Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins

Background  

Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity.     

Exploiting sequence and structure homologs to identify protein-protein binding sites

A rapid increase in the number of experimentally derived three-dimensional structures provides an opportunity to better understand and subsequently predict protein-protein interactions. In this study, structurally conserved residues were derived from multiple structure alignments of the individual components of known complexes and the assigned conservation score was weighted based on the crystallographic B factor to account for the structural flexibility that will result in a poor alignment. Sequence profile and accessible surface area information was then combined with the conservation score to predict protein-protein binding sites using a Support Vector Machine (SVM). The incorporation of the conservation score significantly improved the performance of the SVM. About 52% of the binding sites were precisely predicted (greater than 70% of the residues in the site were identified); 77% of the binding sites were correctly predicted (greater than 50% of the residues in the site were identified), and 21% of the binding sites were partially covered by the predicted residues (some residues were identified). The results support the hypothesis that in many cases protein interfaces require some residues to provide rigidity to minimize the entropic cost upon complex formation.     

Role of structural water for prediction of cation binding sites in apoproteins

Structures of many metal-binding proteins are often obtained without structural cations in their apoprotein forms. Missing cation coordinates are usually updated from structural templates constructed from many holoprotein structures. Such templates usually do not include structural water, the important contributor to the ion binding energy. Structural templates are also inconvenient for taking into account structural modifications around the binding site at apo-/holo- transitions. An approach based upon statistical potentials readily takes into account structural modifications associated with binding as well as contribution of structural water molecules. Here, we construct a set of statistical potentials for Mg²⁺, Ca²⁺, and Zn²⁺ contacting with protein atoms of a different type or structural water oxygens. Each type of the cations tends to form tight contacts with protein atoms of specific types. Structural water contributes relatively more into the binding pseudo-energy of Mg²⁺ and Ca²⁺ than of Zn²⁺. We have developed PIONCA (Protein-Ion Calculator), a fast CUDA GPGPU-based algorithm that predicts ion-binding sites in apoproteins. Comparative tests demonstrate that PIONCA outperforms most of the tools based on structural templates or docking. Our software can be also used for locating bound cations in holoprotein structures with missing cation heteroatoms. PIONCA is equipped with an interactive web interface based upon JSmol.     

Genome-wide prediction,display and refinement of binding sites with information theory-based models

Background  

We present Delila-genome, a software system for identification, visualization and analysis of protein binding sites in complete genome sequences. Binding sites are predicted by scanning genomic sequences with information theory-based (or user-defined) weight matrices. Matrices are refined by adding experimentally-defined binding sites to published binding sites. Delila-Genome was used to examine the accuracy of individual information contents of binding sites detected with refined matrices as a measure of the strengths of the corresponding protein-nucleic acid interactions. The software can then be used to predict novel sites by rescanning the genome with the refined matrices.     

RNANetMotif: Identifying sequence-structure RNA network motifs in RNA-protein binding sites

RNA molecules can adopt stable secondary and tertiary structures, which are essential in mediating physical interactions with other partners such as RNA binding proteins (RBPs) and in carrying out their cellular functions. In vivo and in vitro experiments such as RNAcompete and eCLIP have revealed in vitro binding preferences of RBPs to RNA oligomers and in vivo binding sites in cells. Analysis of these binding data showed that the structure properties of the RNAs in these binding sites are important determinants of the binding events; however, it has been a challenge to incorporate the structure information into an interpretable model. Here we describe a new approach, RNANetMotif, which takes predicted secondary structure of thousands of RNA sequences bound by an RBP as input and uses a graph theory approach to recognize enriched subgraphs. These enriched subgraphs are in essence shared sequence-structure elements that are important in RBP-RNA binding. To validate our approach, we performed RNA structure modeling via coarse-grained molecular dynamics folding simulations for selected 4 RBPs, and RNA-protein docking for LIN28B. The simulation results, e.g., solvent accessibility and energetics, further support the biological relevance of the discovered network subgraphs.     

Analysis and prediction of carbohydrate binding sites

An analysis of the characteristic properties of sugar binding sites was performed on a set of 19 sugar binding proteins. For each site six parameters were evaluated: solvation potential, residue propensity, hydrophobicity, planarity, protrusion and relative accessible surface area. Three of the parameters were found to distinguish the observed sugar binding sites from the other surface patches. These parameters were then used to calculate the probability for a surface patch to be a carbohydrate binding site. The prediction was optimized on a set of 19 non-homologous carbohydrate binding structures and a test prediction was carried out on a set of 40 protein-carbohydrate complexes. The overall accuracy of prediction achieved was 65%. Results were in general better for carbohydrate-binding enzymes than for the lectins, with a rate of success of 87%.     

Using protein binding site prediction to improve protein docking

Predicting protein interaction interfaces and protein complexes are two important related problems. For interface prediction, there are a number of tools, such as PPI-Pred, PPISP, PINUP, Promate, and SPPIDER, which predict enzyme-inhibitor interfaces with success rates of 23% to 55% and other interfaces with 10% to 28% on a benchmark dataset of 62 complexes. Here, we develop, metaPPI, a meta server for interface prediction. It significantly improves prediction success rates to 70% for enzyme-inhibitor and 44% for other interfaces. As shown with Promate, predicted interfaces can be used to improve protein docking. Here, we follow this idea using the meta server instead of individual predictions. We confirm that filtering with predicted interfaces significantly improves candidate generation in rigid-body docking based on shape complementarity. Finally, we show that the initial ranking of candidate solutions in rigid-body docking can be further improved for the class of enzyme-inhibitor complexes by a geometrical scoring which rewards deep pockets. A web server of metaPPI is available at scoppi.tu-dresden.de/metappi. The source code of our docking algorithm BDOCK is also available at www.biotec.tu-dresden.de/~bhuang/bdock.     

