Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis

Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify

Metalloenzymes catalyze a wide range of chemical transformations, with the active site residues playing a key role in modulating chemical reactivity and selectivity. Unlike smaller synthetic catalysts, a metalloenzyme active site is embedded in a larger protein, which makes interrogation of electronic properties and geometric features with quantum mechanical calculations challenging. Here we implement the ability to fetch crystallographic structures from the Protein Data Bank and analyze the metal binding sites in the program molSimplify. We show the usefulness of the newly created protein3D class to extract the local environment around non-heme iron enzymes containing a two histidine motif and prepare 372 structures for quantum mechanical calculations. Our implementation of protein3D serves to expand the range of systems molSimplify can be used to analyze and will enable high-throughput study of metal-containing active sites in proteins.

Chemical features and machine learning assisted predictions of protein-ligand short hydrogen bonds

There are continuous efforts to elucidate the structure and biological functions of short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms reside more than 0.3 Å closer than the sum of their van der Waals radii. In this work, we evaluate 1070 atomic-resolution protein structures and characterize the common chemical features of SHBs formed between the side chains of amino acids and small molecule ligands. We then develop a machine learning assisted prediction of protein-ligand SHBs (MAPSHB-Ligand) model and reveal that the types of amino acids and ligand functional groups as well as the sequence of neighboring residues are essential factors that determine the class of protein-ligand hydrogen bonds. The MAPSHB-Ligand model and its implementation on our web server enable the effective identification of protein-ligand SHBs in proteins, which will facilitate the design of biomolecules and ligands that exploit these close contacts for enhanced functions.

Modeling with Alternate Locations in X-ray Protein Structures

In many molecular modeling applications, the standard procedure is still to handle proteins as single, rigid structures. While the importance of conformational flexibility is widely known, handling it remains challenging. Even the crystal structure of a protein usually contains variability exemplified in alternate side chain orientations or backbone segments. This conformational variability is encoded in PDB structure files by so-called alternate locations (AltLocs). Most modeling approaches either ignore AltLocs or resolve them with simple heuristics early on during structure import. We analyzed the occurrence and usage of AltLocs in the PDB and developed an algorithm to automatically handle AltLocs in PDB files enabling all structure-based methods using rigid structures to take the alternative protein conformations described by AltLocs into consideration. A respective software tool named AltLocEnumerator can be used as a structure preprocessor to easily exploit AltLocs. While the amount of data makes it difficult to show impact on a statistical level, handling AltLocs has a substantial impact on a case-by-case basis. We believe that the inspection and consideration of AltLocs is a very valuable approach in many modeling scenarios.

Electronic structure theory on modeling short-range noncovalent interactions between amino acids

While short-range noncovalent interactions (NCIs) are proving to be of importance in many chemical and biological systems, these atypical bindings happen within the so-called van der Waals envelope and pose an enormous challenge for current computational methods. We introduce SNCIAA, a database of 723 benchmark interaction energies of short-range noncovalent interactions between neutral/charged amino acids originated from protein x-ray crystal structures at the "gold standard" coupled-cluster with singles, doubles, and perturbative triples/complete basis set [CCSD(T)/CBS] level of theory with a mean absolute binding uncertainty less than 0.1 kcal/mol. Subsequently, a systematic assessment of commonly used computational methods, such as the second-order Møller-Plesset theory (MP2), density functional theory (DFT), symmetry-adapted perturbation theory (SAPT), composite electronic-structure methods, semiempirical approaches, and the physical-based potentials with machine learning (IPML) on SNCIAA is carried out. It is shown that the inclusion of dispersion corrections is essential even though these dimers are dominated by electrostatics, such as hydrogen bondings and salt bridges. Overall, MP2, ωB97M-V, and B3LYP+D4 turned out to be the most reliable methods for the description of short-range NCIs even in strongly attractive/repulsive complexes. SAPT is also recommended in describing short-range NCIs only if the δMP2 correction has been included. The good performance of IPML for dimers at close-equilibrium and long-range conditions is not transferable to the short-range. We expect that SNCIAA will assist the development/improvement/validation of computational methods, such as DFT, force-fields, and ML models, in describing NCIs across entire potential energy surfaces (short-, intermediate-, and long-range NCIs) on the same footing.

