Biology subjects

Richardson, J. S.

Publications and source records attributed to Richardson, J. S..

11 recordsLinked to original sources

bioRxiv2025

Categorizing prediction modes within low-pLDDT regions of AlphaFold2 structures

AlphaFold2 protein structure predictions are widely available for structural biology uses. These predictions, especially for eukaryotic proteins, frequently contain extensive regions predicted below the pLDDT 70 level, the rule-of-thumb cutoff for high confidence. This work identifies major modes of behavior within low-pLDDT regions through a survey of human proteome predictions provided by the AlphaFold Protein Structure Database. The near-predictive mode resembles folded protein and can be a nearly accurate prediction. Barbed wire is extremely unproteinlike, being recognized by wide looping coils, an absence of packing contacts, and numerous signature validation outliers, and it likely represents a nonpredicted region. Pseudostructure presents an intermediate behavior with a misleading appearance of isolated and badly formed secondary structure-like elements. These prediction modes are compared with annotations of disorder from MobiDB, showing general correlation between barbed wire/pseudostructure and many measures of disorder, an association between pseudostructure and signal peptides, and an association between near-predictive and regions of conditional folding. To enable users to identify these regions within a prediction, a new Phenix tool is developed encompassing the results of this work, including prediction annotation, visual markup, and residue selection based on these prediction modes. This tool will help users develop expertise in interpreting difficult AlphaFold predictions and identify the near-predictive regions that can aid in molecular replacement when a prediction does not contain enough high-pLDDT regions.

biochemistry↗

bioRxiv2025

RNAprecis: Prediction of full-detail RNA conformation from the experimentally best-observed sparse parameters

We address the problem of predicting high-detail RNA structure geometry from the information available in low-detail experimental maps. Here, low-detail refers to resolutions {approx} 2.5-3.5[A], where the location of the phosphate groups and the glycosidic bonds can be determined from experimental maps but all other backbone atom positions cannot. In contrast, higher-resolution maps allow high-detail determinations of all backbone atomic positions. To this end, we first create a gold standard dataset of highly curated, experimentally supported RNA suites. Second, we develop and employ a modified version of the previously devised algorithm MINT-AGE to learn clusters that are in high correspondence with the gold standards conformational classes of suites based on 3D RNA structure. Since some of the gold standard classes are of very small size, a new modified version of MINT-AGE is able to also identify very small clusters. Third, we create a new conformer prediction algorithm, RNAprecis, which assigns low-detail structures to newly designed 3D shape coordinates. Our improvements include: (i) learned classes augmented to cover also very low sample sizes and (ii) replacing distances from clusters by Bayesian posterior probabilities. On test data containing suites modeled as conformational outliers, RNAprecis shows good results suggesting that our learning method generalizes well. In particular, we show that the modified MINT-AGE clustering can more finely delineate between previously unseen suite conformer separations. For example, the 0a conformer has been separated into two clusters seen in different structural contexts. Such new distinctions can have implications for biochemical interpretation of RNA structure.

molecular biology↗

bioRxiv2022

AlphaFold predictions: great hypotheses but no match for experiment

AI-based methods such as AlphaFold have revolutionized structural biology, often making it possible to predict protein structures with high accuracy. The accuracies of these predictions vary, however, and they do not include ligands, covalent modifications or other environmental factors. Here we focus on very-high-confidence parts of AlphaFold predictions, evaluating how well they can be expected to describe the structure of a protein in a particular environment. We compare predictions with experimental crystallographic maps of the same proteins for 102 crystal structures. In many cases, those parts of AlphaFold predictions that were predicted with very high confidence matched experimental maps remarkably closely. In other cases, these predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. Overall, C atoms in very-high-confidence parts of AlphaFold predictions differed from corresponding crystal structures by a median of 0.6 [A], and about 10% of these differed by more than 2 [A], each about twice the values found for pairs of crystal structures containing the same components but determined in different space groups. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.

