Comprehensive analysis of energy minima of the 20 natural amino acids

Benchmarking Quantum Mechanical Levels of Theory for Valence Parametrization in Force Fields

A wide range of density functional methods and basis sets are available to derive the electronic structure and properties of molecules. Quantum mechanical calculations are too computationally intensive for routine simulation of molecules in the condensed phase, prompting the development of computationally efficient force fields based on quantum mechanical data. Parametrizing general force fields, which cover a vast chemical space, necessitates the generation of sizable quantum mechanical data sets with optimized geometries and torsion scans. To achieve this efficiently, choosing a quantum mechanical method that balances computational cost and accuracy is crucial. In this study, we seek to assess the accuracy of quantum mechanical theory for specific properties such as conformer energies and torsion energetics. To comprehensively evaluate various methods, we focus on a representative set of 59 diverse small molecules, comparing approximately 25 combinations of functional and basis sets against the reference level coupled cluster calculations at the complete basis set limit.

Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors

Machine-learned computational chemistry has led to a paradoxical situation in which molecular properties can be accurately predicted, but they are difficult to interpret. Explainable AI (XAI) tools can be used to analyze complex models, but they are highly dependent on the AI technique and the origin of the reference data. Alternatively, interpretable real-space tools can be employed directly, but they are often expensive to compute. To address this dilemma between explainability and accuracy, we developed SchNet4AIM, a SchNet-based architecture capable of dealing with local one-body (atomic) and two-body (interatomic) descriptors. The performance of SchNet4AIM is tested by predicting a wide collection of real-space quantities ranging from atomic charges and delocalization indices to pairwise interaction energies. The accuracy and speed of SchNet4AIM breaks the bottleneck that has prevented the use of real-space chemical descriptors in complex systems. We show that the group delocalization indices, arising from our physically rigorous atomistic predictions, provide reliable indicators of supramolecular binding events, thus contributing to the development of Explainable Chemical Artificial Intelligence (XCAI) models.

Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein

Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fields. Currently, molecular mechanical (MM) force fields are mainly utilized in MD simulations because of their low computational cost. Quantum mechanical (QM) calculation has high accuracy, but it is exceedingly time consuming for protein simulations. Machine learning (ML) provides the capability for generating accurate potential at the QM level without increasing much computational effort for specific systems that can be studied at the QM level. However, the construction of general machine learned force fields, needed for broad applications and large and complex systems, is still challenging. Here, general and transferable neural network (NN) force fields based on CHARMM force fields, named CHARMM-NN, are constructed for proteins by training NN models on 27 fragments partitioned from the residue-based systematic molecular fragmentation (rSMF) method. The NN for each fragment is based on atom types and uses new input features that are similar to MM inputs, including bonds, angles, dihedrals, and non-bonded terms, which enhance the compatibility of CHARMM-NN to MM MD and enable the implementation of CHARMM-NN force fields in different MD programs. While the main part of the energy of the protein is based on rSMF and NN, the nonbonded interactions between the fragments and with water are taken from the CHARMM force field through mechanical embedding. The validations of the method for dipeptides on geometric data, relative potential energies, and structural reorganization energies demonstrate that the CHARMM-NN local minima on the potential energy surface are very accurate approximations to QM, showing the success of CHARMM-NN for bonded interactions. However, the MD simulations on peptides and proteins indicate that more accurate methods to represent protein-water interactions in fragments and non-bonded interactions between fragments should be considered in the future improvement of CHARMM-NN, which can increase the accuracy of approximation beyond the current mechanical embedding QM/MM level.

Linking the Interatomic Exchange-Correlation Energy to Experimental J-Coupling Constants

The main aim of the current work is to find an experimental connection to the interatomic exchange-correlation energy as defined by the energy decomposition method Interacting Quantum Atoms (IQA). A suitable candidate as (essentially) experimental quantity is the nuclear magnetic resonance (NMR) J-coupling constant denoted ³J(H,H'), which a number of previous studies showed to correlate well with QTAIM's delocalization index (DI), which is essentially a bond order. Inspired by Karplus equations, here, we investigate correlations between ³J(H,H') and a relevant dihedral angle in six simple initial compounds of the shape H₃C-YH_n (Y = C, N, O, Si, P, and S), N-methylacetamide (as prototype of the peptide bond), and five peptide-capped amino acids (Gly, Ala, Val, Ile, and Leu) because of the protein direction of the force field FFLUX. In conclusion, except for methanol, the inter-hydrogen exchange-correlation energy V_xc(H,H') makes the best contact with experiment, through ³J(H,H'), when multiplied with the internuclear distance R_HH'.

