Measuring waning protection from seasonal influenza vaccination during nine influenza seasons, Ontario, Canada, 2010/11 to 2018/19.

BackgroundWaning immunity from seasonal influenza vaccination can cause suboptimal protection during peak influenza activity. However, vaccine effectiveness studies assessing waning immunity using vaccinated and unvaccinated individuals are subject to biases.AimWe examined the association between time since vaccination and laboratory-confirmed influenza to assess the change in influenza vaccine protection over time.MethodsUsing linked laboratory and health administrative databases in Ontario, Canada, we identified community-dwelling individuals aged ≥ 6 months who received an influenza vaccine before being tested for influenza by RT-PCR during the 2010/11 to 2018/19 influenza seasons. We estimated the adjusted odds ratio (aOR) for laboratory-confirmed influenza by time since vaccination (categorised into intervals) and for every 28 days.ResultsThere were 53,065 individuals who were vaccinated before testing for influenza, with 10,264 (19%) influenza-positive cases. The odds of influenza increased from 1.05 (95% CI: 0.91-1.22) at 42-69 days after vaccination and peaked at 1.27 (95% CI: 1.04-1.55) at 126-153 days when compared with the reference interval (14-41 days). This corresponded to 1.09-times increased odds of influenza every 28 days (aOR = 1.09; 95% CI: 1.04-1.15). Individuals aged 18-64 years showed the greatest decline in protection against influenza A(H1N1) (aORper 28 days = 1.26; 95% CI: 0.97-1.64), whereas for individuals aged ≥ 65 years, it was against influenza A(H3N2) (aORper 28 days = 1.20; 95% CI: 1.08-1.33). We did not observe evidence of waning vaccine protection for individuals aged < 18 years.ConclusionsInfluenza vaccine protection wanes during an influenza season. Understanding the optimal timing of vaccination could ensure robust protection during seasonal influenza activity.

Effectiveness of maternal influenza vaccination during pregnancy against laboratory-confirmed seasonal influenza among infants under 6 months of age in Ontario, Canada.

BACKGROUND: Randomized trials conducted in low- and middle-income settings demonstrated efficacy of influenza vaccination during pregnancy against influenza infection among infants <6 months of age. However, vaccine effectiveness (VE) estimates from settings with different population characteristics and influenza seasonality remain limited. METHODS: We conducted a test-negative study in Ontario, Canada. All influenza virus tests among infants <6 months from 2010-2019 were identified and linked with health databases to ascertain information on maternal-infant dyads. VE was estimated from the odds ratio for influenza vaccination during pregnancy among cases versus controls, computed using logistic regression with adjustment for potential confounders. RESULTS: Among 23,806 infants tested for influenza, 1,783 (7.5%) were positive and 1,708 (7.2%) were born to mothers vaccinated against influenza during pregnancy. VE against laboratory-confirmed infant influenza infection was 64% (95% confidence interval [CI]: 50%-74%). VE was similar by trimester of vaccination (1st/2nd: 66%, 40%-80%; 3rd: 63%, 46%-74%), infant age at testing (0-<2 months: 63%, 46%-75%; 2-<6 months: 64%, 36%-79%), and gestational age at birth (≥37 weeks: 64%, 50%-75%; < 37 weeks: 61%, 4%-86%). VE against influenza hospitalization was 67% (95%CI: 50%-78%). CONCLUSIONS: Influenza vaccination during pregnancy offers effective protection to infants <6 months, for whom vaccines are not currently available.

Evaluating the Impact of Statin Use on Influenza Vaccine Effectiveness and Influenza Infection in Older Adults.

