are required for identification of protein-binding interfaces of RNA in the absence of known structures. We have investigated several machine learning classifiers and various features derived from nucleotide sequences to identify protein-binding nucleotides in RNA. We achieve best performance with nucleotide-triplet and nucleotide-quartet feature-based random forest models. An overall accuracy of 84.8%, sensitivity of 83.2%, specificity of 86.1%, MCC of 0.70 and AUC of 0.93 is achieved. We have further implemented the developed models in a user-friendly webserver "Nucpred", which is freely accessible at "http://www.csb.iitkgp.ac.in/applications/Nucpred/index".

Impaired nuclear transport induced by juvenile ALS causing P525L mutation in NLS domain of FUS: A molecular mechanistic study.

Amyotrophic lateral sclerosis (ALS) and fronto-temporal lobar degeneration (FTLD) are progressive neurological disorders affecting motor neurons. Cellular aggregates of fused in sarcoma (FUS) protein are found in cytoplasm of ALS and FTLD patients. Nuclear localisation signal (NLS) domain of FUS binds to Karyopherin ß2 (Kapß2), which drives nuclear transport of FUS from cytoplasm. Several pathogenic mutations are reported in FUS NLS, which are associated with its impaired nuclear transport and cytoplasmic mis-localisation. P525L mutation in NLS is most commonly found in cases of juvenile ALS (jALS), which affects individuals below 25 years of age. jALS progresses aggressively causing death within a year of its onset. This study elucidates the molecular mechanism behind jALS-causing P525L mutation hindering nuclear transport of FUS. We perform multiple molecular dynamics simulations in aqueous and hydrophobic solvent to understand the effect of the mutation at molecular level. Dynamics of Kapß2-FUS complex is better captured in hydrophobic solvent compared to aqueous solvent. P525 and Y526 (PY-motif) of NLS exhibit fine-tuned stereochemical arrangement, which is essential for optimum Kapß2 binding. P525L causes loss of several native contacts at interface leading to weaker binding, which promotes self-aggregation of FUS in cytoplasm. Native complex samples closed conformation, while mutant complex exhibits open conformation exposing hydrophilic residues of Kapß2 to hydrophobic solvent. Mutant complex also fails to exhibit spring-like motion essential for its transport through nuclear pore complex. This study provides a mechanistic insight of binding affinity between NLS and Kapß2 that inhibits self-aggregation of FUS preventing the disease condition.

NCodR: A multi-class support vector machine classification to distinguish non-coding RNAs in Viridiplantae.

Non-coding RNAs (ncRNAs) are major players in the regulation of gene expression. This study analyses seven classes of ncRNAs in plants using sequence and secondary structure-based RNA folding measures. We observe distinct regions in the distribution of AU content along with overlapping regions for different ncRNA classes. Additionally, we find similar averages for minimum folding energy index across various ncRNAs classes except for pre-miRNAs and lncRNAs. Various RNA folding measures show similar trends among the different ncRNA classes except for pre-miRNAs and lncRNAs. We observe different k-mer repeat signatures of length three among various ncRNA classes. However, in pre-miRs and lncRNAs, a diffuse pattern of k-mers is observed. Using these attributes, we train eight different classifiers to discriminate various ncRNA classes in plants. Support vector machines employing radial basis function show the highest accuracy (average F1 of ~96%) in discriminating ncRNAs, and the classifier is implemented as a web server, NCodR.

Conservation and coevolution determine evolvability of different classes of disordered residues in human intrinsically disordered proteins.

Structure, function, and evolution are interdependent properties of proteins. Diversity of protein functions arising from structural variations is a potential driving force behind protein evolvability. Intrinsically disordered proteins or regions (IDPs or IDRs) lack well-defined structure under normal physiological conditions, yet, they are highly functional. Increased occurrence of IDPs in eukaryotes compared to prokaryotes indicates strong correlation of protein evolution and disorderedness. IDPs generally have higher evolution rate compared to globular proteins. Structural pliability allows IDPs to accommodate multiple mutations without affecting their functional potential. Nevertheless, how evolutionary signals vary between different classes of disordered residues (DRs) in IDPs is poorly understood. This study addresses variation of evolutionary behavior in terms of residue conservation and intra-protein coevolution among structural and functional classes of DRs in IDPs. Analyses are performed on 579 human IDPs, which are classified based on length of IDRs, interacting partners and functional classes. We find short IDRs are less conserved than long IDRs or full IDPs. Functional classes which require flexibility and specificity to perform their activity comparatively evolve slower than others. Disorder promoting amino acids evolve faster than order promoting amino acids. Pro, Gly, Ile, and Phe have unique coevolving nature which further emphasizes on their roles in IDPs. This study sheds light on evolutionary footprints in different classes of DRs from human IDPs and enhances our understanding of the structural and functional potential of IDPs.

