Biology subjects

Terwilliger, T. C.

Publications and source records attributed to Terwilliger, T. C..

16 recordsLinked to original sources

bioRxiv2026

The Untangle Challenge for accurate ensemble models

We report the discovery of a new class of local minima that has severely limited the accuracy of macromolecular models. Termed density misfit barrier traps, these minima explain much of the poor fit between macromolecular models and experimental data relative to that of smaller molecules: not just high R factors, but distorted chemical geometry. We postulated that proteins exist as an ensemble of conformations that each have good geometry, but refinement algorithms have been unable to converge to them due to a tangling phenomenon arising from these traps. To demonstrate, a synthetic ground truth data set was generated, consisting of a 2-member ensemble with excellent geometry. A series of starting models, each trapped in increasingly difficult local minima, were prepared, a unified validation score defined, and an open Challenge issued. This Challenge inspired algorithms for escaping such traps, and new programs have been released that are expected to substantially improve the accuracy of macromolecular ensemble models. SynopsisA synthetic 2-member conformational ensemble of a small protein and corresponding electron density data was generated to demonstrate how topological local minima hinder simultaneous agreement with density data and chemical geometry restraints in conventional structure refinement.

biophysics↗

bioRxiv2025

AlphaFold as a Prior: Experimental StructureDetermination Conditioned on a Pretrained Neural Network

Advances in machine learning have transformed structural biology, enabling swift and accurate prediction of protein structure from sequence. However, challenges persist in capturing sidechain packing, condition-dependent conformational dynamics, and biomolecular interactions, primarily due to scarcity of high-quality training data. Emerging techniques, including cryo-electron tomography (cryo-ET) and high-throughput crystallography, promise vast new sources of structural data, but translating experimental observations into mechanistically interpretable atomic models remains a key bottleneck. Here, we address these challenges by improving the efficiency of structural analysis through combining experimental measurements with a landmark protein structure prediction method - AlphaFold2. We present an augmentation of AlphaFold2, ROCKET, that refines its predictions using cryo-EM, cryo-ET, and X-ray crystallography data, and demonstrate that this approach captures biologically important structural variation that AlphaFold2 does not. By performing structure optimization in the space of coevolutionary embeddings, rather than Cartesian coordinates, ROCKET automates difficult modeling tasks, such as flips of functional loops and domain rearrangements, that are beyond the scope of current state-of-the-art methods and, in some instances, even manual human modeling. The ability to efficiently sample these barrier-crossing rearrangements unlocks a new horizon for scalable and automated model building. Crucially, ROCKET does not require retraining of AlphaFold2 and is readily adaptable to multimers, ligand-cofolding, and other data modalities. Conversely, our differentiable crystal-lographic and cryo-EM target functions are capable of augmenting other structure prediction methods. ROCKET thus provides an extensible framework for the integration of experimental observables with biomolecular machine learning.

biophysics↗

bioRxiv2025

Epigenomic insights into extreme longevity in the world's oldest terrestrial animal, Jonathan

Giant tortoises exhibit exceptional longevity, often exceeding the human lifespan. To understand the genomic and epigenomic basis of their longevity, awe analyzed the DNA sequence and methylome of Jonathan, an Aldabra giant tortoise (Aldabrachelys gigantea), estimated to be 192 years old. Relative to other giant tortoises (Aldabrachelys gigantea and Chelonoidis abingdonii), we found Jonathan has gene variants in pathways associated with aging, including DNA repair and telomere regulation. Consistent with his advanced age, Jonathan has significant age-related changes in DNA methylation and methylation entropy, compared with a 5-year-old Aldabra individual. Notably, we found that low entropy regions in Jonathans methylome were enriched for genes involved in the electron transport chain. This suggests that high-fidelity transcription of these genes may be crucial for extreme longevity. With this data, we propose a model for aging, that links efficient mitochondrial energy production with nuclear maintenance of low methylation entropy.