Bond dissociation energies of X-H bonds in proteins

Knowledge of reliable X-H bond dissociation energies (X = C, N, O, S) for amino acids in proteins is key for studying the radical chemistry of proteins. X-H bond dissociation energies of model dipeptides were computed using the isodesmic reaction method at the BMK/6-31+G(2df,p) and G4(MP2)-6X levels of theory. The density functional theory values agree well with the composite-level calculations. By this high level of theory, combined with a careful choice of reference compounds and peptide model systems, our work provides a highly valuable data set of bond dissociation energies with unprecedented accuracy and comprehensiveness. It will likely prove useful to predict protein biochemistry involving radicals, e.g., by machine learning.

Extensive exploration of the conformational landscapes of neutral and terminally blocked prolines in the gas phase: A density functional theory study

Proline is an important amino acid that plays unique roles in the structures of peptides and proteins. The conformations of proline are searched by a thorough method, generating 3888 trial structures optimized at the B97D/6-311++G** level. A total of 23 conformations are found and their structural and energetic data are presented. All the proline conformers exhibit a coplanar feature for four of the five pyrrolidine ring atoms. The coplanar rule reduces the cost of the conformational search by a factor of 40. The theoretical composition-weighted infrared spectrum provides a good explanation of the experimental results. A conformational search of capped proline yields seven unique conformers, all with trans C-termini peptide planes. The trans C-termini rule further cuts by half the cost of the conformational search of proline-containing peptides. The theoretical composition of the cis N-termini peptide bonds at room temperature is 5.5%, agreeing with the experimental estimations of 3%–10%.

A general RNA force field: comprehensive analysis of energy minima of molecular fragments of RNA

Force fields are actively used to study RNA. Development of accurate force fields relies on a knowledge of how the variation of properties of molecules depends on their structure. Detailed scrutiny of RNA's conformational preferences is needed to guide such development. Towards this end, minimum energy structures for each of a set of 16 small RNA-derived molecules were obtained by geometry optimization at the HF/6-31G(d,p), B3LYP/apc-1, and MP2/cc-pVDZ levels of theory. The number of minima computed for a given fragment was found to be related to both its size and flexibility. Atomic electrostatic multipole moments of atoms occurring in the [HO-P(O₃)-CH₂-] fragment of 30 sugar-phosphate-sugar geometries were calculated at the HF/6-31G(d,p) and B3LYP/apc-1 levels of theory, and the transferability of these properties between different conformations was investigated. The atomic multipole moments were found to be highly transferable between different conformations with small standard deviations. These results indicate necessary elements of the development of accurate RNA force fields.

What Types of Chemical Problems Benefit from Density-Corrected DFT? A Probe Using an Extensive and Chemically Diverse Test Suite

For the large and chemically diverse GMTKN55 benchmark suite, we have studied the performance of density-corrected density functional theory (HF-DFT), compared to self-consistent DFT, for several pure and hybrid GGA and meta-GGA exchange–correlation (XC) functionals (PBE, BLYP, TPSS, and SCAN) as a function of the percentage of HF exchange in the hybrid. The D4 empirical dispersion correction has been added throughout. For subsets dominated by dynamical correlation, HF-DFT is highly beneficial, particularly at low HF exchange percentages. This is especially true for noncovalent interactions where the electrostatic component is dominant, such as hydrogen and halogen bonds: for π-stacking, HF-DFT is detrimental. For subsets with significant nondynamical correlation (i.e., where a Hartree–Fock determinant is not a good zero-order wavefunction), HF-DFT may do more harm than good. While the self-consistent series show optima at or near 37.5% (i.e., 3/8) for all four XC functionals—consistent with Grimme’s proposal of the PBE38 functional—HF-BnLYP-D4, HF-PBEn-D4, and HF-TPSSn-D4 all exhibit minima nearer 25% (i.e., 1/4) as the use of HF orbitals greatly mitigates the error at 25% for barrier heights. Intriguingly, for HF-SCANn-D4, the minimum is near 10%, but the weighted mean absolute error (WTMAD2) for GMTKN55 is only barely lower than that for HF-SCAN-D4 (i.e., where the post-HF step is a pure meta-GGA). The latter becomes an attractive option, only slightly more costly than pure Hartree–Fock, and devoid of adjustable parameters other than the three in the dispersion correction. Moreover, its WTMAD2 is only surpassed by the highly empirical M06-2X and by the combinatorially optimized empirical range-separated hybrids ωB97X-V and ωB97M-V.