Computational Modeling of Conformer Stability in Benenodin-1, a Thermally Actuated Lasso Peptide Switch

Benenodin-1 is a thermally actuated lasso peptide rotaxane switch with two primary translational isomers that differ in the relative position of the residue Gln15. The conversion from one conformer to the other involves substantial enthalpy-entropy compensation: one conformer is energetically favored and the other is entropically favored. Here, we take a multi-scale quantum mechanical (QM) and classical molecular dynamic (MD) approach to reveal residue-specific sources of these differences in stability. QM reveals that the two benenodin-1 conformers involve distinct hydrogen bonding networks, with the enthalpically favored conformer having more intra-peptide hydrogen bonds between the Gln15 side chain and nearby residues. The evaluation of configurational entropy over the MD-sampled geometries reveals that the entropically favored conformer has enhanced conformational flexibility. By computing the by-residue-sum entropies, we identify the role of Gln15 and neighboring Glu14 in mediating the entropic variation during the switching process. These computational insights help explain the effects of Glu14Ala and Gln15Ala mutations on the conformational population of benenodin-1 observed experimentally.

Factors That Determine the Variation of Equilibrium and Kinetic Properties of QM/MM Enzyme Simulations: QM Region, Conformation, and Boundary Condition

To analyze the impact of various technical details on the results of quantum mechanical (QM)/molecular mechanical (MM) enzyme simulations, including the QM region size, catechol-O-methyltransferase (COMT) is studied as a model system using an approximate QM/MM method (DFTB3/CHARMM). The results show that key equilibrium and kinetic properties for methyl transfer in COMT exhibit limited variations with respect to the size of the QM region, which ranges from ∼100 to ∼500 atoms in this study. With extensive sampling, local and global structural characteristics of the enzyme are largely conserved across the studied QM regions, while the nature of the transition state (e.g., secondary kinetic isotope effect) and reaction exergonicity are largely maintained. Deviations in the free energy profile with different QM region sizes are similar in magnitude to those observed with changes in other simulation protocols, such as different initial enzyme conformations and boundary conditions. Electronic structural properties, such as the covariance matrix of residual charge fluctuations, appear to exhibit rather long-range correlations, especially when the peptide backbone is included in the QM region; this observation holds when a range-separated DFT approach is used as the QM region, suggesting that delocalization error is unlikely the origin. Overall, the analyses suggest that multiple simulation details determine the results of QM/MM enzyme simulations with comparable contributions.

Effective prediction of short hydrogen bonds in proteins via machine learning method

Short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms lie within 2.7 Å, exhibit prominent quantum mechanical characters and are connected to a wide range of essential biomolecular processes. However, exact determination of the geometry and functional roles of SHBs requires a protein to be at atomic resolution. In this work, we analyze 1260 high-resolution peptide and protein structures from the Protein Data Bank and develop a boosting based machine learning model to predict the formation of SHBs between amino acids. This model, which we name as machine learning assisted prediction of short hydrogen bonds (MAPSHB), takes into account 21 structural, chemical and sequence features and their interaction effects and effectively categorizes each hydrogen bond in a protein to a short or normal hydrogen bond. The MAPSHB model reveals that the type of the donor amino acid plays a major role in determining the class of a hydrogen bond and that the side chain Tyr-Asp pair demonstrates a significant probability of forming a SHB. Combining electronic structure calculations and energy decomposition analysis, we elucidate how the interplay of competing intermolecular interactions stabilizes the Tyr-Asp SHBs more than other commonly observed combinations of amino acid side chains. The MAPSHB model, which is freely available on our web server, allows one to accurately and efficiently predict the presence of SHBs given a protein structure with moderate or low resolution and will facilitate the experimental and computational refinement of protein structures.