Genome-wide prediction of cauliflower miRNAs and lncRNAs and their roles in post-transcriptional gene regulation.

MAIN CONCLUSION: We have predicted miRNAs, their targets and lncRNAs from the genome of Brassica oleracea along with their functional annotation. Selected miRNAs and their targets are experimentally validated. Roles of these non-coding RNAs in post-transcriptional gene regulation are also deciphered. Cauliflower (Brassica oleracea var. Botrytis) is an important vegetable crop for its dietary and medicinal values with rich source of vitamins, dietary fibers, flavonoids and antioxidants. MicroRNAs (miRNAs) are small non-coding RNAs (ncRNAs), which regulate gene expression by inhibiting translation or by degrading messenger RNAs (mRNAs). On the other hand, long non-coding RNAs (lncRNAs) are responsible for the up regulation and the down regulation of transcription. Although the genome of cauliflower is reported, yet the roles of these ncRNAs in post-transcriptional gene regulation (PTGR) remain elusive. In this study, we have computationally predicted 355 miRNAs, of which 280 miRNAs are novel compared to miRBase 22.1. All the predicted miRNAs belong to 121 different families. We have also identified 934 targets of 125 miRNAs along with their functional annotation. These targets are further classified into biological processes, molecular functions and cellular components. Moreover, we have predicted 634 lncRNAs, of which 61 are targeted by 30 novel miRNAs. Randomly chosen 10 miRNAs and 10 lncRNAs are experimentally validated. Five miRNA targets including squamosa promoter-binding-like protein 9, homeobox-leucine zipper protein HDG12-like, NAC domain-containing protein 100, CUP-SHAPED COTYLEDON 1 and kinesin-like protein NACK2 of four miRNAs including bol-miR156a, bol-miR162a, bol-miR164d and bol-miR2673 are also experimentally validated. We have built network models of interactions between miRNAs and their target mRNAs, as well as between miRNAs and lncRNAs. Our findings enhance the knowledge of non-coding genome of cauliflower and their roles in PTGR, and might play important roles in improving agronomic traits of this economically important crop.

Unusual RNA binding of FUS RRM studied by molecular dynamics simulation and enhanced sampling method.

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobe degeneration (FTLD) are two inter-related intractable diseases of motor neuron degeneration. Fused in sarcoma (FUS) is found in cytoplasmic accumulation of ALS and FTLD patients, which readily link the protein with the diseases. The RNA recognition motif (RRM) of FUS has the canonical α-ß folds along with an unusual lysine-rich loop (KK-loop) between α1 and ß2. This KK-loop is highly conserved among FET family proteins. Another contrasting feature of FUS RRM is the absence of critical binding residues, which are otherwise highly conserved in canonical RRMs. These residues in FUS RRM are Thr286, Glu336, Thr338, and Ser367, which are substitutions of lysine, phenylalanine, phenylalanine, and lysine, respectively, in other RRMs. Considering the importance of FUS in RNA regulation and metabolism, and its implication in ALS and FTLD, it is important to elucidate the underlying molecular mechanism of RNA recognition. In this study, we have performed molecular dynamics simulation with enhanced sampling to understand the conformational dynamics of noncanonical FUS RRM and its binding with RNA. We studied two sets of mutations: one with alanine mutation of KK-loop and another with KK-loop mutations along with critical binding residues mutated back to their canonical form. We find that concerted movement of KK-loop and loop between ß2 and ß3 facilitates the folding of the partner RNA, indicating an induced-fit mechanism of RNA binding. Flexibility of the RRM is highly restricted upon mutating the lysine residues of the KK-loop, resulting in weaker binding with the RNA. Our results also suggest that absence of the canonical residues in FUS RRM along with the KK-loop is equally important in regulating its binding dynamics. This study provides a significant structural insight into the binding of FUS RRM with its cognate RNA, which may further help in designing potential drugs targeting noncanonical RNA recognition.

