Can a physics‐based, all‐atom potential find a protein's native structure among misfolded structures? I. Large scale AMBER benchmarking

Wróblewska, Liliana; Skolnick, Jeffrey

doi:10.1002/jcc.20720

Cited by 62 publications

(84 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We performed numerous analyses to establish the significance of our refinement results. One of the common problems in the training of potential functions is the decoy generation procedure (20). Some decoy sets contain certain characteristics that are easy to memorize during training, but are not transferable to other decoy sets.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the energy surface should be funnel-like so that the potential can drive the structure toward lower energy, more native-like conformations. For this to occur, the energy function must have a correlation with native structure similarity (20). Previously (21), we explored the possibility of creating a funnel-like shape for the Amber ff03-based potential (22) by global optimization of the weights of the individual energy components.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Protein model refinement using an optimized physics-based all-atom force field

Jagielska

Wróblewska

Skolnick

2008

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

Self Cite

View full text Add to dashboard Cite

One of the greatest challenges in protein structure prediction is the refinement of low-resolution predicted models to high-resolution structures that are close to the native state. Although contemporary structure prediction methods can assemble the correct topology for a large fraction of protein domains, such approximate models are often not of the resolution required for many important applications, including studies of reaction mechanisms and virtual ligand screening. Thus, the development of a method that could bring those structures closer to the native state is of great importance. We recently optimized the relative weights of the components of the Amber ff03 potential on a large set of decoy structures to create a funnel-shaped energy landscape with the native structure at the global minimum. Such an energy function might be able to drive proteins toward their native structure. In this work, for a test set of 47 proteins, with 100 decoy structures per protein that have a range of structural similarities to the native state, we demonstrate that our optimized potential can drive protein models closer to their native structure. Comparing the lowest-energy structure from each trajectory with the starting decoy, structural improvement is seen for 70% of the models on average. The ability to do such systematic structural refinements by using a physicsbased all-atom potential represents a promising approach to highresolution structure prediction.Amber force field ͉ force field optimization ͉ protein structure prediction ͉ all-atom potential T he past several years have witnessed significant progress in the field of protein structure prediction (1-10), with contemporary methods being able to assemble the correct topology for a large fraction of protein domains. Such approximately correct models typically vary in their structural similarity to the native state, with a rmsd (root mean square deviation) from native that ranges from 1 Å to Ϸ6 Å. Models with a rmsd to native of 1-2 Å are comparable to experimentally obtained structures and can be used in a broad range of applications, including studies of reaction mechanisms and virtual ligand screening (2). In contrast, the range of applicability of lower-resolution models (with a 3-to 6-Å rmsd from native) is smaller (2). Structure prediction methods often use a coarse-grained protein representation to enhance the conformational search efficiency. To further improve model quality, it is possible that additional structural details need to be included. A tempting approach is to use an all-atom detailed protein representation for the final stages of structure prediction, but despite considerable effort, all-atom refinement has seen little success (11-13). Over the years, there have been individual examples of successful refinement (11-16), with the best improvement of Ϸ2 Å (12, 16) and the largest refinement benchmarks consisting of a small set of proteins (12,13,(17)(18)(19). Although isolated examples of refinement have been reported, the methods are far from routin...

show abstract

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Protein model refinement using an optimized physics-based all-atom force field

Jagielska

Wróblewska

Skolnick

2008

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The result of our study of sampling loop conformations to an RMSD of 1.57 Å and yet their detection is inferior by a resolution of $ 1 Å , indicates the need for improvements in the energy function. Other reported studies have clearly shown that the native minimum on an all-atom potential energy function often does not appear to be the lowest free-energy state (see, e.g., Wroblewska and Skolnick 35 ). This difficulty originates from imperfections in the underlying force field plus GB implicit solvent models.…”

Section: Computational Cost and Overall Assessmentmentioning

confidence: 97%

Prediction of protein loop conformations using multiscale modeling methods with physical energy scoring functions