Exploiting intrastructure information for secondary structure prediction with multifaceted pipelines

Predicting the secondary structure of proteins is still a typical step in several bioinformatic tasks, in particular, for tertiary structure prediction. Notwithstanding the impressive results obtained so far, mostly due to the advent of sequence encoding schemes based on multiple alignment, in our view the problem should be studied from a novel perspective, in which understanding how available information sources are dealt with plays a central role. After revisiting a well-known secondary structure predictor viewed from this perspective (with the goal of identifying which sources of information have been considered and which have not), we propose a generic software architecture designed to account for all relevant information sources. To demonstrate the validity of the approach, a predictor compliant with the proposed generic architecture has been implemented and compared with several state-of-the-art secondary structure predictors. Experiments have been carried out on standard data sets, and the corresponding results confirm the validity of the approach. The predictor is available at http://iasc.diee.unica.it/ssp2/ through the corresponding web application or as downloadable stand-alone portable unpack-and-run bundle.     

ProPred: prediction of HLA-DR binding sites.

ProPred is a graphical web tool for predicting MHC class II binding regions in antigenic protein sequences. The server implement matrix based prediction algorithm, employing amino-acid/position coefficient table deduced from literature. The predicted binders can be visualized either as peaks in graphical interface or as colored residues in HTML interface. This server might be a useful tool in locating the promiscuous binding regions that can bind to several HLA-DR alleles. AVAILABILITY: The server is available at http://www.imtech.res.in/raghava/propred/ CONTACT: raghava@imtech.res.in SUPPLEMENTARY INFORMATION: http://www.imtech.res.in/raghava/propred/page3.html     

PSSM-based prediction of DNA binding sites in proteins

Background  

Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.     

Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets

Mechanistic understanding of many key cellular processes often involves identification of RNA binding proteins (RBPs) and RNA binding sites in two separate steps. Here, they are predicted simultaneously by structural alignment to known protein-RNA complex structures followed by binding assessment with a DFIRE-based statistical energy function. This method achieves 98% accuracy and 91% precision for predicting RBPs and 93% accuracy and 78% precision for predicting RNA-binding amino-acid residues for a large benchmark of 212 RNA binding and 6761 non-RNA binding domains (leave-one-out cross-validation). Additional tests revealed that the method makes no false positive prediction from 311 DNA binding domains but correctly detects six domains binding with both DNA and RNA. In addition, it correctly identified 31 of 75 unbound RNA-binding domains with 92% accuracy and 65% precision for predicted binding residues and achieved 86% success rate in its application to SCOP RNA binding domain superfamily (Structural Classification Of Proteins). It further predicts 25 targets as RBPs in 2076 structural genomics targets: 20 of 25 predicted ones (80%) are putatively RNA binding. The superior performance over existing methods indicates the importance of dividing structures into domains, using a Z-score to measure relative structural similarity, and a statistical energy function to measure protein-RNA binding affinity.     

Steric block high affinity oligonucleotide analogues: a new tool for mapping RNA-protein binding sites

Steric-block ON analogues are efficient inhibitors of RNA-protein interaction and therefore have potential to probe RNA sequences for putative protein binding sites and to investigate mechanisms of protein binding. The packaging process of HIV-1 is highly specific involving an interaction between the Gag protein and a conserved sequence that is only present on genomic viral RNA. Using oligonucleotide probes we have confirmed that the terminal purine loop is the major Gag binding site on SL3 and that a secondary Gag binding site exists at an internal purine bulge. We also demonstrate direct binding of oligonucleotide to their binding sites and confirm this interaction does not alter global RNA conformation, making them highly specific, nondisruptive probes of RNA protein interactions.     

Localization of phosphatidylserine binding sites to structural domains of factor Xa.

Binding of short chain phosphatidylserine (C6PS) enhances the proteolytic activity of factor X(a) by 60-fold (Koppaka, V., Wang, J., Banerjee, M., and Lentz, B. R. (1996) Biochemistry 35, 7482-7491). In the present study, we locate three C6PS binding sites to different domains of factor X(a) using a combination of activity, circular dichroism, fluorescence, and equilibrium dialysis measurements on proteolytic and biosynthetic fragments of factor X(a). Our results demonstrate that the structural responses of human and bovine factor X(a) to C6PS binding are somewhat different. Despite this difference, data obtained with fragments from both human and bovine factor X(a) are consistent with a common hypothesis for the location of C6PS binding sites to different structural domains. First, the gamma-carboxyglutamic acid (Gla) domain binds C6PS only in the absence of Ca(2+) (k(d) approximately 1 mm), although this PS site does not influence the functional response of factor X(a). Second, a Ca(2+)-dependent binding site is in the epidermal growth factor domains (EGF(NC)) that are linked by Ca(2+) and C6PS binding to the Gla domain. This site appears to be the lipid regulatory site of factor X(a). Third, a Ca(2+)-requiring site seems to be in the EGF(C)-catalytic domain. This site appears not to be a lipid regulatory site but rather to share residues with the substrate recognition site. Finally, the full functional response to C6PS requires linkage of the Gla, EGF(NC), and catalytic domains in the presence of Ca(2+), meaning that PS regulation of factor X(a) involves linkage between widely separated parts of the protein.