Biology subjects

Keegan, R.

Publications and source records attributed to Keegan, R..

6 recordsLinked to original sources

bioRxiv2026

A validation approach for RNA structures based on deep-learning 3D-structure predictions

Understanding the molecular mechanisms of life depends on accurate macromolecular structures. RNA, in particular, poses a particular challenge for structural biology due to its inherent flexibility and dynamic nature. The challenging nature of structure determination experiments and their interpretation have led to the development of validation approaches to prevent or fix errors in solved structures. Existing methods for RNA structure validation can, as with proteins, be broadly split into two types. Map-based validation tools seek to detect discrepancies between the experimental data and the determined structure. They are excellent for fixing all types of errors, but only when high-resolution experimental data are available.Model-only validation methods, which usually focus on molecular geometry, are excellent for fixing short-length-scale issues even when experimental resolution is low. Conkit-validate is a mapless method for protein validation, which is not based on chemical plausibility, but rather on the similarity between experimental models and structure predictions for the same protein. By comparing contact patterns, this tool leverages evolutionary covariance information, learned by state-of-the-art structural prediction models, into a mapless validation method capable of detecting sequence assignment issues, with notable success in detecting register errors. Here, we extend the conkit-validate method to work on RNA.The results show performance similar to that previously achieved for proteins. %We also show how this new method can detect errors missed by map-based approaches when the experimental data is of low resolution. In a scan of the PDB, its detection rate does not depend on the local resolution of the density, so it remains applicable where map-based approaches are not, and case studies show it can identify register errors where the density alone cannot decide between models.Together, this highlights the complementary value of a contact-prediction based validation approach using information orthogonal to map- and geometry-based approaches to ensure the quality of deposited RNA structures against varying criteria.The performance of the method can be expected to track ongoing improvements in RNA structure prediction methods.

bioinformatics↗

bioRxiv2026

Structure and signaling mechanism of Helicobacter pylori transducer-like protein D

Chemoreceptors, or methyl-accepting chemotaxis proteins (MCPs), are ancient and widespread prokaryotic sensors that direct taxis in response to stimuli and are attractive targets for therapeutic control of bacteria 1-4. Decades of study have yielded substantial mechanistic insight into chemoreceptor function, but the absence of high-resolution full-length structures containing ligand-binding domains (LBD) has limited understanding of how effector sensing is structurally coupled to long-range signal transduction. Here, we present the intact structure of the chemoreceptor transducer-like protein D (TlpD) from the gastric pathogen Helicobacter pylori, in complex with its ligand Zn2+, determined by X-ray crystallography in two crystal forms at 2.4-3.0 [A]. Three different conformations are captured, revealing how interactions in the ligand-binding site of the chemoreceptor zinc-binding (CZB) domain are interconnected with the distal kinase interface. Small changes at the ligand-binding site coincide with cascades of side-chain rearrangements across the dimer, distortion of the receptor coiled-coil, and conformational and dynamic shifts at the kinase interface over 140 [A] away. These near-atomic resolution structures provide a framework for understanding cooperativity and allosteric communication in chemoreceptors, and establish a representative model for a widespread class of soluble chemoreceptors important in bacterial pathogenesis 2,5.

molecular biology↗

bioRxiv2024

Using deep learning predictions reveals a large number of register errors in PDB deposits

The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, especially where the experimental data are of limited resolution. We have previously established a novel protein structure validation approach based on spotting inconsistencies between the residue contacts and distances observed in a structural model and those computationally predicted by methods such as AlphaFold 2. It is particularly well-suited to the detection of register errors. Importantly, the new approach is orthogonal to traditional methods based on stereochemistry or map-model agreement, and is resolution-independent. Here we identify thousands of likely register errors by scanning 3-5[A] resolution structures in the PDB. Unlike most methods, application of our approach yields suggested corrections to the register of affected regions which we show, even by limited implementation, lead to improved refinement statistics in the vast majority of cases. A few limitations and confounding factors such as fold-switching proteins are characterised, but we expect our approach to have broad application in spotting potential issues in current accessions and, through its implementation and distribution in CCP4, helping ensure the accuracy of future deposits.

bioinformatics↗

bioRxiv2024

In the AlphaFold era, when is experimental phasing of protein crystals still required?

The availability of highly accurate protein structure predictions from AlphaFold 2 (AF2) and similar tools has hugely expanded the applicability of Molecular Replacement (MR) for crystal structure solution. Many structures solve routinely using raw models, structures processed to remove unreliable parts or models split into distinct structural units. There is therefore an open question around how many and which cases still require experimental phasing methods such as single-wavelength anomalous diffraction (SAD). Here we address the question using a large set of PDB deposits that were solved by SAD. A large majority (87%) solve using unedited or minimally edited AF2 predictions. A further 17 (4%) yield straightforwardly to MR after splitting of the AF2 prediction using SliceNDice, although different splitting methods succeed on slightly different sets of cases. We also find that further unique targets can be solved by alternative modelling approaches such as ESMFold (four cases), alternative MR approaches such as ARCIMBOLDO and AMPLE (two cases each), and multimeric model building with AlphaFold-Multimer or UniFold (three cases). Ultimately, only 12 cases, or 3% of the SAD-phased set did not yield to any form of MR tested here, offering valuable hints as to the number and characteristics of cases where experimental phasing remains essential for macromolecular structure solution.

bioinformatics↗

bioRxiv2023

Assessment of three-dimensional RNA structure prediction in CASP15

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non- canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

biophysics↗

bioRxiv2022

Using deep learning predictions of inter-residue distances for model validation

Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank (PDB). Experimental limitations can lead to unavoidable uncertainties during the process of model building, which result in the introduction of errors into the deposited model. Many metrics are available for model validation, but most are limited to the consideration of the physico-chemical aspects of the model or its match to the map. The latest advances in the field of deep learning have enabled the increasingly accurate prediction of inter-residue distances, an advance which has played a pivotal role in the recent improvements observed in the field of protein ab initio modelling. Here we present new validation methods based on the use of these precise inter-residue distance predictions, which are compared with the distances observed in the protein model. Sequence register errors are particularly clearly detected, and the register shifts required for their correction can be reliably determined. The method is available in the package ConKit (www.conkit.org).

bioinformatics↗