SIMBAD: a sequence-independent molecular-replacement pipeline.

Adam J Simpkin,María-Natalia Lisa,Ville Uski,Charles Ballard,Marcin Wojdyr,Rui Wu,Yibin Xu,Jens M H Thomas,Alejandro Buschiazzo,William Shepard,Ronan M Keegan,Martin Savko,Andrey Lebedev,Felix Simkovic,Ruslan Sanishvili,Daniel J Rigden

doi:10.1107/s2059798318005752

Abstract

The conventional approach to finding structurally similar search models for use in molecular replacement (MR) is to use the sequence of the target to search against those of a set of known structures. Sequence similarity often correlates with structure similarity. Given sufficient similarity, a known structure correctly positioned in the target cell by the MR process can provide an approximation to the unknown phases of the target. An alternative approach to identifying homologous structures suitable for MR is to exploit the measured data directly, comparing the lattice parameters or the experimentally derived structure-factor amplitudes with those of known structures. Here, SIMBAD, a new sequence-independent MR pipeline which implements these approaches, is presented. SIMBAD can identify cases of contaminant crystallization and other mishaps such as mistaken identity (swapped crystallization trays), as well as solving unsequenced targets and providing a brute-force approach where sequence-dependent search-model identification may be nontrivial, for example because of conformational diversity among identifiable homologues. The program implements a three-step pipeline to efficiently identify a suitable search model in a database of known structures. The first step performs a lattice-parameter search against the entire Protein Data Bank (PDB), rapidly determining whether or not a homologue exists in the same crystal form. The second step is designed to screen the target data for the presence of a crystallized contaminant, a not uncommon occurrence in macromolecular crystallography. Solving structures with MR in such cases can remain problematic for many years, since the search models, which are assumed to be similar to the structure of interest, are not necessarily related to the structures that have actually crystallized. To cater for this eventuality, SIMBAD rapidly screens the data against a database of known contaminant structures. Where the first two steps fail to yield a solution, a final step in SIMBAD can be invoked to perform a brute-force search of a nonredundant PDB database provided by the MoRDa MR software. Through early-access usage of SIMBAD, this approach has solved novel cases that have otherwise proved difficult to solve.

Highlights

In X-ray crystallography, the problem of solving the threedimensional structure of a protein remains a difficult task
The first two steps of SIMBAD, the lattice-parameter and contaminant searches, are quick but thorough approaches to find search models that are suitable for molecular replacement (MR) in cases where a contaminant is present or when a related structure with very similar cell dimensions is available
Invoking these two options on their own is well suited for use as a post-data-collection rapid screening of data sets to ensure that a contaminant is not present

Summary

Introduction

In X-ray crystallography, the problem of solving the threedimensional structure of a protein remains a difficult task. Other approaches make use of idealized fragments or regularly occurring fragments and motifs from known structures as search models in MR. ARCIMBOLDO (Rodrıguez et al, 2009) and Fragon (Jenkins, 2018) are two developments exploiting this approach. All of these applications mainly rely on small but highly accurate fragments being placed correctly in the unit cell of the target. In the most extreme cases, where data are available to 1 Aresolution or better, it has been shown that it is possible to use single atoms as a successful search model (McCoy et al, 2017)

Methods

Results

Discussion

Conclusion