Coping with strong translational noncrystallographic symmetry and extreme anisotropy in molecular replacement with Phaser: human Rab27a

Likelihood-based signal and noise analysis for docking of models into cryo-EM maps

Fast, reliable docking of models into cryo-EM maps requires understanding of the errors in the maps and the models. Likelihood-based approaches to errors have proven to be powerful and adaptable in experimental structural biology, finding applications in both crystallography and cryo-EM. Indeed, previous crystallographic work on the errors in structural models is directly applicable to likelihood targets in cryo-EM. Likelihood targets in Fourier space are derived here to characterize, based on the comparison of half-maps, the direction- and resolution-dependent variation in the strength of both signal and noise in the data. Because the signal depends on local features, the signal and noise are analysed in local regions of the cryo-EM reconstruction. The likelihood analysis extends to prediction of the signal that will be achieved in any docking calculation for a model of specified quality and completeness. A related calculation generalizes a previous measure of the information gained by making the cryo-EM reconstruction.

Identification of the first structurally validated covalent ligands of the small GTPase RAB27A

Rab27A is a small GTPase, which mediates transport and docking of secretory vesicles at the plasma membrane via protein-protein interactions (PPIs) with effector proteins. Rab27A promotes the growth and invasion of multiple cancer types such as breast, lung and pancreatic, by enhancing secretion of chemokines, metalloproteases and exosomes. The significant role of Rab27A in multiple cancer types and the minor role in adults suggest that Rab27A may be a suitable target to disrupt cancer metastasis. Similar to many GTPases, the flat topology of the Rab27A-effector PPI interface and the high affinity for GTP make it a challenging target for inhibition by small molecules. Reported co-crystal structures show that several effectors of Rab27A interact with the Rab27A SF4 pocket ('WF-binding pocket') via a conserved tryptophan-phenylalanine (WF) dipeptide motif. To obtain structural insight into the ligandability of this pocket, a novel construct was designed fusing Rab27A to part of an effector protein (fRab27A), allowing crystallisation of Rab27A in high throughput. The paradigm of KRas covalent inhibitor development highlights the challenge presented by GTPase proteins as targets. However, taking advantage of two cysteine residues, C123 and C188, that flank the WF pocket and are unique to Rab27A and Rab27B among the >60 Rab family proteins, we used the quantitative Irreversible Tethering (qIT) assay to identify the first covalent ligands for native Rab27A. The binding modes of two hits were elucidated by co-crystallisation with fRab27A, exemplifying a platform for identifying suitable lead fragments for future development of competitive inhibitors of the Rab27A-effector interaction interface, corroborating the use of covalent libraries to tackle challenging targets.

How do I get the most out of my protein sequence using bioinformatics tools?

Biochemical and biophysical experiments are essential for uncovering the three-dimensional structure and biological role of a protein of interest. However, meaningful predictions can frequently also be made using bioinformatics resources that transfer knowledge from a well studied protein to an uncharacterized protein based on their evolutionary relatedness. These predictions are helpful in developing specific hypotheses to guide wet-laboratory experiments. Commonly used bioinformatics resources include methods to identify and predict conserved sequence motifs, protein domains, transmembrane segments, signal sequences, and secondary as well as tertiary structure. Here, several such methods available through the MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) are described and how their combined use can provide meaningful information on a protein of unknown function is demonstrated. In particular, the identification of homologs of known structure using HHpred, internal repeats using HHrepID, coiled coils using PCOILS and DeepCoil, and transmembrane segments using Quick2D are focused on.

Introduction to molecular replacement: a time perspective

This article provides an introduction to the crystal phasing technique known as molecular replacement. The available software is reviewed, and the prospects for future developments are considered. Several examples are described in detail to illustrate potential problems. A brief account of past progress is included. The basic crystallographic equations underlying the procedures are given in an appendix.

Detection of translational noncrystallographic symmetry in Patterson functions

Detection of translational noncrystallographic symmetry (TNCS) can be critical for success in crystallographic phasing, particularly when molecular-replacement models are poor or anomalous phasing information is weak. If the correct TNCS is detected then expected intensity factors for each reflection can be refined, so that the maximum-likelihood functions underlying molecular replacement and single-wavelength anomalous dispersion use appropriate structure-factor normalization and variance terms. Here, an analysis of a curated database of protein structures from the Protein Data Bank to investigate how TNCS manifests in the Patterson function is described. These studies informed an algorithm for the detection of TNCS, which includes a method for detecting the number of vectors involved in any commensurate modulation (the TNCS order). The algorithm generates a ranked list of possible TNCS associations in the asymmetric unit for exploration during structure solution.

Phasertng: directed acyclic graphs for crystallographic phasing

Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies.

Measuring and using information gained by observing diffraction data

The information gained by making a measurement, termed the Kullback-Leibler divergence, assesses how much more precisely the true quantity is known after the measurement was made (the posterior probability distribution) than before (the prior probability distribution). It provides an upper bound for the contribution that an observation can make to the total likelihood score in likelihood-based crystallographic algorithms. This makes information gain a natural criterion for deciding which data can legitimately be omitted from likelihood calculations. Many existing methods use an approximation for the effects of measurement error that breaks down for very weak and poorly measured data. For such methods a different (higher) information threshold is appropriate compared with methods that account well for even large measurement errors. Concerns are raised about a current trend to deposit data that have been corrected for anisotropy, sharpened and pruned without including the original unaltered measurements. If not checked, this trend will have serious consequences for the reuse of deposited data by those who hope to repeat calculations using improved new methods.