DSS1 allosterically regulates the conformation of the tower domain of BRCA2 that has dsDNA binding specificity for homologous recombination.

DSS1 is an evolutionary conserved, small intrinsically disordered protein that regulates various cellular functions. Although several studies have elucidated the role of DSS1 in stabilizing BRCA2 and its importance in homologous recombination repair (HRR), yet the structural mechanism behind the stability and HRR remains elusive. In this study, using molecular dynamics simulation we show that DSS1 stabilizes linearly arranged DNA/DSS1 binding domains of BRCA2 with many native contacts. These contacts are absent in the complexes with two missense DSS1 mutants associated with germline breast cancer and somatic mouth carcinoma. Most importantly, our protein energy-based network models show DSS1 allosterically regulates the conformation of the distant tower domain of BRCA2 that has dsDNA binding specificity for HRR. We further postulate that the unique conformation of the tower domain with kinked-helices might be responsible for DNA strand invasion and initiation of HRR. Induced conformation of the tower domain by the kinked-helices is absent in the unbound BRCA2, as well as in the two mutant DSS1-BRCA2 complexes. This suggests that DSS1 allosterically regulates the tower domain conformations of BRCA2 that affects dsDNA binding, essential for HRR. Our results add a new dimension to the function of DSS1 and its role in regulating HRR.

Do sequence neighbours of intrinsically disordered regions promote structural flexibility in intrinsically disordered proteins?

Intrinsically disordered proteins (IDPs) are crucial players in various cellular activities. Several experimental and computational analyses have been conducted to study structural pliability and functional potential of IDPs. In spite of active research in past few decades, what induces structural disorder in IDPs and how is still elusive. Many studies testify that sequential and spatial neighbours often play important roles in determining structural and functional behaviour of proteins. Considering this fact, we assessed sequence neighbours of intrinsically disordered regions (IDRs) to understand if they have any role to play in inducing structural flexibility in IDPs. Our analysis includes 97% eukaryotic IDPs and 3% from bacteria and viruses. Physicochemical and structural parameters including amino acid propensity, hydrophobicity, secondary structure propensity, relative solvent accessibility, B-factor and atomic packing density are used to characterise the neighbouring residues of IDRs (NRIs). We show that NRIs exhibit a unique nature, which makes them stand out from both ordered and disordered residues. They show correlative occurrences of residue pairs like Ser-Thr and Gln-Asn, indicating their tendency to avoid strong biases of order or disorder promoting amino acids. We also find differential preferences of amino acids between N- and C-terminal neighbours, which might indicate a plausible directional effect on the dynamics of adjacent IDRs. We designed an efficient prediction tool using Random Forest to distinguish the NRIs from the ordered residues. Our findings will contribute to understand the behaviour of IDPs, and may provide potential lead in deciphering the role of IDRs in protein folding and assembly.

A structure-based model for the prediction of protein-RNA binding affinity.

Protein-RNA recognition is highly affinity-driven and regulates a wide array of cellular functions. In this study, we have curated a binding affinity data set of 40 protein-RNA complexes, for which at least one unbound partner is available in the docking benchmark. The data set covers a wide affinity range of eight orders of magnitude as well as four different structural classes. On average, we find the complexes with single-stranded RNA have the highest affinity, whereas the complexes with the duplex RNA have the lowest. Nevertheless, free energy gain upon binding is the highest for the complexes with ribosomal proteins and the lowest for the complexes with tRNA with an average of -5.7 cal/mol/Å2 in the entire data set. We train regression models to predict the binding affinity from the structural and physicochemical parameters of protein-RNA interfaces. The best fit model with the lowest maximum error is provided with three interface parameters: relative hydrophobicity, conformational change upon binding and relative hydration pattern. This model has been used for predicting the binding affinity on a test data set, generated using mutated structures of yeast aspartyl-tRNA synthetase, for which experimentally determined ΔG values of 40 mutations are available. The predicted ΔGempirical values highly correlate with the experimental observations. The data set provided in this study should be useful for further development of the binding affinity prediction methods. Moreover, the model developed in this study enhances our understanding on the structural basis of protein-RNA binding affinity and provides a platform to engineer protein-RNA interfaces with desired affinity.

Residue conservation elucidates the evolution of r-proteins in ribosomal assembly and function.

