Dale. E. Tronrud

Macromolecular refinement is an optimization process that aims to produce the most likely macromolecular structural model in the light of experimental data. As such, macromolecular refinement is one of the most complex optimization problems in wide use. Macromolecular refinement programs have to deal with the complex relationship between the parameters of the atomic model and the experimental data, as well as a large number of types of prior knowledge about chemical structure. This paper draws attention to areas of unfinished business in the field of macromolecular refinement. In it, we describe ten refinement topics that we think deserve attention and discuss directions leading to macromolecular refinement software that would make the best use of modern computer resources to meet the needs of structural biologists of the twenty-first century.

Structural biology plays a crucial role in the fight against COVID-19, permitting us to ‘see’ and understand SARS-CoV-2. However, the macromolecular structures of SARS-CoV-2 proteins that were solved with great speed and urgency can contain errors that may hinder drug design. The Coronavirus Structural Task Force has been working behind the scenes to evaluate and improve these structures, making the results freely available at https://insidecorona.net/.

While broadening the applicability of (φ/ψ)-dependent target values for the bond angles in the peptide backbone, sequence/conformation categories with too few residues to analyze via previous methods were encountered. Here, a method of describing a conformation-dependent library (CDL) using two-dimensional Fourier coefficients is reported where the number of coefficients for individual categories is determined via complete cross-validation. Sample sizes are increased further by selective blending of categories with similar patterns of conformational dependence. An additional advantage of the Fourier-synthesis-based CDL is that it uses continuous functions and has no artifactual steps near the edges of populated regions of φ/ψ space. A set of libraries for the seven main-chain bond angles, along with the ω and ζ angles, was created based on a set of Fourier analyses of 48 368 residues selected from high-resolution models in the wwPDB. This new library encompasses both trans- and cis-peptide bonds and outperforms currently used discrete CDLs.

Crystallographic refinement of macromolecular structures relies on stereochemical restraints to mitigate the typically poor data-to-parameter ratio. For proteins, each amino acid has a unique set of geometry restraints which represent stereochemical information such as bond lengths, valence angles, torsion angles, dihedrals and planes. It has been shown that the geometry in refined structures can differ significantly from that present in libraries; for example, it was recently reported that the guanidinium moiety in arginine is not symmetric. In this work, the asymmetry of the N^ɛ—C^ζ—N^η1 and N^ɛ—C^ζ—N^η2 valence angles in the guanidinium moiety is confirmed. In addition, it was found that the C^δ atom can deviate significantly (more than 20°) from the guanidinium plane. This requires the relaxation of the planar restraint for the C^δ atom, as it otherwise causes the other atoms in the group to compensate by distorting the guanidinium core plane. A new set of restraints for the arginine side chain have therefore been formulated, and are available in the software package Phenix, that take into account the asymmetry of the group and the planar deviation of the C^δ atom. This is an example of the need to regularly revisit the geometric restraint libraries used in macromolecular refinement so that they reflect the best knowledge of the structural chemistry of their components available at the time.

During the COVID-19 pandemic, structural biologists rushed to solve the structures of the 28 proteins encoded by the SARS-CoV-2 genome in order to understand the viral life cycle and enable structure-based drug design. In addition to the 204 previously solved structures from SARS-CoV-1, 548 structures covering 16 of the SARS-CoV-2 viral proteins have been released in a span of only 6 months. These structural models serve as the basis for research to understand how the virus hijacks human cells, for structure-based drug design, and to aid in the development of vaccines. However, errors often occur in even the most careful structure determination - and may be even more common among these structures, which were solved quickly and under immense pressure. The Coronavirus Structural Task Force has responded to this challenge by rapidly categorizing, evaluating and reviewing all of these experimental protein structures in order to help downstream users and original authors. In addition, the Task Force provided improved models for key structures online, which have been used by Folding@Home, OpenPandemics, the EU JEDI COVID-19 challenge and others.

Chemical restraints are a fundamental part of crystallographic protein structure refinement. In response to mounting evidence that conventional restraints have shortcomings, it has previously been documented that using backbone restraints that depend on the protein backbone conformation helps to address these shortcomings and improves the performance of refinements [Moriartyet al.(2014),FEBS J.281, 4061–4071]. It is important that these improvements be made available to all in the protein crystallography community. Toward this end, a change in the default geometry library used byPhenixis described here. Tests are presented showing that this change will not generate increased numbers of outliers during validation, or deposition in the Protein Data Bank, during the transition period in which some validation tools still use the conventional restraint libraries.

Many methods of protein structure generation such as NMR‐based solution structure determination and template‐based modeling do not produce a single model, but an ensemble of models consistent with the available information. Current strategies for comparing ensembles lose information because they use only a single representative structure. Here, we describe the ENSEMBLATOR and its novel strategy to directly compare two ensembles containing the same atoms to identify significant global and local backbone differences between them on per‐atom and per‐residue levels, respectively. The ENSEMBLATOR has four components: eePREP (ee for ensemble‐ensemble), which selects atoms common to all models; eeCORE, which identifies atoms belonging to a cutoff‐distance dependent common core; eeGLOBAL, which globally superimposes all models using the defined core atoms and calculates for each atom the two intraensemble variations, the interensemble variation, and the closest approach of members of the two ensembles; and eeLOCAL, which performs a local overlay of each dipeptide and, using a novel measure of local backbone similarity, reports the same four variations as eeGLOBAL. The combination of eeGLOBAL and eeLOCAL analyses identifies the most significant differences between ensembles. We illustrate the ENSEMBLATOR's capabilities by showing how using it to analyze NMR ensembles and to compare NMR ensembles with crystal structures provides novel insights compared to published studies. One of these studies leads us to suggest that a “consistency check” of NMR‐derived ensembles may be a useful analysis step for NMR‐based structure determinations in general. The ENSEMBLATOR 1.0 is available as a first generation tool to carry out ensemble‐ensemble comparisons.

Have you ever seen a feature in your structure and asked, "I wonder how novel this is?" For instance a residue with a certain phi/psi angle, or an Asp residue followed by three further residues that make a tight turn centered on the side chain, or a peptide unit deviating 25° from planarity? If so, the Protein Geometry Database (PGD) is something you'll want to know about. The PGD web service (pgd.science.oregonstate.edu, Berkholz 2009a) manages access to a database containing the geometric details of 1.9 million amino acids. Working with the PGD involves two easy steps - using a search form to find a set of peptides matching your interests, and analyzing the geometric details of the set. The search form allows you to find examples of peptide fragments that have any specified combination of backbone conformations and sequence. Filtering can also be performed based on side chain conformations, or bond angles. You can also specify the quality of models included in the search. Once a set of peptides has been identified, their geometric properties can be analyzed on the web site. You can look at the averages and standard deviations for any bond length, bond angle, or conformational angle, or you can explore relationships between properties by displaying highly customizable plots. An option to export the search results allows you to perform any further analyses you might devise. We will show how the PGD has been used to develop a Conformation Dependent Library of main chain bond angle targets for crystallographic refinement (Berkholz 2009b), as well as to advance our understanding of peptide non-planarity, and of conformational preferences for pairs of residues and of cis-peptides. We will also describe how simple searches for outliers in bond lengths and angles are a powerful validation tool that can both uncovering errors in PDB models and can lead to the discovery of very interesting and real deviations from what is normally considered ideal geometry.