Predicting the Effect of Amino Acid Single-Point Mutations on Protein Stability—Large-Scale Validation of MD-Based Relative Free Energy Calculations

Steinbrecher, Thomas; Zhu, Chongkai; Wang, Lingle; Abel, Robert; Negron, Christopher; Pearlman, David A.; Feyfant, Eric; Duan, Jian‐Xin; Sherman, Woody

doi:10.1016/j.jmb.2016.12.007

Cited by 100 publications

(142 citation statements)

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“…Due to the importance of changes in protein stability upon mutation, a wide variety of techniques exist to predict ΔΔ G Folding ( S 1 → S 2 ). Techniques range from fast physics‐based, knowledge‐based, and machine learning approaches; to implicit solvent methods; to rigorous alchemical free energy calculations in explicit solvent …”

Section: Discussionmentioning

confidence: 99%

“…Studies using free energy methods to estimate changes in peptide and protein free energy upon side chain mutation are much rarer . Last year, two large‐scale applications of free energy methods to protein folding and binding upon mutation appeared . Such studies could in principle be used to guide protein design, but in practice are far too slow to be of practical use in driving design.…”

Section: Introductionmentioning

confidence: 99%

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Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme

2018

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The estimation of changes in free energy upon mutation is central to the problem of protein design. Modern protein design methods have had remarkable success over a wide range of design targets, but are reaching their limits in ligand binding and enzyme design due to insufficient accuracy in mutational free energies. Alchemical free energy calculations have the potential to supplement modern design methods through more accurate molecular dynamics based prediction of free energy changes, but suffer from high computational cost. Multisite λ dynamics (MSλD) is a particularly efficient and scalable free energy method with potential to explore combinatorially large sequence spaces inaccessible with other free energy methods. This work aims to quantify the accuracy of MSλD and demonstrate its scalability. We apply MSλD to the classic problem of calculating folding free energies in T4 lysozyme, a system with a wealth of experimental measurements. Single site mutants considering 32 mutations show remarkable agreement with experiment with a Pearson correlation of 0.914 and mean unsigned error of 1.19 kcal/mol. Multisite mutants in systems with up to five concurrent mutations spanning 240 different sequences show comparable agreement with experiment. These results demonstrate the promise of MSλD in exploring large sequence spaces for protein design.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme

2018

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“…Initial equilibration was performed using standard protocol (see, for example, ref. 58) in Desmond. The production run was 250 ns long using a time step of 2 fs and conformations were saved every 100 ps generating a trajectory with 2500 frames.…”

Section: Methodsmentioning

confidence: 99%

Efficient Generation of Bispecific Murine Antibodies for Pre-Clinical Investigations in Syngeneic Rodent Models

Labrijn

Meesters

Bunce

et al. 2017

Sci Rep

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Therapeutic concepts exploiting tumor-specific antibodies are often established in pre-clinical xenograft models using immuno-deficient mice. More complex therapeutic paradigms, however, warrant the use of immuno-competent mice, that more accurately capture the relevant biology that is being exploited. These models require the use of (surrogate) mouse or rat antibodies to enable optimal interactions with murine effector molecules. Immunogenicity is furthermore decreased, allowing longer-term treatment. We recently described controlled Fab-arm exchange (cFAE) as an easy-to-use method for the generation of therapeutic human IgG1 bispecific antibodies (bsAb). To facilitate the investigation of dual-targeting concepts in immuno-competent mice, we now applied and optimized our method for the generation of murine bsAbs. We show that the optimized combinations of matched point-mutations enabled efficient generation of murine bsAbs for all subclasses studied (mouse IgG1, IgG2a and IgG2b; rat IgG1, IgG2a, IgG2b, and IgG2c). The mutations did not adversely affect the inherent effector functions or pharmacokinetic properties of the corresponding subclasses. Thus, cFAE can be used to efficiently generate (surrogate) mouse or rat bsAbs for pre-clinical evaluation in immuno-competent rodents.

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“…Thus, force fields demonstrate their considerable success even in blind predictions, such as on host-guest [64,65,105] and protein-ligand binding affinities [1,7,52,61,75,77], hydration free energies [21,63], partition and distribution coefficients [3], ligand binding modes and activity [15], and others. Extensive retrospective tests for other properties such as dielectric constants [6,19,32,69] and perturbations in protein stability [80] are also worth noting given their success. It seems safe to say these relatively simple models have succeeded far beyond original expectations, likely in part because of their strong physical basis, careful and time-consuming attention paid in parameterization, and a reasonable balance of speed versus accuracy for many applications.…”

Section: Introductionmentioning

confidence: 99%

Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1

Mobley

Bannan

Rizzi

et al. 2018

Preprint

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Here, we focus on testing and improving force fields for molecular modeling, which see widespread use in diverse areas of computational chemistry and biomolecular simulation. A key issue affecting the accuracy and transferrability of these force fields is the use of atom typing. Traditional approaches to defining molecular mechanics force fields must encode, within a discrete set of atom types, all information which will ever be needed about the chemical environment; parameters are then assigned by looking up combinations of these atom types in tables. This atom typing approach leads to a wide variety of problems such as inextensible atom-typing machinery, enormous difficulty in expanding parameters encoded by atom types, and unnecessarily proliferation of encoded parameters. Here, we describe a new approach to assigning parameters for molecular mechanics force fields based on the industry standard SMARTS chemical perception language (with extensions to identify specific atoms available in SMIRKS). In this approach, each force field term (bonds, angles, and torsions, and nonbonded interactions) features separate definitions assigned in a hierarchical manner without using atom types. We accomplish this using direct chemical perception, where parameters are assigned directly based on substructure queries operating on the molecule(s) being parameterized, thereby avoiding the intermediate step of assigning atom types -a step which can be considered indirect chemical perception. Direct chemical perception allows for substantial simplification of force fields, as well as additional generality in the substructure queries. This approach is applicable to a wide variety of (bio)molecular systems, and can greatly reduce the number of parameters needed to create a complete force field. Further flexibility can also be gained by allowing force field terms to be interpolated based on the assignment of fractional bond orders via the same procedure used to assign partial charges. As an example of the utility of this approach, we provide a minimalist small molecule force field derived from Merck's parm@Frosst (an Amber parm99 descendant), in which a parameter definition file only ≈300 lines long can parameterize a large and diverse spectrum of pharmaceutically relevant small molecule chemical space. We benchmark this minimalist force field on the FreeSolv small molecule hydration free energy set and calculations of densities and dielectric constants from the ThermoML Archive, demonstrating that it achieves comparable accuracy to the Generalized Amber Force Field (GAFF) that consists of many thousands of parameters.

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Predicting the Effect of Amino Acid Single-Point Mutations on Protein Stability—Large-Scale Validation of MD-Based Relative Free Energy Calculations

Cited by 100 publications

References 100 publications

Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme

Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme

Efficient Generation of Bispecific Murine Antibodies for Pre-Clinical Investigations in Syngeneic Rodent Models

Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1

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