Predicting nucleic acid binding interfaces from structural models of proteins

Longitudinal Genotype-Phenotype (Vineland Questionnaire) Characterization of 15 ADNP Syndrome Cases Highlights Mutated Protein Length and Structural Characteristics Correlation with Communicative Abilities Accentuated in Males

Activity-dependent neuroprotective protein (ADNP) is essential for neurodevelopment and de novo mutations in ADNP cause the ADNP syndrome. From brain pathologies point of view, tauopathy has been demonstrated at a young age, implying stunted development coupled with early/accelerated neurodegeneration. Given potential genotype-phenotype differences and age-dependency, we have assessed here a cohort of 15 individuals (1-27-year-old), using 1-3 longitudinal parent (caretaker) interview/s (Vineland 3 questionnaire) over several years. Our results indicated developmental delays, or even developmental arrests, coupled with potential spurts of development at early ages. Severe outcomes correlated with the truncating high impact mutation, in other words, the remaining mutated protein length as well as with the tested individual age, corroborating the hypothesis of developmental delays coupled with accelerated aging. A significant correlation was noted between mutated protein length and communication, implying a high impact of ADNP on communicative skills. Additionally, correlations were discovered between the two previously described epi-genetic signatures in ADNP emphasizing aberrant acquisition of motor behaviors, with truncating mutations around the nuclear localization signal being mostly affected. Finally, all individuals seem to acquire an age equivalent of 1-6 years, requiring disease modification treatment, such as the ADNP-derived drug candidate, NAP (davunetide), which has recently shown efficacy in women suffering from the neurodegenerative disorder, progressive supranuclear palsy (PSP), a late-onset tauopathy.

Ebola virus protein VP40 binding to Sec24c for transport to the plasma membrane

Outbreaks of the Ebola virus (EBOV) continue to occur and while a vaccine and treatment are now available, there remains a dearth of options for those who become sick with EBOV disease. An understanding at the atomic and molecular level of the various steps in the EBOV replication cycle can provide molecular targets for disrupting the virus. An important step in the EBOV replication cycle is the transport of EBOV structural matrix VP40 protein molecules to the plasma membrane inner leaflet, which involves VP40 binding to the host cell's Sec24c protein. Though some VP40 residues involved in the binding are known, the molecular details of VP40-Sec24c binding are not known. We use various molecular computational techniques to investigate the molecular details of how EBOV VP40 binds with the Sec24c complex of the ESCRT-I pathway. We employed different docking programs to identify the VP40-binding site on Sec24c and then performed molecular dynamics simulations to determine the atomic details and binding interactions of the complex. We also investigated how the inter-protein interactions of the complex are affected upon mutations of VP40 amino acids in the Sec24c-binding region. Our results provide a molecular basis for understanding previous coimmunoprecipitation experimental studies. In addition, we found that VP40 can bind to a site on Sec24c that can also bind Sec23 and suggests that VP40 may use the COPII transport mechanism in a manner that may not need the Sec23 protein in order for VP40 to be transported to the plasma membrane.

PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees

Protein-RNA interactions play essential roles in many biological aspects. Quantifying the binding affinity of protein-RNA complexes is helpful to the understanding of protein-RNA recognition mechanisms and identification of strong binding partners. Due to experimentally measured protein-RNA binding affinity data available is still limited to date, there is a pressing demand for accurate and reliable computational approaches. In this paper, we propose a computational approach, PredPRBA, which can effectively predict protein-RNA binding affinity using gradient boosted regression trees. We build a dataset of protein-RNA binding affinity that includes 103 protein-RNA complex structures manually collected from related literature. Then, we generate 37 kinds of sequence and structural features and explore the relationship between the features and protein-RNA binding affinity. We find that the binding affinity mainly depends on the structure of RNA molecules. According to the type of RNA associated with proteins composed of the protein-RNA complex, we split the 103 protein-RNA complexes into six categories. For each category, we build a gradient boosted regression tree (GBRT) model based on the generated features. We perform a comprehensive evaluation for the proposed method on the binding affinity dataset using leave-one-out cross-validation. We show that PredPRBA achieves correlations ranging from 0.723 to 0.897 among six categories, which is significantly better than other typical regression methods and the pioneer protein-RNA binding affinity predictor SPOT-Seq-RNA. In addition, a user-friendly web server has been developed to predict the binding affinity of protein-RNA complexes. The PredPRBA webserver is freely available at http://PredPRBA.denglab.org/.

