D7.4 A REFMAC server for EM and NMR

D7.4 A REFMAC server for EM and NMR

D7.4 A REFMAC server for EM and NMR D7.7: Quality analysis workflow for predicted complexes Joint project: EMBL-EBI Marc F. Lensink (University of Science and Technology of Lille) Background & rationale Proteinprotein interactions and protein assemblies play a crucial role in all cellular processes. A small fraction of protein complexes have been solved experimentally. Computational procedures to generate models of macromolecular

assemblies is important to supplement experimental methods. Validation of experimentally determined structures: coordinated by the wwPDB structure validation task forces (X-ray, NMR, EM, hybrid/integrative methods). These are available to worldwide user. Validation of predicted complexes is established by the CAPRI (Critical Assessment of Predicted Interactions) community, however it is not available to users. This deliverable (D7.7) aims to make this analysis protocol available to users worldwide. CAPRI + evaluation criteria The Critical Assessment of Predicted Interactions (CAPRI)

is a community-wide initiative to organize worldwide experiment for macromolecular complex prediction. Established in 2001. >130 complexes have been predicted by groups worldwide Since its inception, the CAPRI committee has organized six evaluation meetings. During these evaluation meetings, discussions within the community have led to

established standards for the parameters and criteria used to evaluate the quality of the predicted complexes Assess geometric (L-rms, I-rms) and biological (f(nat)) properties of the models Web server Input 1: Structures of Unbound proteins Input 2: Structure of Complex

Input 3: Predicted models Run workflow on EBI farm Landing page + expert interface Result User provide e-mail unique link to result

Currently, result shows a table showing classification of each model Will allow download of superposition files, etc D7.8 Report on prototypes using Big Data approaches Selected projects: Implementation of Convolutional Neural Network for structural biology maps Clustering PDB entries

Extracting mentions of residues from literature Machine learning for cryoEM First prototype for "Big Data" technologies in structural biology Other examples: Steve Ludtke CNNs for cryoET SuRVoS for segmentation Several attempts at particle picking Use case: Analyse 3D maps to learn and recognise features at different scales: e.g. missing components, domains, side chains Protein vs nucleic acid vs solvent EM: effect of map sharpening

xtal: effect of phase quality CNN for maps Data EMD-2984 betagal 2.2 48*48 pixel 2D slices Fitted model 5a1a to annotate as protein or not

Compare deposited map with blurred map 33,148 and 18,750 slices respectively deposited blurred Network Classify as protein or noise Implemented with Keras Tried standard VGG16 model, but reverted to 5layer model

No. images Protein Non-protein Loss Model trained and tested on images from blurred map 33148 15696 17452 0.099 Model trained on blurred map, tested on images from unblurred map 18750 4736 14014

11.842 Model trained and tested on images from unblurred map 18750 4736 14014 0.176 Model trained on unblurred map, tested on images from blurred map 33148 15696 17452 1.631 Accuracy 0.978

0.252 0.938 0.587 Similarity and clustering in PDB The problem New protein/chain, find similar ones in PDB Several methods to assess similarity, e.g. GESAMT: E. Krissinel, Enhanced fold recognition using efficient short fragment clustering, J Mol Biochem., 2012, 1: 76-85 RCC: R.Corral-Corral et al., Machine Learnable Fold Space Representation Based on Residue Cluster Classes, Comp. biol. and chem., 2015, 59: 1-7.

Too many (140k entries/500k chains) to compare with all Representative clean set (65k chains) Thorough statistics (space coverage, similarity measure correlation, pairwise similarity distribution, ) Two proposed approaches Dissimilarity search, dimensionality reduction and clustering

Similarity and clustering in PDB Dissimilarity search If the new structure t is dissimilar to some r, anything similar to r neednt be considered Precompute similarity of all entries with some fixed representative set N Still feasible -- dne once, extensible with new entries On query with t, choose random samples of N Based on precomputed similarities, use them to identify candidates Compute similarity of t with all candidates, choose the best one

Similarity and clustering in PDB Dimensionality reduction and clustering Compute residue cluster classes 26 integer descriptors Embed in real space, perform linear and non-linear dimensionality reductions Apply multiple clustering techniques Evaluate quality of resulting clusters Framework to plug in different algorithms in all steps being developed Similarity and clustering in PDB Plans Develop prototypes Cross-validate results

Use independent similarity assessment techniques Publish! Integrate with PDB Annotations for EuropePMC Natural Language Processing

Annotations will also be imported to PDBe Dashboard at https://pyresid-dash.herokuapp.com/ Uses spaCy Software pyresid available via pip IUCR considering use Work done by Rob Firth STFC, Francisco Talo EBI Rob will attend OpenMinTed conference Challenges: sentence boundary recognition Possible continuation by future grant application Ingest from Proteopedia?

D7.9 Report on existing metadata standards, and proposals for new vocabularies Provenance: D6.4 should add PROV-O to Virtual Folder Projects: translate ARIA metadata to CERIF Workflows: CSIC EOSC Pilot on cryoEM workflows NUTS metadata in Repository, ? NMR-STAR mmCIF UU Contribution to data standards Utrecht member of the integrative modelling task force of PBD Contributing to expending the mmCIF dictionary https://github.com/ihmwg/IHM-dictionary Visit to Salis lab at USSF and RCSB in Rutgers in January

First HADDOCK integrative model deposited in PDB dev: https://pdb-dev.wwpdb.org mmCIF DipCheck now accepts mmCIF input ARP/WARP accepts mmCIF for ligands but not proteins (EMBL-Hamburg) The PDB-REDO databank now stores mmCIF files The PDB_REDO service reads and writes mmCIF Version 8 of PDB-REDO will use mmCIF internally (NKI). 3DBionotes reads mmCIF D7.5 A HADDOCK server for EM

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