The Phenix-AlphaFold webservice: Enabling AlphaFold predictions for use in Phenix

Advances in machine learning have enabled sufficiently accurate predictions of protein structure to be used in macromolecular structure determination with crystallography and cryo-electron microscopy data. The Phenix software suite has AlphaFold predictions integrated into an automated pipeline that can start with an amino acid sequence and data, and automatically perform model-building and refinement to return a protein model fitted into the data. Due to the steep technical requirements of running AlphaFold efficiently, we have implemented a Phenix-AlphaFold webservice that enables all Phenix users to run AlphaFold predictions remotely from the Phenix GUI starting with the official 1.21 release. This webservice will be improved based on how it is used by the research community and the future research directions for Phenix.

Bulk-solvent and overall scaling revisited: faster calculations, improved results. Corrigendum

Equations in Sections 2.3 and 2.4 of the article by Afonine et al. [Acta Cryst. (2013). D69, 625-634] are corrected.

Putting AlphaFold models to work with phenix.process_predicted_model and ISOLDE

AlphaFold has recently become an important tool in providing models for experimental structure determination by X-ray crystallography and cryo-EM. Large parts of the predicted models typically approach the accuracy of experimentally determined structures, although there are frequently local errors and errors in the relative orientations of domains. Importantly, residues in the model of a protein predicted by AlphaFold are tagged with a predicted local distance difference test score, informing users about which regions of the structure are predicted with less confidence. AlphaFold also produces a predicted aligned error matrix indicating its confidence in the relative positions of each pair of residues in the predicted model. The phenix.process_predicted_model tool downweights or removes low-confidence residues and can break a model into confidently predicted domains in preparation for molecular replacement or cryo-EM docking. These confidence metrics are further used in ISOLDE to weight torsion and atom-atom distance restraints, allowing the complete AlphaFold model to be interactively rearranged to match the docked fragments and reducing the need for the rebuilding of connecting regions.

Modeling a unit cell: crystallographic refinement procedure using the biomolecular MD simulation platform Amber

A procedure has been developed for the refinement of crystallographic protein structures based on the biomolecular simulation program Amber. The procedure constructs a model representing a crystal unit cell, which generally contains multiple protein molecules and is fully hydrated with TIP3P water. Periodic boundary conditions are applied to the cell in order to emulate the crystal lattice. The refinement is conducted in the form of a specially designed short molecular-dynamics run controlled by the Amber ff14SB force field and the maximum-likelihood potential that encodes the structure-factor-based restraints. The new Amber-based refinement procedure has been tested on a set of 84 protein structures. In most cases, the new procedure led to appreciably lower R free values compared with those reported in the original PDB depositions or obtained by means of the industry-standard phenix.refine program. In particular, the new method has the edge in refining low-accuracy scrambled models. It has also been successful in refining a number of molecular-replacement models, including one with an r.m.s.d. of 2.15 Å. In addition, Amber-refined structures consistently show superior MolProbity scores. The new approach offers a highly realistic representation of protein-protein interactions in the crystal, as well as of protein-water interactions. It also offers a realistic representation of protein crystal dynamics (akin to ensemble-refinement schemes). Importantly, the method fully utilizes the information from the available diffraction data, while relying on state-of-the-art molecular-dynamics modeling to assist with those elements of the structure that do not diffract well (for example mobile loops or side chains). Finally, it should be noted that the protocol employs no tunable parameters, and the calculations can be conducted in a matter of several hours on desktop computers equipped with graphical processing units or using a designated web service.

PAIREF: paired refinement also for Phenix users

In macromolecular crystallography, paired refinement is generally accepted to be the optimal approach for the determination of the high-resolution cutoff. The software tool PAIREF provides automation of the protocol and associated analysis. Support for phenix.refine as a refinement engine has recently been implemented in the program. This feature is presented here using previously published data for thermolysin. The results demonstrate the importance of the complete cross-validation procedure to obtain a thorough and unbiased insight into the quality of high-resolution data.

CERES: a cryo-EM re-refinement system for continuous improvement of deposited models

The field of electron cryomicroscopy (cryo-EM) has advanced quickly in recent years as the result of numerous technological and methodological developments. This has led to an increase in the number of atomic structures determined using this method. Recently, several tools for the analysis of cryo-EM data and models have been developed within the Phenix software package, such as phenix.real_space_refine for the refinement of atomic models against real-space maps. Also, new validation metrics have been developed for low-resolution cryo-EM models. To understand the quality of deposited cryo-EM structures and how they might be improved, models deposited in the Protein Data Bank that have map resolutions of better than 5 Å were automatically re-refined using current versions of Phenix tools. The results are available on a publicly accessible web page (https://cci.lbl.gov/ceres). The implementation of a Cryo-EM Re-refinement System (CERES) for the improvement of models deposited in the wwPDB, and the results of the re-refinements, are described. Based on these results, contents are proposed for a `cryo-EM Table 1', which summarizes experimental details and validation metrics in a similar way to `Table 1' in crystallography. The consistent use of robust metrics for the evaluation of cryo-EM models and data should accompany every structure deposition and be reported in scientific publications.

A Global Ramachandran Score Identifies Protein Structures with Unlikely Stereochemistry

Ramachandran plots report the distribution of the (ϕ, ψ) torsion angles of the protein backbone and are one of the best quality metrics of experimental structure models. Typically, validation software reports the number of residues belonging to "outlier," "allowed," and "favored" regions. While "zero unexplained outliers" can be considered the current "gold standard," this can be misleading if deviations from expected distributions are not considered. We revisited the Ramachandran Z score (Rama-Z), a quality metric introduced more than two decades ago but underutilized. We describe a reimplementation of the Rama-Z score in the Computational Crystallography Toolbox along with an algorithm to estimate its uncertainty for individual models; final implementations are available in Phenix and PDB-REDO. We discuss the interpretation of the Rama-Z score and advocate including it in the validation reports provided by the Protein Data Bank. We also advocate reporting it alongside the outlier/allowed/favored counts in structural publications.

