Sequence-structure relationships in proteins and copolymers

Yue, Kaizhi; Dill, Ken A.

doi:10.1103/physreve.48.2267

Cited by 96 publications

(93 citation statements)

References 17 publications

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“…The CG method is even competitive in efficiency with CHCC, the complete search algorithm of Yue and Dill (1993). Possible advantages are that: (1) the search time in the CG method may not scale exponentially with the chain length, as CHCC does, and thus might reach longer chain lengths in reasonable computer time; (2) CG does not rely on HP lattice-specific features, and therefore should generalize readily to more realistic protein folding models; and (3) these ideas are simple to implement.…”

Section: Discussionmentioning

confidence: 99%

“…Our method begins by estimating the size of the hydrophobic core. Following Yue and Dill ( 1993), we count the total number of H monomers in the sequence, and construct a core, which is as nearly square as possible, that can contain all the H monomers. This is not the final true core of the native protein; it is an optimal core constructed as if there were no chain connectivity constraint, but it gives a framework for construction.…”

Section: The Conformational Search Strategymentioning

confidence: 99%

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A fast conformational search strategy for finding low energy structures of model proteins

1996

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We describe a new computer algorithm for finding low-energy conformations of proteins. It is a chain-growth method that uses a heuristic bias function to help assemble a hydrophobic core. We call it the Core-directed chain Growth method (CG). We test the CG method on several well-known literature examples of HP lattice model proteins [in which proteins are modeled as sequences of hydrophobic (H) and polar (P) monomers], ranging from 20-64 monomers in two dimensions, and up to 88-mers in three dimensions. Previous nonexhaustive methods-Monte Carlo, a Genetic Algorithm, Hydrophobic Zippers. and ContactInteractions-have been tried on these same model sequences. CG is substantially better at finding the global optima, and avoiding local optima, and it does so in comparable or shorter times. CG finds the global minimum energy of the longest HP lattice model chain for which the global optimum is known, a 3D 88-mer that has only been reachable before by the CHCC complete search method. CG has the potential advantage that it should have nonexponential scaling with chain length. We believe this is a promising method for conformational searching in protein folding algorithms.Keywords: chain growth algorithm; conformational searching; lattice model; protein folding The conformational search problemThere have been many important advances on the road to developing a computer protein folding algorithm (Levitt & Warshel, 1975;Kuntz et al., 1976;Wilson & Doniach, 1989;Skolnick & Kolinski, 1990;Covell, 1992Covell, , 1994Sippl et al., 1992;Vajda et al., 1993;Hinds & Levitt, 1994;Kolinski & Skolnick, 1994;Monge et al., 1994;Wallqvist et al., 1994;Boczko & Brooks, 1995;Srinivasan & Rose, 1995;Sun et al., 1995;Yue & Dill, 1996). In order to devise a computer method that can predict the native structure of a protein from its amino acid sequence alone, it is necessary to have an adequate energy function applied to an appropriate chain representation and searched with a fast conformational search method. Currently, the most popular conformational search methods are Molecular Dynamics (MD) and Monte Carlo (MC) and its variants-simulated annealing and genetic algorithms. But these conformational search methods are too slow and "inefficient;" that is, they get stuck in energy traps and are unable to reach the global minima of their energy functions in a reasonable amount of computer time (hours to weeks on workstations). Here we describe a Reprint requests to: Ken A. Dill, Department of Pharmaceutical Chemistry, Box 1204, University of California, San Francisco, California 94143-1204; e-mail: dill@maxwell.ucsf.edu. method that improves on the speed and efficiency of existing search methods.The main problem in developing a conformational search strategy for protein folding is that the energy landscape is large, and sometimes rugged, and we seek the global minima (rather than local minima), of which there are an exceedingly small number. We are searching for a needle in a haystack . The success of a search strategy can be judged ...

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Section: Discussionmentioning

confidence: 99%

Section: The Conformational Search Strategymentioning

confidence: 99%

A fast conformational search strategy for finding low energy structures of model proteins

1996

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“…Elsewhere, various search methods have been tested in lattice models 26,27,28,29 . However, we were interested in comparing zipping and assembly with replica-exchange methods, which are generalized ensemble approaches to reducing barriers and increasing sampling efficiencies 30,31 .…”

Section: Zanda Outperforms Replica-exchange Monte Carlo (Remc) In Reachmentioning

confidence: 99%

Exploring zipping and assembly as a protein folding principle

Voelz

Dill

2007

Proteins

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It has been proposed that proteins fold by a process called "Zipping & Assembly" (Z&A). Zipping refers to the growth of local substructures within the chain, and assembly refers to the coming together of already-formed pieces. Our interest here is in whether Z&A is a general method that can fold most of sequence space, to global minima, efficiently. Using the HP model, we can address this question by enumerating full conformation and sequence spaces. We find that Z&A reaches the global energy minimum native states, even though it searches only a very small fraction of conformational space, for most sequences in the full sequence space. We find that Z&A, a mechanism-based search, is more efficient in our tests than the Replica Exchange search method. Folding efficiency is increased for chains having: (a) small loop-closure steps, consistent with observations by Plaxco et al. 1 that folding rates correlate with contact order, (b) neither too few nor too many nucleation sites per chain, and (c) assembly steps that do not occur too early in the folding process. We find that the efficiency increases with chain length, although our range of chain lengths is limited. We believe these insights may be useful for developing faster protein conformational search algorithms.

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“…Therefore, U HP σiσj = −δ σiH δ σj H . This parameterization, which we will traditionally call the HP model in the following, has been extensively used to identify ground states of HP sequences, some of which are believed to show up qualitative properties comparable with realistic proteins whose 20-letter sequence was transcribed into the 2-letter code of the HP model [16,18,[37][38][39].…”

Section: The Hp Modelmentioning

confidence: 99%

“…Therefore, sophisticated algorithms were developed to find lowest-energy states for chains of up to 136 monomers. The methods applied are based on very different algorithms, ranging from exact enumeration in two dimensions [11,12] and three dimensions on cuboid (compact) lattices [4,[13][14][15], and hydrophobic-core construction methods [16,17] over genetic algorithms [18][19][20][21][22], Monte Carlo simulations with different types of move sets [23][24][25][26], and generalized ensemble approaches [27] to Rosenbluth chaingrowth methods [28] of the 'Go with the Winners' type [29][30][31][32][33][34][35]. With some of these algorithms, thermodynamic quantities of lattice heteropolymers were studied as well [14,27,31,[34][35][36].…”

Section: The Hydrophobic-polar (Hp) Lattice Protein Modelmentioning

confidence: 99%

Thermodynamics of Protein Folding from Coarse-Grained Models’ Perspectives

Bachmann

Janke

Rugged Free Energy Landscapes

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Sequence-structure relationships in proteins and copolymers

Cited by 96 publications

References 17 publications

A fast conformational search strategy for finding low energy structures of model proteins

A fast conformational search strategy for finding low energy structures of model proteins

Exploring zipping and assembly as a protein folding principle

Thermodynamics of Protein Folding from Coarse-Grained Models’ Perspectives

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