Biology subjects

Chen, V. B.

Publications and source records attributed to Chen, V. B..

3 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↗

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↗