BACKGROUND: Older adults are recommended to receive influenza vaccination annually, and many use statins. Statins have immunomodulatory properties that might modify influenza vaccine effectiveness (VE) and alter influenza infection risk. METHODS: Using the test-negative design and linked laboratory and health administrative databases in Ontario, Canada, we estimated VE against laboratory-confirmed influenza among community-dwelling statin users and nonusers aged ≥66 years during the 2010-2011 to 2018-2019 influenza seasons. We also estimated the odds ratio for influenza infection comparing statin users and nonusers by vaccination status. RESULTS: Among persons tested for influenza across the 9 seasons, 54 243 had continuous statin exposure before testing and 48 469 were deemed unexposed. The VE against laboratory-confirmed influenza was similar between statin users and nonusers (17% [95% confidence interval, 13%-20%] and 17% [13%-21%] respectively; test for interaction, P = .87). In both vaccinated and unvaccinated persons, statin users had higher odds of laboratory-confirmed influenza than nonusers (odds ratios for vaccinated and unvaccinated persons 1.15 [95% confidence interval, 1.10-1.21] and 1.15 [1.10-1.20], respectively). These findings were consistent by mean daily dose and statin type. VE did not differ between users and nonusers of other cardiovascular drugs, except for ß-blockers. We did not observe that vaccinated and unvaccinated users of these drugs had increased odds of influenza, except for unvaccinated ß-blocker users. CONCLUSIONS: Influenza VE did not differ between statin users and nonusers. Statin use was associated with increased odds of laboratory-confirmed influenza in vaccinated and unvaccinated persons, but these associations might be affected by residual confounding.

Seeing the PDB.

Ever since the first structures of proteins were determined in the 1960s, structural biologists have required methods to visualize biomolecular structures, both as an essential tool for their research and also to promote 3D comprehension of structural results by a wide audience of researchers, students, and the general public. In this review to celebrate the 50th anniversary of the Protein Data Bank, we present our own experiences in developing and applying methods of visualization and analysis to the ever-expanding archive of protein and nucleic acid structures in the worldwide Protein Data Bank. Across that timespan, Jane and David Richardson have concentrated on the organization inside and between the macromolecules, with ribbons to show the overall backbone "fold" and contact dots to show how the all-atom details fit together locally. David Goodsell has explored surface-based representations to present and explore biological subjects that range from molecules to cells. This review concludes with some ideas about the current challenges being addressed by the field of biomolecular visualization.

Improving SARS-CoV-2 structures: Peer review by early coordinate release.

This work builds upon the record-breaking speed and generous immediate release of new experimental three-dimensional structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins and complexes, which are crucial to downstream vaccine and drug development. We have surveyed those structures to catch the occasional errors that could be significant for those important uses and for which we were able to provide demonstrably higher-accuracy corrections. This process relied on new validation and correction methods such as CaBLAM and ISOLDE, which are not yet in routine use. We found such important and correctable problems in seven early SARS-CoV-2 structures. Two of the structures were soon superseded by new higher-resolution data, confirming our proposed changes. For the other five, we emailed the depositors a documented and illustrated report and encouraged them to make the model corrections themselves and use the new option at the worldwide Protein Data Bank for depositors to re-version their coordinates without changing the Protein Data Bank code. This quickly and easily makes the better-accuracy coordinates available to anyone who examines or downloads their structure, even before formal publication. The changes have involved sequence misalignments, incorrect RNA conformations near a bound inhibitor, incorrect metal ligands, and cis-trans or peptide flips that prevent good contact at interaction sites. These improvements have propagated into nearly all related structures done afterward. This process constitutes a new form of highly rigorous peer review, which is actually faster and more strict than standard publication review because it has access to coordinates and maps; journal peer review would also be strengthened by such access.

Influenza Vaccine Effectiveness Against All-Cause Mortality Following Laboratory-Confirmed Influenza in Older Adults, 2010-2011 to 2015-2016 Seasons in Ontario, Canada.

BACKGROUND: Older adults are at increased risk of mortality from influenza infections. We estimated influenza vaccine effectiveness (VE) against mortality following laboratory-confirmed influenza. METHODS: Using a test-negative design study and linked laboratory and health administrative databases in Ontario, Canada, we estimated VE against all-cause mortality following laboratory-confirmed influenza for community-dwelling adults aged >65 years during the 2010-2011 to 2015-2016 influenza seasons. RESULTS: Among 54 116 older adults tested for influenza across the 6 seasons, 6837 died within 30 days of specimen collection. Thirteen percent (925 individuals) tested positive for influenza, and 50.6% were considered vaccinated for that season. Only 23.2% of influenza test-positive cases had influenza recorded as their underlying cause of death. Before and after multivariable adjustment, we estimated VE against all-cause mortality following laboratory-confirmed influenza to be 20% (95% confidence interval [CI], 8%-30%) and 20% (95% CI, 7%-30%), respectively. This estimate increased to 34% after correcting for influenza vaccination exposure misclassification. We observed significant VE against deaths following influenza confirmation during 2014-2015 (VE = 26% [95% CI, 5%-42%]). We also observed significant VE against deaths following confirmation of influenza A/H1N1 and A/H3N2, and against deaths with COPD as the underlying cause. CONCLUSIONS: These results support the importance of influenza vaccination in older adults, who account for most influenza-associated deaths annually.