Investigation of major amino acid residues of anti-norfloxacin monoclonal antibodies responsible for binding with fluoroquinolones

It is important to understand the amino acid residues that govern the properties of the binding between antibodies and ligands. We studied the binding of two anti-norfloxacins, anti-nor 132 and anti-nor 155, and the fluoroquinolones norfloxacin, enrofloxacin, ciprofloxacin, and ofloxacin. Binding cross-reactivities tested by an indirect competitive enzyme-linked immunosorbent assay indicated that anti-nor 132 (22–100%) had a broader range of cross-reactivity than anti-nor 155 (62–100%). These cross-reactivities correlated with variations in the numbers of interacting amino acid residues and their positions. Molecular docking was employed to investigate the molecular interactions between the fluoroquinolones and the monoclonal antibodies. Homology models of the heavy chain and light chain variable regions of each mAb 3D structure were docked with the fluoroquinolones targeting the crucial part of the complementarity-determining regions. The fluoroquinolone binding site of anti-nor 155 was a region of the HCDR3 and LCDR3 loops in which hydrogen bonds were formed with TYR (H:35), ASN (H:101), LYS (H:106), ASN (L:92), and ASN (L:93). These regions were further away in anti-nor 132 and could not contact the fluoroquinolones. Another binding region consisting of HIS (L:38) and ASP (H:100) was found for norfloxacin, enrofloxacin, and ciprofloxacin, whereas only ASP (H:100) was found for ofloxacin.

Harder, better, faster, stronger: Large-scale QM and QM/MM for predictive modeling in enzymes and proteins

Computational prediction of enzyme mechanism and protein function requires accurate physics-based models and suitable sampling. We discuss recent advances in large-scale quantum mechanical (QM) modeling of biochemical systems that have reduced the cost of high-accuracy models. Tradeoffs between sampling and accuracy have motivated modeling with molecular mechanics (MM) in a multiscale QM/MM or iterative approach. Limitations to both conventional density-functional theory and classical MM force fields remain for describing noncovalent interactions in comparison to experiment or wavefunction theory. Because predictions of enzyme action (i.e. electrostatics), free energy barriers, and mechanisms are sensitive to the protocol and embedding method in QM/MM, convergence tests and systematic methods for quantifying QM-level interactions are a needed, active area of development.

FMODB: The World's First Database of Quantum Mechanical Calculations for Biomacromolecules Based on the Fragment Molecular Orbital Method

We developed the world's first web-based public database for the storage, management, and sharing of fragment molecular orbital (FMO) calculation data sets describing the complex interactions between biomacromolecules, named FMO Database (https://drugdesign.riken.jp/FMODB/). Each entry in the database contains relevant background information on how the data was compiled as well as the total energy of each molecular system and interfragment interaction energy (IFIE) and pair interaction energy decomposition analysis (PIEDA) values. Currently, the database contains more than 13 600 FMO calculation data sets, and a comprehensive search function implemented at the front-end. The procedure for selecting target proteins, preprocessing the experimental structures, construction of the database, and details of the database front-end were described. Then, we demonstrated a use of the FMODB by comparing IFIE value distributions of hydrogen bond, ion-pair, and XH/π interactions obtained by FMO method to those by molecular mechanics approach. From the comparison, the statistical analysis of the data provided standard reference values for the three types of interactions that will be useful for determining whether each interaction in a given system is relatively strong or weak compared to the interactions contained within the data in the FMODB. In the final part, we demonstrate the use of the database to examine the contribution of halogen atoms to the binding affinity between human cathepsin L and its inhibitors. We found that the electrostatic term derived by PIEDA greatly correlated with the binding affinities of the halogen containing cathepsin L inhibitors, indicating the importance of QM calculation for quantitative analysis of halogen interactions. Thus, the FMO calculation data in FMODB will be useful for conducting statistical analyses to drug discovery, for conducting molecular recognition studies in structural biology, and for other studies involving quantum mechanics-based interactions.

