bbc 2015

Recommendations

Info

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P42. EARLY FOLDING AND LOCAL INTERACTIONS R. Pancsa 1 , M. Varadi 1 , E. Cilia 2,3 , D. Raimondi 1,2,3 & W. F. Vranken 1,3,* . Structural Biology Research Centre, VIB and Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium 1 ; Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium 2 ; Interuniversity Institute of Bioinformatics in Brussels (IB) 2 , Brussels, Belgium 3 . * wvranken@vub.ac.be INTRODUCTION Protein folding is in its early stages largely determined by the protein sequence and complex local interactions between amino acids, resulting in the formation of foldons that provide the context for further folding into the native state. These early folding processes are therefore important to understand subsequent folding steps and their influence on, for example, aggregation, but they are difficult to study experimentally. We here address this issue computationally by assembling and analysing a dataset on early folding residues from hydrogen deuterium exchange (HDX) data from NMR and MS, and analyse how they relate to the sequence-based backbone dynamics predictions from DynaMine (Cilia et al. 2013, 2014) and evolutionary information from multiple sequence alignments. METHODS We assembled a dataset of HDX experimental data from NMR and MS from literature for 57 proteins totalling 4172 residues. The data was classified by the into early, intermediate and late classes depending on the folding time where protection of the backbone NH was observed, and into strong, medium and weak classes depending on how long the amides remain protected upon unfolding the native state. This resulted in 219 residue sets that are organised in XML files and loaded into a database that is made available online via http://start2fold.eu. The DynaMine predictions were run locally with a new version of the software that handles C- and N-terminal effects. These original predictions were then normalised by shifting them so that the maximum prediction value for each protein is always 1.0, so not affecting the relative differences between the prediction values within each protein, but effectively normalising the values between different proteins. MSAs were generated for each sequence in the dataset using HHblits and Jackhmmer with 3 iterations and E value threshold of 10 -4 . All the retrieved homologs have minimum 90% coverage with the query sequence. By using HHfilter, a post processing tool provided in the HHblits package, we built two different sets of MSAs by varying the maximum pairwise sequence identity threshold between the collected homologs in each MSA. The (ungapped) sequences in the MSAs were predicted without normalisation in order to preserve the differences within a protein family, and mapped back to the full (gapped) MSA. Our analysis shows that the DynaMine-predicted rigidity of the protein backbone represents where the protein is likely to adopt specific lower free energy conformations based on sequence-encoded local interactions, as evidenced by the HDX data on early folding (Figure 1). This effect is also present on a per-residue basis. FIGURE 1. Distribution of DynaMine predictions for early folding residues (green) and non-early folding residues (brown) for the original (left) and normalized (right) values. When relating the secondary structure elements as observed in the native fold to the early folding residues, we observe that the ‘early folding’ secondary structure elements also tend to be more rigid overall. Finally, we examined whether early folding is conserved in evolution on the basis of multiple sequence alignments. Although there is no conservation of individual amino acids, the physical characteristic of a rigid backbone seems to be conserved. We therefore propose that the backbone dynamics of the protein is a fundamental physical feature conserved by proteins that can provide important insights into their folding mechanisms and stability. REFERENCES Cilia, E., Pancsa, R., Tompa, P., Lenaerts, T., & Vranken, W. F. (2013). From protein sequence to dynamics and disorder with DynaMine. Nature Communications, 4, 2741. http://doi.org/10.1038/ncomms3741 Cilia, E., Pancsa, R., Tompa, P., Lenaerts, T., & Vranken, W. F. (2014). The DynaMine webserver: predicting protein dynamics from sequence. Nucleic Acids Research, 12(Web Server), W264–W270. http://doi.org/10.1093/nar/gku270 RESULTS & DISCUSSION 86