Acute Myocardial Infarction after Laboratory-Confirmed Influenza Infection.

BACKGROUND: Acute myocardial infarction can be triggered by acute respiratory infections. Previous studies have suggested an association between influenza and acute myocardial infarction, but those studies used nonspecific measures of influenza infection or study designs that were susceptible to bias. We evaluated the association between laboratory-confirmed influenza infection and acute myocardial infarction. METHODS: We used the self-controlled case-series design to evaluate the association between laboratory-confirmed influenza infection and hospitalization for acute myocardial infarction. We used various high-specificity laboratory methods to confirm influenza infection in respiratory specimens, and we ascertained hospitalization for acute myocardial infarction from administrative data. We defined the "risk interval" as the first 7 days after respiratory specimen collection and the "control interval" as 1 year before and 1 year after the risk interval. RESULTS: We identified 364 hospitalizations for acute myocardial infarction that occurred within 1 year before and 1 year after a positive test result for influenza. Of these, 20 (20.0 admissions per week) occurred during the risk interval and 344 (3.3 admissions per week) occurred during the control interval. The incidence ratio of an admission for acute myocardial infarction during the risk interval as compared with the control interval was 6.05 (95% confidence interval [CI], 3.86 to 9.50). No increased incidence was observed after day 7. Incidence ratios for acute myocardial infarction within 7 days after detection of influenza B, influenza A, respiratory syncytial virus, and other viruses were 10.11 (95% CI, 4.37 to 23.38), 5.17 (95% CI, 3.02 to 8.84), 3.51 (95% CI, 1.11 to 11.12), and 2.77 (95% CI, 1.23 to 6.24), respectively. CONCLUSIONS: We found a significant association between respiratory infections, especially influenza, and acute myocardial infarction. (Funded by the Canadian Institutes of Health Research and others.).

Influenza Vaccine Effectiveness in Preventing Hospitalizations in Older Patients With Chronic Obstructive Pulmonary Disease.

BACKGROUND: Annual influenza immunization is recommended for people with chronic obstructive pulmonary disease (COPD) by all major COPD clinical practice guidelines. We sought to determine the seasonal influenza vaccine effectiveness (VE) against laboratory-confirmed influenza-associated hospitalizations among older adults with COPD. METHODS: We conducted a test-negative study of influenza VE in community-dwelling older adults with COPD in Ontario, Canada using health administrative data and respiratory specimens collected from patients tested for influenza during the 2010-11 to 2015-16 influenza seasons. Influenza vaccination was ascertained from physician and pharmacist billing claims. Multivariable logistic regression was used to estimate the adjusted odds ratio of influenza vaccination in people with, compared to those without, laboratory-confirmed influenza. RESULTS: Receipt of seasonal influenza vaccine was associated with an adjusted 22% (95% confidence interval [CI], 15%-27%) reduction in laboratory-confirmed influenza-associated hospitalization. Adjustment for potential misclassification of vaccination status increased this to 43% (95% CI, 35%-52%). Vaccine effectiveness was not found to vary by patient- or influenza-related variables. CONCLUSIONS: During the studied influenza seasons, influenza vaccination was at least modestly effective in reducing laboratory-confirmed influenza-associated hospitalizations in people with COPD. The imperfect effectiveness emphasizes the need for better influenza vaccines and other preventive strategies.

The impact of repeated vaccination using 10-year vaccination history on protection against influenza in older adults: a test-negative design study across the 2010/11 to 2015/16 influenza seasons in Ontario, Canada.

