Protein Force Field Research Articles

Electrostatics analysis of the mutational and pH effects of the N-terminal domain self-association of the major ampullate spidroin.

Spider silk is a fascinating material combining mechanical properties such as maximum strength and high toughness comparable or better than man-made materials, with biocompatible degradability characteristics. Experimental measurements have shown that pH triggers the dimer formation of the N-terminal domain (NTD) of the major ampullate spidroin 1 (MaSp 1). A coarse-grained model accounting for electrostatics, van der Waals and pH-dependent charge-fluctuation interactions, by means of Monte Carlo simulations, gave us a more comprehensive view of the NTD dimerization process. A detailed analysis of the electrostatic properties and free energy derivatives for the NTD homoassociation was carried out at different pH values and salt concentrations for the protein wild type and for several mutants. We observed an enhancement of dipole-dipole interactions at pH 6 due to the ionization of key amino acids, a process identified as the main driving force for dimerization. Analytical estimates based on the DVLO theory framework corroborate our findings. Molecular dynamics simulations using the OPEP coarse-grained force field for proteins show that the mutant E17Q is subject to larger structural fluctuations when compared to the wild type. Estimates of the association rate constants for this mutant were evaluated by the Debye-Smoluchowski theory and are in agreement with the experimental data when thermally relaxed structures are used instead of the crystallographic data. Our results can contribute to the design of new mutants with specific association properties.

OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins.

The parametrization and validation of the OPLS3 force field for small molecules and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addition of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chemical space, OPLS3 employs over an order of magnitude more reference data and associated parameter types relative to other commonly used small molecule force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small molecule conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a number of proteins. Together, the improvements made to both the small molecule and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.

Transferable kriging machine learning models for the multipolar electrostatics of helical deca-alanine

We exploit the transferability of quantum topological atoms in the construction of a multipolar polarizable protein force field QCTFF. A helical oligopeptide of 10 alanine residues (103 atoms) has its total electrostatic energy predicted using the kriging machine learning method with a mean error of 6.4 kJ mol−1. This error is similar to that found in smaller molecules presented in past QCTFF publications. Kriging relates the molecular geometry to atomic multipole moments that describe the ab initio electron density. Atom types are constructed from similar atoms within the helix. As the atoms within a given atom type share a local chemical environment, they can share a kriging model with a reduced number of input descriptors (i.e. features). The feature reduction decreases the kriging training times by more than 23 times but increases the prediction error by only 1.3 %. In transferability tests, transferable models give a 5.7 % error when predicting moments of an atom outside the training set, compared to the 3.9 % error when tested against data belonging to atoms included in the training data. The transferable kriging models successfully predict atomic multipole moments with useful accuracy, opening an avenue to QCTFF modelling of a whole protein.

3DRobot: automated generation of diverse and well-packed protein structure decoys

Computationally generated non-native protein structure conformations (or decoys) are often used for designing protein folding simulation methods and force fields. However, almost all the decoy sets currently used in literature suffer from uneven root mean square deviation (RMSD) distribution with bias to non-protein like hydrogen-bonding and compactness patterns. Meanwhile, most protein decoy sets are pre-calculated and there is a lack of methods for automated generation of high-quality decoys for any target proteins. We developed a new algorithm, 3DRobot, to create protein structure decoys by free fragment assembly with enhanced hydrogen-bonding and compactness interactions. The method was benchmarked with three widely used decoy sets from ab initio folding and comparative modeling simulations. The decoys generated by 3DRobot are shown to have significantly enhanced diversity and evenness with a continuous distribution in the RMSD space. The new energy terms introduced in 3DRobot improve the hydrogen-bonding network and compactness of decoys, which eliminates the possibility of native structure recognition by trivial potentials. Algorithms that can automatically create such diverse and well-packed non-native conformations from any protein structure should have a broad impact on the development of advanced protein force field and folding simulation methods. AVAILIABLITY AND IMPLEMENTATION: http://zhanglab.ccmb.med.umich.edu/3DRobot/ jiay@phy.ccnu.edu.cn; zhng@umich.edu Supplementary data are available at Bioinformatics online.

Grid-based backbone correction to the ff12SB protein force field for implicit-solvent simulations.