biochemistry↗

bioRxiv2022

Accelerating crystal structure determination with iterative AlphaFold prediction

Experimental structure determination can be accelerated with AI-based structure prediction methods such as AlphaFold. Here we present an automatic procedure requiring only sequence information and crystallographic data that uses AlphaFold predictions to produce an electron density map and a structural model. Iterating through cycles of structure prediction is a key element of our procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. We applied this procedure to X-ray data for 215 structures released by the Protein Data Bank in a recent 6-month period. In 87% of cases our procedure yielded a model with at least 50% of C atoms matching those in the deposited models within 2[A]. Predictions from our iterative template-guided prediction procedure were more accurate than those obtained without templates. We suggest a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization.

biochemistry↗

bioRxiv2022

Improving AlphaFold modeling using implicit information from experimental density maps

Machine learning prediction algorithms such as AlphaFold1 and RoseTTAFold2 can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy3-6. We hypothesized that by implicitly including experimental information, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt based on experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We find that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for crystallographic and electron cryo-microscopy map interpretation.

biochemistry↗

bioRxiv2021

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 pre-filtered data typically contains 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 pre-filtered datasets have been released on the Zenodo online web service and is freely available for all uses under a Creative Commons license. Currently, one dataset is residue-filtered on mainchain plus C{beta} atoms, and a second dataset is full-residue filtered; each is available at 4 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, pre-filtered reference datasets constitutes a notable advance for structural bioinformatics and the fields that depend upon it. The Top2018 dataset provides the first representative sample of 3D protein structure for which excellence of experimental data constrains the detailed local conformation to be correct for essentially all 3 million residues included. Earlier generations of residue-filtered datasets were central in developing MolProbity validation used worldwide, and now Zenodo has enabled anyone to use out latest version as a sound basis for structural bioinformatics, protein design, prediction, improving biomedically important structures, or other applications.

biochemistry↗

bioRxiv2020

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

This paper describes outcomes of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 [A]) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several novel metrics were introduced. The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual laboratory experiments and holdings of structure data archives such as the Protein Data Bank. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived from these benchmark maps by 13 participating teams, representing both widely used and novel modeling approaches. We also evaluate the pros and cons of the commonly used metrics to assess model quality and recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed density in the cryo-EM map.

biochemistry↗

bioRxiv2019

CaBLAM for CryoEM Chiropraxis, UnDowser to Rethink \"Waters\", and NGL Viewer to Recapture Online 3D Graphics in MolProbity Validation

The MolProbity web service provides macromolecular model validation to help correct local errors, for the structural biology community worldwide. Here we highlight new validation features, and also describe how we are fighting back against recent outside changes that degrade or endanger that mission. Sophisticated hacking of the MolProbity server has required continual monitoring and various security measures short of restricting user access. Refinement software now increasingly restrains validation criteria in order to supplement the sparser experimental data at 3-4[A] resolutions typical of modern cryoEM. But unfortunately the broad density allows optimization without fixing underlying problems, which means these structures often score much better than they really are. CaBLAM, our first new tool designed for this regime, was described in the previous Tools issue, and here we demonstrate its effectiveness in diagnosing local errors even when other validation outliers have been artificially removed. The deprecation of Java applets now prevents KiNG interactive online display of outliers on the 3D model during a MolProbity run, but that important functionality is now recaptured with a modified version of the Javascript NGL Viewer. Other new changes are more straightforwardly good. We are moving to the Neo4j database (graphical rather than relational), and will soon have cleaner as well as much larger reference datasets. In addition to several minor new features, we have developed a tool called UnDowser that analyzes the properties and context of modeled but clashing HOH \"waters\" to diagnose what they might actually represent. A dozen distinct scenarios are illustrated and described.

biophysics↗

bioRxiv2019

Improved chemistry restraints for crystallographic refinement by integrating the Amber force field into Phenix