genomics↗

bioRxiv2024

Likelihood-based interactive local docking into cryo-EM maps in ChimeraX

The interpretation of cryo-EM maps often includes the docking of known or predicted structures of the components, which is particularly useful when the map resolution is worse than 4 [A]. Although it can be effective to search the entire map to find the best placement of a component, the process can be slow when the maps are large. However, frequently there is a well-founded hypothesis about where particular components are located. In such cases, a local search using a map subvolume will be much faster because the search volume is smaller, and more sensitive because optimizing the search volume for the rotation search step enhances signal-to-noise. A Fourier-space likelihood-based local search approach, based on the previously-published em_placement software, has been implemented in the new emplace_local program. Tests confirm that the local search approach enhances speed and sensitivity of the computations. An interactive graphical interface in the ChimeraX molecular graphics program provides a convenient way to set up and evaluate docking calculations, particularly in defining the part of the map into which the components should be placed. SynopsisLikelihood-based cryo-EM docking using our emplace_local software is faster and more sensitive than our related software, em_placement, when the approximate location of a component is known, and is available conveniently through a plugin to the ChimeraX visualization software.

biophysics↗

bioRxiv2022

Likelihood-based signal and noise analysis for docking of models into cryo-EM maps

Fast, reliable docking of models into cryo-EM maps requires understanding of the errors in the maps and the models. Likelihood-based approaches to errors have proven to be powerful and adaptable in experimental structural biology, finding applications in both crystallography and cryo-EM. Indeed, previous crystallographic work on the errors in structural models is directly applicable to likelihood targets in cryo-EM. Likelihood targets in Fourier space are derived here to characterise, based on the comparison of half-maps, the direction- and resolution-dependent variation in the strength of both signal and noise in the data. Because the signal depends on local features, the signal and noise are analysed in local regions of the cryo-EM reconstruction. The likelihood analysis extends to prediction of the signal that will be achieved in any docking calculation for a model of specified quality and completeness. A related calculation generalises a previous measure of the information gained by making the cryo-EM reconstruction.

biophysics↗

bioRxiv2022

Likelihood-based docking of models into cryo-EM maps

Optimized docking of models into cryo-EM maps requires exploiting an understanding of the signal expected in the data to minimize the calculation time while maintaining sufficient signal. The likelihood-based rotation function used in crystallography can be employed to establish plausible orientations in a docking search. A phased likelihood translation function yields scores for the placement and rigid-body refinement of oriented models. Optimised strategies for choices of the resolution of data from the cryo-EM maps to use in the calculations and the size of search volumes are based on expected log-likelihood-gain scores, computed in advance of the search calculation. Tests demonstrate that the new procedure is fast, robust and effective at placing models into even challenging cryo-EM maps. SynopsisExploiting analogies to crystallographic molecular replacement, a strategy for docking into cryo-EM maps is informed by calculations of expected log-likelihood-gain scores.

biophysics↗

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↗

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↗

bioRxiv2020

Density modification of cryo-EM maps

Density modification uses expectations about features of a map such as a flat solvent and expected distributions of density in the region of the macromolecule to improve individual Fourier terms representing the map. This process transfers information from one part of a map to another and can improve the accuracy of a map. Here the assumptions behind density modification for maps from electron cryomicroscopy are examined and a procedure is presented that allows incorporation of model-based information. Density modification works best in cases where unfiltered, unmasked maps with clear boundaries between macromolecule and solvent are visible and where there is substantial noise in the map, both in the region of the macromolecule and the solvent. It also is most effective if the characteristics of the map are relatively constant within regions of the macromolecule and the solvent. Model-based information can be used to improve density modification, but model bias can in principle occur. Here model bias is reduced by using ensemble models that allow estimation of model uncertainty. A test of model bias is presented suggesting that even if the expected density in a region of a map is specified incorrectly by using an incorrect model, the incorrect expectations do not strongly affect the final map. SynopsisThe prerequisites for density modification of maps from electron cryomicroscopy are examined and a procedure for incorporating model-based information is presented.

biochemistry↗

bioRxiv2019

Improvement of cryo-EM maps by density modification

A density modification procedure for improving maps produced by single-particle electron cryo-microscopy is presented. The theoretical basis of the method is identical to that of maximum-likelihood density modification, previously used to improve maps from macromolecular X-ray crystallography. Two key differences from applications in crystallography are that the errors in Fourier coefficients are largely in the phases in crystallography but in both phases and amplitudes in electron cryo-microscopy, and that half-maps with independent errors are available in electron cryo-microscopy. These differences lead to a distinct approach for combination of information from starting maps with information obtained in the density modification process. The applicability of density modification theory to electron cryo-microscopy was evaluated using half-maps for apoferritin at a resolution of 3.1 [A] and a matched 1.8 [A] reference map. Error estimates for the map obtained by density modification were found to closely agree with true errors as estimated by comparison with the reference map. The density modification procedure was applied to a set of 104 datasets where half-maps, a full map and a model all had been deposited. The procedure improved map-model correlation and increased the visibility of details in the maps. The procedure requires two unmasked half-maps and a sequence file or other source of information on the volume of the macromolecule that has been imaged.