Deciphering the structural preference encoded in amide-I vibrations of lysine dipeptide in gas phase and in aqueous solution

Protein's biological function is critically associated with its structural feature, which is encoded in its amino acid sequence. For evaluation of conformational fluctuation and folding mechanism, DFT calculations were performed on the model compound - lysine dipeptide (LYSD) in gas phase to demonstrate the correlation between amide-I vibrations and secondary structure. Molecular dynamics simulations were carried out for the structural dynamics of LYSD in aqueous solution. The results show that LYSD tends form C_7eq, C₅, β, PP_II and α conformations in the gas phase and primarily presented PP_II and α conformations in aqueous solution. The obtained amide-I vibrational frequencies of LYSD conformers were assigned, thus build the correlations between amide-I probes and secondary structure of LYSD. These results provide theoretical insights into the structural feature of LYSD through amide-I vibrations, and would shed light on site specific structural prediction of polypeptides.

On the faithfulness of molecular mechanics representations of proteins towards quantum-mechanical energy surfaces

Force fields based on molecular mechanics (MM) are the main computational tool to study the relationship between protein structure and function at the molecular level. To validate the quality of such force fields, high-level quantum-mechanical (QM) data are employed to test their capability to reproduce the features of all major conformational substates of a series of blocked amino acids. The phase-space overlap between MM and QM is quantified in terms of the average structural reorganization energies over all energy minima. Here, the structural reorganization energy is the MM potential-energy difference between the structure of the respective QM energy minimum and the structure of the closest MM energy minimum. Thus, it serves as a measure for the relative probability of visiting the QM minimum during an MM simulation. We evaluate variants of the AMBER, CHARMM, GROMOS and OPLS biomolecular force fields. In addition, the two blocked amino acids alanine and serine are used to demonstrate the dependence of the measured agreement on the QM method, the phase, and the conformational preferences. Blocked serine serves as an example to discuss possible improvements of the force fields, such as including polarization with Drude particles, or using tailored force fields. The results show that none of the evaluated force fields satisfactorily reproduces all energy minima. By decomposing the average structural reorganization energies in terms of individual energy terms, we can further assess the individual weaknesses of the parametrization strategies of each force field. The dominant problem for most force fields appears to be the van der Waals parameters, followed to a lesser degree by dihedral and bonded terms. Our results show that performing a simple QM energy optimization from an MM-optimized structure can be a first test of the validity of a force field for a particular target molecule.

Side chain flexibility and the symmetry of protein homodimers

A comprehensive analysis of crystallographic data of 565 high-resolution protein homodimers comprised of over 250,000 residues suggests that amino acids form two groups that differ in their tendency to distort or symmetrize the structure of protein homodimers. Residues of the first group tend to distort the protein homodimer and generally have long or polar side chains. These include: Lys, Gln, Glu, Arg, Asn, Met, Ser, Thr and Asp. Residues of the second group contribute to protein symmetry and are generally characterized by short or aromatic side chains. These include: Ile, Pro, His, Val, Cys, Leu, Trp, Tyr, Phe, Ala and Gly. The distributions of the continuous symmetry measures of the proteins and the continuous chirality measures of their building blocks highlight the role of side chain geometry and the interplay between entropy and symmetry in dictating the conformational flexibility of proteins.

Application of a transparent window vibrational probe (azido probe) to the structural dynamics of model dipeptides and amyloid β-peptide

The azido asymmetric stretching motion is widely used for the elucidation of the intrinsic conformational preference and folding mechanism of protein since it has strong vibrational absorbance in the spectral transparent windows. However, the possible secondary structural disturbance induced by the insertion of azido group in the side chain of polypeptides should be carefully evaluated. Here, DFT calculation and enhanced sampling method were employed for model dipeptides with or without azido substitution, and the outcome results show that the lower potential energy basins of isolated model dipeptides are consistent with the preferred structural distributions of model dipeptides in aqueous solution. The azido asymmetric stretching frequency shows its sensitivity to the backbone configurations just like amide-I vibration does, and the azido vibration exhibits great potential as a structural reporter in the transparent window. For the evaluation of the application of azido group in biologically related system, the structural dynamics of Aβ_37-42 and N₃-Aβ_37-42 fragments and the self-assemble process of their protofiliments in aqueous solution were demonstrated. The outcome results show that the structural fluctuations of Aβ_37-42 and its protofilament in aqueous solution are quite similar with or without azido substitution, and the dewetting transitions of Aβ_37-42 and N₃-Aβ_37-42 β-sheet layers are both complete within 30 ns and assemble into stable protofilaments. Therefore, the azido asymmetric vibrational motion is a minimally invasive structural probe and would not introduce much disturbance to the structural dynamics of polypeptides.