BindUP: a web server for non-homology-based prediction of DNA and RNA binding proteins

Gene expression is a multi-step process involving many layers of regulation. The main regulators of the pathway are DNA and RNA binding proteins. While over the years, a large number of DNA and RNA binding proteins have been identified and extensively studied, it is still expected that many other proteins, some with yet another known function, are awaiting to be discovered. Here we present a new web server, BindUP, freely accessible through the website http://bindup.technion.ac.il/, for predicting DNA and RNA binding proteins using a non-homology-based approach. Our method is based on the electrostatic features of the protein surface and other general properties of the protein. BindUP predicts nucleic acid binding function given the proteins three-dimensional structure or a structural model. Additionally, BindUP provides information on the largest electrostatic surface patches, visualized on the server. The server was tested on several datasets of DNA and RNA binding proteins, including proteins which do not possess DNA or RNA binding domains and have no similarity to known nucleic acid binding proteins, achieving very high accuracy. BindUP is applicable in either single or batch modes and can be applied for testing hundreds of proteins simultaneously in a highly efficient manner.

SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues

Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinder^F and a template predictor SNBRFinder^T. SNBRFinder^F was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinder^T was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinder^F was clearly superior to the commonly used sequence profile-based predictor and SNBRFinder^T can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

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.

eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands

Molecular structures and functions of the majority of proteins across different species are yet to be identified. Much needed functional annotation of these gene products often benefits from the knowledge of protein-ligand interactions. Towards this goal, we developed eFindSite, an improved version of FINDSITE, designed to more efficiently identify ligand binding sites and residues using only weakly homologous templates. It employs a collection of effective algorithms, including highly sensitive meta-threading approaches, improved clustering techniques, advanced machine learning methods and reliable confidence estimation systems. Depending on the quality of target protein structures, eFindSite outperforms geometric pocket detection algorithms by 15-40 % in binding site detection and by 5-35 % in binding residue prediction. Moreover, compared to FINDSITE, it identifies 14 % more binding residues in the most difficult cases. When multiple putative binding pockets are identified, the ranking accuracy is 75-78 %, which can be further improved by 3-4 % by including auxiliary information on binding ligands extracted from biomedical literature. As a first across-genome application, we describe structure modeling and binding site prediction for the entire proteome of Escherichia coli. Carefully calibrated confidence estimates strongly indicate that highly reliable ligand binding predictions are made for the majority of gene products, thus eFindSite holds a significant promise for large-scale genome annotation and drug development projects. eFindSite is freely available to the academic community at http://www.brylinski.org/efindsite .

Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters

We present a set of four parameters that in combination can predict DNA-binding residues on protein structures to a high degree of accuracy. These are the number of evolutionary conserved residues (N_cons) and their spatial clustering (ρ_e), hydrogen bond donor capability (D_p) and residue propensity (R_p). We first used these parameters to characterize 130 interfaces in a set of 126 DNA-binding proteins (DBPs). The applicability of these parameters both individually and in combination, to distinguish the true binding region from the rest of the protein surface was then analyzed. R_p shows the best performance identifying the true interface with the top rank in 83% cases. Importantly, we also used the unbound-bound test cases of the protein–DNA docking benchmark to test the efficacy of our method. When applied to the unbound form of the DBPs, R_p can distinguish 86% cases. Finally, we have applied the SVM approach for recognizing the interface region using the above parameters along with the individual amino acid composition as attributes. The accuracy of prediction is 90.5% for the bound structures and 93.6% for the unbound form of the proteins.