Volume 15 Supplement 8
Selected articles from the Third IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS 2013): Bioinformatics
Computational prediction of hinge axes in proteins
 Rittika Shamsuddin^{1},
 Milka Doktorova^{2},
 Sheila Jaswal^{3},
 Audrey LeeSt John^{4}Email author and
 Kathryn McMenimen^{4}
DOI: 10.1186/1471210515S8S2
© Shamsuddin et al.; licensee BioMed Central Ltd. 2014
Published: 14 July 2014
Abstract
Background
A protein's function is determined by the wide range of motions exhibited by its 3D structure. However, current experimental techniques are not able to reliably provide the level of detail required for elucidating the exact mechanisms of protein motion essential for effective drug screening and design. Computational tools are instrumental in the study of the underlying structurefunction relationship. We focus on a special type of proteins called "hinge proteins" which exhibit a motion that can be interpreted as a rotation of one domain relative to another.
Results
This work proposes a computational approach that uses the geometric structure of a single conformation to predict the feasible motions of the protein and is founded in recent work from rigidity theory, an area of mathematics that studies flexibility properties of general structures. Given a single conformational state, our analysis predicts a relative axis of motion between two specified domains. We analyze a dataset of 19 structures known to exhibit this hingelike behavior. For 15, the predicted axis is consistent with a motion to a second, known conformation. We present a detailed case study for three proteins whose dynamics have been wellstudied in the literature: calmodulin, the LAO binding protein and the BenceJones protein.
Conclusions
Our results show that incorporating rigiditytheoretic analyses can lead to effective computational methods for understanding hinge motions in macromolecules. This initial investigation is the first step towards a new tool for probing the structuredynamics relationship in proteins.
Keywords
protein flexibility rigidity theory linear algebraBackground
Proteins play a significant role in virtually all biological processes. These macromolecules are composed of sequences of amino acids folded into 3D shapes of varying size and complexity. The structures of many proteins have been determined experimentally and are easily accessible [1]. The key to protein function, however, is the wide range of motions exhibited by the molecules, from local vibrational fluctuations to larger global movements significantly altering the conformational state [2]. The motions of biological interest occur on the timescales of picoseconds to nanoseconds, which makes their study challenging. Only a few experimental techniques, such as NMR and singlemolecule FRET, are capable of probing dynamics at this level [3–6]. However, these techniques are not able to reliably provide the level of detail required for elucidating the exact mechanisms of protein motion and the underlying structurefunction relationship, essential for effective drug screening and design. Theoretical models and computational tools are instrumental for gaining better mechanistic understanding and predictive power.
Related work
Computational methods for predicting hinges in proteins generally focus on determining which residues comprise the "hinge" joint, expected to allow flexibility that results in a motion of two larger domains. The most closely related approaches include Stonehinge [8], HingeProt [9], and DynDom [10]. Both Stonehinge and HingeProt rely on analysis of rigidity and flexibility properties of the protein by using elastic network models; Stonehinge additionally incorporates the same underlying rigidity theory as KINARI [11] to find a cluster decomposition. These methods seek to pinpoint the location of the "hinge" joint; while this is done from a single conformation as input, a predicted axis of motion is not part of the output. The approach of DynDom does identify an axis of motion, but requires two conformations as input.
Contributions
We present a computational approach for predicting the type of motion allowed by a protein; as input, we require a single structure with two domains identified for which relative motion should be studied. Our analysis models the protein as a geometric structure studied in rigidity theory and predicts the relative axis of motion. We use KINARI [11] to perform initial rigidity analysis, resulting in a decomposition of the structure into rigid regions, or "clusters." This reduces the complexity of the protein, allowing subsequent computational analysis for predicting the axis of motion. We take steric hindrance, a molecular property not modeled by the theory, into account by incorporating Rosetta energy calculations [12] when sampling conformations near the native state. We evaluate our approach on 19 structures of proteins known to exhibit hingelike motions and verify that the predicted axis of motion is consistent with a second conformation for 15 of them. To illustrate our results, we present a case study of three of the proteins from our dataset: calmodulin, the LAO binding protein and the BenceJones protein.