IntroductionAnnual influenza vaccination is recommended for older adults, but evidence regarding the impact of repeated vaccination has been inconclusive.AimWe investigated vaccine effectiveness (VE) against laboratory-confirmed influenza and the impact of repeated vaccination over 10 previous seasons on current season VE among older adults.MethodsWe conducted an observational test-negative study in community-dwelling adults aged > 65 years in Ontario, Canada for the 2010/11 to 2015/16 seasons by linking laboratory and health administrative data. We estimated VE using multivariable logistic regression. We assessed the impact of repeated vaccination by stratifying by previous vaccination history.ResultsWe included 58,304 testing episodes for respiratory viruses, with 11,496 (20%) testing positive for influenza and 31,004 (53%) vaccinated. Adjusted VE against laboratory-confirmed influenza for the six seasons combined was 21% (95% confidence interval (CI): 18 to 24%). Patients who were vaccinated in the current season, but had received no vaccinations in the previous 10 seasons, had higher current season VE (34%; 95%CI: 9 to 52%) than patients who had received 1-3 (26%; 95%CI: 13 to 37%), 4-6 (24%; 95%CI: 15 to 33%), 7-8 (13%; 95%CI: 2 to 22%), or 9-10 (7%; 95%CI: -4 to 16%) vaccinations (trend test p = 0.001). All estimates were higher after correcting for misclassification of current season vaccination status. For patients who were not vaccinated in the current season, residual protection rose significantly with increasing numbers of vaccinations received previously.ConclusionsAlthough VE appeared to decrease with increasing numbers of previous vaccinations, current season vaccination likely provides some protection against influenza regardless of the number of vaccinations received over the previous 10 influenza seasons.

Assessment of detailed conformations suggests strategies for improving cryoEM models: Helix at lower resolution, ensembles, pre-refinement fixups, and validation at multi-residue length scale.

We find that the overall quite good methods used in the CryoEM Model Challenge could still benefit greatly from several strategies for improving local conformations. Our assessments primarily use validation criteria from the MolProbity web service. Those criteria include MolProbity's all-atom contact analysis, updated versions of standard conformational validations for protein and RNA, plus two recent additions: first, flags for cis-nonPro and twisted peptides, and second, the CaBLAM system for diagnosing secondary structure, validating Cα backbone, and validating adjacent peptide CO orientations in the context of the Cα trace. In general, automated ab initio building of starting models is quite good at backbone connectivity but often fails at local conformation or sequence register, especially at poorer than 3.5 Šresolution. However, we show that even if criteria (such as Ramachandran or rotamer) are explicitly restrained to improve refinement behavior and overall validation scores, automated optimization of a deposited structure seldom corrects specific misfittings that start in the wrong local minimum, but just hides them. Therefore, local problems should be identified, and as many as possible corrected, before starting refinement. Secondary structures are confusing at 3-4 Šbut can be better recognized at 6-8 Å. In future model challenges, specific steps being tested (such as segmentation) and the required documentation (such as PDB code of starting model) should each be explicitly defined, so competing methods on a given task can be meaningfully compared. Individual local examples are presented here, to understand what local mistakes and corrections look like in 3D, how they probably arise, and what possible improvements to methodology might help avoid them. At these resolutions, both structural biologists and end-users need meaningful estimates of local uncertainty, perhaps through explicit ensembles. Fitting problems can best be diagnosed by validation that spans multiple residues; CaBLAM is such a multi-residue tool, and its effectiveness is demonstrated.

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.

New tools provide a second look at HDV ribozyme structure, dynamics and cleavage.

The hepatitis delta virus (HDV) ribozyme is a self-cleaving RNA enzyme essential for processing viral transcripts during rolling circle viral replication. The first crystal structure of the cleaved ribozyme was solved in 1998, followed by structures of uncleaved, mutant-inhibited and ion-complexed forms. Recently, methods have been developed that make the task of modeling RNA structure and dynamics significantly easier and more reliable. We have used ERRASER and PHENIX to rebuild and re-refine the cleaved and cis-acting C75U-inhibited structures of the HDV ribozyme. The results correct local conformations and identify alternates for RNA residues, many in functionally important regions, leading to improved R values and model validation statistics for both structures. We compare the rebuilt structures to a higher resolution, trans-acting deoxy-inhibited structure of the ribozyme, and conclude that although both inhibited structures are consistent with the currently accepted hammerhead-like mechanism of cleavage, they do not add direct structural evidence to the biochemical and modeling data. However, the rebuilt structures (PDBs: 4PR6, 4PRF) provide a more robust starting point for research on the dynamics and catalytic mechanism of the HDV ribozyme and demonstrate the power of new techniques to make significant improvements in RNA structures that impact biologically relevant conclusions.

