Reduce the need for human intervention in protein model building Xpleo1 : Modeling missing protein fragments in weak or ambiguous density Consensus Modeler : a tool to combine experimental protein models 1 Acta Cryst. (2005). D61, 2-13

How to Model Missing Fragments? Disorder and Dynamics in Proteins X-ray Crystallography: well-ordered crystals for diffraction Mobile fragments challenging Dynamic disorder: weak density Multi-modal disorder: ambiguous/overlapping density Existing modeling techniques break down: -> Gaps in main-chain High potential pay-off: Mobile regions often facilitate binding Impact on structural/functional studies, drug design: virtual screening, lead optimization How to Model Missing Fragments?

Modeling a Missing Fragment Partial model of the molecule: Missing fragments 3-15 residues in length ‘Anchoring’ residues of the fragment correctly positioned Known sequence Start with attaching an open conformation to an anchor Method consists of two stages: Generate a set of promising, closed conformations that match the electron density reasonably well. Torsion angle refinement of stage-1 conformers while maintaining closure Stage 1: Main chain only. Stage 2: Side chains added from backbone dependent rotamer library. c-angle ‘free’ refinement.

Results aaRMSD = 0.6Å Final model Xpleo conformation TM0813: residues 83-96 initial model: RESOLVE resolution: 2.0Å, truncated at 2.8Å contour: 1.0s PDB: 1J5X

Consensus Modeler: an Algorithm to Combine Protein Models Goal: Obtain optimal starting model for manual refinement from combining all traces obtained from automated data processing Idea: Mix and match multiple incomplete inputs to increase completeness Error reduction: Compare input models to identify and correct errors Obtain a ‘globally optimal’ model: DP algorithm

Algorithm Shift origin, superimpose all traces using crystallographic symmetry operations Superimpose NCS related molecules using SS matching algorithm1 Represent models as a graph, with each vertex a residue connected by weighted edges. Edge weight represents ‘goodness of fit’ of its pair of residues in the final model Determine path through graph that globally maximizes the goodness of fit traces Output: - Single, optimally complete protein model - Consensus reflection file; combined HL coefficients - All superposed input traces, all origin-shifted reflection files residues 1 Krissinel and Henrick, 2003

Edge Weights Problems in Models: Gaps ‘Dummy’ residues in graph represent gaps in output Gaps ≤ 3 residues filled automatically Frame Shift Two models place different fragments at same location in density Divergence Input models disagree (peptide flip, main-chain in side-chain density, etc.) Undocked Fragments Solvent chains, GLY chains Edge Weights Represent: Agreement with same residue in other models Density Fit (CC), Ramachandran values, Geometry Continuity of the output model (Dummy Residues, Discontinuities in Sequence)

Results Target/CrystalID rsds res SG mol models best trace Consensus PC07317D/22317 253 2.25 C2 1 2 78% 86% PC02663D/22977 203 2.0 8 88% 93% TM0771/20687 310 I4 3 69% 79% TM1622/13219 160 1.9 P3221 37 84% PC04261E/24045 311 2.56 P43212 6 73%1 1 31% docked