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A Finite Element framework for computation of  
protein normal modes and mechanical response 
Mark Bathe 
Arnold Sommerfeld Zentrum für Theoretische Physik and Center for NanoScience 
Ludwig–Maximilians–Universität München 
Theresienstrasse 37, 80333 Munich, Germany 
Abstract 
A coarse-grained computational procedure based on the Finite Element Method is 
proposed to calculate the normal modes and mechanical response of proteins and their 
supramolecular assemblies. Motivated by the elastic network model, proteins are 
modeled as homogeneous isotropic elastic solids with volume defined by their solvent-
excluded surface. The discretized Finite Element representation is obtained using a 
surface simplification algorithm that facilitates the generation of models of arbitrary 
prescribed spatial resolution. The procedure is applied to compute the normal modes of a 
mutant of T4 phage lysozyme and of filamentous actin, as well as the critical Euler 
buckling load of the latter when subject to axial compression. Results compare favorably 
with all-atom normal mode analysis, the Rotation Translation Blocks procedure, and 
experiment. The proposed methodology establishes a computational framework for the 
calculation of protein mechanical response that facilitates the incorporation of specific 
atomic-level interactions into the model, including aqueous-electrolyte-mediated 
electrostatic effects. The procedure is equally applicable to proteins with known atomic 
coordinates as it is to electron density maps of proteins, protein complexes, and 
supramolecular assemblies of unknown atomic structure. 
Introduction 
 Equilibrium conformational fluctuations of proteins about their folded, native 
structure play an important role in their biological function.1-4 Three prominent 
approaches used to compute conformational fluctuations of proteins are, in order of 
increasing computational efficiency and decreasing modeling resolution, molecular 
dynamics (MD), all-atom normal mode analysis (NMA), and coarse-grained elastic NMA 
(eNMA). MD attempts to sample the equilibrium distribution of states in the vicinity of 
the native structure via time-integration of Newton’s equations of motion, typically 
modeling solvent explicitly.5 All-atom NMA assumes harmonic fluctuations about the 
native state in solving the free vibration problem for the protein while treating the solvent 
implicitly.2,3,6 Finally, eNMA employs a coarse-grained elastic description of the protein 
in which specific atomic interactions are replaced by a simple network of linear elastic 
springs, typically connecting Cα atoms within an arbitrary cut-off radius.7,8 Successively 
coarser and thus computationally more efficient eNMA descriptions are obtained by 
reducing the total number of interaction sites in the system.9-12 The idea of treating 
proteins as effective elastic media in calculating their normal modes dates back at least to 
Suezaki and Go.13 
 Despite their relative simplicity, elastic coarse-grained models have proven 
remarkably successful in calculating the slow, large length-scale vibrational modes of 
proteins and their supramolecular assemblies. As shown recently by Lu and Ma,14 their 
success may partially be attributed to the fact that biomolecular shape plays a dominant 
role in determining the lowest normal modes of proteins. Indeed, large length-scale 
modes naturally average over heterogeneous interactions present at atomic length-scales, 
thereby rendering elastic descriptions valid in this regime. Global structural averages 
such as backbone fluctuations and inter-residue correlations are in turn also successfully 
predicted because they are dominated by these low frequency modes. 
 The success of eNMA motivates the current work, in which the elastic network 
model for proteins is cast in the framework of the well established Finite Element Method 
(FEM).15,16 In formulating the model, the protein is defined by its mass density, ρ, 
isotropic elastic modulus, E, and solvent-excluded surface (SES), which is obtained by 
rolling a water molecule-size probe-sphere over its van der Waals surface.17-20 As an 
initial exploration of the utility of the FEM in analyzing protein mechanical response, the 
normal modes of a mutant of T4 lysozyme and of F-actin are computed, as well as the 
critical Euler buckling load of F-actin when subject to axial compression. NMA results 
for T4 lysozyme are compared with all-atom NMA, the Rotation Translation Blocks 
(RTB) procedure,21,22 which treats residues as rigid but retains atomic-level interactions 
as modeled by the implicit solvent force-field EEF1,23 and experiment. 
 Similar to eNMA, the proposed FE-based procedure offers several advantages 
over all-atom NMA, including the elimination of costly energy minimization that may 
distort the initial protein structure, direct applicability to x-ray data of proteins with 
unknown atomic structure,24-26 and a significant speed-up of the NMA itself due to a 
drastic reduction in the number of degrees of freedom simulated. 
 Additionally, the FEM offers several distinct advantages over existing elastic 
network models that provide the primary motivation for the current work. Principal 
among these is the suitability of the FEM to calculate the mechanical response of proteins 
and their supramolecular assemblies to applied bending, buckling, and other generalized 
loading scenarios, which is needed to probe the structure-function relation of 
supramolecular assemblies such as viral capsids,27,28 microtubules,29,30 F-actin 
bundles,31,32 and molecular motors.33,34 Moreover, casting the coarse-grained elastic 
model in the framework of the FEM opens two important avenues of model refinement 
that are currently being pursued. First, the atomic Hessian can be projected onto the FE-
space in order to incorporate atomic-level interactions into the model, thereby eliminating 
the a priori assumption of homogeneous isotropic elastic response. This idea is similar to 
the initial version of the Rotation Translation Blocks (RTB) procedure proposed in 
Durand et al.,35, as well as related works in modeling crystals.36,37 The incorporation of 
atomic-level interactions may be particularly important in modeling binding interfaces 
present between constituent monomers in supramolecular assemblies such as F-actin, 
MTs, and viral coat protein subunits, particularly near the onset of mechanical failure. 