Force fields, such as Amber's ff12SB, can be fairly accurate models of the physical forces in proteins and other biomolecules. When coupled with accurate solvation models, force fields are able to bring insight into the conformational preferences, transitions, pathways, and free energies for these biomolecules. When computational speed/cost matters, implicit solvent is often used but at the cost of accuracy. We present an empirical grid-like correction term, in the spirit of cMAPs, to the combination of the ff12SB protein force field and the GBneck2 implicit-solvent model. Ff12SB-cMAP is parametrized on experimental helicity data. We provide validation on a set of peptides and proteins. Ff12SB-cMAP successfully improves the secondary structure biases observed in ff12SB + Gbneck2. Ff12SB-cMAP can be downloaded ( https://github.com/laufercenter/Amap.git ) and used within the Amber package. It can improve the agreement of force fields + implicit solvent with experiments.

A Maximum-Likelihood Approach to Force-Field Calibration.

A new approach to the calibration of the force fields is proposed, in which the force-field parameters are obtained by maximum-likelihood fitting of the calculated conformational ensembles to the experimental ensembles of training system(s). The maximum-likelihood function is composed of logarithms of the Boltzmann probabilities of the experimental conformations, calculated with the current energy function. Because the theoretical distribution is given in the form of the simulated conformations only, the contributions from all of the simulated conformations, with Gaussian weights in the distances from a given experimental conformation, are added to give the contribution to the target function from this conformation. In contrast to earlier methods for force-field calibration, the approach does not suffer from the arbitrariness of dividing the decoy set into native-like and non-native structures; however, if such a division is made instead of using Gaussian weights, application of the maximum-likelihood method results in the well-known energy-gap maximization. The computational procedure consists of cycles of decoy generation and maximum-likelihood-function optimization, which are iterated until convergence is reached. The method was tested with Gaussian distributions and then applied to the physics-based coarse-grained UNRES force field for proteins. The NMR structures of the tryptophan cage, a small α-helical protein, determined at three temperatures (T = 280, 305, and 313 K) by Hałabis et al. ( J. Phys. Chem. B 2012 , 116 , 6898 - 6907 ), were used. Multiplexed replica-exchange molecular dynamics was used to generate the decoys. The iterative procedure exhibited steady convergence. Three variants of optimization were tried: optimization of the energy-term weights alone and use of the experimental ensemble of the folded protein only at T = 280 K (run 1); optimization of the energy-term weights and use of experimental ensembles at all three temperatures (run 2); and optimization of the energy-term weights and the coefficients of the torsional and multibody energy terms and use of experimental ensembles at all three temperatures (run 3). The force fields were subsequently tested with a set of 14 α-helical and two α + β proteins. Optimization run 1 resulted in better agreement with the experimental ensemble at T = 280 K compared with optimization run 2 and in comparable performance on the test set but poorer agreement of the calculated folding temperature with the experimental folding temperature. Optimization run 3 resulted in the best fit of the calculated ensembles to the experimental ones for the tryptophan cage but in much poorer performance on the training set, suggesting that use of a small α-helical protein for extensive force-field calibration resulted in overfitting of the data for this protein at the expense of transferability. The optimized force field resulting from run 2 was found to fold 13 of the 14 tested α-helical proteins and one small α + β protein with the correct topologies; the average structures of 10 of them were predicted with accuracies of about 5 Å C(α) root-mean-square deviation or better. Test simulations with an additional set of 12 α-helical proteins demonstrated that this force field performed better on α-helical proteins than the previous parametrizations of UNRES. The proposed approach is applicable to any problem of maximum-likelihood parameter estimation when the contributions to the maximum-likelihood function cannot be evaluated at the experimental points and the dimension of the configurational space is too high to construct histograms of the experimental distributions.

The Dependence of Amyloid-β Dynamics on Protein Force Fields and Water Models.

We studied the dynamics of Aβ40 , involved in Alzheimer's disease, by using 21 methods combined from Amber03, Amber99sb-ILDN, Charmm27, Charmm22*, OPLS-2001, OPLS-2006, OPLS-2008, Gromos96-43a1, Gromos96-53a6, Gromos96-54a7, and the water models SPC, TIP3P, TIP4P. Major differences in the structural ensembles were systematized: Amber03, Charmm27, and Gromos96-54a7 stabilize the helices; Gromos96-43a1 and Gromos53a6 favor the β-strands (with Charmm22* and Amber99sb-ILDN in between), and OPLS produces unstructured ensembles. The accuracy of the NMR chemical shifts was in the order: Charmm22*>Amber99sb-ILDN>OPLS-2008≈Gromos96-43a1>Gromos96-54a7≈OPLS-2001>OPLS-2006>Gromos96-53a6>Charmm27>Amber03. The computed (3) JHNHα -coupling constants were sensitive to experiment type and Karplus parameterization. Overall, the ensembles of Charmm22* and Amber99sb-ILDN provided the best agreement with experimental NMR and circular dichroism data, providing a model for the real Aβ monomer ensemble. Also, the polar water model TIP3P significantly favored helix and compact conformations.