Ribosomes are the translational machineries having two unequal subunits, small subunit (SSU) and large subunit (LSU) across all the domains of life. Origin and evolution of ribosome are encoded in its structure, and the core of the ribosome is highly conserved. Here, we have used Shannon entropy to analyze the evolution of ribosomal proteins (r-proteins) across the three domains of life. Moreover, we have analyzed the residue conservation at protein-protein (PP) and protein-RNA (PR) interfaces in SSU and LSU. Furthermore, we have studied the evolution of early, intermediate and late binding r-proteins. We show that the r-proteins of Thermus thermophilus are better conserved during the evolution. Furthermore, we find the late binders are better conserved than the early and the intermediate binders. The residues at the interior of the r-proteins are the most conserved followed by those at the interface and the solvent accessible surface. Additionally, we show that the residues at the PP interfaces are better conserved than those at the PR interfaces. However, between PR and PP interfaces, the multi-interface residues at the former are better conserved than those at the latter ones. Our findings may provide insights into the evolution of r-proteins in ribosomal assembly and function.

Dissecting macromolecular recognition sites in ribosome: implication to its self-assembly.

Interactions between macromolecules play a crucial role in ribosome assembly that follows a highly coordinated process involving RNA folding and binding of ribosomal proteins (r-proteins). Although extensive studies have been carried out to understand macromolecular interactions in ribosomes, most of them are confined to either large or small ribosomal-subunit of few species. A comparative analysis of macromolecular interactions across different domains is still missing. We have analyzed the structural and physicochemical properties of protein-protein (PP), protein-RNA (PR) and RNA-RNA (RR) interfaces in small and large subunits of ribosomes, as well as in between the two subunits. Additionally, we have also developed Random Forest (RF) classifier to catalog the r-proteins. We find significant differences as well as similarities in macromolecular recognition sites between ribosomal assemblies of prokaryotes and eukaryotes. PR interfaces are substantially larger and have more ionic interactions than PP and RR interfaces in both prokaryotes and eukaryotes. PP, PR and RR interfaces in eukaryotes are well packed compared to those in prokaryotes. However, the packing density between the large and the small subunit interfaces in the entire assembly is strikingly low in both prokaryotes and eukaryotes, indicating the periodic association and dissociation of the two subunits during the translation. The structural and physicochemical properties of PR interfaces are used to predict the r-proteins in the assembly pathway into early, intermediate and late binders using RF classifier with an accuracy of 80%. The results provide new insights into the classification of r-proteins in the assembly pathway.

Dissecting water binding sites at protein-protein interfaces: a lesson from the atomic structures in the Protein Data Bank.

We dissect the protein-protein interfaces into water preservation (WP), water hydration (WH) and water dehydration (WD) sites by comparing the water-mediated hydrogen bonds (H-bond) in the bound and unbound states of the interacting subunits. Upon subunit complexation, if a H-bond between an interface water and a protein polar group is retained, we assign it as WP site; if it is lost, we assign it as WD site and if a new H-bond is created, we assign it as WH site. We find that the density of WD sites is highest followed by WH and WP sites except in antigen and (or) antibody complexes, where the density of WH sites is highest followed by WD and WP sites. Furthermore, we find that WP sites are the most conserved followed by WD and WH sites in all class of complexes except in antigen and (or) antibody complexes, where WD sites are the most conserved followed by WH and WP sites. A significant number of WP and WH sites are involved in water bridges that stabilize the subunit interactions. At WH sites, the residues involved in water bridges are significantly better conserved than the other residues. However, no such difference is observed at WP sites. Interestingly, WD sites are generally replaced with direct H-bonds upon subunit complexation. Significantly, we observe many water-mediated H-bonds remain preserved in spite of large conformational changes upon subunit complexation. These findings have implications in predicting and engineering water binding sites at protein-protein interfaces.

Identification and characterization of differentially expressed Phaseolus vulgaris miRNAs and their targets during mungbean yellow mosaic India virus infection reveals new insight into Phaseolus-MYMIV interaction.