Aqueous-Phase Conformations of Lactose, Maltose, and Sucrose and the Assessment of Low-Cost DFT Methods with the DSCONF Set of Conformers for the Three Disaccharides

In the present study, we have examined a range of quantum chemistry methods for the calculation of conformers for lactose, maltose, and sucrose. We find that the DSD-PBE-P86/aug'-cc-pVTZ//B3-LYP-D3BJ/6-311+G(2d,p) protocol yields good relative energies in comparison with reference CCSD(T)/CBS//B3-LYP-D3BJ/maug-cc-pVTZ values. We have surveyed a total of ∼550 conformers for the three disaccharides with the chosen DSD-PBE-P86 method in conjunction with continuum aqueous solvation. In each case, the lowest free energy conformer is characterized by hydrogen bond(s) between the two rings. Another finding is that the major contributors to the overall variations in aqueous free energies are the electronic energies and the solvation energies. To facilitate investigations of larger systems, we have compiled the DSCONF set of conformers for the three disaccharides, and we have assessed lower cost methods with this set. We find MS1-D3/6-31+G(2d,p) to be cost-effective and accurate for both geometry optimization and the calculation of relative energies for disaccharides. In addition, we note that MS1-D3 has previously been found to yield good relative energies for the WATER27 set of water clusters. We thus deem this method to be appropriate for the study of saccharide conformations in both gas phase and aqueous solution.

Cost-Effective Potential for Accurate Polarizable Embedding Calculations in Protein Environments

The fragment-based polarizable embedding (PE) model combined with an appropriate electronic structure method constitutes a highly efficient and accurate multiscale approach for computing spectroscopic properties of a central moiety including effects from its molecular environment through an embedding potential. There is, however, a comparatively high computational overhead associated with the computation of the embedding potential, which is derived from first-principles calculations on individual fragments of the environment. To reduce the computational cost associated with the calculation of embedding potential parameters, we developed a set of amino acid-specific transferable parameters tailored for large-scale PE-based calculations that include proteins. The amino acid-based parameters are obtained by simultaneously fitting to a set of reference electric potentials based on structures derived from a backbone-dependent rotamer library. The developed cost-effective polarizable protein potential (CP³) consists of atom-centered charges and isotropic dipole-dipole polarizabilities of the standard amino acids. In terms of reproduction of electric potentials, the CP³ is shown to perform consistently and with acceptable accuracy across both small tripeptide test systems and larger proteins. We show, through applications on realistic protein systems, that acceptable accuracy can be obtained by using a pure CP³ representation of the protein environment, thus altogether omitting the cost associated with the calculation of embedding potential parameters. High accuracy comparable to that of the full fragment-based approach can be achieved through a mixed description where the CP³ is used only to describe amino acids beyond a threshold distance from the central quantum part.

Mapping the amide-I vibrations of model dipeptides with secondary structure sensitivity and amino acid residue specificity, and its application to amyloid β-peptide in aqueous solution

Vibrational spectroscopy has been known as particularly well-suited for deciphering the polypeptide's structure. To decode structural information encoded in IR spectra, we developed amide-I frequency maps on the basis of model dipeptides to correlate the amide-I frequency of interest to the combination of the calculated secondary structure dependent amide-I frequency by using DFT method and the electrostatic potentials that projected onto the amide unit from the micro-environment within molecular mechanics force field. The constructed maps were applied to model dipeptides and amyloid β-peptide fragment (Aβ_25-35). The dipeptide specified map (DS map) and the hybrid map (HYB map) predicted amide-I bands of Aβ_25-35 in solution satisfactorily reproduce experimental observation, and indicate the preference of forming β-sheet and random coil structure for Aβ_25-35 in D₂O just as the results of cluster analysis suggested. These maps with secondary structural sensitivity and amino acid residue specificity open up a way for the interpretation of amide-I vibrations and show their potentials in the understanding of molecular structure of polypeptides in solution.