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P43. BINDING SITE SIMILARITY DRUG REPOSITIONING: A GENERAL AND SYSTEMATIC METHOD FOR DRUG DISCOVERY AND SIDE EFFECTS DETECTION Daniele Parisi & Yves Moreau. I developed a protocol based on prediction of druggable cavities, comparison of these putative binding sites and crossdocking between bound ligands and the binding site detected to be similar to the one of the complex, in order to study the cross reactivity of known compounds. It is a general method because it can find applications both in drug repositioning and in the study of adverse effects, and it is systematic because it consists in several subsequent steps. It would indicate ligands to screen, reducing the number of candidates and allowing companies or universities to save money and time from unnecessary tests. INTRODUCTION The ability of small molecules to interact with multiple proteins is referred to as polypharmacology [1] , and the strategy that aims to exploit the positive aspects of polypharmacology is drug repositioning, whereby existing drugs are investigated for efficacy against targets for other indications. Existing drugs are privileged structures with verified bioavailability and compatibility. Furthermore, virtual screening allows to conduct repositioning of existing drugs against novel disease targets without the expense of purchasing thousands of compounds [2] . The combination of structure-based virtual screening (such as estimation of similarity of protein-ligand binding sites and consequent cross-docking) and drug repositioning represents a highly efficient and fast methodology for predicting cross-reactivity and putative side effects of drug candidates [3] . METHODS Each step of my work is related to a bioinformatics technique or tool, resulting to be the coupling of different software. 1. At first there is the choice of the query (a single protein as PDB file) and the templates (a set of PDB structures). At least one of the two categories has to present a ligand bound in a cavity; 2. prediction of druggable cavities in all the protein structures using a geometry-based or an energy-based algorithm (Fpocket, geometry-based tool, in my case); 3. comparison of the query binding sites to the binding sites of the templates for assessing the similarity. It can be carried out by an alignment or alignment-free algorithm (I used Apoc, an alignment based tool); 4. cross-docking of the ligand available in the pair of similar binding sites, into the other cavity, in order to study the binding with a different target for toxicity or new therapeutic indications (AutodockVina); 5. Fingerprinting of the new complex ligand-cavity for scoring the docking poses. I applied this protocol on two different queries (Thrombin and Dihydrofolate reductase), using a data set of 1067 druggable proteins as tamplates (Druggable Cavity Directory). RESULTS & DISCUSSION The method works well in repositioning ligands among proteins of the same family (intraprotein), but is not able to detect interprotein similarities (among not related proteins). It happens because of the big size of the predicted cavities (larger than the mere space occupied by the ligand) coupled to the alignment-based algorithm used, which make difficult to have a sufficient similarity rate and exponentially increase the false negatives. For my further works I will divide the cavity space in subpockets, disengage the similarity from the sequence by using pharmacophoric maps, and couple the structure based similarity to the ligand based and network based. All the information will be fused with data integrations algorithms. REFERENCES On the origins of drug polypharmacology, Xavier Jalencas and Jordi Mestres, Med. Chem. Commun., 2013, 4, 80. Drug repositioning by structure-based virtual screening, Dik-Lung Ma, Daniel Shiu-Hin Chana and Chung-Hang Leung, Chem. Soc. Rev., 2013, 42, 2130. Comparison and Druggability Prediction of Protein−Ligand Binding Sites from Pharmacophore-Annotated Cavity Shapes, Jérémy Desaphy, Karima Azdimousa, Esther Kellenberger, and Didier Rognan, J. Chem. Inf. Model. 2012, 52, 2287−2299. 87
Page 1 and 2:
10 th Benelux Bioinformatics Confer
Page 3 and 4:
10th Benelux Bioinformatics Confere
Page 5 and 6:
Page 7 and 8:
Page 9 and 10:
Page 11 and 12:
Page 13 and 14:
Page 15 and 16:
Page 17 and 18:
Page 19 and 20:
BeNeLux Bioinformatics Conference -
Page 21 and 22:
Page 23 and 24:
Page 25 and 26:
Page 27 and 28:
Page 29 and 30:
Page 31 and 32:
Page 33 and 34:
Page 35 and 36: BeNeLux Bioinformatics Conference -
Page 85: BeNeLux Bioinformatics Conference -
Page 115: 10th Benelux Bioinformatics Confere
show all

bbc 2015

Create successful ePaper yourself

Delete template?

Save as template?