Biology subjects

Mulvaney, T.

Publications and source records attributed to Mulvaney, T..

4 recordsLinked to original sources

bioRxiv2023

CASP15 cryoEM protein and RNA targets: refinement and analysis using experimental maps

CASP assessments primarily rely on comparing predicted coordinates with experimental reference structures. However, errors in the reference structures can potentially reduce the accuracy of the assessment. This issue is particularly prominent in cryoEM-determined structures, and therefore, in the assessment of CASP15 cryoEM targets, we directly utilized density maps to evaluate the predictions. A method for ranking the quality of protein chain predictions based on rigid fitting to experimental density was found to correlate well with the CASP assessment scores. Overall, the evaluation against the density map indicated that the models are of high accuracy although local assessment of predicted side chains in a 1.52 [A] resolution map showed that side-chains are sometimes poorly positioned. The top 136 predictions associated with 9 protein target reference structures were selected for refinement, in addition to the top 40 predictions for 11 RNA targets. To this end, we have developed an automated hierarchical refinement pipeline in cryoEM maps. For both proteins and RNA, the refinement of CASP15 predictions resulted in structures that are close to the reference target structure, including some regions with better fit to the density. This refinement was successful despite large conformational changes and secondary structure element movements often being required, suggesting that predictions from CASP-assessed methods could serve as a good starting point for building atomic models in cryoEM maps for both proteins and RNA. Loop modeling continued to pose a challenge for predictors with even short loops failing to be accurately modeled or refined at times. The lack of consensus amongst models suggests that modeling holds the potential for identifying more flexible regions within the structure.

bioinformatics↗

bioRxiv2023

PICKLUSTER: A protein-interface clustering and analysis plug-in for UCSF ChimeraX

MotivationThe identification and characterization of interfaces in protein complexes is crucial for understanding the mechanisms of molecular recognition. These interfaces are also attractive targets for protein inhibition. However, targeting protein interfaces can be challenging for large interfaces that consist of multiple interacting regions. We present PICKLUSTER -a program for identifying sub-interfaces in protein-protein complexes using distance clustering. The division of the interface into smaller "sub-interfaces" offers a more focused approach for targeting protein-protein interfaces. Availability and implementationThe plug-in PICKLUSTER is implemented for the molecular visualization program UCSF ChimeraX 1.4 and subsequent versions and and is freely available in the ChimeraX toolshed or, together with the source code, from https://gitlab.com/topf-lab/pickluster.git). Contactmaya.topf@cssb-hamburg.de

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↗

bioRxiv2023

ChemEM: flexible docking of small molecules in Cryo-EM structures using difference maps

The rapid advancement of the "resolution revolution" has propelled cryo-electron microscopy (cryo-EM) to the forefront of structure-based drug discovery. However, the majority of cryo-EM structures are solved at medium resolution (3-4[A]), an unexplored territory for small-molecule docking, due to difficulty in positioning ligands and the surrounding side-chains. Therefore, the development of software capable of reliably and automatically docking ligands into cryo-EM maps at such resolutions is of utmost importance. ChemEM is a novel method that employs cryo-EM data, difference mapping, and a physico-chemical scoring function to flexibly dock one or multiple ligands in a protein binding site. To validate its effectiveness, ChemEM was assessed using a highly curated benchmark containing 33 experimental cryo-EM structures, spanning a resolution range of 2.2-5.6 [A]. In all but one case, the method placed the ligands in the density in an accurate conformation, often better than the PDB deposited solutions. Even without the use of cryo-EM density, the ChemEM scoring function outperformed the well-established docking software AutoDock Vina. Furthermore, the study demonstrates that useful information is present in the map even at low resolutions. ChemEM unlocks the potential of medium-resolution cryo-EM structures for drug discovery.

bioinformatics↗