Quantum Corrections to the Free Energy Difference between Peptides and Proteins Conformers.

The calculation of the free energy of conformation is key in understanding the function of biomolecules and has attracted significant interest in recent years. Most current computational approaches evaluate the difference in conformational free energy in the classical limit based on the common "dogma" that only the lowest-frequency modes make a significant contribution to it, i.e. they assume that quantum mechanical corrections are negligible. Here, I show for three biomolecular systems described in the rigid-rotor, harmonic-oscillator approximation that the zero-point energy contribution, although small, is not negligible even at room temperature. I find that a quantum correction arises from the intermediate-frequency vibrational modes and that its magnitude is strongly correlated with the number of atoms in the system. A straightforward, though approximate, way to account for this quantum correction in the calculation of conformational free-energy differences by classical molecular dynamics is presented. The relevance of the quantum correction analyzed in this paper is discussed in the context of conventional force fields for proteins.

Structural Ensemble of CD4 Cytoplasmic Tail (402-419) Reveals a Nearly Flat Free-Energy Landscape with Local α-Helical Order in Aqueous Solution.

The human cluster determinant 4 (CD4), expressed primarily on the surface of T helper cells, serves as a coreceptor in T-cell receptor recognition of MHC II antigen complexes. Besides its cellular functions, CD4 serves as a primary receptor of human immunodeficiency virus (HIV) type 1. The cytoplasmic tail of CD4 (residues 402-419) is known to be involved in direct interaction with the HIV-1 proteins Vpu and Nef. These two viral accessory proteins (Vpu and Nef) downregulate CD4 in HIV-1 infected cells by multiple strategies and make the body susceptible to all forms of infections. In this work, we carried out extensive replica exchange molecular dynamics simulations in explicit water with three popular protein force fields Amber ff99SB, Amber ff99SB*-ILDN, and CHARMM36 to characterize the equilibrium conformational ensemble of CD4-tail (402-419) and further validated the simulated ensembles with known NMR data. We found that ff99SB*-ILDN gives a better description of the structural ensemble of this peptide compared with ff99SB and CHARMM36. The peptide adopts multiple distinct conformations with varying degree of residual secondary structures. In particular, we observed 28, 7, and 5% average α-helical, β-strand, and 3(10)-helix content, respectively, for ff99SB*-ILDN. The peptide chain shows the tendency of helix formation in a cooperative manner, seeding at residues 407-410, and subsequently extending toward both ends of the chain. Furthermore, we constructed Markov state model (MSM) from large-scale molecular dynamics simulations to study the dynamics of transitions between different metastable states explored by this peptide. The mean first passage times computed from MSM indicate rapid interconversion of these states, and the time scales of transitions range from several nanoseconds to hundreds of microseconds. Our results show good agreement with experimental data and could help to understand the key molecular mechanisms of T-cell activation and HIV-mediated receptor interference.

Improved Peptide and Protein Torsional Energetics with the OPLSAA Force Field.

The development and validation of new peptide dihedral parameters are reported for the OPLS-AA force field. High accuracy quantum chemical methods were used to scan φ, ψ, χ1, and χ2 potential energy surfaces for blocked dipeptides. New Fourier coefficients for the dihedral angle terms of the OPLS-AA force field were fit to these surfaces, utilizing a Boltzmann-weighted error function and systematically examining the effects of weighting temperature. To prevent overfitting to the available data, a minimal number of new residue-specific and peptide-specific torsion terms were developed. Extensive experimental solution-phase and quantum chemical gas-phase benchmarks were used to assess the quality of the new parameters, named OPLS-AA/M, demonstrating significant improvement over previous OPLS-AA force fields. A Boltzmann weighting temperature of 2000 K was determined to be optimal for fitting the new Fourier coefficients for dihedral angle parameters. Conclusions are drawn from the results for best practices for developing new torsion parameters for protein force fields.