Methodology
Our approach is based on results from infinitesimal rigidity theory. We begin with a brief overview of the relevant theoretical concepts, then present our analysis pipeline. For a more thorough treatment of classical rigidity theory, see [13]; for further explanation of the theory behind identifying revolute and prismatic joints, see [7].
Preliminaries
Infinitesimal rigidity theory
Assuming the framework is not in a singular position, the dimension of the motion space after pinning defines the number of degrees of freedom available to the framework; this is equivalent to the minimum number of bars whose addition would stabilize the framework. For example, the 4bar mechanism in Figure 2(a) has 1 degree of freedom, as the addition of a single bar creates a rigid structure (Figure 2(b)). In 2D, generic rigidity of a barandjoint structure is characterized by a graphtheoretic property proven by Laman [14]; however, in 3D, no analogous result is known. (Intuitively, the term "generic" indicates that the structure is not in a "special position"  the technical definition of genericity is outside the scope of this paper.) For 3D bodybarhinge frameworks, composed of rigid bodies with fixedlength bars or hinges between them, a similar graphtheoretic characterization is given by Tay [15, 16]. A bar imposes a distance constraint between two points on the respective bodies, and a hinge allows only a rotational degree of freedom.
The KINARI software that we use models a protein as a bodybarhinge structure by assigning bars or hinges to chemical interactions computed to be present in the protein; for example, a covalent bond is modeled as a hinge, allowing only the dihedral angle to vary [11]. The infinitesimal rigidity theory of bodybarhinge structures is analogous to that of barandjoint structures: a rigidity matrix encodes the firstorder behavior of the constraints, and its null space gives the motion space.
Instantaneous motions of bodybarhinge frameworks
Since we use the motion space of a bodybarhinge framework in our analysis, we now provide a few more details about instantaneous motions in 3D relevant to this work.
By Chasles' Theorem, every rigid body motion in 3D can be described by a screw motion: a rotation and translation along a screw axis. One can imagine a screw motion as being analogous to traveling along an alpha helix, with the screw axis defined by the helix's direction and placement. As a consequence, every instantaneous rigid body motion can be described by a twist: an instantaneous rotation and translation along a twist axis.
For a bodybarhinge framework with n bodies, a motion of the whole structure assigns a twist to each body and can be described by a vector of length 6n. The motion space for the framework, which is a vector space of dimension d, may be described by a set of d basis vectors b_{1}, . . . , b_{ d }; a motion vector s in the space can be expressed as a linear combination of the basis vectors: $s=\sum _{i=1}^{d}{c}_{i}{\text{b}}_{i}$ for some set of "weight" coefficients c_{1}, . . . , c_{ d } ϵ ℝ.
Approach
Analyzed protein dataset.
Protein  PDB ID  KINARI cutoff  Pinned cluster  Moving cluster  Twist purity 

Calmodulin (calciumfree)  1CFD  default  0  1  77.1269 
Calmodulin (Ca 2+ bound; open)  1CLL  2  0  1  95.435 
Calmodulin (Ca2+ bound; closed)  2BBM(A)  default  4  7  150.339 
LAO binding protein (open)  1LST  default  0  1  91.5669 
LAO binding protein (closed)  2LAO  default  0  1  98.1407 
BenceJones protein (open)  4BJL(B)  1.25  0  1  97.0153 
BenceJones protein (closed)  4BJL(A)  1.25  0  1  99.0822 
cAMPdependent protein kinase (open)  1CTP  1.9  0  2  89.7223 
cAMPdependent protein kinase (closed)  1ATP  1.9  0  1  93.2537 
Adenylate kinase (open)  2AK3(A)  default  0  1  95.2801 
Adenylate kinase (closed)  1AKE(A)  2.5  1  0  91.4916 
Glutamine binding protein (open)  1GGG(A)  default  1  0  105.321 
Glutamine binding protein (closed)  1WDN  2  0  1  90 
DNA polymerase β (open)  2BPG(A)  default  0  1  109.657 
DNA polymerase β (closed)  1BPD  default  0  1  86.6385 
Inorganic pyrophosphatase (open)  1K23(A)  default  0  1  91.6584 
Inorganic pyrophosphatase (closed)  1K20(A)  3  1  0  80.6779 
Ribose binding protein (open)  1URP(C)  2  0  1  75.4811 
Ribose binding protein (closed)  2DRI  2.65  1  0  89.4672 
Rigid cluster decomposition with KINARI
We use the KINARIWeb application [11] to model each protein structure as an initial set of bodies (generally one per atom) with constraints between them (determined by interatomic chemical interactions). Depending on the nature of the interactions, KINARI represents them as bars or hinges allowing certain degrees of freedom; these choices are adjustable parameters and can thus be modified. Once set, the software analyzes the rigidity of the structure and produces a cluster decomposition that reduces the complexity of the initial bodybarhinge model, where bodies in the original model are grouped into larger rigid clusters.
