Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures

  • Yunqi Li,

    Affiliation Center for Bioinformatics and Department of Molecular Bioscience, University of Kansas, Lawrence, Kansas, United States of America

  • Ambrish Roy,

    Affiliation Center for Bioinformatics and Department of Molecular Bioscience, University of Kansas, Lawrence, Kansas, United States of America

  • Yang Zhang

    yzhang@ku.edu

    Affiliation Center for Bioinformatics and Department of Molecular Bioscience, University of Kansas, Lawrence, Kansas, United States of America

HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures

  • Yunqi Li, 
  • Ambrish Roy, 
  • Yang Zhang
PLOS
x

Abstract

Hydrogen constitutes nearly half of all atoms in proteins and their positions are essential for analyzing hydrogen-bonding interactions and refining atomic-level structures. However, most protein structures determined by experiments or computer prediction lack hydrogen coordinates. We present a new algorithm, HAAD, to predict the positions of hydrogen atoms based on the positions of heavy atoms. The algorithm is built on the basic rules of orbital hybridization followed by the optimization of steric repulsion and electrostatic interactions. We tested the algorithm using three independent data sets: ultra-high-resolution X-ray structures, structures determined by neutron diffraction, and NOE proton-proton distances. Compared with the widely used programs CHARMM and REDUCE, HAAD has a significantly higher accuracy, with the average RMSD of the predicted hydrogen atoms to the X-ray and neutron diffraction structures decreased by 26% and 11%, respectively. Furthermore, hydrogen atoms placed by HAAD have more matches with the NOE restraints and fewer clashes with heavy atoms. The average CPU cost by HAAD is 18 and 8 times lower than that of CHARMM and REDUCE, respectively. The significant advantage of HAAD in both the accuracy and the speed of the hydrogen additions should make HAAD a useful tool for the detailed study of protein structure and function. Both an executable and the source code of HAAD are freely available at http://zhang.bioinformatics.ku.edu/HAAD.

Introduction

Hydrogen constitutes nearly half of all atoms in protein molecules and plays an important role in controlling the folding kinetics and in stabilizing the native state through hydrophobic interactions and hydrogen bonding [1], [2], [3], [4]. The non-polar hydrogen atoms in alkyl and aromatic groups contribute to hydrophobic interactions, while the polar hydrogen atoms participate directly in hydrogen bonds. Hydrogen atoms mediate a number of important interactions and considering the energetic contribution associated with them is important in studies such as the analysis of ligand-protein and protein-protein interactions [5], [6], ligand screening [7], and structure-based drug design [8], [9]. Moreover, the exact location of hydrogen atoms plays a critical role in developing atomic-level potentials for refining high-resolution protein structures [10], [11], [12], [13], [14] and is essential for interpreting structural features such as bifurcated hydrogen bonds [15]. However, most protein structures solved by X-ray crystallography in the Protein Data Bank (PDB) and structural models generated by computer programs (e.g. SCWRL [16] and MODELLER [17]) lack hydrogen atoms, which necessitates the development of programs that can predict hydrogen positions accurately and quickly.

There are several algorithms dedicated to predicting the positions of hydrogen atoms [18], [19], [20], [21], [22], [23], [24]. In general, hydrogen atoms are first placed using local geometric restraints and then their positions are optimized by conformational search guided by an energy function[18], [20], [21], [22], [23], or by heuristic approaches[19], [24]. For example, WHAT IF [24] determines the position of non-polar hydrogen atoms using fixed bond lengths and bond angles, while for the polar hydrogen atoms, it considers possible hydrogen bonds and the protonation state of each amino acid. REDUCE [19] searches for the most favorable position of hydrogen atoms by a “contact dot” method and samples the atomic “repulsion surface”. MCCE [18] places the non-hydroxyl hydrogen atoms using standard geometric values for the bond lengths and bond angles, while the hydroxyl hydrogen atom positions are optimized by Monte Carlo simulations guided by an energy function consisting of torsion, excluded volume, solvation, and electrostatic terms. HBUILD[20] uses a unique dihedral angle parameter, defined in the CHARMM22 force field, for the placement of hydrogen atoms. Forest and Honig[18] recently compared the accuracy of several hydrogen addition methods, including REDUCE[19], CHARMM (using the HBUILD subroutine)[20], [21], CNS[22], MCCE[18], GROMACS[23] and WHAT IF[24]. Based on a test using seven protein structures solved by X-ray crystallography and neutron diffraction, the authors concluded that REDUCE, WHAT IF and MCCE are among the best methods for placing hydrogen atoms. HBUILD, implemented in the CHARMM package [20], [21], was also shown to have a comparable performance after energy optimization. Despite the good performance of these programs, an algorithm that is of higher prediction accuracy is always desirable for atomic-level structure modeling and drug screening [9]. Especially, for atomic protein structure simulations[25] and atomic force field based protein structure refinement [26], where detailed hydrogen-bonding energy terms have to be calculated at each step of the modeling movements, high-speed determination of hydrogen atom positions is of key importance.

In this work, we develop a new method, called HAAD (Hydrogen Atom ADdition), for quickly constructing hydrogen atoms by combining local geometry restraints and conformational search. The purpose is to reduce steric repulsion and enhance hydrogen bonding networks in the protein structure. On a comprehensive benchmark, we test our method based on three sets of experimental data: high-resolution X-ray crystallography, structures from neutron diffraction, and NOE proton-proton distance restraints. The widely used methods HUBILD and REDUCE are used as a reference for accuracy measurement. The successes or failures of the algorithms in positioning different types of hydrogen atoms are discussed.