Folding thermodynamics of β‐hairpins studied by replica‐exchange molecular dynamics simulations

We study the differences in folding stability of β-hairpin peptides, including GB1 hairpin and a point mutant GB1 K10G, as well as tryptophan zippers (TrpZips): TrpZip1, TrpZip2, TrpZip3-1, and TrpZip4. By performing replica-exchange molecular dynamics simulations with Amber03* force field (a modified version of Amber ff03) in explicit solvent, we observe ab initio folding of all the peptides except TrpZip3-1, which is experimentally known to be the least stable among the peptides studied here. By calculating the free energies of unfolding of the peptides at room temperature and folding midpoint temperatures for thermal unfolding of peptides, we find that TrpZip4 and GB1 K10G peptides are the most stable β-hairpins followed by TrpZip1, GB1, and TrpZip2 in the given order. Hence, the proposed K10G mutation of GB1 peptide results in enhanced stability compared to wild-type GB1. An important goal of our study is to test whether simulations with Amber 03* model can reproduce experimentally predicted folding stability differences between these peptides. While the stabilities of GB1 and TrpZip1 yield close agreement with experiment, TrpZip2 is found to be less stable than predicted by experiment. However, as heterogenous folding of TrpZip2 may yield divergent thermodynamic parameters by different spectroscopic methods, mismatching of results with previous experimental values are not conclusive of model shortcomings. For most of the cases, molecular simulations with Amber03* can successfully reproduce experimentally known differences between the mutated peptides, further highlighting the predictive capabilities of current state-of-the-art all-atom protein force fields.

Residue-Specific Force Field (RSFF2) Improves the Modeling of Conformational Behavior of Peptides and Proteins.

A recent report of (3)J(HNHα) scalar coupling constants for hundreds of two-residue peptides has provided an important opportunity to test simulation force fields for proteins. Here, we compare the abilities of three derivatives of the Amber ff99SB force field to reproduce these data. We report molecular dynamics (MD) simulations of 256 two-residue peptides and show that the recently developed residue-specific force field (RSFF2) produces a dramatic improvement in the agreement with experimental (3)J(HNHα) coupling constants. We further show that RSFF2 also appears to produce a modest improvement in reproducing the (3)J(HNHα) coupling constants of five model proteins. Perhaps surprisingly, an analysis of neighboring residue effects (NREs) on the (3)J(HNHα) coupling constants of the two-residue peptides indicates little difference between the force fields' abilities to reproduce experimental NREs. We speculate that this might indicate limitations in the force fields' descriptions of nonbonded interactions between adjacent side chains or with terminal capping groups.

Parametrization of Backbone Flexibility in a Coarse-Grained Force Field for Proteins (COFFDROP) Derived from All-Atom Explicit-Solvent Molecular Dynamics Simulations of All Possible Two-Residue Peptides.

Recently, we reported the parametrization of a set of coarse-grained (CG) nonbonded potential functions, derived from all-atom explicit-solvent molecular dynamics (MD) simulations of amino acid pairs and designed for use in (implicit-solvent) Brownian dynamics (BD) simulations of proteins; this force field was named COFFDROP (COarse-grained Force Field for Dynamic Representations Of Proteins). Here, we describe the extension of COFFDROP to include bonded backbone terms derived from fitting to results of explicit-solvent MD simulations of all possible two-residue peptides containing the 20 standard amino acids, with histidine modeled in both its protonated and neutral forms. The iterative Boltzmann inversion (IBI) method was used to optimize new CG potential functions for backbone-related terms by attempting to reproduce angle, dihedral, and distance probability distributions generated by the MD simulations. In a simple test of the transferability of the extended force field, the angle, dihedral, and distance probability distributions obtained from BD simulations of 56 three-residue peptides were compared to results from corresponding explicit-solvent MD simulations. In a more challenging test of the COFFDROP force field, it was used to simulate eight intrinsically disordered proteins and was shown to quite accurately reproduce the experimental hydrodynamic radii (Rhydro), provided that the favorable nonbonded interactions of the force field were uniformly scaled downward in magnitude. Overall, the results indicate that the COFFDROP force field is likely to find use in modeling the conformational behavior of intrinsically disordered proteins and multidomain proteins connected by flexible linkers.

Development of a coarse-grained α-chitin model on the basis of MARTINI forcefield.

At nanoscale, atomistic simulation is widely used for investigating crystalline chitin fibers, the structural component for many biological materials. However, the longitudinal dimension of naturally occurring chitin fibers exceeds hundreds of nanometer, beyond the investigation range of all-atom simulation due to the limitation of computational power. Under this context, coarse-grained simulation is a useful alternative that facilitates the investigation of a large system. We develop a coarse-grained model for describing the structural and mechanical properties of α-chitin. The developed coarse-grained model can reasonably predict these properties. Moreover, this model is consistent with existing coarse-grained force fields for proteins. The present model of α-chitin possesses good potential and applicability in the investigation of natural chitin-based materials at the length scale of several hundred nanometers.

Modulation of the disordered conformational ensembles of the p53 transactivation domain by cancer-associated mutations.

Intrinsically disordered proteins (IDPs) are frequently associated with human diseases such as cancers, and about one-fourth of disease-associated missense mutations have been mapped into predicted disordered regions. Understanding how these mutations affect the structure-function relationship of IDPs is a formidable task that requires detailed characterization of the disordered conformational ensembles. Implicit solvent coupled with enhanced sampling has been proposed to provide a balance between accuracy and efficiency necessary for systematic and comparative assessments of the effects of mutations as well as post-translational modifications on IDP structure and interaction. Here, we utilize a recently developed replica exchange with guided annealing enhanced sampling technique to calculate well-converged atomistic conformational ensembles of the intrinsically disordered transactivation domain (TAD) of tumor suppressor p53 and several cancer-associated mutants in implicit solvent. The simulations are critically assessed by quantitative comparisons with several types of experimental data that provide structural information on both secondary and tertiary levels. The results show that the calculated ensembles reproduce local structural features of wild-type p53-TAD and the effects of K24N mutation quantitatively. On the tertiary level, the simulated ensembles are overly compact, even though they appear to recapitulate the overall features of transient long-range contacts qualitatively. A key finding is that, while p53-TAD and its cancer mutants sample a similar set of conformational states, cancer mutants could introduce both local and long-range structural modulations to potentially perturb the balance of p53 binding to various regulatory proteins and further alter how this balance is regulated by multisite phosphorylation of p53-TAD. The current study clearly demonstrates the promise of atomistic simulations for detailed characterization of IDP conformations, and at the same time reveals important limitations in the current implicit solvent protein force field that must be sufficiently addressed for reliable description of long-range structural features of the disordered ensembles.

Accuracy of Protein Embedding Potentials: An Analysis in Terms of Electrostatic Potentials.

Quantum-mechanical embedding methods have in recent years gained significant interest and may now be applied to predict a wide range of molecular properties calculated at different levels of theory. To reach a high level of accuracy in embedding methods, both the electronic structure model of the active region and the embedding potential need to be of sufficiently high quality. In fact, failures in quantum mechanics/molecular mechanics (QM/MM)-based embedding methods have often been associated with the QM/MM methodology itself; however, in many cases the reason for such failures is due to the use of an inaccurate embedding potential. In this paper, we investigate in detail the quality of the electronic component of embedding potentials designed for calculations on protein biostructures. We show that very accurate explicitly polarizable embedding potentials may be efficiently designed using fragmentation strategies combined with single-fragment ab initio calculations. In fact, due to the self-interaction error in Kohn-Sham density functional theory (KS-DFT), use of large full-structure quantum-mechanical calculations based on conventional (hybrid) functionals leads to less accurate embedding potentials than fragment-based approaches. We also find that standard protein force fields yield poor embedding potentials, and it is therefore not advisable to use such force fields in general QM/MM-type calculations of molecular properties other than energies and structures.

QCTFF: On the construction of a novel protein force field

In this perspective, we explain the strategy behind QCTFF, the current name for a novel atomistic protein force field. The atoms are constructed using Quantum Chemical Topology (QCT). These topological atoms determine how a system's energy is partitioned. We give a brief account of the results hitherto obtained, and a glimpse of unpublished results. Combining this QCT partitioning with the universal quantum expression of energy, leads to four types of fundamental energy contributions. The first of these is intra‐atomic and the remaining three interatomic: (i) atomic self‐energy, (ii) Coulomb energy, (iii) exchange energy, and (iv) correlation energy. All structural and dynamic effects emerge from the interplay of these contributions. The machine learning method kriging captures well how they change in response to a change in nuclear configuration. Second, the Coulomb energy is represented by a converging multipolar series expansion when the nuclei are sufficiently far apart. © 2015 The Authors International Journal of Quantum Chemistry Published by Wiley Periodicals, Inc.

