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

Abstract

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...

Comparison between self‐guided Langevin dynamics and molecular dynamics simulations for structure refinement of protein loop conformations

“…Notable examples of computational refinement of protein models include the work of Levitt and coworkers, [1][2][3] the work of the Baker lab [4][5][6] and Zhu et al, [7] the work from the Skolnick group, [8,9] Jacobson and coworkers, [10] and Chen and Brooks, [11] among others that participated in recent Critical Assessment of Protein Structure Prediction meetings. [12] An integral component of structure refinement is the modeling of protein loops.…”

Generalized ensemble methods for de novo structure prediction

“…Rosetta incorporates (i) a low-resolution representation of a protein that uses the main chain atoms and a side-chain centroid and (ii) a high-resolution representation that uses all atoms. The low-resolution Rosetta energy function includes the van der Waals hard sphere repulsion (vdw), environment (env), pair (pair), C␤ packing density (cb), secondary structure packing [helixhelix pairing (hh), helix-strand pairing (hs), strand-strand pairing (ss), strand pair distance/register (rsigma) and strand arrangement into sheets (sheet)], radius of gyration (rg) energetic contributions, contact order (co), and Ramachandran torsion angle filters (rama) (2,14). Additional hydrogen bonding (short-(hb srbb) and long-range (hb lrbb) backbone-backbone hydrogen bond) energy terms are added right before (score6) and used during full-atom refinement (score12).…”

“…For longer proteins, the computational cost and ruggedness of the all-atom energy function makes solving this problem particularly challenging as evidenced by the modest success of fullatom refinement (12)(13)(14). For this reason, there are multiscale approaches that start with low-resolution or reduced-model energy functions and then use all-atom energy functions on a few selected conformations [often relying on additional steps such as use of sequence homologs (2) or clustering (3, 4)] been developed (4,6,12,13).…”

“…These methods strive to search energy space better by computing the density of states, sampling expanded ranges of temperatures, or computing other physical quantities affecting transitions between the states during the search. In particular, advanced methods such as Temperature Replica Exchange Monte Carlo (TREM) (8) and Hamiltonian Replica Exchange Monte Carlo (HREM) (10), have been shown to outperform standard Monte Carlo in terms of sampling for both simplified and all-atom force fields of small proteins (8,10,11).For longer proteins, the computational cost and ruggedness of the all-atom energy function makes solving this problem particularly challenging as evidenced by the modest success of fullatom refinement (12)(13)(14). For this reason, there are multiscale approaches that start with low-resolution or reduced-model energy functions and then use all-atom energy functions on a few selected conformations [often relying on additional steps such as use of sequence homologs (2) or clustering (3, 4)] been developed (4, 6, 12, 13).…”

Comparative Modeling: The State of the Art and Protein Drug Target Structure Prediction

“…Alternative evaluation methods use physics-based energy functions to score the energy of the predicted model, taking into account bonded and non-bonded interactions between the atoms in the system. Therefore, classical physics-based energies for molecular dynamics simulations, such as AMBER [73] and CHARMM [74], have been used to assess the quality of predicted models [75,76]. A more complete description of the methods for model assessment has been recently published [70].…”