Methodology

There are three kinds of hybrid orbital, i.e. sp3, sp2 and sp, associated with the heavy atoms (C, N, O and S) in proteins [27]. Given the 3D coordinates of the heavy atoms, the spatial orientations of the hybrid orbital can be used to determine the positions of hydrogen atoms (H-atoms). The position of an H-atom connected to a heavy atom is determined relative to other heavy atoms connected to the same central heavy atom. Basically, if the heavy atom has an sp3 hybrid orbital, the four connected atoms tend to form a tetrahedron centered at this heavy atom; if it has a sp2 hybrid orbital, the three atoms connected to it tend to form a triangle with the heavy atom in the center; if it has a sp hybrid orbital, the heavy atom and the two bonded atoms tend to form a triangle with the three atoms on its vertices.

In our method, H-atoms are initially placed based on the local geometry, which is determined by the hybrid orbital of the heavy atom to which the hydrogen atom is connected. In general, three constraints are required to fix the spatial position of an H-atom. Two of them are the bond length and the bond angle, which are constant and taken from the CHARMM22 force field [28]. The third constraint is determined based on the classes of the given H-atom; H-atoms are classified based on the type of the hybrid orbital and the number of H-atoms connected to the central heavy atom (see Table 1).

thumbnail
Table 1. Classification of hydrogen atoms, and their bond lengths and locations.

https://doi.org/10.1371/journal.pone.0006701.t001

In Figure 1, we present an illustration of how the local geometry is determined by the hybrid orbital. We label the central heavy atom under consideration as A and the neighboring central heavy atom as B, with A1, A2, B1, B2 and B3 denoting the groups connected to these central atoms, where for the exclusive cases the atoms are labeled with their element symbol. The atoms involved in an sp3 hybrid orbital have a preference for a staggered conformations because this state ensures the minimum local steric repulsion between the atoms [29], [30]. Therefore, we place H-atoms in the sp3H3 class in a staggered conformation (labeled A1, A2 & H in Fig 1a) without further optimization, although they may have rotational freedom around the A–B bond. To assign the position of sp3H2 H-atoms, we first identify the tetrahedron centered at A with two of its vertices at B and the heavy atom A1, and then put the two H-atoms at the remaining vertices of the tetrahedron (A2 and H in Figure 1a), while retaining the standard bond lengths and bond angles. In the case of sp3H1 H-atoms, because the three heavy atoms at B, A1 and A2 form three vertices of the tetrahedron centered at A, the sp3H1 H-atom is placed at the remaining vertex of the tetrahedron (H in Figure 1a), with the standard parameters.

thumbnail
Figure 1. Illustration of hydrogen atom placement based on local geometry.

(a) The hydrogen atoms are bonded to the heavy atom A with an sp3 hybrid orbital; (b) and (c) the local geometry for sp2 hydrogen atoms; (d) local geometry for sp hydrogen atoms. The labels A and B denote the position which may hold C, N or other atoms in the protein chain; the labels A1, A2 and B1, B2, B3 represent atoms or atomic groups. The excluded volumes are ordered as A1≥A2≥H, and B1≥B2≥B3. The dotted lines indicate the geometry determined by the hybrid orbital. In (d), H0 is at the initial position with a trans-conformation; H is at the position obtained after considering non-bonded interactions.

https://doi.org/10.1371/journal.pone.0006701.g001

For constructing the sp2H2 and sp2H1 H-atoms, we first decide on the orientation of the conjugated plane or the aromatic ring with respect to the neighboring heavy atoms; the normal vector of the conjugated plane is determined by taking the cross product of two vectors between the heavy atoms. For the sp2H2 H-atoms (illustrated in Fig. 1b), the normal vector of the conjugated plane is the cross product of the unit vectors B→N and B2→B; then the two H-atoms are placed at positions A1 and H, which are within the conjugated plane respected to the B→N vector with the exact bond angle from CHARMM22 force field. For sp2H1 H-atoms, two conformations are possible. The first is to place the H-atom in the peptide plane as illustrated in Fig. 1b, where A1 and B1 represent the alpha carbon atoms. The position of H in this case is decided by using the same method as the one used to determine the position of the sp2H2 H-atoms while holding the trans-conformation. The second possible conformation is for a hydrogen in an aromatic ring, as illustrated in Fig. 1c. The normal vector of the conjugated plane is defined by the cross product of the unit vectors of A1→A and A2→A; and the H-atom is then placed in the conjugated plane along the vector satisfying the bond length and the bond angle.

H-atoms in the spH1 category constitute less than 2% of all H-atoms in proteins. However, the placement of spH1 H-atoms is usually less accurate than that of other H-atoms due to the fact that these H-atoms have a rotational freedom and can be located at any position around the circle in a cone (see Figure 1d). To decide on the position of spH1 atoms, we initially place the H-atoms in a trans-conformation using a similar protocol to the spH1 atoms (H0 in Fig. 1d), and then relocate them based on the global minimum of the energy function(1)where i runs through all spH1 H-atoms and N is the total number of atoms in the protein chain. δij = 10(δi+δjrij) when rij<δi+δj; otherwise equals to zero. ρij = ρiρj when rij≤4 Å; otherwise equals to zero. Here δi and ρi are the van der Waals radius and the partial charge of the ith atom from the CHARMM22 force field [28], and rij is the distance between the ith and the jth atom. The first term in Eq. (1) is used to minimize steric clashes of the ith H-atom with other atoms, while the second term accounts for the electrostatic interactions and guides atoms of opposite partial charges to be placed close to each other. Since the hydrogen bond donor and acceptor atoms have opposite partial charges, minimization of ρij tends to encourage the formation of more hydrogen bonds. We search the conformational space by rotating the dihedral angle B1-B-O-H in a 10° interval starting from the initial position and finally adopt the position on the cone with the smallest energy.

