Dataset Information

Accurate Prediction of Amide Exchange in the Fast Limit Reveals Thrombin Allostery.

ABSTRACT: Amide hydrogen/deuterium exchange mass spectrometry (HDXMS) of proteins has become extremely popular for identifying ligand-binding sites, protein-protein interactions, intrinsic disorder, and allosteric changes upon protein modification. Such phenomena are revealed when amide exchange is measured in the fast limit, that is, within a few minutes of exchange in deuterated buffer. The HDXMS data have a resolution of the length of peptides and are difficult to interpret because many different phenomena lead to changes in hydrogen/deuterium exchange. We present a quantitative analysis of accelerated molecular dynamics simulations that provides impressive agreement with peptide-length HDXMS data. Comparative analysis of thrombin and a single-point mutant reveals that the simulation analysis can distinguish the subtle differences in exchange due to mutation. In addition, the results provide a deeper understanding of the underlying changes in dynamics revealed by the HDXMS that extend far from the site of mutation.

SUBMITTER: Markwick PRL

PROVIDER: S-EPMC6342732 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Accurate Prediction of Amide Exchange in the Fast Limit Reveals Thrombin Allostery.

Markwick Phineus R L PRL Peacock Riley B RB Komives Elizabeth A EA

Biophysical journal 20181124 1

Amide hydrogen/deuterium exchange mass spectrometry (HDXMS) of proteins has become extremely popular for identifying ligand-binding sites, protein-protein interactions, intrinsic disorder, and allosteric changes upon protein modification. Such phenomena are revealed when amide exchange is measured in the fast limit, that is, within a few minutes of exchange in deuterated buffer. The HDXMS data have a resolution of the length of peptides and are difficult to interpret because many different pheno ...[more]

PMID: 30558884

Similar Datasets

Project description:A wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses are mediated by RNA-protein interactions. However, experimental determination of the structures of protein-RNA complexes is expensive and technically challenging. Hence, a number of computational tools have been developed for predicting protein-RNA interfaces. Some of the state-of-the-art protein-RNA interface predictors rely on position-specific scoring matrix (PSSM)-based encoding of the protein sequences. The computational efforts needed for generating PSSMs severely limits the practical utility of protein-RNA interface prediction servers. In this work, we experiment with two approaches, random sampling and sequence similarity reduction, for extracting a representative reference database of protein sequences from more than 50 million protein sequences in UniRef100. Our results suggest that random sampled databases produce better PSSM profiles (in terms of the number of hits used to generate the profile and the distance of the generated profile to the corresponding profile generated using the entire UniRef100 data as well as the accuracy of the machine learning classifier trained using these profiles). Based on our results, we developed FastRNABindR, an improved version of RNABindR for predicting protein-RNA interface residues using PSSM profiles generated using 1% of the UniRef100 sequences sampled uniformly at random. To the best of our knowledge, FastRNABindR is the only protein-RNA interface residue prediction online server that requires generation of PSSM profiles for query sequences and accepts hundreds of protein sequences per submission. Our approach for determining the optimal BLAST database for a protein-RNA interface residue classification task has the potential of substantially speeding up, and hence increasing the practical utility of, other amino acid sequence based predictors of protein-protein and protein-DNA interfaces.

Dataset Information

Accurate Prediction of Amide Exchange in the Fast Limit Reveals Thrombin Allostery.

Publications

Accurate Prediction of Amide Exchange in the Fast Limit Reveals Thrombin Allostery.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets