Bingding Huang

LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation

BackgroundIdentifying pockets on protein surfaces is of great importance for many structure-based drug design applications and protein-ligand docking algorithms. Over the last ten years, many geometric methods for the prediction of ligand-binding sites have been developed.ResultsWe present LIGSITEcsc, an extension and implementation of the LIGSITE algorithm. LIGSITEcscis based on the notion of surface-solvent-surface events and the degree of conservation of the involved surface residues. We compare our algorithm to four other approaches, LIGSITE, CAST, PASS, and SURFNET, and evaluate all on a dataset of 48 unbound/bound structures and 210 bound-structures. LIGSITEcscperforms slightly better than the other tools and achieves a success rate of 71% and 75%, respectively.ConclusionThe use of the Connolly surface leads to slight improvements, the prediction re-ranking by conservation to significant improvements of the binding site predictions. A web server for LIGSITEcscand its source code is available at scoppi.biotec.tu-dresden.de/pocket.

MetaPocket: a meta approach to improve protein ligand binding site prediction.

The identification of ligand-binding sites is often the starting point for protein function annotation and structure-based drug design. Many computational methods for the prediction of ligand-binding sites have been developed in recent decades. Here we present a consensus method metaPocket, in which the predicted sites from four methods: LIGSITE(cs), PASS, Q-SiteFinder, and SURFNET are combined together to improve the prediction success rate. All these methods are evaluated on two datasets of 48 unbound/bound structures and 210 bound structures. The comparison results show that metaPocket improves the success rate from approximately 70 to 75% at the top 1 prediction. MetaPocket is available at http://metapocket.eml.org .

Computational approaches to identifying and characterizing protein binding sites for ligand design

Given the three‐dimensional structure of a protein, how can one find the sites where other molecules might bind to it? Do these sites have the properties necessary for high affinity binding? Is this protein a suitable target for drug design? Here, we discuss recent developments in computational methods to address these and related questions. Geometric methods to identify pockets on protein surfaces have been developed over many years but, with new algorithms, their performance is still improving. Simulation methods show promise in accounting for protein conformational variability to identify transient pockets but lack the ease of use of many of the (rigid) shape‐based tools. Sequence and structure comparison approaches are benefiting from the constantly increasing size of sequence and structure databases. Energetic methods can aid identification and characterization of binding pockets, and have undergone recent improvements in the treatment of solvation and hydrophobicity. The “druggability” of a binding site is still difficult to predict with an automated procedure. The methodologies available for this purpose range from simple shape and hydrophobicity scores to computationally demanding free energy simulations. Copyright

Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction

MOTIVATION Protein-ligand binding sites are the active sites on protein surface that perform protein functions. Thus, the identification of those binding sites is often the first step to study protein functions and structure-based drug design. There are many computational algorithms and tools developed in recent decades, such as LIGSITE(cs/c), PASS, Q-SiteFinder, SURFNET, and so on. In our previous work, MetaPocket, we have proved that it is possible to combine the results of many methods together to improve the prediction result. RESULTS Here, we continue our previous work by adding four more methods Fpocket, GHECOM, ConCavity and POCASA to further improve the prediction success rate. The new method MetaPocket 2.0 and the individual approaches are all tested on two datasets of 48 unbound/bound and 210 bound structures as used before. The results show that the average success rate has been raised 5% at the top 1 prediction compared with previous work. Moreover, we construct a non-redundant dataset of drug-target complexes with known structure from DrugBank, DrugPort and PDB database and apply MetaPocket 2.0 to this dataset to predict drug binding sites. As a result, >74% drug binding sites on protein target are correctly identified at the top 3 prediction, and it is 12% better than the best individual approach. AVAILABILITY The web service of MetaPocket 2.0 and all the test datasets are freely available at http://projects.biotec.tu-dresden.de/metapocket/ and http://sysbio.zju.edu.cn/metapocket.

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 /approximately bhuang/bdock.

MetaDBSite: a meta approach to improve protein DNA-binding sites prediction

BackgroundProtein-DNA interactions play an important role in many fundamental biological activities such as DNA replication, transcription and repair. Identification of amino acid residues involved in DNA binding site is critical for understanding of the mechanism of gene regulations. In the last decade, there have been a number of computational approaches developed to predict protein-DNA binding sites based on protein sequence and/or structural information.ResultsIn this article, we present metaDBSite, a meta web server to predict DNA-binding residues for DNA-binding proteins. MetaDBSite integrates the prediction results from six available online web servers: DISIS, DNABindR, BindN, BindN-rf, DP-Bind and DBS-PRED and it solely uses sequence information of proteins. A large dataset of DNA-binding proteins is constructed from the Protein Data Bank and it serves as a gold-standard benchmark to evaluate the metaDBSite approach and the other six predictors.ConclusionsThe comparison results show that metaDBSite outperforms single individual approach. We believe that metaDBSite will become a useful and integrative tool for protein DNA-binding residues prediction. The MetaDBSite web-server is freely available at http://projects.biotec.tu-dresden.de/metadbsite/ and http://sysbio.zju.edu.cn/metadbsite.

Alternative polyadenylation in glioblastoma multiforme and changes in predicted RNA binding protein profiles.

Alternative polyadenylation (APA) is widely present in the human genome and plays a key role in carcinogenesis. We conducted a comprehensive analysis of the APA products in glioblastoma multiforme (GBM, one of the most lethal brain tumors) and normal brain tissues and further developed a computational pipeline, RNAelements (http://sysbio.zju.edu.cn/RNAelements/), using covariance model from known RNA binding protein (RBP) targets acquired by RNA Immunoprecipitation (RIP) analysis. We identified 4530 APA isoforms for 2733 genes in GBM, and found that 182 APA isoforms from 148 genes showed significant differential expression between normal and GBM brain tissues. We then focused on three genes with long and short APA isoforms that show inconsistent expression changes between normal and GBM brain tissues. These were myocyte enhancer factor 2D, heat shock factor binding protein 1, and polyhomeotic homolog 1 (Drosophila). Using the RNAelements program, we found that RBP binding sites were enriched in the alternative regions between the first and the last polyadenylation sites, which would result in the short APA forms escaping regulation from those RNA binding proteins. To the best of our knowledge, this report is the first comprehensive APA isoform dataset for GBM and normal brain tissues. Additionally, we demonstrated a putative novel APA-mediated mechanism for controlling RNA stability and translation for APA isoforms. These observations collectively lay a foundation for novel diagnostics and molecular mechanisms that can inform future therapeutic interventions for GBM.

Structural modeling of histone methyltransferase complex Set1C from Saccharomyces cerevisiae using constraint-based docking.

Set1C is a histone methyltransferase playing an important role in yeast gene regulation. Modeling the structure of this eight‐subunit protein complex is an important open problem to further elucidate its functional mechanism. Recently, there has been progress in modeling of larger complexes using constraints to restrict the combinatorial explosion in binary docking of subunits. Here, we model the subunits of Set1C and develop a constraint‐based docking approach, which uses high‐quality protein interaction as well as functional data to guide and constrain the combinatorial assembly procedure. We obtained 22 final models. The core complex consisting of the subunits Set1, Bre2, Sdc1 and Swd2 is conformationally conserved in over half of the models, thus, giving high confidence. We characterize these high‐confidence and the lower confidence interfaces and discuss implications for the function of Set1C.