HAAD is a standalone program written in FORTRAN90. The average CPU time required for constructing all H-atoms in a protein structure with ∼200 amino acids is 0.06 seconds on a 2.6 GHz AMD processor machine, which is about 8 times faster than REDUCE (0.46 seconds) and 18 times faster than HBUILD (1.09 seconds) according to our test on 230 protein structures. The on-line server, the executable and source code of the HAAD program are freely available at http://zhang.bioinformatics.ku.edu/HAAD/.

Materials

For a given protein structure with fixed heavy atom positions, the possible variation in H-atom positions is relatively small, especially compared to the possible topology changes resulting from changing the backbone conformation. Therefore, high-resolution structures including H-atoms are essential for evaluating hydrogen addition algorithms. For this purpose, two sets of experimental protein structures containing chains of at least 30 residues with explicitly solved H-atoms were selected from the PDB. The first set includes ultra-high-resolution protein structures solved by X-ray crystallography experiments with a resolution better than 1.0 Å (Table 2); the second set includes structures solved by high-resolution neutron diffraction, in which the relative orientation of the groups containing H-atoms are accurately determined [31].

thumbnail
Table 2. List of the proteins solved by high-resolution X-ray and neutron diffraction experiments used for analysis.

https://doi.org/10.1371/journal.pone.0006701.t002

To assess the accuracy of predicted H-atom positions on these two sets of proteins, all the H-atoms in these protein structures were first removed, and then added using HBUILD (from CHARMM) [20], [21], REDUCE [19] and HAAD. We choose HBUILD and REDUCE for comparison because they are widely used and are among the most accurate methods based on recent assessments [18]. Because REDUCE may flip the side chains of Gln, Asn, and His to resolve clashes during H-atom construction which results in additional errors when assessing the models by REDUCE, to have a fair comparison, we excluded those proteins from our benchmark set, in which side chains were flipped, by checking whether the root mean square deviation (RMSD) of all heavy atoms is equal to zero between the structures before and after adding the hydrogen by REDUCE. Finally, 22 X-ray structures and 15 neutron diffraction structures were selected for the comparison and analysis. It is worth mentioning that in the analysis of the protein structures solved by the neutron diffraction, we exclude deuterium atoms in the experimentally solved structures from the comparison with the predicted H-atom positions, because deuterium atoms have different bond lengths and van der Waals radii than H-atoms.

Protein structures solved by NMR are usually determined by satisfying the spatial distance restraints [32] which can be derived from the proton-proton distances in the Nuclear Overhauser Effect (NOE) data. Because of the limited number of NOEs, there are usually a number of NMR models in the PDB files which fit equally well to the NOE data and thus result in uncertainty in the heavy atom coordinates. Especially, the H-atoms in NMR are usually determined by running existing H-adding software and the accuracy of the software programs can be questionable. Thus, we do not consider the NMR models as objective criterions for examining the developed H-adding algorithms. Instead, we test the algorithms based on the original NOE data with proton-proton distances and the corresponding X-ray diffraction structures. For this purpose, we collected 13 proteins, as shown in Table 3, from the PDB which have been solved by both NMR (for collecting NOE) and X-ray crystallography, and have their NOE data deposited in BMRB [33]. We first rebuild all the H-atoms based on the X-ray heavy atom structures, and generate an inter-proton distance map which is then compared with the original NOE distance restraints. Although the X-ray structures and the NOE data are obtained in different environments and may reflect structural diversity and have different resolutions, the assumption here is that the correctly positioned H-atoms should, on average, have the maximum convergence with NOE proton distance map, because they are from the same proteins. It needs to be mentioned that in the comparison of the distance map with the NOE data, we only consider those NOE distance restraints which have a mean distance of no more than 5 Å, because the Nuclear Overhauser effect above this distance becomes relatively weak [34].

thumbnail
Table 3. List of proteins having both an X-ray structure and NOE data deposited in PDB, which are used for analysis.

https://doi.org/10.1371/journal.pone.0006701.t003

To evaluate the accuracy of hydrogen positions using the NOE distance restraints, the ratio of matched proton-proton pair (fmatch) is counted through the definition of(2)where rij is the distance between the ith and the jth H-atoms predicted by the hydrogen addition programs based on the heavy atom of X-ray structures; rij,NOE ( = 5 Å) is a mean distance cutoff of the NOE restraint data for the corresponding atom pairs and NNOE is the number of NOE distance restraints with the mean proton-proton distances below 5 Å as shown in Table 3. The step function δ(x) = 1 if x≤0; otherwise it is equal to 0.

Results

Deviation of predicted H-atom from X-ray and neutron diffraction structures

RMSD is commonly used as a direct measure for assessing the accuracy of the predicted H-atoms in comparison with those in high-resolution experimental structures. Instead of an all-atom superposition as done in usual RMSD calculation [35], we first superimpose the structure of the heavy atoms and then directly calculate the root mean square of the distances between corresponding H-atom pairs.