PEPCONF, a diverse data set of peptide conformational energies

We present an extensive and diverse database of peptide conformational energies. Our database contains five different classes of model geometries: dipeptides, tripeptides, and disulfide-bridged, bioactive, and cyclic peptides. In total, the database consists of 3775 conformational energy data points and 4530 conformer geometries. All the reference energies have been calculated at the LC-ωPBE-XDM/aug-cc-pVTZ level of theory, which is shown to yield conformational energies with an accuracy in the order of tenths of a kcal/mol when compared to complete-basis-set coupled-cluster reference data. The peptide conformational data set (PEPCONF) is presented as a high-quality reference set for the development and benchmarking of molecular-mechanics and semi-empirical electronic structure methods, which are the most commonly used techniques in the modeling of medium to large proteins.

Electrostatic Effects on the Stability of Peptide Radicals

We explored the influence of external electric fields (EEFs) on the stability of a glycine dipeptide model radical using high-level quantum chemical methods. Remotely located ions (Cl^-/Na⁺) are used to implement EEF effects. The effects of these ions are reproduced using background point charges and oriented EEFs. Remote charges as far as 900 pm from the C_α radical center can be significantly stabilizing or destabilizing as a function of their relative orientation. The magnitude of these effects is also strongly dependent on the distance between the radical center and the charge location. After examining the strengths and weaknesses of some frequently used quantum mechanics methods in describing these effects properly, a comparison is made on the stability of dipeptide radicals bearing protonable or deprotonable side chains. In this group, the stability of the respective C_α radicals mainly depends on the preferred orientation of the charge-carrying side chain.

An improved two-rotor function for conformational potential energy surfaces of 20 amino acid diamides

Predicting the three-dimensional structure of a protein from its amino acid sequence requires a complete understanding of the molecular forces that influences the protein folding process. Each possible conformation has its corresponding potential energy, which characterizes its thermodynamic stability. This is needed to identify the primary intra- and inter-molecular interactions, so that we can reduce the dimensionality of the problem, and create a relatively simple representation of the system. Investigating this problem using quantum chemical methods produces accurate results; however, this also entails large computational resources. In this study, an improved two-rotor potential energy function is proposed to represent the backbone interactions in amino acids through a linear combination of a Fourier series and a mixture of Gaussian functions. This function is applied to approximate the 20 amino acid diamide Ramachandran-type PESs, and results yielded an average RMSE of 2.36 kJ mol−1, which suggest that the mathematical model precisely captures the general topology of the conformational potential energy surface. Furthermore, this paper provides insights on the conformational preferences of amino acid diamides through local minima geometries and energy ranges, using the improved mathematical model. The proposed mathematical model presents a simpler representation that attempts to provide a framework on building polypeptide models from individual amino acid functions, and consequently, a novel method for rapid but accurate evaluation of potential energies for biomolecular simulations.

Electronic structure of dipeptides in the gas-phase and as an adsorbed monolayer

UPS and DFT reveal how frontier energy levels and molecular orbitals of peptides are modified upon peptide binding to a gold substrate.

Modelling the Effect of Conformation on Hydrogen-Atom Abstraction from Peptides

Computational quantum chemistry is used to examine the effect of conformation on the kinetics of hydrogen-atom abstraction by HO• from amides of glycine and proline as peptide models. In accord with previous findings, it is found that there are substantial variations possible in the conformations and the corresponding energies, with the captodative effect, hydrogen bonding, and solvation being some of the major features that contribute to the variations. The ‘minimum-energy-structure-pathway’ strategy that is often employed in theoretical studies of peptide chemistry with small models certainly provides valuable fundamental information. However, one may anticipate different reaction outcomes in structurally constrained systems due to modified reaction thermodynamics and kinetics, as demonstrated explicitly in the present study. Thus, using a ‘consistent-conformation-pathway’ approach may indeed be more informative in such circumstances, and in this regard theory provides information that would be difficult to obtain from experimental studies alone.