Biophysical highlights from 54 years of macromolecular crystallography.

The United Nations has declared 2014 the International Year of Crystallography, and in commemoration, this review features a selection of 54 notable macromolecular crystal structures that have illuminated the field of biophysics in the 54 years since the first excitement of the myoglobin and hemoglobin structures in 1960. Chronological by publication of the earliest solved structure, each illustrated entry briefly describes key concepts or methods new at the time and key later work leveraged by knowledge of the three-dimensional atomic structure.

Studying and polishing the PDB's macromolecules.

Macromolecular crystal structures are among the best of scientific data, providing detailed insight into these complex and biologically important molecules with a relatively low level of error and subjectivity. However, there are two notable problems with getting the most information from them. The first is that the models are not perfect: there is still opportunity for improving them, and users need to evaluate whether the local reliability in a structure is up to answering their question of interest. The second is that protein and nucleic acid molecules are highly complex and individual, inherently handed and three-dimensional, and the cooperative and subtle interactions that govern their detailed structure and function are not intuitively evident. Thus there is a real need for graphical representations and descriptive classifications that enable molecular 3D literacy. We have spent our career working to understand these elegant molecules ourselves, and building tools to help us and others determine and understand them better. The Protein Data Bank (PDB) has of course been vital and central to this undertaking. Here we combine some history of our involvement as depositors, illustrators, evaluators, and end-users of PDB structures with commentary on how best to study and draw scientific inferences from them.

The role of local backrub motions in evolved and designed mutations.

Amino acid substitutions in protein structures often require subtle backbone adjustments that are difficult to model in atomic detail. An improved ability to predict realistic backbone changes in response to engineered mutations would be of great utility for the blossoming field of rational protein design. One model that has recently grown in acceptance is the backrub motion, a low-energy dipeptide rotation with single-peptide counter-rotations, that is coupled to dynamic two-state sidechain rotamer jumps, as evidenced by alternate conformations in very high-resolution crystal structures. It has been speculated that backrubs may facilitate sequence changes equally well as rotamer changes. However, backrub-induced shifts and experimental uncertainty are of similar magnitude for backbone atoms in even high-resolution structures, so comparison of wildtype-vs.-mutant crystal structure pairs is not sufficient to directly link backrubs to mutations. In this study, we use two alternative approaches that bypass this limitation. First, we use a quality-filtered structure database to aggregate many examples for precisely defined motifs with single amino acid differences, and find that the effectively amplified backbone differences closely resemble backrubs. Second, we directly apply a provably-accurate, backrub-enabled protein design algorithm to idealized versions of these motifs, and discover that the lowest-energy computed models match the average-coordinate experimental structures. These results support the hypothesis that backrubs participate in natural protein evolution and validate their continued use for design of synthetic proteins.

The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions.

Model building and refinement, and the validation of their correctness, are very effective and reliable at local resolutions better than about 2.5 Šfor both crystallography and cryo-EM. However, at local resolutions worse than 2.5 Šboth the procedures and their validation break down and do not ensure reliably correct models. This is because in the broad density at lower resolution, critical features such as protein backbone carbonyl O atoms are not just less accurate but are not seen at all, and so peptide orientations are frequently wrongly fitted by 90-180°. This puts both backbone and side chains into the wrong local energy minimum, and they are then worsened rather than improved by further refinement into a valid but incorrect rotamer or Ramachandran region. On the positive side, new tools are being developed to locate this type of pernicious error in PDB depositions, such as CaBLAM, EMRinger, Pperp diagnosis of ribose puckers, and peptide flips in PDB-REDO, while interactive modeling in Coot or ISOLDE can help to fix many of them. Another positive trend is that artificial intelligence predictions such as those made by AlphaFold2 contribute additional evidence from large multiple sequence alignments, and in high-confidence parts they provide quite good starting models for loops, termini or whole domains with otherwise ambiguous density.

Use of knowledge-based restraints in phenix.refine to improve macromolecular refinement at low resolution.

