PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
August 27, 2003Protein Science762 citationsOpen Access

Matthews coefficient probabilities: Improved estimates for unit cell contents of proteins, DNA, and protein–nucleic acid complex crystals

View Full Paper
KKKatherine A. KantardjieffBRBernhard Rupp

Key Points

  • The research aims to improve estimates of the number of molecules in the asymmetric unit of crystals, specifically for proteins and nucleic acids.
  • Surveyed 15641 entries from the Protein Data Bank (PDB)
  • Reanalyzed the distribution of the Matthews coefficient based on updated data
  • Examined V(M) distribution for nucleic acid crystals and included resolution as a factor.
  • Identified a changed range of values and frequencies for V(M) over 30 years
  • Established resolution as a significant factor influencing V(M)
  • Provided improved estimates for probabilities of molecules in the asymmetric unit.

Abstract

Estimating the number of molecules in the crystallographic asymmetric unit is one of the first steps in a macromolecular structure determination. Based on a survey of 15641 crystallographic Protein Data Bank (PDB) entries the distribution of V(M), the crystal volume per unit of protein molecular weight, known as Matthews coefficient, has been reanalyzed. The range of values and frequencies has changed in the 30 years since Matthews first analysis of protein crystal solvent content. In the statistical analysis, complexes of proteins and nucleic acids have been treated as a separate group. In addition, the V(M) distribution for nucleic acid crystals has been examined for the first time. Observing that resolution is a significant discriminator of V(M), an improved estimator for the probabilities of the number of molecules in the crystallographic asymmetric unit has been implemented, using resolution as additional information.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Kantardjieff et al. (2003) studied this question.

synapsesocial.com/papers/6a23a1698ac8ec529b08f4dfhttps://doi.org/10.1110/ps.0350503
Ask AI
Helpful
Bookmark
Share
View Full Paper