Conserved Binding Regions Provide the Clue for Peptide-Based Vaccine Development: A Chemical Perspective

Synthetic peptides have become invaluable biomedical research and medicinal chemistry tools for studying functional roles, i.e., binding or proteolytic activity, naturally-occurring regions’ immunogenicity in proteins and developing therapeutic agents and vaccines. Synthetic peptides can mimic protein sites; their structure and function can be easily modulated by specific amino acid replacement. They have major advantages, i.e., they are cheap, easily-produced and chemically stable, lack infectious and secondary adverse reactions and can induce immune responses via T- and B-cell epitopes. Our group has previously shown that using synthetic peptides and adopting a functional approach has led to identifying Plasmodium falciparumconserved regions binding to host cells. Conserved high activity binding peptides’ (cHABPs) physicochemical, structural and immunological characteristics have been taken into account for properly modifying and converting them into highly immunogenic, protection-inducing peptides (mHABPs) in the experimental Aotus monkey model. This article describes stereo–electron and topochemical characteristics regarding major histocompatibility complex (MHC)-mHABP-T-cell receptor (TCR) complex formation. Some mHABPs in this complex inducing long-lasting, protective immunity have been named immune protection-inducing protein structures (IMPIPS), forming the subunit components in chemically synthesized vaccines. This manuscript summarizes this particular field and adds our recent findings concerning intramolecular interactions (H-bonds or π-interactions) enabling proper IMPIPS structure as well as the peripheral flanking residues (PFR) to stabilize the MHCII-IMPIPS-TCR interaction, aimed at inducing long-lasting, protective immunological memory.

Unfavorable regions in the ramachandran plot: Is it really steric hindrance? The interacting quantum atoms perspective

Accurate description of the intrinsic preferences of amino acids is important to consider when developing a biomolecular force field. In this study, we use a modern energy partitioning approach called Interacting Quantum Atoms to inspect the cause of the φ and ψ torsional preferences of three dipeptides (Gly, Val, and Ile). Repeating energy trends at each of the molecular, functional group, and atomic levels are observed across both (1) the three amino acids and (2) the φ/ψ scans in Ramachandran plots. At the molecular level, it is surprisingly electrostatic destabilization that causes the high-energy regions in the Ramachandran plot, not molecular steric hindrance (related to the intra-atomic energy). At the functional group and atomic levels, the importance of key peptide atoms (Oi-1 , Ci , Ni , Ni+1 ) and some sidechain hydrogen atoms (Hγ ) are identified as responsible for the destabilization seen in the energetically disfavored Ramachandran regions. Consistently, the Oi-1 atoms are particularly important for the explanation of dipeptide intrinsic behavior, where electrostatic and steric destabilization unusually complement one another. The findings suggest that, at least for these dipeptides, it is the peptide group atoms that dominate the intrinsic behavior, more so than the sidechain atoms. © 2017 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Chiral Ramachandran Plots I: Glycine

Ramachandran plots (RPs) map the wealth of conformations of the polypeptide backbone and are widely used to characterize protein structures. A limitation of the RPs is that they are based solely on two dihedral angles for each amino acid residue and provide therefore only a partial picture of the conformational richness of the protein. Here we extend the structural RP analysis of proteins from a two-dimensional (2D) map to a three-dimensional map by adding the quantitative degree of chirality-the continuous chirality measure (CCM)-of the amino acid residue at each point in the RP. This measure encompasses all bond angles and bond lengths of an amino acid residue. We focus in this report on glycine (Gly) because, due to its flexibility, it occupies a large portion of the 2D map, thus allowing a detailed study of the chirality measure, and in order to evaluate the justification of classically labeling Gly as the only achiral amino acid. We have analyzed in detail 4366 Gly residues extracted from high resolution crystallographic data of 160 proteins. This analysis reveals not only that Gly is practically always conformationally chiral, but that upon comparing with the backbone of all amino acids, the quantitative chirality values of Gly are of similar magnitudes to those of the (chiral) amino acids. Structural trends and energetic considerations are discussed in detail. Generally we show that adding chirality to Ramachandran plots creates far more informative plots that highlight the sensitivity of the protein structure to minor conformational changes.

Probing the Importance of Charge Flux in Force Field Modeling

We analyze the conformational dependence of atomic charges and molecular dipole moments for a selection of ∼900 conformations of peptide models of the 20 neutral amino acids. Based on a set of reference density functional theory calculations, we partition the changes into effects due to changes in bond distances, bond angles, and torsional angles and into geometry and charge flux contributions. This allows an assessment of the limitations of fixed charge force fields and indications for how to design improved force fields. The torsional degrees of freedom are the main contribution to conformational changes of atomic charges and molecular dipole moments, but indirect effects due to change in bond distances and angles account for ∼25% of the variation. Charge flux effects dominate for changes in bond distances and are also the main component of the variation in bond angles, while they are ∼25% compared to the geometry variations for torsional degrees of freedom. The geometry and charge flux contributions to some extent produce compensating effects.