Deriving and refining atomic models in crystallography and cryo-EM: the latest Phenix tools to facilitate structure analysis

In structural biology, deriving and refining atomic models into maps obtained from X-ray crystallography or cryo electron microscopy (cryo-EM) is essential for the detailed interpretation of a structure and its functional implications through interactions so that for example hydrogen bonds, drug specificity and associated molecular mechanisms can be analysed. This commentary summarizes the latest features of the Phenix software and also highlights the fact that cryo-EM increasingly contributes to data depositions in the PDB and EMDB.

Cryo_fit: Democratization of flexible fitting for cryo-EM

Cryo-electron microscopy (cryo-EM) is becoming a method of choice for describing native conformations of biomolecular complexes at high resolution. The rapid growth of cryo-EM in recent years has created a high demand for automated solutions, both in hardware and software. Flexible fitting of atomic models to three-dimensional (3D) cryo-EM reconstructions by molecular dynamics (MD) simulation is a popular technique but often requires technical expertise in computer simulation. This work introduces cryo_fit, a package for the automatic flexible fitting of atomic models in cryo-EM maps using MD simulation. The package is integrated with the Phenix software suite. The module was designed to automate the multiple steps of MD simulation in a reproducible manner, as well as facilitate refinement and validation through Phenix. Through the use of cryo_fit, scientists with little experience in MD simulation can produce high quality atomic models automatically and better exploit the potential of cryo-EM.

Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix

Diffraction (X-ray, neutron and electron) and electron cryo-microscopy are powerful methods to determine three-dimensional macromolecular structures, which are required to understand biological processes and to develop new therapeutics against diseases. The overall structure-solution workflow is similar for these techniques, but nuances exist because the properties of the reduced experimental data are different. Software tools for structure determination should therefore be tailored for each method. Phenix is a comprehensive software package for macromolecular structure determination that handles data from any of these techniques. Tasks performed with Phenix include data-quality assessment, map improvement, model building, the validation/rebuilding/refinement cycle and deposition. Each tool caters to the type of experimental data. The design of Phenix emphasizes the automation of procedures, where possible, to minimize repetitive and time-consuming manual tasks, while default parameters are chosen to encourage best practice. A graphical user interface provides access to many command-line features of Phenix and streamlines the transition between programs, project tracking and re-running of previous tasks.

Evaluation of models determined by neutron diffraction and proposed improvements to their validation and deposition

The Protein Data Bank (PDB) contains a growing number of models that have been determined using neutron diffraction or a hybrid method that combines X-ray and neutron diffraction. The advantage of neutron diffraction experiments is that the positions of all atoms can be determined, including H atoms, which are hardly detectable by X-ray diffraction. This allows the determination of protonation states and the assignment of H atoms to water molecules. Because neutrons are scattered differently by hydrogen and its isotope deuterium, neutron diffraction in combination with H/D exchange can provide information on accessibility, dynamics and chemical lability. In this study, the deposited data, models and model-to-data fit for all PDB entries that used neutron diffraction as the source of experimental data have been analysed. In many cases, the reported Rwork and Rfree values were not reproducible. In such cases, the model and data files were analysed to identify the reasons for this mismatch. The issues responsible for the discrepancies are summarized and explained. The analysis unveiled limitations to the annotation, deposition and validation of models and data, and a lack of community-wide accepted standards for the description of neutron models and data, as well as deficiencies in current model refinement tools. Most of the issues identified concern the handling of H atoms. Since the primary use of neutron macromolecular crystallography is to locate and directly visualize H atoms, it is important to address these issues, so that the deposited neutron models allow the retrieval of the maximum amount of information with the smallest effort of manual intervention. A path forward to improving the annotation, validation and deposition of neutron models and hybrid X-ray and neutron models is suggested.

Molprobity's ultimate rotamer-library distributions for model validation

Here we describe the updated MolProbity rotamer-library distributions derived from an order-of-magnitude larger and more stringently quality-filtered dataset of about 8000 (vs. 500) protein chains, and we explain the resulting changes and improvements to model validation as seen by users. To include only side-chains with satisfactory justification for their given conformation, we added residue-specific filters for electron-density value and model-to-density fit. The combined new protocol retains a million residues of data, while cleaning up false-positive noise in the multi- χ datapoint distributions. It enables unambiguous characterization of conformational clusters nearly 1000-fold less frequent than the most common ones. We describe examples of local interactions that favor these rare conformations, including the role of authentic covalent bond-angle deviations in enabling presumably strained side-chain conformations. Further, along with favored and outlier, an allowed category (0.3-2.0% occurrence in reference data) has been added, analogous to Ramachandran validation categories. The new rotamer distributions are used for current rotamer validation in MolProbity and PHENIX, and for rotamer choice in PHENIX model-building and refinement. The multi-dimensional χ distributions and Top8000 reference dataset are freely available on GitHub. These rotamers are termed "ultimate" because data sampling and quality are now fully adequate for this task, and also because we believe the future of conformational validation should integrate side-chain with backbone criteria. Proteins 2016; 84:1177-1189. © 2016 Wiley Periodicals, Inc.

A new default restraint library for the protein backbone in Phenix: a conformation-dependent geometry goes mainstream

The default geometry restraints used in Phenix for the protein backbone have been upgraded to account for the known conformation-dependencies of bond angles and lengths.

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 [Moriarty et 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 by Phenix is 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.