The refinement of biomolecular crystallographic models relies on geometric restraints to help address the paucity of experimental data typical in these experiments. Limitations in these restraints can degrade the quality of the resulting atomic models. Here we present an integration of the full all-atom Amber molecular dynamics force field into Phenix crystallographic refinement, which enables a more complete modeling of biomolecular chemistry. The advantages of the force field include a carefully derived set of torsion angle potentials, an extensive and flexible set of atom types, Lennard-Jones treatment of non-bonded interactions and a full treatment of crystalline electrostatics. The new combined method was tested against conventional geometry restraints for over twenty-two thousand protein structures. Structures refined with the new method show substantially improved model quality. On average, Ramachandran and rotamer scores are somewhat better; clash scores and MolProbity scores are significantly improved; and the modelling of electrostatics leads to structures that exhibit more, and more correct, hydrogen bonds than those refined with traditional geometry restraints. We find in general that model improvements are greatest at lower resolutions, prompting plans to add the Amber target function to real-space refinement for use in electron cryo-microscopy. This work opens the door to the future development of more advanced applications such as Amber-based ensemble refinement, quantum mechanical representation of active sites and improved geometric restraints for simulated annealing.\n\nIMPORTANTthis document contains embedded data - to preserve data integrity, please ensure where possible that the IUCr Word tools (available from http://journals.iucr.org/services/docxtemplate/) are installed when editing this document.\n\nSynopsisThe full Amber force field has been integrated into Phenix as an alternative refinement target. With a slight loss in speed, it achieves improved stereochemistry, fewer steric clashes and better hydrogen bonds.

molecular biology↗

bioRxiv2018

Priming of leaf litter decomposition by algae seems of minor importance in natural streams during autumn

Allochthonous detritus from terrestrial origin is one of the main energy sources in forested headwater streams, but its poor nutritional quality makes it difficult to use by heterotrophs. It has been suggested that algae growing on this detritus can enhance its nutritional quality and promote decomposition. So far, most evidence of this \"priming\" effect is derived from laboratory or mesocosm experiments, and it is unclear what its importance is under natural conditions. We measured accrual of algae, phosphorus uptake capacity, and decomposition of poplar leaves in autumn in open- and closed-canopy reaches in 3 forest and 3 agricultural streams. Chlorophyll a abundance did not change significantly neither with stream type nor with canopy cover, although some between open and closed reaches, although in some agricultural streams it was higher in open than in closed canopy reaches. Canopy cover did not affect either phosphate uptake capacity or microbial decomposition. On the other hand, although there was no effect of canopy cover on invertebrate fragmentation rate, a significant interaction between canopy cover and stream suggests priming occurs at least in some streams. Overall, the results point to a weak effect of algae on litter decomposition in natural streams during autumn.

ecology↗

bioRxiv2018

Cis-nonPro Peptides: Genuine Occurrences and their Functional Roles

While cis peptides preceding proline can occur about 5% of the time, cis peptides preceding any other residue ("cis-nonPro" peptides) are an extremely rare feature in protein structures, of considerable importance for two opposite reasons. On one hand, their genuine occurrences are mostly found at sites critical to biological function, from the active sites of carbohydrate enzymes to rare adjacent-residue disulfide bonds. On the other hand, a cis-nonPro can easily be misfit into weak or ambiguous electron density, which led to a high incidence of unjustified cis-nonPro over the 2006-2015 decade. This paper uses high-resolution crystallographic data and especially stringent quality-filtering at the residue level to identify genuine occurrences of cis-nonPro, and to survey both individual examples and broad patterns of their functionality. We explain the procedure developed to identify genuine cis-nonPro examples with almost no false positives. We then survey a large sample of the varied functional roles and structural contexts of cis-nonPro, including the uses of specific amino acids for particular purposes. We emphasize aspects not previously covered: that cis-nonPro always (except for vicinal disulfides) occur in highly ordered structure, and especially the great concentration of occurrence in proteins that process or bind carbohydrates (identified by occurrence on the CAZy website).

biochemistry↗