A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions

We present the updated and extended GMTKN55 benchmark database for more accurate and extensive energetic evaluation of density functionals and other electronic structure methods with detailed guidelines for method users.

First-principles data set of 45,892 isolated and cation-coordinated conformers of 20 proteinogenic amino acids

We present a structural data set of the 20 proteinogenic amino acids and their amino-methylated and acetylated (capped) dipeptides. Different protonation states of the backbone (uncharged and zwitterionic) were considered for the amino acids as well as varied side chain protonation states. Furthermore, we studied amino acids and dipeptides in complex with divalent cations (Ca²⁺, Ba²⁺, Sr²⁺, Cd²⁺, Pb²⁺, and Hg²⁺). The database covers the conformational hierarchies of 280 systems in a wide relative energy range of up to 4 eV (390 kJ/mol), summing up to a total of 45,892 stationary points on the respective potential-energy surfaces. All systems were calculated on equal first-principles footing, applying density-functional theory in the generalized gradient approximation corrected for long-range van der Waals interactions. We show good agreement to available experimental data for gas-phase ion affinities. Our curated data can be utilized, for example, for a wide comparison across chemical space of the building blocks of life, for the parametrization of protein force fields, and for the calculation of reference spectra for biophysical applications.

Systematic Improvement of Potential-Derived Atomic Multipoles and Redundancy of the Electrostatic Parameter Space

We assess the accuracy of force field (FF) electrostatics at several levels of approximation from the standard model using fixed partial charges to conformational specific multipole fits including up to quadrupole moments. Potential-derived point charges and multipoles are calculated using least-squares methods for a total of ∼1000 different conformations of the 20 natural amino acids. Opposed to standard charge fitting schemes the procedure presented in the current work employs fitting points placed on a single isodensity surface, since the electrostatic potential (ESP) on such a surface determines the ESP at all points outside this surface. We find that the effect of multipoles beyond partial atomic charges is of the same magnitude as the effect due to neglecting conformational dependency (i.e., polarizability), suggesting that the two effects should be included at the same level in FF development. The redundancy at both the partial charge and multipole levels of approximation is quantified. We present an algorithm which stepwise reduces or increases the dimensionality of the charge or multipole parameter space and provides an upper limit of the ESP error that can be obtained at a given truncation level. Thereby, we can identify a reduced set of multipole moments corresponding to ∼40% of the total number of multipoles. This subset of parameters provides a significant improvement in the representation of the ESP compared to the simple point charge model and close to the accuracy obtained using the complete multipole parameter space. The selection of the ∼40% most important multipole sites is highly transferable among different conformations, and we find that quadrupoles are of high importance for atoms involved in π-bonding, since the anisotropic electric field generated in such regions requires a large degree of flexibility.

Realistic sampling of amino acid geometries for a multipolar polarizable force field

The Quantum Chemical Topological Force Field (QCTFF) uses the machine learning method kriging to map atomic multipole moments to the coordinates of all atoms in the molecular system. It is important that kriging operates on relevant and realistic training sets of molecular geometries. Therefore, we sampled single amino acid geometries directly from protein crystal structures stored in the Protein Databank (PDB). This sampling enhances the conformational realism (in terms of dihedral angles) of the training geometries. However, these geometries can be fraught with inaccurate bond lengths and valence angles due to artefacts of the refinement process of the X-ray diffraction patterns, combined with experimentally invisible hydrogen atoms. This is why we developed a hybrid PDB/nonstationary normal modes (NM) sampling approach called PDB/NM. This method is superior over standard NM sampling, which captures only geometries optimized from the stationary points of single amino acids in the gas phase. Indeed, PDB/NM combines the sampling of relevant dihedral angles with chemically correct local geometries. Geometries sampled using PDB/NM were used to build kriging models for alanine and lysine, and their prediction accuracy was compared to models built from geometries sampled from three other sampling approaches. Bond length variation, as opposed to variation in dihedral angles, puts pressure on prediction accuracy, potentially lowering it. Hence, the larger coverage of dihedral angles of the PDB/NM method does not deteriorate the predictive accuracy of kriging models, compared to the NM sampling around local energetic minima used so far in the development of QCTFF.