Table 4 summarizes the RMSD of the H-atoms added by the three different methods used in our study. It shows that the H-atoms added by HAAD have a lower RMSD to the experimental structures than those added by HBUILD and REDUCE in all the H-atom categories except spH1. For the spH1 atoms, the average RMSD from HAAD (1.111 Å) is lower than that from HBUILD (1.217 Å) but slightly higher than that from REDUCE (1.094 Å). The average RMSD for all 46,753 H-atoms is 0.208 Å, 0.234 Å, and 0.282 Å for HAAD, REDUCE, and HBUILD, respectively.

thumbnail
Table 4. Summary of the accuracy of hydrogen atoms placement by different methods as compared to high resolution X-ray and neutron diffraction structures.

https://doi.org/10.1371/journal.pone.0006701.t004

In Figure 2, we split the H-atoms added by each algorithm to all structures in our test sets into two categories: those having a small deviation (distance≤0.2 Å) and those having a large deviation (distance>0.2 Å) from their respective native positions. In the small deviation category (Figure 2a), all the three programs have an appreciable accuracy, with 93.5% of H-atoms added by HAAD falling in this category, while 92.3% and 91.2% H-atoms by REDUCE and HBUILD are in this category, respectively. At a more restrictive distance cutoff of RMSD≤0.1 Å, the performance difference becomes more pronounced, with 88.0% of H-atoms added by HAAD falling in this category, while only 76.6% and 59.9% of H-atoms predicted by REDUCE and HBUILD are in this category, respectively.

thumbnail
Figure 2. The RMSD distribution in the small deviation (a) and the large deviation category (b).

https://doi.org/10.1371/journal.pone.0006701.g002

In the large deviation category (Figure 2b), the three methods show similar distributions. The largest observed deviations reaches 1.85 Å got 9 H-atoms placed by REDUCE, 8 of them belong to the spH1 class. The profile of the RMSD distribution for all the H-atoms is in agreement with the distribution of the mean square displacements of H-atoms in experimental structures [13], [18]. Overall, HAAD has the ability to place H-atoms with a smaller deviation from their positions in the experimentally solved high-resolution structures than other programs.

To find out which atoms contribute most of the large deviations, we show the distance distribution of the spH1 H-atoms in Figure 3a. For all the H-atoms in the 37 structures with a distance ≥1.0 Å, 422 out of 454 H-atoms rebuilt by HAAD, similarly 481 out of 1031 by HBUILD, and 368 out of 475 by REDUCE, belong to the spH1 class. These data again show that the spH1 H-atoms are the major contributions to the large deviation category, and that spH1 is the most difficult class of H-atoms to be accurately predicted.

thumbnail
Figure 3. The RMSD distribution of spH1 hydrogen atoms and examples.

(a) The RMSD distribution in the spH1 category. (b) An example from 1gci, showing the OH group in Y89 as an acceptor of a hydrogen bond with the NZ atom in K27. (c) An example from 1ab1, showing the OH group in S11 as a donor of a hydrogen bond with the O atom in I7. The yellow dashed line indicates the hydrogen bond; the grey, red, blue and white balls represent C, O, N and H atoms, respectively. The green sphere indicates the favorable position of the hydrogen as corresponding to the local geometry, which becomes unfavorable because of the formation of hydrogen bonds.

https://doi.org/10.1371/journal.pone.0006701.g003

In addition to the fact that the spH1 H-atoms have a large degree of positional uncertainty according to the hybridization model shown in Figure 1d, we assume that the relocation of H-atoms in –OH groups due to the formation of hydrogen bonds is another reason contributing to lower accuracy of predicted spH1 H-atom positions, The –OH group can serve either as a donor or an acceptor or both in a protein chain and the hydrogen bonding energy is favorable enough to change the stereochemistry and conformation of this group. In fact, we observed a number of cases where H-atoms are obviously relocated due to the formation of hydrogen bonds. Figure 3b shows one example, the hydrogen in the -OH group of Y89 (TYR) of the protein 1gci, which has been driven away from the aromatic conjugated ring plane (i.e. the favorable position corresponding to the local geometry as shown by the green sphere) to decrease the steric repulsion from the donor NZ in K27 (LYS), with which a hydrogen bond is formed (indicated by the yellow dashed line). Figure 3c is another example, from protein 1ab1, where the hydrogen in OG group of the S11 (SER) side chain is drawn away from the position corresponding to the minimum of the local steric repulsion (green sphere), because the OG atom serves as a donor of a hydrogen bond whose acceptor is the O atom in I7 (ILE). Since hydrogen bonds involving –OH groups can be formed both in the buried core region (with other polar groups) and ath the exposed protein surface (with solvent molecules), the position of an spH1 H-atoms does not depend on whether the –OH group is buried or not.

Atomic clashes of predicted H-atoms with other atoms

The number of atomic clashes between the added H-atoms and other heavy atoms is an another important evaluation criterion to assess the quality of hydrogen addition algorithms [36]. Two atoms clash when the distance between them is less than the sum of their van der Waals radii. Ideally, the atoms in the native structures have no (or very few) clashes, suggesting that structures with fewer atom clashes should be more reliable and native-like.

The normalized number of clashes made by H-atoms in category T in a protein can be calculated by(3)where vij equals to the sum of the van der Waals radius of the ith and the jth atoms with values taken from the CHARMM22 force field (see ‘par_all22_prot.inp’ in the CHARMM22 package). δ(x) = 1 if x<0, otherwise equals 0. NH,T is the number of H-atoms in category T, where T may denote all H-atoms (“all”), polar H-atoms (“polar”) and non-polar H-atoms (“non-polar”). When counting the number of clashed atom pairs, atom pairs with strong chemical geometry restraints, i.e. fewer than three covalent bonds apart, are excluded. Because the polar H-atoms can easily undergo an exchange with the solvent [13] and the properties of hydrogen and deuterium are different, we also exclude those atom pairs from the comparison that involve deuterium atoms in the neutron diffraction structures.

