<i>Ab initio</i>
            simulations of protein-folding pathways by molecular dynamics with the united-residue model of polypeptide chains

Liwo, Adam; Khalili, Mey; Scheraga, Harold A.

doi:10.1073/pnas.0408885102

Cited by 261 publications

(357 citation statements)

References 49 publications

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“…Transferable ''knowledge-based'' potentials derived from atom-atom contact frequencies in protein-structure databases have been used with some success (13,14) and are detailed in a number of recent reviews (1)(2)(3)(4)7). Although these methods have been applied to questions of structure prediction (13,15,16), the sampling techniques and potential design procedures used in many such studies have limited applicability to the ab initio simulation of ensemble protein-folding mechanisms.…”

mentioning

confidence: 99%

Understanding ensemble protein folding at atomic detail

Hubner

Deeds

Shakhnovich

2006

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

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It has long been known that a protein's amino acid sequence dictates its native structure. However, despite significant recent advances, an ensemble description of how a protein achieves its native conformation from random coil under physiologically relevant conditions remains incomplete. Here we present a detailed all-atom model with a transferable potential that is capable of ab initio folding of entire protein domains using only sequence information. The computational efficiency of this model allows us to perform thousands of microsecond-time scale-folding simulations of the engrailed homeodomain and to observe thousands of complete independent folding events. We apply a graph-theoretic analysis to this massive data set to elucidate which intermediates and intermediary states are common to many trajectories and thus important for the folding process. This method provides an atomically detailed and complete picture of a folding pathway at the ensemble level. The approach that we describe is quite general and could be used to study the folding of proteins on time scales orders of magnitude longer than currently possible.engrailed homeodomain ͉ folding pathway ͉ graph theory ͉ Monte Carlo ͉ protein folding O ne of the longest-standing goals of computational biophysics is the creation of a model for protein folding that is both predictive of protein structure and folding mechanism at the atomic level. Despite numerous advances in recent years (1-5), no method has yet been able to follow the folding of an entire protein domain from random coil to its native state over realistic time scales using an atomically detailed representation and a fully transferable potential. Significant progress has been made on the computational prediction of native-state structures for many proteins (6), but such algorithms do not use a single realistic representation of the polypeptide chain and its dynamics from start to finish and as such cannot shed light on the mechanistic details of the folding process. For instance, the highly successful structure prediction ROSETTA algorithm (6) uses a C-␤ representation of the protein for many of its early calculations and is based on dynamics that move entire segments of the chain to instantly adopt complete structural motifs drawn from a database to search conformational space efficiently.Attempts to access the important features of the physical folding process using explicit approximations of physical forces and molecular dynamics simulation protocols have encountered significant barriers in computational efficiency (7). Individual simulations using such protocols are currently limited to tens to hundreds of nanosecond time scales, and although rare fastfolding events can be observed in such simulations (7), these methods cannot access folding events or intermediates that occur on longer time scales (7). Given that even the fastest-folding protein domains fold on the order of tens of microseconds, much of this work has thus focused on protein fragments or designed peptide sequences (7) that...

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mentioning

confidence: 99%

Understanding ensemble protein folding at atomic detail

Hubner

Deeds

Shakhnovich

2006

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

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“…In the same way, the regular and polarized water models by The model developed by Nguyen and coworkers was thought to combine with ePRIME [152], and be delivered as BioModi [149], a unified CG force-field for DNA, proteins and general polymers. The DNA model from Scheraga's group [128] was derived with the same philosophy used to derive the UNRES [153] force-field for proteins, and is expected to be compatible. Finally, the incorporation of proteins to the SIRAH force-field [154], allows the comprehensive simulation of DNA, proteins, water and ions in a multiscale (all-atom/coarsegrain) manner [125••].…”

Section: Coarse-grain Studiesmentioning

confidence: 99%

Multiscale simulation of DNA

Dans¹,

Walther

Gómez

et al. 2016

Current Opinion in Structural Biology

142

131

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DNA is not only among the most important molecules in life, but a meeting point for biology, physics and chemistry, being studied by numerous techniques. Theoretical methods can help in gaining a detailed understanding of DNA structure and function, but their practical use is hampered by the multiscale nature of this molecule. In this regard, the study of DNA covers a broad range of different topics, from sub-Angstrom details of the electronic distributions of nucleobases, to the mechanical properties of millimeter-long chromatin fibers. Some of the biological processes involving DNA occur in femtoseconds, while others require years. In this review, we describe the most recent theoretical methods that have been considered to study DNA, from the electron to the chromosome, enriching our knowledge on this fascinating molecule.

