Producing High-Accuracy Lattice Models from Protein Atomic Coordinates Including Side Chains

Mann, Martin; Saunders, Rhodri; Smith, Cameron; Backofen, Rolf; Deane, Charlotte M.

doi:10.1155/2012/148045

Cited by 25 publications

(30 citation statements)

References 41 publications

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“…For examples, see Figure 1. The modeling quality of real protein conformations by lattice protein structures depends strongly on the underlying lattice used [51][52][53]. Figure 1 depicts the exponential lattice-dependent growth of the structure space that is detailed in Ref.…”

Section: Lattice Protein Modelsmentioning

confidence: 99%

Exact methods for lattice protein models

Mann

Backofen²

2014

Bio-Algorithms and Med-Systems

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Lattice protein models are well-studied abstractions of globular proteins. By discretizing the structure space and simplifying the energy model over regular proteins, they enable detailed studies of protein structure formation and evolution. However, even in the simplest lattice protein models, the prediction of optimal structures is computationally difficult. Therefore, often, heuristic approaches are applied to find such conformations.Commonly, heuristic methods find only locally optimal solutions. Nevertheless, there exist methods that guarantee to predict globally optimal structures. Currently, only one such exact approach is publicly available, namely the constraint-based protein structure prediction method and variants. Here, we review exact approaches and derived methods. We discuss fundamental concepts like hydrophobic core construction and their use in optimal structure prediction, as well as possible applications like combinations of different energy models.

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Section: Lattice Protein Modelsmentioning

confidence: 99%

Exact methods for lattice protein models

Mann

Backofen²

2014

Bio-Algorithms and Med-Systems

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“…While typical lattice models account only for the backbone representation of protein sequences, recent research aims at incorporating the space required for side chain packing; see the tool in [24]. As pointed out by Moreno-Hernández and Levitt in [20], protein structure predictions carried out in practice may consist of multiple stages that combine computational models of different complexities, where simplified models are often used in the early stages of structure predictions.…”

Section: Introductionmentioning

confidence: 99%

A Firefly-Inspired Method for Protein Structure Prediction in Lattice Models

et al. 2014

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We introduce a Firefly-inspired algorithmic approach for protein structure prediction over two different lattice models in three-dimensional space. In particular, we consider three-dimensional cubic and three-dimensional face-centred-cubic (FCC) lattices. The underlying energy models are the Hydrophobic-Polar (H-P) model, the Miyazawa–Jernigan (M-J) model and a related matrix model. The implementation of our approach is tested on ten H-P benchmark problems of a length of 48 and ten M-J benchmark problems of a length ranging from 48 until 61. The key complexity parameter we investigate is the total number of objective function evaluations required to achieve the optimum energy values for the H-P model or competitive results in comparison to published values for the M-J model. For H-P instances and cubic lattices, where data for comparison are available, we obtain an average speed-up over eight instances of 2.1, leaving out two extreme values (otherwise, 8.8). For six M-J instances, data for comparison are available for cubic lattices and runs with a population size of 100, where, a priori, the minimum free energy is a termination criterion. The average speed-up over four instances is 1.2 (leaving out two extreme values, otherwise 1.1), which is achieved for a population size of only eight instances. The present study is a test case with initial results for ad hoc parameter settings, with the aim of justifying future research on larger instances within lattice model settings, eventually leading to the ultimate goal of implementations for off-lattice models.

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“…Recently, lattice models have been used to characterize the placement of termini in native protein structures [15], and they have found application also in the investigation of RNA folding [16]. For many applications, the accuracy gap between all-atom models and lattice models is indeed significant, and attempts to bridge it have been proposed by projecting the former onto the latter via optimization methods [17], [18].…”

Section: Introductionmentioning

confidence: 99%

On the Characterization and Software Implementation of General Protein Lattice Models

Bechini

2013

PLoS ONE

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models of proteins have been widely used as a practical means to computationally investigate general properties of the system. In lattice models any sterically feasible conformation is represented as a self-avoiding walk on a lattice, and residue types are limited in number. So far, only two- or three-dimensional lattices have been used. The inspection of the neighborhood of alpha carbons in the core of real proteins reveals that also lattices with higher coordination numbers, possibly in higher dimensional spaces, can be adopted. In this paper, a new general parametric lattice model for simplified protein conformations is proposed and investigated. It is shown how the supporting software can be consistently designed to let algorithms that operate on protein structures be implemented in a lattice-agnostic way. The necessary theoretical foundations are developed and organically presented, pinpointing the role of the concept of main directions in lattice-agnostic model handling. Subsequently, the model features across dimensions and lattice types are explored in tests performed on benchmark protein sequences, using a Python implementation. Simulations give insights on the use of square and triangular lattices in a range of dimensions. The trend of potential minimum for sequences of different lengths, varying the lattice dimension, is uncovered. Moreover, an extensive quantitative characterization of the usage of the so-called “move types” is reported for the first time. The proposed general framework for the development of lattice models is simple yet complete, and an object-oriented architecture can be proficiently employed for the supporting software, by designing ad-hoc classes. The proposed framework represents a new general viewpoint that potentially subsumes a number of solutions previously studied. The adoption of the described model pushes to look at protein structure issues from a more general and essential perspective, making computational investigations over simplified models more straightforward as well.

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Producing High-Accuracy Lattice Models from Protein Atomic Coordinates Including Side Chains

Cited by 25 publications

References 41 publications

Exact methods for lattice protein models

Exact methods for lattice protein models

A Firefly-Inspired Method for Protein Structure Prediction in Lattice Models

On the Characterization and Software Implementation of General Protein Lattice Models

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