Capturing molecular energy landscapes with probabilistic conformational roadmaps

Apaydın, Mehmet Serkan; Singh, Amit; Brutlag, Douglas L.; Latombe, Jean‐Claude

doi:10.1109/robot.2001.932670

Cited by 33 publications

(38 citation statements)

References 13 publications

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“…An energy function can easily be integrated into this kind of exploration algorithm. Indeed, impressive results have been obtained by conformational search methods inspired by sampling-based motion planning techniques applied to computer assisted drug design [29], protein folding [27,58] and ligand-protein docking [59,60]. Given this energy function, geometrically feasible conformations generated by our approach could be evaluated and labeled, and then only the subset of the conformational space C lowE below a certain energetic limit should be explored.…”

Section: Discussion and Prospectsmentioning

confidence: 99%

Geometric algorithms for the conformational analysis of long protein loops

et al. 2004

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The efficient filtering of unfeasible conformations would considerably benefit the exploration of the conformational space when searching for minimum energy structures or during molecular simulation. The most important conditions for filtering are the maintenance of molecular chain integrity and the avoidance of steric clashes. These conditions can be seen as geometric constraints on a molecular model. In this paper, we discuss how techniques issued from recent research in Robotics can be applied to this filtering. Two complementary techniques are presented: one for conformational sampling and another for computing conformational changes satisfying such geometric constraints. The main interest of the proposed techniques is their application to the structural analysis of long protein loops. First experimental results demonstrate the efficacy of the approach for studying the mobility of loop 7 in amylosucrase from Neisseria polysaccharea.The supposed motions of this 17-residue loop would play a very important role in the activity of this enzyme.

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Section: Discussion and Prospectsmentioning

confidence: 99%

Geometric algorithms for the conformational analysis of long protein loops

et al. 2004

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“…This mapping out of the conformational space is similar in spirit to the probabilistic roadmaps used in robotics for motion planning (34) and recently applied to binding site landscapes (35).…”

Section: Energy Optimization and Iterative Refinementmentioning

confidence: 91%

Funnel sculpting for in silico assembly of secondary structure elements of proteins

Fain

Levitt

2003

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

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We present a method for designing a funnel-shaped free-energy surface that reproducibly assembles secondary structure elements of proteins into their native conformations from a random extended configuration. Assuming a priori knowledge of secondary structure, our method can design a funnel-shaped surface for folding of ␣, ␤, and ␣␤ structures individually. We design energy surfaces that fold up to five unrelated sequences with the same energy parameters. We develop a measure of the foldability of an energy landscape in silico and present an alternative way to view energy landscapes.I n 1973, Anfinsen (1) showed that the native state of a protein corresponds to a minimum of free energy. Using his discovery as a starting point, scientists have tried to derive the free energy surface of proteins and to predict the folded protein structure.One of the dominant prediction methods used today is the decoy-then-scoring paradigm (45). First, a large number of decoys are generated to sample the conformation space. Usually considerable effort is exerted to explore and enrich the near-native fraction of the decoy set. After the sampling, a discrimination function picks out the best structure(s) from the ensemble. In this approach, the sampling of conformation space and the scoring of structures are separate. This paradigm is suitable for today's rapid increase of computational power: sampling is trivially parallelizable and, consequently, progress can result from simple increase in the number of operations spent on the task. Dovetailed into the decoy-generate-then-score paradigm are several different methods of extracting energy parameters for proteins. We mention a few examples: Z-score minimization (2-4) requires that the native state energy lie as low as possible relative to some reference state, usually represented by an ensemble of compact structures. Linear optimization (5, 6) maximizes a variety of conditions subject to the logical constraint that the energy of the native state (suitably parametrized as a linear sum) is simply lower than all alternate conformations. Potentials of mean force (7) assume that some relevant variables, e.g., residue-residue distances, are Boltzmann-distributed with the native state being the overwhelmingly more populated one. Finally, some approaches have trial-and-error components, where researchers notice and introduce features that make their predictions more native-like (8, 9).The price extracted by the generate-decoy-then-score separation is the necessity of elaborate and computationally intensive sampling techniques to uncover the low energy states. Several clever schemes (see ref. 9 and references therein) use sequence information by either finding a close homologue or using short segment homology in fragment insertion. So far, researchers have not been able to consistently fold proteins using the generate-then-score paradigm (10, 11). Folding FunnelsScientists are not alone in having trouble folding proteins with current methods. A polypeptide can assume such a vast number of conf...

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“…As a joint rotation changes the positions of following links, so does rotation about a bond change the positions of following atoms [21]. These analogies have long been employed by robotics researchers to apply algorithms that plan motions for kinematic chains with revolute dofs to the study of protein conformations [26][27][28][29][30][31][32][33][34][35][36][37]. Unlike typical articulated mechanisms, protein chains have a high number of dofs.…”

Section: Short Primer On Protein Modelingmentioning

confidence: 99%

Modeling Structures and Motions of Loops in Protein Molecules

Shehu

Kavraki

2012

Entropy

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Unlike the secondary structure elements that connect in protein structures, loop fragments in protein chains are often highly mobile even in generally stable proteins. The structural variability of loops is often at the center of a protein's stability, folding, and even biological function. Loops are found to mediate important biological processes, such as signaling, protein-ligand binding, and protein-protein interactions. Modeling conformations of a loop under physiological conditions remains an open problem in computational biology. This article reviews computational research in loop modeling, highlighting progress and challenges. Important insight is obtained on potential directions for future research.

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Capturing molecular energy landscapes with probabilistic conformational roadmaps

Cited by 33 publications

References 13 publications

Geometric algorithms for the conformational analysis of long protein loops

Geometric algorithms for the conformational analysis of long protein loops

Funnel sculpting for in silico assembly of secondary structure elements of proteins

Modeling Structures and Motions of Loops in Protein Molecules

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