We only adjust the parameter for hydrogen bonds, which are calculated by KINARI based on the geometry of the structure and are assigned an energy value denoting their strength (the smaller the energy, the stronger the bond). By default, all hydrogen bonds, including the weakest ones, are modeled as hinges with a single degree of freedom. However, this representation may overly restrict the motion of the structure by producing very large rigid clusters where the domains of interest are grouped into the same cluster. In this case, we adjust the hydrogen bond energy cutoff parameter to remove the weakest hydrogen bonds from the model (preserving the default modeling of a hydrogen bond as a hinge). The rigidity analysis is then performed again to produce a new cluster decomposition. This process is repeated until an energy value is found that produces a cluster decomposition with the two domains in distinct clusters.
We proceed with the analysis using the corresponding bodybarhinge (BBH) framework output by KINARI: each cluster is itself a rigid body, connected to other clusters with bars and hinges. The clusters are labeled by size, with Cluster 0 containing the largest number of atoms. To maintain consistency, we will refer to rigid bodies as "clusters" for the remainder of this paper.
Motion space calculation
 1.
Randomly generate d weight coefficients between 0 and 1: c_{ i } for i = 1, . . . , d.
 2.
Compute the resulting linear combination of basis vectors: $s=\sum _{i=1}^{d}{c}_{i}{\text{b}}_{i}$
 3.
Interpret s = (t_{1}, . . . , t_{ n }), where each t_{ i } ϵ ℝ^{6}. Let each t_{ i } = (ω_{ i }, v_{ i }), where ω_{ i }, v_{ i } ϵ ℝ^{3}.
For each cluster i, let {p_{1}, . . . , p_{ ni }} be the set of positions of the n_{ i } atoms found in the cluster. For each atom position p_{ j }, compute p'_{ j } = ω_{ i } × p_{ j } + v_{ i } and move the atom in the direction p'_{ j } to compute its new position.
 4.
Output s (twists for each cluster) and a PDB with the updated positions.
Steric hindrance
Rigidity theory does not consider collisions (see Figure 2(a)), as it is based on a system of linear equations; collisions would require inequality constraints. However, due to the close packing of atoms in a protein, steric hindrance plays a significant role in the allowable conformations near the native state. We generate the samples by moving each atom an "infinitesimal" distance using the computed motion space, but the direction of many of these motions may be biologically infeasible. Therefore, we use PyRosetta [12, 18] to calculate the energy of each generated PDB structure and determine how favorable the motion is. Note that we do not relax the structure, but instead use the computed score to select the most appropriate set of motions for further analysis.
Twist analysis and aggregated data
For each twist (ω, v), we compute the angle α (in degrees) between the two 3vectors ω and v using the dot product 〈ω, v〉; α can take on values ranging from 0° to 180°. Recall that the twist is a pure rotation or translation if 〈ω, v〉 = 0: if v is the zero vector, then the twist is a pure rotation about a line through the origin with direction ω; if ω is the zero vector, then the twist is a pure translation in the direction of v. Therefore, we refer to the computed angle α as the twist purity; a value of α close to 90° corresponds to a dot product close to 0. Values further from 90° correspond to more general screw motions (with both rotational and translational components).