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“…However, this modification meant introducing some elements of knowledge-based information, which we want to avoid. To overcome problems related to a global search procedure, we recently developed Langevin dynamics (46) for the UNRES potential, which is currently being introduced as our main conformational search method. Molecular dynamics facilitates extending our prediction capabilities not only to predict protein 3D structure more accurately but also to predict protein-folding pathways.…”

Section: Discussionmentioning

confidence: 99%

Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: Assessment in two blind tests

Ołdziej

Czaplewski

Liwo

et al. 2005

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

136

119

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Recent improvements in the protein-structure prediction method developed in our laboratory, based on the thermodynamic hypothesis, are described. The conformational space is searched extensively at the united-residue level by using our physics-based UNRES energy function and the conformational space annealing method of global optimization. The lowest-energy coarse-grained structures are then converted to an all-atom representation and energyminimized with the ECEPP͞3 force field. The procedure was assessed in two recent blind tests of protein-structure prediction. During the first blind test, we predicted large fragments of ␣ and ␣؉␤ proteins [60 -70 residues with C ␣ rms deviation (rmsd) <6 Å]. However, for ␣؉␤ proteins, significant topological errors occurred despite low rmsd values. In the second exercise, we predicted whole structures of five proteins (two ␣ and three ␣؉␤, with sizes of 53-235 residues) with remarkably good accuracy. In particular, for the genomic target TM0487 (a 102-residue ␣؉␤ protein from Thermotoga maritima), we predicted the complete, topologically correct structure with 7.3-Å C ␣ rmsd. So far this protein is the largest ␣؉␤ protein predicted based solely on the amino acid sequence and a physics-based potential-energy function and search procedure. For target T0198, a phosphate transport system regulator PhoU from T. maritima (a 235-residue mainly ␣-helical protein), we predicted the topology of the whole six-helix bundle correctly within 8 Å rmsd, except the 32 C-terminal residues, most of which form a ␤-hairpin. These and other examples described in this work demonstrate significant progress in physics-based protein-structure prediction.global optimization ͉ thermodynamic hypothesis T o date, the great majority of successful algorithms for proteinstructure prediction are knowledge-based approaches; they make explicit use of homology modeling (1, 2) or fold recognition methods (2-6). This feature even pertains to most of the methods considered as ab initio (7,8), which, in theory, should not make explicit use of structural databases. However, in-depth understanding of the physical principles of formation of protein structure requires the development of physics-based methods for proteinstructure prediction (9). Moreover, such methods will be independent of structural databases used in the training of knowledgebased methods. Furthermore, physics-based methods will enable us to study the structures of proteins that seem to possess a degenerate native state, such as the prion proteins, to simulate protein-folding pathways, to understand the mechanisms of protein folding, and to study interactions of proteins with other biomacromolecules and their assemblies (e.g., nucleic acids, polysaccharides, lipids, etc.). The underlying principle of physics-based methods for proteinstructure prediction is Anfinsen's thermodynamic hypothesis (10), according to which protein molecules adopt the conformations that are the global minima of their potential-energy surfaces. The methods based on this hypoth...

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Ab initio simulations of protein-folding pathways by molecular dynamics with the united-residue model of polypeptide chains

Cited by 261 publications

References 49 publications

Understanding ensemble protein folding at atomic detail

Understanding ensemble protein folding at atomic detail

Multiscale simulation of DNA

Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: Assessment in two blind tests

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