The number of atomic clashes between the predicted H-atoms and other atoms for all the 37 high resolution structures are shown in Fig. 4 and the average values are summarized in Table 5. Some of the structures solved by neutron diffraction have an Npolar equal to 0 because no polar H-atom is compared in these structures. On average, for all the H-atoms, the experimental structures have the lowest average number of atomic clashes, i.e. Nall = 1.48. The number of clashing atoms in structures generated by HAAD is 2% higher than that in the experimental structures, but 5% lower than that in models from HBUILD and 6% lower than that in models from REDUCE.

thumbnail
Figure 4. The average number of atom clashes made by hydrogen atoms in various categories, in models of 37 protein structures.

The dashed line marks the boundary between X-ray (left) and neutron diffraction structures (right).

https://doi.org/10.1371/journal.pone.0006701.g004

thumbnail
Table 5. Comparison of the average number of atom clashes and its standard deviation (in parentheses) of the predicted hydrogen atoms in the models built by different methods.

https://doi.org/10.1371/journal.pone.0006701.t005

Consistency of H-atom predictions with NOE distance restraints

In Figure 5, we present the comparison of predicted H-atoms with data from NMR experiments. Because H-atoms in NMR models are usually added based on existing H-adding algorithms, to eliminate the algorithm-dependent bias, we compare our H-adding prediction directly with the original NOE proton-proton distance data, where the structure models with the H-atoms are reconstructed by HAAD, HBUILD and REDUCE based on the X-ray heavy-atom structure of the same proteins. As shown in Eq. (2), fmatch is defined as the number of matches between NOE restraints and the predicted H-atom distances divided by the number of NOEs. fmatch as calculated based on the NMR structural models is also shown for a reference comparison. For proteins with multiple NMR models, the model which has the minimum RMSD to the X-ray structure is presented.

thumbnail
Figure 5. The number of hydrogen atom pairs matching the NOE proton-proton distance restraints in models of 13 proteins.

Models are from in the NMR structures, and the structures built by the three methods based on either X-ray or NMR heavy-atom structures. For 1 kdf, the hydrogen in the NMR model is not available.

https://doi.org/10.1371/journal.pone.0006701.g005

Despite considerable systematic errors due to the fact that the proteins are solved in different environments by NMR and X-ray crystallography, there are obvious differences between the models made by the three different methods. From the 13 proteins that were used in this analysis, HBUILD has 4 (1 ties with HAAD), REDUCE has 4 (1 ties with HBUILD), and HAAD has 7 (1 ties with HBUILD) cases with the highest fmatch values. The average fmatch for all the 12 proteins (except for 1 kdf that has no H-atoms in the NMR structure) are 0.598, 0.584, 0.583 and 0.588 for the NMR model, and the structure models by HBUILD, REDUCE and HAAD, respectively. The fmatch for 1 kdf is comparable and is equal to 0.398, 0.393 and 0.376 for HBUILD, REDUCE and HAAD, respectively.

The main reason of choosing X-ray diffraction structures instead of NMR models as starting model for constructing H-atoms in the above experiment is that the X-ray structure is much less program-dependent while NMR models are usually built based on molecular simulations under NOE restraints. In case that the number of NOEs is limited, several models can be generated. In the right columns of Figure 5, we also compare the NOE data with the H-atoms predicted on the NMR heavy atom structures that are closest to the X-ray structure. Similarly, HBUILD gets 3 (1 ties with REDUCE), REDUCE gets 2 (1 ties with HBUILD) and HAAD gets 8 cases which have the highest fmatch values. The average fmatch in all the 12 proteins (except for 1 kdf) are 0.595, 0.594 and 0.598 for the H-atom models built by HBUILD, REDUCE and HAAD. These data show that the H-atoms in the models build by HAAD have a greater consistency with the NOE distance restraint data that the other two programs.

Discussion

In general, non-polar H-atoms have a smaller RMSD than polar H-atoms in all three methods. This can be explained by the large positional uncertainty of polar H-atoms induced because of their hydrogen-bonding capability. Quantitatively, the free energy cost of moving a H-atom from the staggered conformation to an eclipsed conformation is around 3.0 kcal/mol [29]. But the free energy gained by forming a hydrogen bond in the polar H-atoms is about 5.0 kcal/mol [37]. This renders the polar H-atoms to readily depart from their standard staggered conformations when a hydrogen bond can be formed, making the emplacement of H-atoms based on local geometry a formidable task. The accurate prediction of polar H-atom positions requires further consideration of both local steric repulsion and non-local hydrogen-bonding networks.

On the other hand, for non-polar H-atoms, the average free energy gain for a hydrophobic interaction pair is about 0.18 kcal/mol [37], which is too weak to move the H-atoms away from their most stable rotational conformation (with minimum local steric repulsion), suggesting that non-polar H-atoms are most likely located close to the position determined by the hybridization state of the central heavy atom. This corresponds to the way of placing non-polar H-atoms in our method, and is also supported by protein structures obtained from neutron diffraction [38].