2007

View full text Add to dashboard Cite

This article examines ab initio methods for the prediction of protein loops by a computational strategy of multiscale conformational sampling and physical energy scoring functions. Our approach consists of initial sampling of loop conformations from lattice-based low-resolution models followed by refinement using all-atom simulations. To allow enhanced conformational sampling, the replica exchange method was implemented. Physical energy functions based on CHARMM19 and CHARMM22 parameterizations with generalized Born (GB) solvent models were applied in scoring loop conformations extracted from the lattice simulations and, in the case of all-atom simulations, the ensemble of conformations were generated and scored with these models. Predictions are reported for 25 loop segments, each eight residues long and taken from a diverse set of 22 protein structures. We find that the simulations generally sampled conformations with low global root-mean-square-deviation (RMSD) for loop backbone coordinates from the known structures, whereas clustering conformations in RMSD space and scoring detected less favorable loop structures. Specifically, the lattice simulations sampled basins that exhibited an average global RMSD of 2.21 +/- 1.42 A, whereas clustering and scoring the loop conformations determined an RMSD of 3.72 +/- 1.91 A. Using CHARMM19/GB to refine the lattice conformations improved the sampling RMSD to 1.57 +/- 0.98 A and detection to 2.58 +/- 1.48 A. We found that further improvement could be gained from extending the upper temperature in the all-atom refinement from 400 to 800 K, where the results typically yield a reduction of approximately 1 A or greater in the RMSD of the detected loop. Overall, CHARMM19 with a simple pairwise GB solvent model is more efficient at sampling low-RMSD loop basins than CHARMM22 with a higher-resolution modified analytical GB model; however, the latter simulation method provides a more accurate description of the all-atom energy surface, yet demands a much greater computational cost.

show abstract

“…It has recently been shown that MD does not help discriminate the native state from its decoys, in that the correlation between energy and rmsd vanishes after MD simulation with AMBER and a GB potential (45). Other recent work used replica exchange sampling with CHARMM22/GBSW potential to refine homology modeling targets with spatial restraints; it worked well but it is unclear whether this would have happened without the restraints (20).…”

Section: Molecular Dynamics In Explicit Solventmentioning

confidence: 99%

Solvent dramatically affects protein structure refinement

Chopra

Summa

Levitt

2008

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

100

106

View full text Add to dashboard Cite

One of the most challenging problems in protein structure prediction is improvement of homology models (structures within 1-3 Å C ␣ rmsd of the native structure), also known as the protein structure refinement problem. It has been shown that improvement could be achieved using in vacuo energy minimization with molecular mechanics and statistically derived continuously differentiable hybrid knowledge-based (KB) potential functions. Globular proteins, however, fold and function in aqueous solution in vivo and in vitro. In this work, we study the role of solvent in protein structure refinement. Molecular dynamics in explicit solvent and energy minimization in both explicit and implicit solvent were performed on a set of 75 native proteins to test the various energy potentials. A more stringent test for refinement was performed on 729 near-native decoys for each native protein. We use a powerfully convergent energy minimization method to show that implicit solvent (GBSA) provides greater improvement for some proteins than the KB potential: 24 of 75 proteins showing an average improvement of >20% in C ␣ rmsd from the native structure with GBSA, compared to just 7 proteins with KB. Molecular dynamics in explicit solvent moved the structures further away from their native conformation than the initial, unrefined decoys. Implicit solvent gives rise to a deep, smooth potential energy attractor basin that pulls toward the native structure.energy minimization ͉ implicit solvent ͉ knowledge-based ͉ molecular dynamics ͉ explicit solvent E xperimental determination of protein structures is very expensive, costing U.S. $250,000 in 2000 (1) and $66,000 today (2) and can be a notoriously difficult task, especially for membrane proteins. With the continuing exponential growth of genome sequence data, there is an increasing need for methods that accurately compute the high-resolution native structure of a protein, for use in biological applications that include virtual ligand screening (3), structure-based protein function prediction (4) and structure-based drug design (5). Homology or template based modeling has been the most successful method for protein structure prediction in the critical assessment of protein structure prediction (CASP) experiments (6, 7). The power of this technique progressively increases as ever more structures are solved by world-wide structural genomics initiatives (8, 9). Nevertheless, obtaining a model with the same accuracy as a crystal structure is still an unsolved problem: structure refinement of a rough model (within 1-3 Å rmsd) to bring it closer to the native structure remains a major challenge (6, 10). Work on structure refinement has been ongoing for many decades, starting from the first Molecular Mechanics (MM) energy minimization (11, 12) and continuing to a recent study with knowledgebased (KB) statistically derived potentials (13). During this period many different potentials and a variety of simulation methodologies such as energy minimization, molecular dynamics, and replica exchange Mon...

show abstract

Can a physics‐based, all‐atom potential find a protein's native structure among misfolded structures? I. Large scale AMBER benchmarking

Cited by 62 publications

References 45 publications

Protein model refinement using an optimized physics-based all-atom force field

Protein model refinement using an optimized physics-based all-atom force field

Prediction of protein loop conformations using multiscale modeling methods with physical energy scoring functions

Solvent dramatically affects protein structure refinement

Contact Info

Product

Resources

About