Incorporation of evolutionary information into Rosetta comparative modeling.
James Thompson,David Baker +1 more
Reads0
Chats0
TLDR
A probabilistic approach to derive spatial restraints from proteins of known structure using advances in alignment technology and the growth in the number of structures in the Protein Data Bank is developed.Abstract:
Prediction of protein structures from sequences is a fundamental problem in computational biology. Algorithms that attempt to predict a structure from sequence primarily use two sources of information. The first source is physical in nature: proteins fold into their lowest energy state. Given an energy function that describes the interactions governing folding, a method for constructing models of protein structures, and the amino acid sequence of a protein of interest, the structure prediction problem becomes a search for the lowest energy structure. Evolution provides an orthogonal source of information: proteins of similar sequences have similar structure, and therefore proteins of known structure can guide modeling. The relatively successful Rosetta approach takes advantage of the first, but not the second source of information during model optimization. Following the classic work by Andrej Sali and colleagues, we develop a probabilistic approach to derive spatial restraints from proteins of known structure using advances in alignment technology and the growth in the number of structures in the Protein Data Bank. These restraints define a region of conformational space that is high-probability, given the template information, and we incorporate them into Rosetta's comparative modeling protocol. The combined approach performs considerably better on a benchmark based on previous CASP experiments. Incorporating evolutionary information into Rosetta is analogous to incorporating sparse experimental data: in both cases, the additional information eliminates large regions of conformational space and increases the probability that energy-based refinement will hone in on the deep energy minimum at the native state.read more
Citations
More filters
Journal ArticleDOI
High resolution comparative modeling with RosettaCM
Yifan Song,Frank DiMaio,Ray Yu-Ruei Wang,David E. Kim,Chris Miles,T. J. Brunette,James Thompson,David Baker,David Baker +8 more
TL;DR: An improved method for comparative modeling, RosettaCM, which optimizes a physically realistic all-atom energy function over the conformational space defined by homologous structures, yields models with more accurate side-chain and backbone conformations than other methods.
Journal ArticleDOI
GalaxyWEB server for protein structure prediction and refinement
TL;DR: The GalaxyWEB server predicts protein structure from sequence by template-based modeling and refines loop or terminus regions by ab initio modeling and generates reliable core structures from multiple templates and re-builds unreliable loops or termini by using an optimization-based refinement method.
Journal ArticleDOI
Structural Probing of a Protein Phosphatase 2A Network by Chemical Cross-Linking and Mass Spectrometry
Franz Herzog,Abdullah Kahraman,Daniel Boehringer,Raymond H. Mak,Andreas Bracher,Thomas Walzthoeni,Alexander Leitner,Martin Beck,Franz-Ulrich Hartl,Nenad Ban,Lars Malmström,Ruedi Aebersold +11 more
TL;DR: This study establishes XL-MS as an integral part of hybrid structural biology approaches for the analysis of endogenous protein complexes by gaining distance restraints on a modular interaction network of protein complexes affinity-purified from human cells by applying an adaptedXL-MS protocol.
Journal ArticleDOI
Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently
TL;DR: Improving enzymes by directed evolution requires the navigation of very large search spaces; this work surveys how to do this intelligently.
Journal ArticleDOI
Assessment of contact predictions in CASP12: co-evolution and deep learning coming of age
TL;DR: The analysis of predictions submitted for CASP12 includes predictions of 34 groups for 38 domains classified as free modeling targets which are not accessible to homology‐based modeling due to a lack of structural templates.
References
More filters
Journal ArticleDOI
The Protein Data Bank
Helen M. Berman,John D. Westbrook,Zukang Feng,Gary L. Gilliland,Talapady N. Bhat,Helge Weissig,Ilya N. Shindyalov,Philip E. Bourne +7 more
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Journal ArticleDOI
Comparative Protein Modelling by Satisfaction of Spatial Restraints
Andrej Sali,Tom L. Blundell +1 more
TL;DR: A comparative protein modelling method designed to find the most probable structure for a sequence given its alignment with related structures, which is automated and illustrated by the modelling of trypsin from two other serine proteinases.
Journal ArticleDOI
Amino acid substitution matrices from protein blocks
TL;DR: This work has derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins, leading to marked improvements in alignments and in searches using queries from each of the groups.
Journal ArticleDOI
Protein homology detection by HMM--HMM comparison
TL;DR: A method for detecting distant homologous relationships between proteins based on the generalized alignment of protein sequences with a profile hidden Markov model (HMM) to the case of pairwise alignment of profile HMMs is presented.
Journal ArticleDOI
The relation between the divergence of sequence and structure in proteins.
Cyrus Chothia,Arthur M. Lesk +1 more
TL;DR: The root mean square deviation in the positions of the main chain atoms, delta, is related to the fraction of mutated residues, H, by the expression: delta(A) = 0.40 e1.87H.
Related Papers (5)
Comparative Protein Modelling by Satisfaction of Spatial Restraints
Andrej Sali,Tom L. Blundell +1 more
ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules.
Andrew Leaver-Fay,Michael D. Tyka,Steven M. Lewis,Oliver F. Lange,James Thompson,Ron Jacak,Kristian W. Kaufman,P. Douglas Renfrew,Colin A. Smith,William Sheffler,Ian W. Davis,Seth Cooper,Adrien Treuille,Daniel J. Mandell,Florian Richter,Yih-En Andrew Ban,Sarel J. Fleishman,Jacob E. Corn,David E. Kim,Sergey Lyskov,Monica Berrondo,Stuart Mentzer,Zoran Popović,James J. Havranek,John Karanicolas,Rhiju Das,Jens Meiler,Tanja Kortemme,Jeffrey J. Gray,Brian Kuhlman,David Baker,Philip Bradley +31 more