Despite of the lack of folded structure, intrinsically disordered regions (IDRs) of proteins play versatile roles in various biological processes, and many nonsynonymous single nucleotide variants (nsSNVs) in IDRs are associated with human diseases. The continuous accumulation of nsSNVs resulted from the wide application of NGS has driven the development of disease-association prediction methods for decades. However, their performance on nsSNVs in IDRs remains inferior, possibly due to the domination of nsSNVs from structured regions in training data. Therefore, it is highly demanding to build a disease-association predictor specifically for nsSNVs in IDRs with better performance. We present IDRMutPred, a machine learning-based tool specifically for predicting disease-associated germline nsSNVs in IDRs. Based on 17 selected optimal features that are extracted from sequence alignments, protein annotations, hydrophobicity indices and disorder scores, IDRMutPred was trained using three ensemble learning algorithms on the training dataset containing only IDR nsSNVs. The evaluation on the two testing datasets shows that all the three prediction models outperform 17 other popular general predictors significantly, achieving the ACC between 0.856 and 0.868 and MCC between 0.713 and 0.737. IDRMutPred will prioritize disease-associated IDR germline nsSNVs more reliably than general predictors. The software is freely available at http://www.wdspdb.com/IDRMutPred. Supplementary data are available at Bioinformatics online.

Diamond-Blackfan Anemia (DBA) is an inherited rare disease characterized with severe pure red cell aplasia, and it is caused by the defective ribosome biogenesis stemming from the impairment of ribosomal proteins. Among all DBA-associated ribosomal proteins, RPS19 affects most patients and carries most DBA mutations. Revealing how these mutations lead to the impairment of RPS19 is highly demanded for understanding the pathogenesis of DBA, but a systematic study is currently lacking. In this work, based on the complex structure of human ribosome, we comprehensively studied the structural basis of DBA mutations of RPS19 by using computational methods. Main structure elements and five conserved surface patches involved in RPS19-18S rRNA interaction were identified. We further revealed that DBA mutations would destabilize RPS19 through disrupting the hydrophobic core or breaking the helix, or perturb the RPS19-18S rRNA interaction through destroying hydrogen bonds, introducing steric hindrance effect, or altering surface electrostatic property at the interface. Moreover, we trained a machine-learning model to predict the pathogenicity of all possible RPS19 mutations. Our work has laid a foundation for revealing the pathogenesis of DBA from the structural perspective.

Background: The wide application of gene se-quencing has accumulated numerous amino acid substi-tutions (AAS) with unknown significance, posing sig-nificant challenges to predicting and understanding their pathogenicity. While various prediction methods have been proposed, most are sequence-based and lack insights for molecular mechanisms from the perspective of pro-tein structures. Moreover, prediction performance must be improved. Methods: Herein, we trained a random forest (RF) prediction model, namely AAS3D-RF, under-scoring sequence and three-dimensional (3D) structure-based features to explore the relationship between dis- eases and AASs. Results: AAS3D-RF was trained on more than 14,000 AASs with 21 selected features, and obtained accuracy (ACC) between 0.811 and 0.839 and Matthews correlation coefficient (MCC) between 0.591 and 0.684 on two independent testing datasets, superior to seven existing tools. In addition, AAS3D-RF possesses unique structure-based features, context-dependent substi-tution score (CDSS) and environment-dependent residue contact energy (ERCE), which could be applied to interpret whether pathogenic AASs would introduce incompatibili-ties to the protein structural microenvironments. Conclu-sion: AAS3D-RF serves as a valuable tool for both pre-dicting and understanding pathogenic AASs.