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Using Integrative Modeling Platform to compute, validate, and archive a model of a protein complex structure
Author(s) -
Saltzberg Daniel J.,
Viswanath Shruthi,
Echeverria Ignacia,
Chemmama Ilan E.,
Webb Ben,
Sali Andrej
Publication year - 2021
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.3995
Subject(s) - computer science , data mining , sampling (signal processing) , ensemble forecasting , biological system , computational biology , machine learning , biology , filter (signal processing) , computer vision
Biology is advanced by producing structural models of biological systems, such as protein complexes. Some systems are recalcitrant to traditional structure determination methods. In such cases, it may still be possible to produce useful models by integrative structure determination that depends on simultaneous use of multiple types of data. An ensemble of models that are sufficiently consistent with the data is produced by a structural sampling method guided by a data-dependent scoring function. The variation in the ensemble of models quantified the uncertainty of the structure, generally resulting from the uncertainty in the input information and actual structural heterogeneity in the samples used to produce the data. Here, we describe how to generate, assess, and interpret ensembles of integrative structural models using our open source Integrative Modeling Platform program (https://integrativemodeling.org).