Traditional methods for macromolecular refinement often have limited success at low resolution (3.0-3.5 Šor worse), producing models that score poorly on crystallographic and geometric validation criteria. To improve low-resolution refinement, knowledge from macromolecular chemistry and homology was used to add three new coordinate-restraint functions to the refinement program phenix.refine. Firstly, a `reference-model' method uses an identical or homologous higher resolution model to add restraints on torsion angles to the geometric target function. Secondly, automatic restraints for common secondary-structure elements in proteins and nucleic acids were implemented that can help to preserve the secondary-structure geometry, which is often distorted at low resolution. Lastly, we have implemented Ramachandran-based restraints on the backbone torsion angles. In this method, a ϕ,ψ term is added to the geometric target function to minimize a modified Ramachandran landscape that smoothly combines favorable peaks identified from nonredundant high-quality data with unfavorable peaks calculated using a clash-based pseudo-energy function. All three methods show improved MolProbity validation statistics, typically complemented by a lowered R(free) and a decreased gap between R(work) and R(free).

Ecosystem effects of a tropical cyclone on a network of lakes in northeastern North America.

Here we document the regional effects of Tropical Cyclone Irene on thermal structure and ecosystem metabolism in nine lakes and reservoirs in northeastern North America using a network of high-frequency, in situ, automated sensors. Thermal stability declined within hours in all systems following passage of Irene, and the magnitude of change was related to the volume of water falling on the lake and catchment relative to lake volume. Across systems, temperature change predicted the change in primary production, but changes in mixed-layer thickness did not affect metabolism. Instead, respiration became a driver of ecosystem metabolism that was decoupled from in-lake primary production, likely due to addition of terrestrially derived carbon. Regionally, energetic disturbance of thermal structure was shorter-lived than disturbance from inflows of terrestrial materials. Given predicted regional increases in intense rain events with climate change, the magnitude and longevity of ecological impacts of these storms will be greater in systems with large catchments relative to lake volume, particularly when significant material is available for transport from the catchment. This case illustrates the power of automated sensor networks and associated human networks in assessing both system response and the characteristics that mediate physical and ecological responses to extreme events.

The Phenix software for automated determination of macromolecular structures.

X-ray crystallography is a critical tool in the study of biological systems. It is able to provide information that has been a prerequisite to understanding the fundamentals of life. It is also a method that is central to the development of new therapeutics for human disease. Significant time and effort are required to determine and optimize many macromolecular structures because of the need for manual interpretation of complex numerical data, often using many different software packages, and the repeated use of interactive three-dimensional graphics. The Phenix software package has been developed to provide a comprehensive system for macromolecular crystallographic structure solution with an emphasis on automation. This has required the development of new algorithms that minimize or eliminate subjective input in favor of built-in expert-systems knowledge, the automation of procedures that are traditionally performed by hand, and the development of a computational framework that allows a tight integration between the algorithms. The application of automated methods is particularly appropriate in the field of structural proteomics, where high throughput is desired. Features in Phenix for the automation of experimental phasing with subsequent model building, molecular replacement, structure refinement and validation are described and examples given of running Phenix from both the command line and graphical user interface.

The importance of residue-level filtering and the Top2018 best-parts dataset of high-quality protein residues.

We have curated a high-quality, "best-parts" reference dataset of about 3 million protein residues in about 15,000 PDB-format coordinate files, each containing only residues with good electron density support for a physically acceptable model conformation. The resulting prefiltered data typically contain the entire core of each chain, in quite long continuous fragments. Each reference file is a single protein chain, and the total set of files were selected for low redundancy, high resolution, good MolProbity score, and other chain-level criteria. Then each residue was critically tested for adequate local map quality to firmly support its conformation, which must also be free of serious clashes or covalent-geometry outliers. The resulting Top2018 prefiltered datasets have been released on the Zenodo online web service and are freely available for all uses under a Creative Commons license. Currently, one dataset is residue filtered on main chain plus Cß atoms, and a second dataset is full-residue filtered; each is available at four different sequence-identity levels. Here, we illustrate both statistics and examples that show the beneficial consequences of residue-level filtering. That process is necessary because even the best of structures contain a few highly disordered local regions with poor density and low-confidence conformations that should not be included in reference data. Therefore, the open distribution of these very large, prefiltered reference datasets constitutes a notable advance for structural bioinformatics and the fields that depend upon it.