Probing Protein Fold Space with a Simplified Model

Mináry, Péter; Levitt, Michael

doi:10.1016/j.jmb.2007.10.087

Cited by 35 publications

(37 citation statements)

References 71 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consistent with this perspective, a core of functional diversity corresponding mostly to the more ancient a/b folds in the protein structure space has been identified [270]. Interestingly, a computer modelling study of the dynamic properties of protein fold space suggests that a/b folds are also more stable than other fold classes [271]. In the space of all possible amino acid sequences, an overwhelming majority of sequences do not have a biological function.…”

Section: Applications Of Biophysics-based Models To Understand Proteimentioning

confidence: 87%

Biophysics of protein evolution and evolutionary protein biophysics

Sikosek

Chan

2014

J. R. Soc. Interface.

219

207

View full text Add to dashboard Cite

The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence-structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by 'hidden' conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution.

show abstract

Section: Applications Of Biophysics-based Models To Understand Proteimentioning

confidence: 87%

Biophysics of protein evolution and evolutionary protein biophysics

Sikosek

Chan

2014

J. R. Soc. Interface.

219

207

View full text Add to dashboard Cite

show abstract

“…Better stability of native folds (3.92 Å from native in average) was reported recently by Minary and Levitt [53]. They used a 3-bead model based on an all-atomistic statistical potential [54].…”

Section: Resultsmentioning

confidence: 93%

A coarse‐grained potential for fold recognition and molecular dynamics simulations of proteins

Marynen

Elber

2009

Proteins

View full text Add to dashboard Cite

A coarse grained potential for protein simulations and fold ranking is presented. The potential is based on a two-point model of individual amino acids and a specific implementation of hydrogen bonding. Parameters are determined for distance dependent pair interactions, pseudo bonds, angles, and torsions. A scaling factor for a hydrogen bonding term is also determined. Iterative sampling for 4867 proteins reproduces distributions of internal coordinates and distances observed in the Protein Data Bank. The adjustment of the potential and re-sampling are in the spirit of the generalized ensemble approach. No native structure information (e.g. secondary structure) is used in the calculation of the potential, or in the simulation of a particular protein. The potential is subject to two tests: (i) simulations of 956 globular proteins in the neighborhood of their native folds (these proteins were not used in the training set), and (ii) discrimination between native and decoy structures for 2470 proteins with 305,000 decoys, and the "Decoys 'R' Us" dataset. In the first test, 58% of tested proteins stay within 5 Å from the native fold in Molecular Dynamics simulations of more than twenty nanoseconds using the new potential. The potential is also useful in differentiating between correct and approximate folds providing significant signal for structure prediction algorithms. Sampling with the potential consistently regenerates the distribution of distances and internal coordinates it learned. Nevertheless, during Molecular Dynamics simulations structures are found that reproduce the learned distributions but are far from the native fold.

show abstract

“…In addition to the EM energy, one may optionally introduce a molecular energy based on a knowledge-based potential (34,35). This new term helps avoid improbable conformations and collisions between different parts of the molecule.…”

Section: Resultsmentioning

confidence: 99%

Multiscale natural moves refine macromolecules using single-particle electron microscopy projection images

Zhang

Mináry

Levitt

2012

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

The method presented here refines molecular conformations directly against projections of single particles measured by electron microscopy. By optimizing the orientation of the projection at the same time as the conformation, the method is well-suited to twodimensional class averages from cryoelectron microscopy. Such direct use of two-dimensional images circumvents the need for a three-dimensional density map, which may be difficult to reconstruct from projections due to structural heterogeneity or preferred orientations of the sample on the grid. Our refinement protocol exploits Natural Move Monte Carlo to model a macromolecule as a small number of segments connected by flexible loops, on multiple scales. After tests on artificial data from lysozyme, we applied the method to the Methonococcus maripaludis chaperonin. We successfully refined its conformation from a closed-state initial model to an open-state final model using just one class-averaged projection. We also used Natural Moves to iteratively refine against heterogeneous projection images of Methonococcus maripaludis chaperonin in a mix of open and closed states. Our results suggest a general method for electron microscopy refinement specially suited to macromolecules with significant conformational flexibility. The algorithm is available in the program Methodologies for Optimization and Sampling In Computational Studies.2D projection | structure refinement | stochastic optimization R ecent advances in single-particle cryoelectron microscopy, or cryo-EM, have enabled 3D structure determination of macromolecules to near-atomic resolution without crystallization, provided that the sample particles are homogeneous and adopt the same conformation (1-6). However, macromolecules are generally flexible in solution and can adopt multiple conformations in order to carry out their functions. Therefore, their cryo-EM images usually represent a heterogeneous mixture of macromolecular conformations. As a result, the power of single particle cryo-EM is limited, in that the reconstruction of a highresolution 3D density map requires hundreds of thousands to millions of particle images of the same conformation.Supervised classification (7) and maximum-likelihood methods (8) have been used to identify multiple structures from samples in which macromolecules experience moderate conformational fluctuations or exist in a small number of discrete structural states. Other approaches based on statistical bootstrapping (9, 10) have been used to separate different substrate-binding modes of macromolecules or to define flexible fragments within a molecular complex. However, these single-particle image processing techniques are severely limited when there are large conformational changes or nondiscrete conformational states (11, 12) that prevent correct determination of the orientation parameters for each raw particle image. Various computational techniques have been developed to model large conformational changes by flexible-fitting of a molecular model into the density map of a...

show abstract

Probing Protein Fold Space with a Simplified Model

Cited by 35 publications

References 71 publications

Biophysics of protein evolution and evolutionary protein biophysics

Biophysics of protein evolution and evolutionary protein biophysics

A coarse‐grained potential for fold recognition and molecular dynamics simulations of proteins

Multiscale natural moves refine macromolecules using single-particle electron microscopy projection images

Contact Info

Product

Resources

About