biochemistry↗

bioRxiv2018

New tools for the analysis and validation of Cryo-EM maps and atomic models

Recent advances in the field of electron cryo-microscopy (cryo-EM) have resulted in a rapidly increasing number of atomic models of bio-macromolecules solved using this technique and deposited in the Protein Data Bank and the Electron Microscopy Data Bank. Similar to macromolecular crystallography, validation tools for these models and maps are required. While some of these validation tools may be borrowed from crystallography, new methods specifically for cryo-EM validation are required. We discuss new computational methods and tools implemented in Phenix, including d99 to estimate resolution, phenix.auto_sharpen to improve maps, and phenix.mtriage to analyze cryo-EM maps. We suggest that cryo-EM half-maps and masks are deposited to facilitate evaluation and validation of cryo-EM derived atomic models and maps. We also present the application of these tools to deposited cryo-EM atomic models and maps.

bioinformatics↗

bioRxiv2018

A fully automatic method yielding initial models from high-resolution electron cryo-microscopy maps

A fully automated procedure for optimization and interpretation of reconstructions from cryo-EM is developed and applied to 476 datasets with resolution of 4.5 [A] or better, including reconstructions of 47 ribosomes and 32 other protein-RNA complexes. The median fraction of residues in the deposited structures reproduced automatically was 71% for reconstructions determined at resolutions of 3 [A] or better and 47% for those at lower resolution.

biochemistry↗

bioRxiv2018

Real-space refinement in Phenix for cryo-EM and crystallography

This article describes the implementation of real-space refinement in the phenix.real_space_refine program from the Phenix suite. Use of a simplified refinement target function enables fast calculation, which in turn makes it possible to identify optimal data-restraints weight as part of routine refinements with little runtime cost. Refinement of atomic models against low-resolution data benefits from the inclusion of as much additional information as is available. In addition to standard restraints on covalent geometry, phenix.real_space_refine makes use of extra information such as secondary-structure and rotamer-specific restraints, as well as restraints or constraints on internal molecular symmetry. Re-refinement of 385 cryo-EM derived models available in the PDB at resolutions of 6 [A] or better shows significant improvement of models and the fit of these models to the target maps.\n\nSynopsisA description of the implementation of real-space refinement in the phenix.real_space_refine program from the Phenix suite and its application to re-refinement of cryo-EM derived models.

bioinformatics↗

bioRxiv2018

Automated map sharpening by maximization of detail and connectivity

SynopsisA procedure for optimizing the sharpening of a map based on maximizing the level of detail and connectivity of the map is developed and applied to 361 pairs of deposited cryo-EM maps and associated models.\n\nAbstractWe present an algorithm for automatic map sharpening that is based on optimization of detail and connectivity of the sharpened map. The detail in the map is reflected in the surface area of an iso-contour surface that contains a fixed fraction of the volume of the map, where a map with high level of detail has a high surface area. The connectivity of the sharpened map is reflected in the number of connected regions defined by the same iso-contour surfaces, where a map with high connectivity has a small number of connected regions. By combining these two measures in a metric we term \"adjusted surface area\", we can evaluate map quality in an automated fashion. We use this metric to choose optimal map sharpening parameters without reference to a model or other interpretations of the map. Map sharpening by optimization of adjusted surface area can be carried out for a map as a whole or it can be carried out locally, yielding a locally-sharpened map. To evaluate the performance of various approaches, we use a simple metric based on map-model correlation that can reproduce visual choices of optimally-sharpened maps. The map-model correlation is calculated using a model with B-factors (atomic displacement factors, ADP) set to zero. We use this model-based metric to evaluate map sharpening, use it to evaluate map sharpening approaches and find that optimization of adjusted surface area can be an effective tool for map sharpening.

biochemistry↗