When are two hydrogen bonds better than one? Accurate first-principles models explain the balance of hydrogen bond donors and acceptors found in proteins

Hydrogen bonds (HBs) play an essential role in the structure and catalytic action of enzymes, but a complete understanding of HBs in proteins challenges the resolution of modern structural (i.e., X-ray diffraction) techniques and mandates computationally demanding electronic structure methods from correlated wavefunction theory for predictive accuracy. Numerous amino acid sidechains contain functional groups (e.g., hydroxyls in Ser/Thr or Tyr and amides in Asn/Gln) that can act as either HB acceptors or donors (HBA/HBD) and even form simultaneous, ambifunctional HB interactions. To understand the relative energetic benefit of each interaction, we characterize the potential energy surfaces of representative model systems with accurate coupled cluster theory calculations. To reveal the relationship of these energetics to the balance of these interactions in proteins, we curate a set of 4000 HBs, of which >500 are ambifunctional HBs, in high-resolution protein structures. We show that our model systems accurately predict the favored HB structural properties. Differences are apparent in HBA/HBD preference for aromatic Tyr versus aliphatic Ser/Thr hydroxyls because Tyr forms significantly stronger O–H⋯O HBs than N–H⋯O HBs in contrast to comparable strengths of the two for Ser/Thr. Despite this residue-specific distinction, all models of residue pairs indicate an energetic benefit for simultaneous HBA and HBD interactions in an ambifunctional HB. Although the stabilization is less than the additive maximum due both to geometric constraints and many-body electronic effects, a wide range of ambifunctional HB geometries are more favorable than any single HB interaction.

Correlated wavefunction theory predicts and high-resolution crystal structure analysis confirms the important, stabilizing effect of simultaneous hydrogen bond donor and acceptor interactions in proteins.

Deflating the RNA Mg2+ bubble. Stereochemistry to the rescue!

Proper evaluation of the ionic structure of biomolecular systems through X ray and cryo-EM techniques remains challenging but is essential for advancing our understanding of the underlying structure/activity/solvent relationships. However, numerous studies overestimate the number of Mg2+ in deposited structures due to assignment errors finding their origin in improper consideration of stereochemical rules. Herein, to tackle such issues, we re-evaluate the PDBid 6QNR and 6SJ6 models of the ribosome ionic structure. We establish that stereochemical principles need to be carefully pondered when evaluating ion binding features, even when K+ anomalous signals are available as it is the case for the 6QNR PDB entry. For ribosomes, assignment errors can result in misleading conceptions of their solvent structure. For instance, present stereochemical analysis result in a significant decrease of the number of assigned Mg2+ in 6QNR, suggesting that K+ and not Mg2+ is the prevalent ion in the ribosome 1st solvation shell. We stress that the use of proper stereochemical guidelines in combination or not with other identification techniques, such as those pertaining to the detection of transition metals, of some anions and of K+ anomalous signals, is critical for deflating the current Mg2+ bubble witnessed in many ribosome and other RNA structures. We also stress that for the identification of lighter ions such as Mg2+, Na+, …, for which no anomalous signals can be detected, stereochemistry coupled with high resolution structures (<2.4 Å) remain the best currently available option.

The KDEL trafficking receptor exploits pH to tune the strength of an unusual short hydrogen bond

The endoplasmic reticulum (ER) is the main site of protein synthesis in eukaryotic cells and requires a high concentration of luminal chaperones to function. During protein synthesis, ER luminal chaperones are swept along the secretory pathway and must be retrieved to maintain cell viability. ER protein retrieval is achieved by the KDEL receptor, which recognises a C-terminal Lys-Asp-Glu-Leu (KDEL) sequence. Recognition of ER proteins by the KDEL receptor is pH dependent, with binding occurring under acidic conditions in the Golgi and release under conditions of higher pH in the ER. Recent crystal structures of the KDEL receptor in the apo and peptide bound state suggested that peptide binding drives the formation of a short-hydrogen bond that locks the KDEL sequence in the receptor and activates the receptor for COPI binding in the cytoplasm. Using quantum mechanical calculations we demonstrate that the strength of this short hydrogen bond is reinforced following protonation of a nearby histidine, providing a conceptual link between receptor protonation and KDEL peptide binding. Protonation also controls the water networks adjacent to the peptide binding site, leading to a conformational change that ultimately allows the receptor-complex to be recognized by the COPI system.