Among the different categories, the spH1 H-atoms have the largest deviation from the native position, and all three methods failed to achieve an average RMSD below 1.0 Å. This is not surprising considering the fact that the spH1 H-atom positions have the largest degree of uncertainty according to the hybridization model (see Figure 1d). The positions of the sp3H3 H-atoms are the second hardest to predict because they have a rotational freedom around the sigma bond (B-A in Figure 1a). In most of the experimental structures, they occupy positions that are close to be not exactly at the positions corresponding to a staggered conformation. For the other four hybridization categories, almost all the H-atoms can be correctly placed by HAAD within an average deviation below 0.2 Å.

With regard to the atomic clashes of H-atoms with others, it is observed that the absolute number of atomic clashes involving non-polar H-atoms is much higher than the number of clashes made by polar H-atoms; this is because non-polar H-atoms are mostly located in the hydrophobic core, which is usually tightly packed [39], and thus have a higher chance to clash with other atoms. On the other hand, the polar H-atoms are mostly in the interface or on the surface, where the atomic packing density is lower than in the core region. Moreover, the polar H-atoms frequently mix with charged groups where the electrostatic repulsion acts against atomic packing. Therefore, the non-polar H-atoms have a smaller free space to accommodate to than polar H-atoms, which result in more atom clashes in the non-polar H-atoms than that in the polar ones.

It has been reported that the length of bonds between hydrogen and heavy atoms are systematically underestimated in X-ray diffraction [39], [40]; this may be partially the reason why there are still some atomic clashes in the experimental structures. Overall, the number of clashes in the HAAD models is closer to that observed in the experimental structures than the numbers from REDUCE and HBUILD models, which demonstrates that the method we used for constructing H-atoms is more efficient in reducing the atom clashes.

Summary

We developed a new algorithm, HAAD, for quickly predicting the positions of H-atoms in protein structures. The method is built on the basic theory of orbital hybridization, followed by the optimization of steric repulsion and electrostatic interactions.

HAAD constructs H-atoms in protein structures with an appreciable accuracy. In three independent tests based on experimental data from ultra-high-resolution X-ray structures, neutron diffraction experiments, and NOE proton-proton distance restraint data, the overall accuracy of the hydrogen positioning by HAAD is consistently higher than that of other methods used for hydrogen construction. The average RMSD of H-atoms placed by HAAD from their corresponding positions in the ultra-high-resolution experimental structures is ∼26% lower than that obtained with HBUILD, a subroutine for hydrogen construction in CHARMM [21], and 10.7% lower than that by REDUCE [19]. When comparing the NOE restraint data with the hydrogen positions built from both the X-ray structures and the NMR models of the same proteins, the models built by HAAD have a higher number of H-atom pairs consistent with the original NOE data than models built by other methods. Although we are aware of the fact that positions of H-atoms in most experimental structures have a high uncertainty compared with the accuracy we addressed here, we believe that our evaluations using a large-scale data (46,753 H-atoms and 15,776 NOE proton-proton distances), including ultra-high-resolution structures, should provide a statistically meaningful differentiation between the respective performances of the tested methods.

As an additional assessment, the number of steric clashes in the HAAD models is relatively lower than in other models. Because the non-polar H-atoms are usually located in the densely packed hydrophobic core, they have a much higher number of clashes than the polar H-atoms which tend to be located on the surface. The number of total clashes in the HAAD models is only 2% higher than the experimental structures, and 5–6% lower than that in models by HBUILD and REDUCE.

In general, the accuracy of predicted polar H-atoms is lower than that of non-polar H-atoms; the accuracy for hydrogen in –OH groups is the lowest among all the different categories of H-atoms. This is mainly due to the fact that the hydrogen-bonding interactions of the polar and spH1 H-atoms with other charged groups (including solvent molecules) tend to drive the H-atoms away from the locally optimal position with minimum steric repulsion. Therefore, further refinement of the global hydrogen-bonding networks, as well as including the interactions with water molecules, may help improve the accuracy of adding polar and spH1 H-atoms, although it will require more CPU cost; a new version of HAAD along this line is in development. Nevertheless, the encouraging results in improving the hydrogen accuracy and the ability of quickly constructing H-atoms should make the current version of HAAD an important tool for detailed studies of protein structure and function, especially in large-scale and atomic-level simulations where the positions of hydrogen atoms need to be quickly and accurately determined.

Acknowledgments

The authors are grateful to Dr. Andras Szilagyi for reading through the manuscript.

Author Contributions

Conceived and designed the experiments: LY ZY. Performed the experiments: LY RA. Analyzed the data: LY RA. Contributed reagents/materials/analysis tools: LY. Wrote the paper: LY RA ZY.

