Improved Energy Selection of Nativelike Protein Loops from Loop Decoys

Lin, Matthew S.; Head‐Gordon, Teresa

doi:10.1021/ct700292u

Cited by 10 publications

(16 citation statements)

References 46 publications

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“…It has to be noted that only a four‐fold random sampling was performed for an illustrative comparison. The success of this simple application shows the potential of the current method for loop ensemble generation enriched with native‐like conformations when combined with more conformational search and more extensive use of good scoring functions 8, 67…”

Section: Resultsmentioning

confidence: 99%

Protein loop modeling by using fragment assembly and analytical loop closure

et al. 2010

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Protein loops are often involved in important biological functions such as molecular recognition, signal transduction, or enzymatic action. The three dimensional structures of loops can provide essential information for understanding molecular mechanisms behind protein functions. In this paper, we develop a novel method for protein loop modeling, where the loop conformations are generated by fragment assembly and analytical loop closure. The fragment assembly method reduces the conformational space drastically, and the analytical loop closure method finds the geometrically consistent loop conformations efficiently. We also derive an analytic formula for the gradient of any analytical function of dihedral angles in the space of closed loops. The gradient can be used to optimize various restraints derived from experiments or databases, for example restraints for preferential interactions between specific residues or for preferred backbone angles. We demonstrate that the current loop modeling method outperforms previous methods that employ residue-based torsion angle maps or different loop closure strategies when tested on two sets of loop targets of lengths ranging from 4 to 12.

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Section: Resultsmentioning

confidence: 99%

Protein loop modeling by using fragment assembly and analytical loop closure

et al. 2010

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“…Current approaches to molecular modeling of the protein loops use either de novo restoration of the loops connecting the fixed atomic points or homology modeling of the loops. Both techniques successfully restore 3D structures of the loops with lengths up to 12 residues (see recent studies22, 25–32; most modeling studies prior to 2006 were referenced earlier33). The latest de novo studies reproduced 3D structures for 13‐membered loops,34, 35 and homology modeling was applied to 14‐membered loops 36.…”

Section: Introductionmentioning

confidence: 99%

Modeling the possible conformations of the extracellular loops in G‐protein‐coupled receptors

Nikiforovich¹,

Taylor

Marshall

et al. 2009

Proteins

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This study presents the results of a de novo approach modeling possible conformational dynamics of the extracellular (EC) loops in G-protein-coupled receptors (GPCRs), specifically in bovine rhodopsin (bRh), squid rhodopsin (sRh), human β-2 adrenergic receptor (β2AR), turkey β-1 adrenergic receptor (β1AR) and human A2 adenosine receptor (A2AR). The approach deliberately sacrificed a detailed description of any particular 3D structure of the loops in GPCRs in favor of a less precise description of many possible structures. Despite this, the approach found ensembles of the low-energy conformers of the EC loops that contained structures close to the available X-ray snapshots. For the smaller EC1 and EC3 loops (6-11 residues), our results were comparable with the best recent results obtained by other authors employing much more sophisticated techniques. For the larger EC2 loops (25-34 residues), our results consistently yielded structures significantly closer to the X-ray snapshots than the results of the other authors for loops of similar size. The results suggested possible large-scale movements of the EC loops in GPCRs that might determine their conformational dynamics. The approach was also validated by accurately reproducing the docking poses for low-molecular weight ligands in β2AR, β1AR and A2AR demonstrating the possible influence of the conformations of the EC loops on the binding sites of ligands. The approach correctly predicted the system of disulfide bridges between the EC loops in A2AR and elucidated the probable pathways for forming this system.

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“…In our previous work, 24,25 we developed a solvation energy model (HPMF) that captures the solvent-induced hydrophobic interactions, and we coupled it with the AMBERff99 protein force field and the GB model to use in protein globular structure prediction and local structure refinement or loop modeling. Here, we update the AMBER protein force field from ff99 26 to ff99sb 27 and add an energy term 31 that mediates the backbone hydrogen bond formations.…”

Section: Discussionmentioning

confidence: 99%

“…(1) are taken from our previous model, where V GB is the generalized Born (GB) description of the electrostatic component of solvent free energy, 28 and V HPMF is the HPMF to describe the hydrophobic solute-solute interaction induced by water. 29,30 We refer the reader to our previous work 24,25 for the functional form and the parameter details regarding these terms. Finally, we have added an energy term developed by Kabsch and Sander 31 to provide a means for improving the geometry of protein backbone hydrogen bond formation.…”

Section: Energy Functionmentioning

confidence: 99%

“…We have also demonstrated the energy function's application in loop prediction, and the results show that it can reliably model the nativeness of the target loops, regardless of the length of the loops. 25 In this work, we present our energy function's application in the protein structural refinement regime. Because we primarily focus on the energy function, we do limited global optimization around a given start state, leaving it to future work to couple it to more extensive global sampling.…”

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confidence: 99%

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Reliable protein structure refinement using a physical energy function

Lin

Head‐Gordon

2010

J Comput Chem

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In the past decade, significant progress has been made in protein structure prediction. However, refining models to a level of resolution that is comparable with experimental results and can be used in studies like enzymatic activity still remains a major challenge. We have previously demonstrated that our modular protein-solvent energy function, uniquely involving a potential of mean force description for hydrophobic solvation, works well in protein globular structure prediction and loop modeling. In this work, we couple protein-solvent energy function with our global optimization method stochastic perturbation with soft constraints and use them to refine a collection of template models from submitted predictions to recent Critical Assessment of Techniques for Protein Structure Prediction blind prediction contests. A prediction protocol based on a selection of structures with the lowest energy is able to successfully refine all of the test proteins, and, more importantly, our energy function does not show degradation in prediction when sampling is exhausted.

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Improved Energy Selection of Nativelike Protein Loops from Loop Decoys

Cited by 10 publications

References 46 publications

Protein loop modeling by using fragment assembly and analytical loop closure

Protein loop modeling by using fragment assembly and analytical loop closure

Modeling the possible conformations of the extracellular loops in G‐protein‐coupled receptors

Reliable protein structure refinement using a physical energy function

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