Quantum effects and 1H NMR chemical shifts of a bifurcated short hydrogen bond

The monoprotonated compound N,N',N''-tris(p-tolyl)azacalix[3](2,6)pyridine (TAPH) contains an intramolecular hydrogen bond that is formed from three N atoms in its cavity. Constrained by the macrocyclic molecular structure, the separations between the N atoms in this bifurcated hydrogen bond are about 2.6 Å, considerably shorter than those typically observed for hydrogen bonded systems in the condensed phases. As such, TAPH exhibits significantly elongated N-H lengths in its hydrogen bond and a downfield ¹H NMR chemical shift of 22.1 ppm. In this work, we carry out ab initio molecular dynamics and ab initio path integral molecular dynamics simulations of TAPH in the acetonitrile solution to reveal the geometry and proton sharing conditions of the bifurcated short hydrogen bond and uncover how the interplay of electronic and nuclear quantum effects gives rise to its far downfield ¹H chemical shift. Taking a linear short hydrogen bond as a reference, we demonstrate the distinct features of competing quantum effects and electronic shielding effects in the bifurcated hydrogen bond of TAPH. We further use the degree of deshielding on the proton as a measure of the hydrogen bonding interactions and evaluate the strength of the bifurcated short hydrogen bond as compared to its linear counterpart.

Physical Mechanisms Governing Substituent Effects on Arene-Arene Interactions in a Protein Milieu

Arene-arene interactions play important roles in protein-ligand complex formation. Here, we investigate the characteristics of arene-arene interactions between small organic molecules and aromatic amino acids in protein interiors. The study is based on X-ray crystallographic data and quantum mechanical calculations using the enzyme acetylcholinesterase and selected inhibitory ligands as a model system. It is shown that the arene substituents of the inhibitors dictate the strength of the interaction and the geometry of the resulting complexes. Importantly, the calculated interaction energies correlate well with the measured inhibitor potency. Non-hydrogen substituents strengthened all interaction types in the protein milieu, in keeping with results for benzene dimer model systems. The interaction energies were dispersion-dominated, but substituents that induced local dipole moments increased the electrostatic contribution and thus yielded more strongly bound complexes. These findings provide fundamental insights into the physical mechanisms governing arene-arene interactions in the protein milieu and thus into molecular recognition between proteins and small molecules.

Both Configuration and QM Region Size Matter: Zinc Stability in QM/MM Models of DNA Methyltransferase

Quantum-mechanical/molecular-mechanical (QM/MM) methods are essential to the study of metalloproteins, but the relative importance of sampling and degree of QM treatment in achieving quantitative predictions is poorly understood. We study the relative magnitude of configurational and QM-region sensitivity of energetic and electronic properties in a representative Zn²⁺ metal binding site of a DNA methyltransferase. To quantify property variations, we analyze snapshots extracted from 250 ns of molecular dynamics simulation. To understand the degree of QM-region sensitivity, we perform analysis using QM regions ranging from a minimal 49-atom region consisting only of the Zn²⁺ metal and its four coordinating Cys residues up to a 628-atom QM region that includes residues within 12 Å of the metal center. Over the configurations sampled, we observe that illustrative properties (e.g., rigid Zn²⁺ removal energy) exhibit large fluctuations that are well captured with even minimal QM regions. Nevertheless, for both energetic and electronic properties, we observe a slow approach to asymptotic limits with similarly large changes in absolute values that converge only with larger (ca. 300-atom) QM region sizes. For the smaller QM regions, the electronic description of Zn²⁺ binding is incomplete: the metal binds too tightly and is too stabilized by the strong electrostatic potential of MM point charges, and the Zn-S bond covalency is overestimated. Overall, this work suggests that efficient sampling with QM/MM in small QM regions is an effective method to explore the influence of enzyme structure on target properties. At the same time, accurate descriptions of electronic and energetic properties require a larger QM region than the minimal metal-coordinating residues in order to converge treatment of both metal-local bonding and the overall electrostatic environment.