References

  1. 1. Rose GD, Fleming PJ, Banavar JR, Maritan A (2006) A backbone-based theory of protein folding. Proc Natl Acad Sci U S A 103: 16623–16633.GD RosePJ FlemingJR BanavarA. Maritan2006A backbone-based theory of protein folding.Proc Natl Acad Sci U S A1031662316633
  2. 2. Baud F, Pebay-Peyroula E, Cohen-Addad C, Odani S, Lehmann MS (1993) Crystal structure of hydrophobic protein from soybean; a member of a new cysteine-rich family. J Mol Biol 231: 877–887.F. BaudE. Pebay-PeyroulaC. Cohen-AddadS. OdaniMS Lehmann1993Crystal structure of hydrophobic protein from soybean; a member of a new cysteine-rich family.J Mol Biol231877887
  3. 3. Dill KA, Bromberg S, Yue K, Fiebig KM, Yee DP, et al. (1995) Principles of protein folding–a perspective from simple exact models. Protein Sci 4: 561–602.KA DillS. BrombergK. YueKM FiebigDP Yee1995Principles of protein folding–a perspective from simple exact models.Protein Sci4561602
  4. 4. SaEI Shakhnovich (2008) Understanding ensemble protein folding at atomic detail. J Phys: Condens Matter 20: 283101.Shakhnovich SaEI2008Understanding ensemble protein folding at atomic detail.J Phys: Condens Matter20283101
  5. 5. Jones S, Thornton JM (1996) Principles of protein-protein interactions. Proc Natl Acad Sci U S A 93: 13–20.S. JonesJM Thornton1996Principles of protein-protein interactions.Proc Natl Acad Sci U S A931320
  6. 6. Wang W, Donini O, Reyes CM, Kollman PA (2001) Biomolecular simulations: recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions. Annu Rev Biophys Biomol Struct 30: 211–243.W. WangO. DoniniCM ReyesPA Kollman2001Biomolecular simulations: recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions.Annu Rev Biophys Biomol Struct30211243
  7. 7. Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11: 580–594.G. Klebe2006Virtual ligand screening: strategies, perspectives and limitations.Drug Discov Today11580594
  8. 8. Anderson AC (2003) The process of structure-based drug design. Chem Biol 10: 787–797.AC Anderson2003The process of structure-based drug design.Chem Biol10787797
  9. 9. Zhang Y (2009) Protein structure prediction: When is it useful? Corr Opin Struct Biol: . Y. Zhang2009Protein structure prediction: When is it useful?Corr Opin Struct Biol:
  10. 10. Cohen M, Reichmann D, Neuvirth H, Schreiber G (2008) Similar chemistry, but different bond preferences in inter versus intra-protein interactions. Proteins 72: 741–753.M. CohenD. ReichmannH. NeuvirthG. Schreiber2008Similar chemistry, but different bond preferences in inter versus intra-protein interactions.Proteins72741753
  11. 11. Gochin M, Roder H (1995) Protein structure refinement based on paramagnetic NMR shifts: applications to wild-type and mutant forms of cytochrome c. Protein Sci 4: 296–305.M. GochinH. Roder1995Protein structure refinement based on paramagnetic NMR shifts: applications to wild-type and mutant forms of cytochrome c.Protein Sci4296305
  12. 12. Kimura SR, Tebben AJ, Langley DR (2008) Expanding GPCR homology model binding sites via a balloon potential: A molecular dynamics refinement approach. Proteins 71: 1919–1929.SR KimuraAJ TebbenDR Langley2008Expanding GPCR homology model binding sites via a balloon potential: A molecular dynamics refinement approach.Proteins7119191929
  13. 13. Engler N, Ostermann A, Niimura N, Parak FG (2003) Hydrogen atoms in proteins: positions and dynamics. Proc Natl Acad Sci U S A 100: 10243–10248.N. EnglerA. OstermannN. NiimuraFG Parak2003Hydrogen atoms in proteins: positions and dynamics.Proc Natl Acad Sci U S A1001024310248
  14. 14. Chen J, Brooks CL 3rd (2007) Can molecular dynamics simulations provide high-resolution refinement of protein structure? Proteins 67: 922–930.J. ChenCL Brooks 3rd2007Can molecular dynamics simulations provide high-resolution refinement of protein structure?Proteins67922930
  15. 15. McDonald IK, Thornton JM (1994) Satisfying hydrogen bonding potential in proteins. J Mol Biol 238: 777–793.IK McDonaldJM Thornton1994Satisfying hydrogen bonding potential in proteins.J Mol Biol238777793
  16. 16. Canutescu AA, Shelenkov AA, Dunbrack RL, Jr (2003) A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci 12: 2001–2014.AA CanutescuAA ShelenkovRL, Dunbrack Jr2003A graph-theory algorithm for rapid protein side-chain prediction.Protein Sci1220012014
  17. 17. Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234: 779–815.A. SaliTL Blundell1993Comparative protein modelling by satisfaction of spatial restraints.J Mol Biol234779815
  18. 18. Forrest LR, Honig B (2005) An assessment of the accuracy of methods for predicting hydrogen positions in protein structures. Proteins 61: 296–309.LR ForrestB. Honig2005An assessment of the accuracy of methods for predicting hydrogen positions in protein structures.Proteins61296309
  19. 19. Word JM, Lovell SC, Richardson JS, Richardson DC (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285: 1735–1747.JM WordSC LovellJS RichardsonDC Richardson1999Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation.J Mol Biol28517351747
  20. 20. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, et al. (1983) CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. Journal of Computational Chemistry 4: 187–217.BR BrooksRE BruccoleriBD OlafsonDJ StatesS. Swaminathan1983CHARMM: A program for macromolecular energy, minimization, and dynamics calculations.Journal of Computational Chemistry4187217
  21. 21. Brunger AT, Karplus M (1988) Polar hydrogen positions in proteins: empirical energy placement and neutron diffraction comparison. Proteins 4: 148–156.AT BrungerM. Karplus1988Polar hydrogen positions in proteins: empirical energy placement and neutron diffraction comparison.Proteins4148156
