Modeling Structures and Motions of Loops in Protein Molecules

Shehu, Amarda; Kavraki, Lydia E.

doi:10.3390/e14020252

Cited by 43 publications

(38 citation statements)

References 157 publications

(282 reference statements)

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“…are also easily accessible. Of particular interest may be the solvent accessible surface area analysis, which provides additional context‐specific information for generation of protein surface loop ensembles . Flexibility analysis may prove beneficial for predicting highly flexible protein structures such as intrinsically disordered regions.…”

Section: Discussionmentioning

confidence: 99%

Dynameomics: Data‐driven methods and models for utilizing large‐scale protein structure repositories for improving fragment‐based loop prediction

2014

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Protein function is intimately linked to protein structure and dynamics yet experimentally determined structures frequently omit regions within a protein due to indeterminate data, which is often due protein dynamics. We propose that atomistic molecular dynamics simulations provide a diverse sampling of biologically relevant structures for these missing segments (and beyond) to improve structural modeling and structure prediction. Here we make use of the Dynameomics data warehouse, which contains simulations of representatives of essentially all known protein folds. We developed novel computational methods to efficiently identify, rank and retrieve small peptide structures, or fragments, from this database. We also created a novel data model to analyze and compare large repositories of structural data, such as contained within the Protein Data Bank and the Dynameomics data warehouse. Our evaluation compares these structural repositories for improving loop predictions and analyzes the utility of our methods and models. Using a standard set of loop structures, containing 510 loops, 30 for each loop length from 4 to 20 residues, we find that the inclusion of Dynameomics structures in fragment-based methods improves the quality of the loop predictions without being dependent on sequence homology. Depending on loop length, 25-75% of the best predictions came from the Dynameomics set, resulting in lower main chain root-mean-square deviations for all fragment lengths using the combined fragment library. We also provide specific cases where Dynameomics fragments provide better predictions for NMR loop structures than fragments from crystal structures. Online access to these fragment libraries is available at http://www.dynameomics.org/fragments.

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Section: Discussionmentioning

confidence: 99%

Dynameomics: Data‐driven methods and models for utilizing large‐scale protein structure repositories for improving fragment‐based loop prediction

2014

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“…Motion planning has recently been applied to a wide range of biologically important subjects including rna folding [18], protein loop modeling [19]–[22], protein folding/binding [23]–[25], conformational flexibility [17], [26] and conformational transitions [27], [28], among others.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we define a library of moves that each individually has been used in prior work for conformational sampling, but not in an integrated way as is done here. This library includes: energy minimization, loop sampling [22], random dihedral angle perturbation, and “natural moves” similar to [38]. An expansive planning algorithm thus grows a tree of conformations that preferentially expands away from a set of starting states towards less-explored regions of the energetic landscape.…”

Section: Introductionmentioning

confidence: 99%

SIMS: A Hybrid Method for Rapid Conformational Analysis

2013

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Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (sims), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. sims can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that sims is applied to and demonstrate a rapid solution for each. These include the automatic determination of “active” residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose-Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems.

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“…Therefore, conformations produced by ARAP interpolation may be close to conformations produced by changes in internal coordinates, but may also include changes in bond lengths and bond angles. This helps ARAP interpolation overcome two obstacles faced by internal coordinates moves: the impossibility to reach the target conformation due to fixed bond lengths and bond angles, as well as the loop closure problem [59].…”

Section: Arap Energy and Dihedral Anglesmentioning

confidence: 99%

As-Rigid-As-Possible molecular interpolation paths

Nguyen

Jaillet

Redon

2017

J Comput Aided Mol Des

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This paper proposes a new method to generate interpolation paths between two given molecular conformations. It relies on the As-Rigid-AsPossible (ARAP) paradigm used in Computer Graphics to manipulate complex meshes while preserving their essential structural characteristics. The adaptation of ARAP approaches to the case of molecular systems is presented in this contribution. Experiments conducted on a large set of benchmarks show how such a strategy can efficiently compute relevant interpolation paths with large conformational rearrangements.

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Modeling Structures and Motions of Loops in Protein Molecules

Cited by 43 publications

References 157 publications

Dynameomics: Data‐driven methods and models for utilizing large‐scale protein structure repositories for improving fragment‐based loop prediction

Dynameomics: Data‐driven methods and models for utilizing large‐scale protein structure repositories for improving fragment‐based loop prediction

SIMS: A Hybrid Method for Rapid Conformational Analysis

As-Rigid-As-Possible molecular interpolation paths

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