We aggregate data over the twists used to generate the lowest energy samples, and compute the mean twist and the mean twist purity for the moving cluster. From the mean twist, we extract the twist axis, referring to it as the average axis of motion. This axis, as well as the corresponding twist purity, give a quantitative description of the motion of the moving cluster relative to the pinned cluster.
Results and discussion
Aggregated twist data.
PDB ID  Mean twist  Pair of points on mean twist axis  

1CFD  (6.5286,5.7547,3.9852,1.6767,2.3933,1)  (0.0413,0.1442,0.2759)  (6.4873,5.6105,4.2610) 
1CLL  (7.7431,7.9801,8.0737,1.2118,1.0044,1)  (0.0007,0.0928,0.0924)  (7.7424,7.8873,8.1661) 
2BBM(A)  (1.8069,0.4130,2.3290,0.2820,0.0460,1)  (0.0345,0.1298,0.0038)  (1.7723,0.5428,2.3328) 
1LST  (118.5740,78.1629,34.8737,1.3842,1.4945,1)  (0.0012,0.0078,0.0133)  (118.5752,78.1551,34.8870) 
2LAO  (82.1258,3.2921,25.4673,1.6032,3.1679,1)  (0.0105,0.0166,0.0359)  (82.1153,3.2755,25.5032) 
4BJL(B)  (46.0200,25.3217,16.3867,0.4717,1.2093,1)  (0.0149,0.0126,0.0223)  (46.0349,25.3091,16.3644) 
4BJL(A)  (106.0130,32.4798,8.2077,0.1640,1.7414,1)  (0.0015,0.0087,0.0154)  (106.0115,32.4885,8.1923) 
1CTP  (213.8180,115.6160,64.7866,0.8091,0.9292,1)  (0.0009,0.0042,0.0046)  (213.8171,115.6202,64.7820) 
1ATP  (3.7499,7.0372,2.2543,0.1487,0.2992,1)  (0.1123,0.0595,0.0011)  (3.8622,6.9777,2.2531) 
2AK3(A)  (86.7048,71.6582,63.0325,1.2556,0.4909,1)  (0.0024,0.0100,0.0080)  (86.7072,71.6482,63.0405) 
1AKE(A)  (133.7850,177.6240,402.4180,9.6463,2.8397,1)  (0.0046,0.0190,0.0099)  (133.7804,177.6050,402.4279) 
1GGG(A)  (24.8789,0.3595,9.9989,0.0595,0.1928,1)  (0.0022,0.0354,0.0067)  (24.8811,0.3949,9.9922) 
1WDN  (461.8080,212.3000,116.8090,7.9733,17.8943,1)  (0.0085,0.0017,0.0366)  (461.7995,212.2983,116.8456) 
2BPG(A)  (15.3978,7.1701,24.6064,2.5494,0.9374,1)  (0.0338,0.0874,0.0043)  (15.3640,7.2575,24.6021) 
1BPD  (2.5596,4.0506,1.9642,0.8227,0.3232,1)  (0.1747,0.1557,0.0934)  (2.7344,3.8949,2.0576) 
1K23(A)  (51.0180,42.4661,98.4056,0.2768,2.2575,1)  (0.0188,0.0056,0.0073)  (51.0368,42.4605,98.3983) 
1K20(A)  (53.8385,382.0230,168.5690,10.6181,2.5881,1)  (0.0003,0.0098,0.0221)  (53.8388,382.0132,168.5911) 
1URP(C)  (1.3473,0.5327,0.7839,0.4220,0.3946,1)  (0.3103,0.3746,0.2788)  (1.0370,0.9073,1.0626) 
2DRI  (56.9057,23.2339,54.2220,0.6751,0.6885,1)  (0.0090,0.0139,0.0035)  (56.9147,23.2200,54.2185) 
The computational complexity of the entire approach is O(n^{3}), dominated by the calculation of the null space (Mathematica). In practice, though, the lineartime sample generation program (written in Java) is the most time consuming step; depending on the number of clusters in the model, processing time ranged from less than 30 minutes up to 5 hours on a MacBook Pro with a 2.6 GHz Intel Core i7 processor and 8GB of memory. However, the focus of this study was not on execution time; future analysis will rely on an optimized codebase and precise timing experiments.