  22. 22. Brunger AT, Adams PD, Clore GM, DeLano WL, Gros P, et al. (1998) Crystallography & NMR system: A new software suite for macromolecular structure determination. Acta Crystallogr D Biol Crystallogr 54: 905–921.AT BrungerPD AdamsGM CloreWL DeLanoP. Gros1998Crystallography & NMR system: A new software suite for macromolecular structure determination.Acta Crystallogr D Biol Crystallogr54905921
  23. 23. Lindahl E, Hess B, Dvd Spoel (2001) GROMACS 3.0: a package for molecular simulation and trajectory analysis. J Mol Model 7: 306–317.E. LindahlB. HessSpoel Dvd2001GROMACS 3.0: a package for molecular simulation and trajectory analysis.J Mol Model7306317
  24. 24. Vriend G (1990) WHAT IF: a molecular modeling and drug design program. J Mol Graph 8: 52-56–29.G. Vriend1990WHAT IF: a molecular modeling and drug design program.J Mol Graph852-5629
  25. 25. Li YQ, Zhang Y (2009) REMO: A new protocol to refine full atomic protein models from C-alpha traces by optimizing hydrogen-bonding networks. Proteins: . YQ LiY. Zhang2009REMO: A new protocol to refine full atomic protein models from C-alpha traces by optimizing hydrogen-bonding networks.Proteins:
  26. 26. Verma A, Wenzel W (2007) Protein structure prediction by all-atom free-energy refinement. BMC Struct Biol 7: 12.A. VermaW. Wenzel2007Protein structure prediction by all-atom free-energy refinement.BMC Struct Biol712
  27. 27. Pauling L (1931) the nature of the chemical bond. Application of results obtained from the quantum mechanics and from a theory of paramagnetic susceptibility to the structure of molecules. J Am Chem Soc 53: 1367–1400.L. Pauling1931the nature of the chemical bond. Application of results obtained from the quantum mechanics and from a theory of paramagnetic susceptibility to the structure of molecules.J Am Chem Soc5313671400
  28. 28. MacKerell AD, Bashford D, Bellott M, Dunbrack RL, Evanseck JD, et al. (1998) All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins. J Phys Chem B 102: 3586–3616.AD MacKerellD. BashfordM. BellottRL DunbrackJD Evanseck1998All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins.J Phys Chem B10235863616
  29. 29. Bickelhaupt FM, Baerends EJ (2003) The case for steric repulsion causing the staggered conformation of ethane. Angew Chem Int Ed Engl 42: 4183–4188.FM BickelhauptEJ Baerends2003The case for steric repulsion causing the staggered conformation of ethane.Angew Chem Int Ed Engl4241834188discussion 4188-4194. discussion 4188-4194.
  30. 30. Pophristic V, Goodman L (2001) Hyperconjugation not steric repulsion leads to the staggered structure of ethane. Nature 411: 565–568.V. PophristicL. Goodman2001Hyperconjugation not steric repulsion leads to the staggered structure of ethane.Nature411565568
  31. 31. Kossiakoff AA (1982) Protein dynamics investigated by the neutron diffraction-hydrogen exchange technique. Nature 296: 713–721.AA Kossiakoff1982Protein dynamics investigated by the neutron diffraction-hydrogen exchange technique.Nature296713721
  32. 32. Herrmann T, Guntert P, Wuthrich K (2002) Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA. J Mol Biol 319: 209–227.T. HerrmannP. GuntertK. Wuthrich2002Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA.J Mol Biol319209227
  33. 33. Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, et al. (2008) BioMagResBank. Nucleic Acids Res 36: D402–408.EL UlrichH. AkutsuJF DoreleijersY. HaranoYE Ioannidis2008BioMagResBank.Nucleic Acids Res36D402408
  34. 34. Nilges M (1997) Ambiguous distance data in the calculation of NMR structures. Fold Des 2: S53–57.M. Nilges1997Ambiguous distance data in the calculation of NMR structures.Fold Des2S5357
  35. 35. Kabsch W (1978) A discussion of the solution for the best rotation to relate two sets of vecotrs. Acta Cryst A 34: 827–828.W. Kabsch1978A discussion of the solution for the best rotation to relate two sets of vecotrs.Acta Cryst A34827828
  36. 36. Read RJ, Chavali G (2007) Assessment of CASP7 predictions in the high accuracy template-based modeling category. Proteins 69: 27–37.RJ ReadG. Chavali2007Assessment of CASP7 predictions in the high accuracy template-based modeling category.Proteins692737
  37. 37. Li Y, Lee J, Lal J, An L, Huang Q (2008) Effects of pH on the Interactions and Conformation of Bovine Serum Albumin: Comparison between Chemical Force Microscopy and Small-Angle Neutron Scattering. J Phys Chem B 112: 3797–3806.Y. LiJ. LeeJ. LalL. AnQ. Huang2008Effects of pH on the Interactions and Conformation of Bovine Serum Albumin: Comparison between Chemical Force Microscopy and Small-Angle Neutron Scattering.J Phys Chem B11237973806
  38. 38. Kossiakoff AA (1983) Neutron protein crystallography: advances in methods and applications. Annu Rev Biophys Bioeng 12: 159–182.AA Kossiakoff1983Neutron protein crystallography: advances in methods and applications.Annu Rev Biophys Bioeng12159182
  39. 39. Seeliger D, de Groot BL (2007) Atomic contacts in protein structures. A detailed analysis of atomic radii, packing, and overlaps. Proteins 68: 595–601.D. SeeligerBL de Groot2007Atomic contacts in protein structures. A detailed analysis of atomic radii, packing, and overlaps.Proteins68595601
  40. 40. Rowland RS, Taylor R (1996) Intermolecular Nonbonded Contact Distances in Organic Crystal Structures: Comparison with Distances Expected from van der Waals Radii. J Phys Chem 100: 7384–7391.RS RowlandR. Taylor1996Intermolecular Nonbonded Contact Distances in Organic Crystal Structures: Comparison with Distances Expected from van der Waals Radii.J Phys Chem10073847391