Can We Execute Stable Microsecond-Scale Atomistic Simulations of Protein-RNA Complexes?

We report over 30 μs of unrestrained molecular dynamics simulations of six protein-RNA complexes in explicit solvent. We utilize the AMBER ff99bsc0χ(OL3) RNA force field combined with the ff99SB protein force field and its more recent ff12SB version with reparametrized side-chain dihedrals. The simulations show variable behavior, ranging from systems that are essentially stable to systems with progressive deviations from the experimental structure, which we could not stabilize anywhere close to the starting experimental structure. For some systems, microsecond-scale simulations are necessary to achieve stabilization after initial sizable structural perturbations. The results show that simulations of protein-RNA complexes are challenging and every system should be treated individually. The simulations are affected by numerous factors, including properties of the starting structures (the initially high force field potential energy, resolution limits, conformational averaging, crystal packing, etc.), force field imbalances, and real flexibility of the studied systems. These factors, and thus the simulation behavior, differ from system to system. The structural stability of simulated systems does not correlate with the size of buried interaction surface or experimentally determined binding affinities but reflects the type of protein-RNA recognition. Protein-RNA interfaces involving shape-specific recognition of RNA are more stable than those relying on sequence-specific RNA recognition. The differences between the protein force fields are considerably smaller than the uncertainties caused by sampling and starting structures. The ff12SB improves description of the tyrosine side-chain group, which eliminates some problems associated with tyrosine dynamics.

SIRAH: a structurally unbiased coarse-grained force field for proteins with aqueous solvation and long-range electrostatics.

Modeling of macromolecular structures and interactions represents an important challenge for computational biology, involving different time and length scales. However, this task can be facilitated through the use of coarse-grained (CG) models, which reduce the number of degrees of freedom and allow efficient exploration of complex conformational spaces. This article presents a new CG protein model named SIRAH, developed to work with explicit solvent and to capture sequence, temperature, and ionic strength effects in a topologically unbiased manner. SIRAH is implemented in GROMACS, and interactions are calculated using a standard pairwise Hamiltonian for classical molecular dynamics simulations. We present a set of simulations that test the capability of SIRAH to produce a qualitatively correct solvation on different amino acids, hydrophilic/hydrophobic interactions, and long-range electrostatic recognition leading to spontaneous association of unstructured peptides and stable structures of single polypeptides and protein-protein complexes.

A CHARMM-Compatible Force Field for N- and Aplha-Methylated Peptides with (Phi,Psi) Energy Correction Grid

Synthetic peptides are a convenient avenue for biomedical applications such as the design of drugs in which natural peptides and key motifs from proteins are rigidified and metabolically stabilized.[1][2] As the relative flexibility of peptides often encumbers the experimental study of their structure, they are an attractive target for Molecular Dynamics computational studies. However, since they exhibit similar subtle sequence-structure relationships as natural proteins, their study requires an empirical force field with a similar level of refinement as protein force fields such as CHARMM36.[3] While nonstandard side chains can often directly be studied with existing protein force fields, and the study of D-amino acids is straightforwardly enabled by constructing a mirror-image potential, backbone modifications invalidate dihedral force field terms associated with the (Phi,Psi) conformational energy surface. Within the framework of the CHARMM36 force field, we present the parametrization of N- and alpha-methylated amino acids, two staples in the peptide chemist's arsenal for increasing peptides’ metabolic stability and secondary structure rigidity.[4][5] This effort includes the construction of dedicated CMAP cross terms, which consist of a 2D grid of energy corrections in (Phi,Psi) space. The resulting parameter set is validated against experimental data on relevant peptides. 1. V. J. Hruby, P. M. Balse, Curr. Med. Chem. 2000, 7, 945-970. 2. C. Adessi, C. Soto, Curr. Med. Chem. 2002, 9, 963-978. 3. R. Best, X. Zhu et al., J. Chem. Theory Comput. 2012, 8, 3257-3273. 4. J. Chatterjee, C. Gilon et al., Acc. Chem. Res. 2008, 41, 1331-1342. 5. R. Gratias, R. Konat et al., J. Am. Chem. Soc. 1998, 120, 4763-4770.