FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2051567790> ?p ?o ?g. }

• W2051567790 endingPage "4658" @default.
• W2051567790 startingPage "4643" @default.
• W2051567790 abstract "Recently, electron microscopy measurement of single particles has enabled us to reconstruct a low-resolution 3D density map of large biomolecular complexes. If structures of the complex subunits can be solved by x-ray crystallography at atomic resolution, fitting these models into the 3D density map can generate an atomic resolution model of the entire large complex. The fitting of multiple subunits, however, generally requires large computational costs; therefore, development of an efficient algorithm is required. We developed a fast fitting program, gmfit, which employs a Gaussian mixture model (GMM) to represent approximated shapes of the 3D density map and the atomic models. A GMM is a distribution function composed by adding together several 3D Gaussian density functions. Because our model analytically provides an integral of a product of two distribution functions, it enables us to quickly calculate the fitness of the density map and the atomic models. Using the integral, two types of potential energy function are introduced: the attraction potential energy between a 3D density map and each subunit, and the repulsion potential energy between subunits. The restraint energy for symmetry is also employed to build symmetrical origomeric complexes. To find the optimal configuration of subunits, we randomly generated initial configurations of subunit models, and performed a steepest-descent method using forces and torques of the three potential energies. Comparison between an original density map and its GMM showed that the required number of Gaussian distribution functions for a given accuracy depended on both resolution and molecular size. We then performed test fitting calculations for simulated low-resolution density maps of atomic models of homodimer, trimer, and hexamer, using different search parameters. The results indicated that our method was able to rebuild atomic models of a complex even for maps of 30 A resolution if sufficient numbers (eight or more) of Gaussian distribution functions were employed for each subunit, and the symmetric restraints were assigned for complexes with more than three subunits. As a more realistic test, we tried to build an atomic model of the GroEL/ES complex by fitting 21-subunit atomic models into the 3D density map obtained by cryoelectron microscopy using the C7 symmetric restraints. A model with low root mean-square deviations (14.7 A) was obtained as the lowest-energy model, showing that our fitting method was reasonably accurate. Inclusion of other restraints from biological and biochemical experiments could further enhance the accuracy." @default.
• W2051567790 created "2016-06-24" @default.
• W2051567790 creator A5001281803 @default.
• W2051567790 date "2008-11-01" @default.
• W2051567790 modified "2023-09-30" @default.
• W2051567790 title "Multiple Subunit Fitting into a Low-Resolution Density Map of a Macromolecular Complex Using a Gaussian Mixture Model" @default.
• W2051567790 cites W1511008459 @default.
• W2051567790 cites W1965564984 @default.
• W2051567790 cites W1991540499 @default.
• W2051567790 cites W2002924743 @default.
• W2051567790 cites W2005752630 @default.
• W2051567790 cites W2007107624 @default.
• W2051567790 cites W2010113343 @default.
• W2051567790 cites W2021419438 @default.
• W2051567790 cites W2026367652 @default.
• W2051567790 cites W2027185514 @default.
• W2051567790 cites W2032219149 @default.
• W2051567790 cites W2036348412 @default.
• W2051567790 cites W2043542498 @default.
• W2051567790 cites W2046221312 @default.
• W2051567790 cites W2054591207 @default.
• W2051567790 cites W2056236390 @default.
• W2051567790 cites W2056331547 @default.
• W2051567790 cites W2058690225 @default.
• W2051567790 cites W2065120788 @default.
• W2051567790 cites W2066644195 @default.
• W2051567790 cites W2071109581 @default.
• W2051567790 cites W2090013975 @default.
• W2051567790 cites W2090492074 @default.
• W2051567790 cites W2093017347 @default.
• W2051567790 cites W2095482495 @default.
• W2051567790 cites W2097463865 @default.
• W2051567790 cites W2111523821 @default.
• W2051567790 cites W2112087820 @default.
• W2051567790 cites W2113654464 @default.
• W2051567790 cites W2117010916 @default.
• W2051567790 cites W2119342149 @default.
• W2051567790 cites W2126126419 @default.
• W2051567790 cites W2128148365 @default.
• W2051567790 cites W2142338805 @default.
• W2051567790 cites W2144659448 @default.
• W2051567790 cites W2146646206 @default.
• W2051567790 cites W2149184368 @default.
• W2051567790 cites W2152012752 @default.
• W2051567790 cites W2152764495 @default.
• W2051567790 cites W64861853 @default.
• W2051567790 doi "https://doi.org/10.1529/biophysj.108.137125" @default.
• W2051567790 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2576401" @default.
• W2051567790 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18708469" @default.
• W2051567790 hasPublicationYear "2008" @default.
• W2051567790 type Work @default.
• W2051567790 sameAs 2051567790 @default.
• W2051567790 citedByCount "102" @default.
• W2051567790 countsByYear W20515677902012 @default.
• W2051567790 countsByYear W20515677902013 @default.
• W2051567790 countsByYear W20515677902014 @default.
• W2051567790 countsByYear W20515677902015 @default.
• W2051567790 countsByYear W20515677902016 @default.
• W2051567790 countsByYear W20515677902017 @default.
• W2051567790 countsByYear W20515677902018 @default.
• W2051567790 countsByYear W20515677902019 @default.
• W2051567790 countsByYear W20515677902020 @default.
• W2051567790 countsByYear W20515677902021 @default.
• W2051567790 countsByYear W20515677902022 @default.
• W2051567790 countsByYear W20515677902023 @default.
• W2051567790 crossrefType "journal-article" @default.
• W2051567790 hasAuthorship W2051567790A5001281803 @default.
• W2051567790 hasBestOaLocation W20515677901 @default.
• W2051567790 hasConcept C105795698 @default.
• W2051567790 hasConcept C110121322 @default.
• W2051567790 hasConcept C11413529 @default.
• W2051567790 hasConcept C121332964 @default.
• W2051567790 hasConcept C121864883 @default.
• W2051567790 hasConcept C134306372 @default.
• W2051567790 hasConcept C138268822 @default.
• W2051567790 hasConcept C14036430 @default.
• W2051567790 hasConcept C147597530 @default.
• W2051567790 hasConcept C153258448 @default.
• W2051567790 hasConcept C154945302 @default.
• W2051567790 hasConcept C163716315 @default.
• W2051567790 hasConcept C185592680 @default.
• W2051567790 hasConcept C186060115 @default.
• W2051567790 hasConcept C186370098 @default.
• W2051567790 hasConcept C186603090 @default.
• W2051567790 hasConcept C197055811 @default.
• W2051567790 hasConcept C30475298 @default.
• W2051567790 hasConcept C33923547 @default.
• W2051567790 hasConcept C41008148 @default.
• W2051567790 hasConcept C50644808 @default.
• W2051567790 hasConcept C61224824 @default.
• W2051567790 hasConcept C62520636 @default.
• W2051567790 hasConcept C7218915 @default.
• W2051567790 hasConcept C78458016 @default.
• W2051567790 hasConcept C86803240 @default.
• W2051567790 hasConceptScore W2051567790C105795698 @default.
• W2051567790 hasConceptScore W2051567790C110121322 @default.
• W2051567790 hasConceptScore W2051567790C11413529 @default.
• W2051567790 hasConceptScore W2051567790C121332964 @default.

• next