Symmetry and 1H NMR chemical shifts of short hydrogen bonds: impact of electronic and nuclear quantum effects

Electronic and nuclear quantum effects determine the symmetry and highly downfield ¹H NMR chemical shifts of short hydrogen bonds.

How Significant Are Unusual Protein-Ligand Interactions? Insights from Database Mining

We present a new approach to derive interaction propensities of protein-ligand atom pairs from mining of the Protein Data Bank. To ensure solid statistics, we use a line-of-sight contact filter and normalize the observed frequency of hits by a statistical null model based on exposed surface areas of atom types in the protein-ligand binding site. This allows us to investigate which intermolecular interactions and geometries are found more often than expected by chance in protein-ligand complexes. We focus our study on some of the unusual interactions that were postulated to be favorable, including σ-hole bonding of halogen and sulfur atoms, weak hydrogen bonding with fluorine as acceptor, and different types of dipolar interactions. Our results confirm some and challenge other common assumptions on these interactions and highlight other contact types that are yet underexplored in structure-based drug design.

Quantum Mechanical Description of Electrostatics Provides a Unified Picture of Catalytic Action Across Methyltransferases

Methyl transferases (MTases) are a well-studied class of enzymes for which competing enzymatic enhancement mechanisms have been suggested, ranging from structural methyl group CH···X hydrogen bonds (HBs) to electrostatic- and charge-transfer-driven stabilization of the transition state (TS). We identified all Class I MTases for which reasonable resolution (<2.0 Å) crystal structures could be used to form catalytically competent ternary complexes for multiscale (i.e., quantum-mechanical/molecular-mechanical or QM/MM) simulation of the S_N2 methyl transfer reaction coordinate. The four Class I MTases studied have both distinct functions (e.g., protein repair or biosynthesis) and substrate nucleophiles (i.e., C, N, or O). While CH···X HBs stabilize all reactant complexes, no universal TS stabilization role is found for these interactions in MTases. A consistent picture is instead obtained through analysis of charge transfer and electrostatics, wherein much of cofactor-substrate charge separation is maintained in the TS region, and electrostatic potential is correlated with substrate nucleophilicity (i.e., intrinsic reactivity).

Unraveling the structural and chemical features of biological short hydrogen bonds

Short hydrogen bonds are ubiquitous in biological macromolecules and exhibit distinctive proton potential energy surfaces and proton sharing properties.

The three-dimensional architecture of biomolecules often creates specialized structural elements, notably short hydrogen bonds that have donor–acceptor separations below 2.7 Å. In this work, we statistically analyze 1663 high-resolution biomolecular structures from the Protein Data Bank and demonstrate that short hydrogen bonds are prevalent in proteins, protein–ligand complexes and nucleic acids. From these biological macromolecules, we characterize the preferred location, connectivity and amino acid composition in short hydrogen bonds and hydrogen bond networks, and assess their possible functional importance. Using electronic structure calculations, we further uncover how the interplay of the structural and chemical features determines the proton potential energy surfaces and proton sharing conditions in biological short hydrogen bonds.

The upcoming subatomic resolution revolution

Subatomic resolution macromolecular crystallography has been revealing the most fascinating details of macromolecular structures for many years. This most extreme form of macromolecular crystallography is going through rapid changes. A new generation of superbrilliant X-ray sources and detectors is facilitating the rapid acquisition of high-quality datasets. Equally important, a new breed of methods and highly integrated advanced computational tools for structure refinement and analysis is poised to change the way we use subatomic resolution data and reposition high-resolution macromolecular crystallography in medicinal chemistry studies. Subatomic resolution macromolecular crystallography may soon be a routine source of detailed molecular information besides precise geometries, including binding energies and other chemical descriptors, opening new possibilities of application.