Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

The CCP4 suite: integrative software for macromolecular crystallography.

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.

ModelCraft: an advanced automated model-building pipeline using Buccaneer.

Interactive model building can be a difficult and time-consuming step in the structure-solution process. Automated model-building programs such as Buccaneer often make it quicker and easier by completing most of the model in advance. However, they may fail to do so with low-resolution data or a poor initial model or map. The Buccaneer pipeline is a relatively simple program that iterates Buccaneer with REFMAC to refine the model and update the map. A new pipeline called ModelCraft has been developed that expands on this to include shift-field refinement, machine-learned pruning of incorrect residues, classical density modification, addition of water and dummy atoms, building of nucleic acids and final rebuilding of side chains. Testing was performed on 1180 structures solved by experimental phasing, 1338 structures solved by molecular replacement using homologues and 2030 structures solved by molecular replacement using predicted AlphaFold models. Compared with the previous Buccaneer pipeline, ModelCraft increased the mean completeness of the protein models in the experimental phasing cases from 91% to 95%, the molecular-replacement cases from 50% to 78% and the AlphaFold cases from 82% to 91%.

Predicting protein model correctness in Coot using machine learning.

Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model-building software can contribute to reducing this burden. This article presents a new correctness score that is produced by combining multiple sources of information using a neural network. The residues in 639 automatically built models were marked as correct or incorrect by comparing them with the coordinates deposited in the PDB. A number of features were also calculated for each residue using Coot, including map-to-model correlation, density values, B factors, clashes, Ramachandran scores, rotamer scores and resolution. Two neural networks were created using these features as inputs: one to predict the correctness of main-chain atoms and the other for side chains. The 639 structures were split into 511 that were used to train the neural networks and 128 that were used to test performance. The predicted correctness scores could correctly categorize 92.3% of the main-chain atoms and 87.6% of the side chains. A Coot ML Correctness script was written to display the scores in a graphical user interface as well as for the automatic pruning of chains, residues and side chains with low scores. The automatic pruning function was added to the CCP4i2 Buccaneer automated model-building pipeline, leading to significant improvements, especially for high-resolution structures.

Carbohydrate structure: the rocky road to automation.

With the introduction of intuitive graphical software, structural biologists who are not experts in crystallography are now able to build complete protein or nucleic acid models rapidly. In contrast, carbohydrates are in a wholly different situation: scant automation exists, with manual building attempts being sometimes toppled by incorrect dictionaries or refinement problems. Sugars are the most stereochemically complex family of biomolecules and, as pyranose rings, have clear conformational preferences. Despite this, all refinement programs may produce high-energy conformations at medium to low resolution, without any support from the electron density. This problem renders the affected structures unusable in glyco-chemical terms. Bringing structural glycobiology up to 'protein standards' will require a total overhaul of the methodology. Time is of the essence, as the community is steadily increasing the production rate of glycoproteins, and electron cryo-microscopy has just started to image them in precisely that resolution range where crystallographic methods falter most.

Three-dimensional structures of two heavily N-glycosylated Aspergillus sp. family GH3 ß-D-glucosidases.

The industrial conversion of cellulosic plant biomass into useful products such as biofuels is a major societal goal. These technologies harness diverse plant degrading enzymes, classical exo- and endo-acting cellulases and, increasingly, cellulose-active lytic polysaccharide monooxygenases, to deconstruct the recalcitrant ß-D-linked polysaccharide. A major drawback with this process is that the exo-acting cellobiohydrolases suffer from severe inhibition from their cellobiose product. ß-D-Glucosidases are therefore important for liberating glucose from cellobiose and thereby relieving limiting product inhibition. Here, the three-dimensional structures of two industrially important family GH3 ß-D-glucosidases from Aspergillus fumigatus and A. oryzae, solved by molecular replacement and refined at 1.95 Šresolution, are reported. Both enzymes, which share 78% sequence identity, display a three-domain structure with the catalytic domain at the interface, as originally shown for barley ß-D-glucan exohydrolase, the first three-dimensional structure solved from glycoside hydrolase family GH3. Both enzymes show extensive N-glycosylation, with only a few external sites being truncated to a single GlcNAc molecule. Those glycans N-linked to the core of the structure are identified purely as high-mannose trees, and establish multiple hydrogen bonds between their sugar components and adjacent protein side chains. The extensive glycans pose special problems for crystallographic refinement, and new techniques and protocols were developed especially for this work. These protocols ensured that all of the D-pyranosides in the glycosylation trees were modelled in the preferred minimum-energy (4)C1 chair conformation and should be of general application to refinements of other crystal structures containing O- or N-glycosylation. The Aspergillus GH3 structures, in light of other recent three-dimensional structures, provide insight into fungal ß-D-glucosidases and provide a platform on which to inform and inspire new generations of variant enzymes for industrial application.

Overview of the CCP4 suite and current developments.

The CCP4 (Collaborative Computational Project, Number 4) software suite is a collection of programs and associated data and software libraries which can be used for macromolecular structure determination by X-ray crystallography. The suite is designed to be flexible, allowing users a number of methods of achieving their aims. The programs are from a wide variety of sources but are connected by a common infrastructure provided by standard file formats, data objects and graphical interfaces. Structure solution by macromolecular crystallography is becoming increasingly automated and the CCP4 suite includes several automation pipelines. After giving a brief description of the evolution of CCP4 over the last 30 years, an overview of the current suite is given. While detailed descriptions are given in the accompanying articles, here it is shown how the individual programs contribute to a complete software package.