Case studies
We now present detailed studies of our analysis on three proteins: calmodulin, the LysineArginineOrnithine (LAO) binding protein and the BenceJones protein. We chose these proteins as they are welldocumented in the literature as undergoing conformational changes through hingelike motions. Studies using NMR [19], xray crystallography [20], MD simulation studies [21] and algorithms that combine information from normal modes, experimental thermal factors, bond constraint networks, energetics, and sequence [22], all agree on the mechanism and measurement for hinge motions in calmodulin. The motions of both the LAO binding and BenceJones proteins were studied in [23], and the structure of the LAO crystal analyzed in detail in [24].
Calmodulin
Calmodulin is a multifunctional, calciumbinding, intermediate messenger protein, which is expressed in all eukaryotic cells. Metabolism, apoptosis, muscle contraction and memory are only few of the many crucial processes mediated by the protein [25]. Calmodulin contains 4 calcium binding sites, with a pair in each of the EFhand globular domains found at the N and Ctermini; these are connected by a helix with a "weak" center around residue 78. This helix plays a key role in conformational changes in calmodulin: (1) tightening when binding calcium, and (2) unraveling for subsequent peptide binding. We analyze both conformational changes and, as we discuss below, the predicted axes of motions agree, computed to be roughly in the same direction as this helix.
LAO binding protein
BenceJones protein
Axis of motion analysis
Our results demonstrate the potential that rigiditytheoretic analysis has for predicting protein motion, establishing the initial groundwork for future studies. While the use of Jmol to visually validate the predicted axis is intuitive, it can be subjective and highlights the need for a robust computational method that quantifies the validity of the computed data.
These structures require further investigation, as the twist data we aggregate over correspond to motions that maintain the geometric modeling of chemical interactions while minimizing steric hindrance. We hypothesize that an inconsistent axis may be due to:

♦ an infeasible axis produced by the averaging of feasible twists;

♦ a feasible, but "unexpected" pathway between the two conformations, such as the unfolding of an alpha helix (a potential explanation for the closed conformation of adenylate kinase, Figure 11);

♦ or, a feasible motion to a conformation that has not been experimentally determined.
Conclusions
Using rigidity theory, we developed a computational approach for predicting an axis of motion for two domains of a protein, requiring only a single conformation as input. We evaluated our approach on a dataset of 19 protein structures, verifying a consistent axis of motion for 15 of them, and presented a detailed discussion of proteins whose motions are welldocumented: calmodulin, the LAO binding protein and the BenceJones proteins.
Our results show that rigidity theory can be applied to analyze proteins and accurately predict information that may elucidate conformational changes tied to protein function. To the best of our knowledge, calculation of twists from a single conformation has not been done before; however, it would be interesting to compare with standard optimization techniques (such as simulated annealing) by seeking twists whose resulting conformation minimizes energy.
This initial investigation represents the first step to a more comprehensive study. We wish to find the minimum number of samples to generate, as this is the most timeconsuming step; a sample set of 100 for calmodulin [PDB:1CFD] seemed to exhibit the same behavior as the sample set of 1000. Since the current approach required close interaction with KINARI to produce an appropriate cluster decomposition, we plan to automate this part of the process in the future, enabling an evaluation of the method on a larger dataset. We ultimately expect to develop a web tool that will allow users to analyze a single structure by uploading or choosing a PDB file. Finally, we seek to develop a computational measure for evaluating the validity of our results instead of visually comparing two conformations using Jmol.
Structures and figures
Declarations
Acknowledgements
AL and KM received partial support from the Clare Boothe Luce Foundation; RS was partially supported by funding from the Howard Hughes Medical Institute while at Mount Holyoke College. We are grateful to the anonymous reviewers for their helpful feedback.
Declarations
The publication charges for this work were paid for by the Four College BioMath Consortium (4CBC) funded by NSF grant DBI1129046. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
This article has been published as part of BMC Bioinformatics Volume 15 Supplement 8, 2014: Selected articles from the Third IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS 2013): Bioinformatics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S8.
Authors’ Affiliations
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