Phaseolus vulgaris is an economically important legume in tropical and subtropical regions of Asia, Africa, Latin-America and parts of USA and Europe. However, its production gets severely affected by mungbean yellow mosaic India virus (MYMIV). We aim to identify and characterize differentially expressed miRNAs during MYMIV-infection in P. vulgaris. A total of 422 miRNAs are identified of which 292 are expressed in both MYMIV-treated and mock-treated samples, 109 are expressed only in MYMIV-treated and 21 are expressed only in mock-treated samples. Selected up- and down-regulated miRNAs are validated by RT-qPCR. 3367 target ORFs are identified for 270 miRNAs. Selected targets are validated by 5' RLM-RACE. Differentially expressed miRNAs regulate transcription factors and are involved in improving stress tolerance to MYMIV. These findings will provide an insight into the role of miRNAs during MYMIV infection in P. vulgaris in particular and during any biotic stress conditions in Leguminosae family in general.

An account of solvent accessibility in protein-RNA recognition.

Protein-RNA recognition often induces conformational changes in binding partners. Consequently, the solvent accessible surface area (SASA) buried in contact estimated from the co-crystal structures may differ from that calculated using their unbound forms. To evaluate the change in accessibility upon binding, we compare SASA of 126 protein-RNA complexes between bound and unbound forms. We observe, in majority of cases the interface of both the binding partners gain accessibility upon binding, which is often associated with either large domain movements or secondary structural transitions in RNA-binding proteins (RBPs), and binding-induced conformational changes in RNAs. At the non-interface region, majority of RNAs lose accessibility upon binding, however, no such preference is observed for RBPs. Side chains of RBPs have major contribution in change in accessibility. In case of flexible binding, we find a moderate correlation between the binding free energy and change in accessibility at the interface. Finally, we introduce a parameter, the ratio of gain to loss of accessibility upon binding, which can be used to identify the native solution among the flexible docking models. Our findings provide fundamental insights into the relationship between flexibility and solvent accessibility, and advance our understanding on binding induced folding in protein-RNA recognition.

Genome-wide identification of miRNAs and lncRNAs in Cajanus cajan.

BACKGROUND: Non-coding RNAs (ncRNAs) are important players in the post transcriptional regulation of gene expression (PTGR). On one hand, microRNAs (miRNAs) are an abundant class of small ncRNAs (~22nt long) that negatively regulate gene expression at the levels of messenger RNAs stability and translation inhibition, on the other hand, long ncRNAs (lncRNAs) are a large and diverse class of transcribed non-protein coding RNA molecules (> 200nt) that play both up-regulatory as well as down-regulatory roles at the transcriptional level. Cajanus cajan, a leguminosae pulse crop grown in tropical and subtropical areas of the world, is a source of high value protein to vegetarians or very poor populations globally. Hence, genome-wide identification of miRNAs and lncRNAs in C. cajan is extremely important to understand their role in PTGR with a possible implication to generate improve variety of crops. RESULTS: We have identified 616 mature miRNAs in C. cajan belonging to 118 families, of which 578 are novel and not reported in MirBase21. A total of 1373 target sequences were identified for 180 miRNAs. Of these, 298 targets were characterized at the protein level. Besides, we have also predicted 3919 lncRNAs. Additionally, we have identified 87 of the predicted lncRNAs to be targeted by 66 miRNAs. CONCLUSIONS: miRNA and lncRNAs in plants are known to control a variety of traits including yield, quality and stress tolerance. Owing to its agricultural importance and medicinal value, the identified miRNA, lncRNA and their targets in C. cajan may be useful for genome editing to improve better quality crop. A thorough understanding of ncRNA-based cellular regulatory networks will aid in the improvement of C. cajan agricultural traits.

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.

A structural perspective of RNA recognition by intrinsically disordered proteins.

Protein-RNA recognition is essential for gene expression and its regulation, which is indispensable for the survival of the living organism at one hand, on the other hand, misregulation of this recognition may lead to their extinction. Polymorphic conformation of both the interacting partners is a characteristic feature of such molecular recognition that promotes the assembly. Many RNA binding proteins (RBP) or regions in them are found to be intrinsically disordered, and this property helps them to play a central role in the regulatory processes. Sequence composition and the length of the flexible linkers between RNA binding domains in RBPs are crucial in making significant contacts with its partner RNA. Polymorphic conformations of RBPs can provide thermodynamic advantage to its binding partner while acting as a chaperone. Prolonged extensions of the disordered regions in RBPs also contribute to the stability of the large cellular machines including ribosome and viral assemblies. The involvement of these disordered regions in most of the significant cellular processes makes RBPs highly associated with various human diseases that arise due to their misregulation.