Second, the FE-based protein model may be coupled directly to field calculations 
including the Poisson–Boltzmann Equation to model solvent-mediated electrostatic 
interactions38-40 and the Stokes Equations to model solvent-damping in dynamic response 
calculations.41,42 
Methods 
 The FEM is a mature field that is discussed in detail in references such as Bathe15 
and Zienkiewicz and Taylor.16 Accordingly, the focus here is on its application to 
proteins and readers are kindly referred to the above-referenced books for details on its 
theoretical foundations. 
 Generation of the FE model requires three steps: (1) definition and discretization 
of the protein volume; (2) definition of the local effective mass density and constitutive 
behavior of the protein; and (3) application of boundary conditions such as displacement- 
or force-based loading. The protein volume is defined by its bounding SES, which is also 
called the Richards Molecular Surface or simply the Molecular Surface. This surface is 
defined by the closest point of contact of a solvent-sized probe-sphere that is rolled over 
the van der Waals surface of the protein, which defines the molecular volume that is 
never penetrated by any part of the solvent probe-sphere.17-19 The SES is computed using 
MSMS ver. 2.6.1, which generates a high density triangulated approximation (one 
triangular vertex per Å2) to the exact SES.20 The MSMS-discretized SES is subsequently 
decimated to arbitrary prescribed spatial resolution using the surface simplification 
algorithm QSLIM.43-45 The QSLIM algorithm employs iterative vertex-pair contraction 
together with a quadric error metric to retain a near-optimal representation of the original 
surface while reducing the total number of faces by an arbitrary, user-specified amount.43 
The protein volume that is bounded by the closed SES is subsequently discretized with 
3D tetrahedral finite elements via automatic mesh generation using the commercial Finite 
Element program ADINA ver. 8.4 (Watertown, MA, USA). Application of the proposed 
FE-based procedure directly to x-ray data would require definition of the molecular 
volume from the electron density map using Voronoi tessellation or a similar procedure, 
as proposed by Wriggers et al.,46, and performed by Ming et al.24 
 The protein constitutive response is modeled using the standard Hooke’s law, 
which treats the protein as a homogeneous, isotropic, elastic continuum with Young’s 
modulus E and Poisson ratio ν.47 While this is conceptually similar to elastic-network 
based models, it is rigorously distinct: Elastic network models typically connect Cα 
atoms by springs of equal stiffness, which results in general in a locally anisotropic and 
inhomogeneous elastic material with length-scale dependent mechanical properties. In 
contrast, the FE-model defined here treats the protein as strictly homogeneous, with an 
isotropic elastic material response that is length-scale invariant. The mass density of the 
protein is taken to be homogeneous, although it could equally be defined as a spatially-
varying function from the underlying atomic constitution or from electron density data. 
 Finally, arbitrary boundary conditions consisting of displacement- or force-based 
loading may be applied to the molecule, modeling the effects of the protein environment. 
In the current application, the free vibration problem is solved for T4 lysozyme and F-
actin in the absence of any boundary condition and the linearized buckling problem for F-
actin is solved by applying co-axial compressive point loads to the ends of the molecule. 
 Given the protein volume, constitutive behavior, and boundary conditions, the 
FEM uses numerical volume-integration to derive a set of algebraic equations that is 
linear in the finite element nodal displacement degrees of freedom, u , 
 + =Mu Ku R  (1) 
where M is the diagonal mass-matrix, K is the elastic stiffness matrix, and R is a forcing 
vector that results from natural (force-based) boundary conditions.15 In the case of the 
free vibration problem relevant to the NMA of proteins, 0=R . Substitution of the 
oscillatory solution, cos( )tω γ= +u y , into the free-vibration form of Eq. (1) results in 
the generalized eigenvalue problem, 
 2 0ω− =Ky My  (2) 
which after definition of the eigenvalues, 2:λ ω= , may be written in secular form, 
 det 0λ− =K M . (3) 
Various efficient FE procedures exist to obtain the solution to the generalized eigenvalue 
problem, yielding the eigenvalues and eigenvectors, ( , )i iλ y . In the present application an 
accelerated subspace-iteration method15,48 is used for T4 lysozyme and F-actin. The 
substructure synthesis procedure49 commonly available in structural mechanics FE 
programs could also be applied to calculate the normal modes of F-actin, as recently 
proposed by Ming et al.50 
 The eigenvectors corresponding to the FE nodal degrees of freedom are linearly 
interpolated to the Cα positions given by the atomic coordinates that were used to define 
the FE model. Standard equilibrium thermal averages may then be computed in the 
standard way, including the fluctuation of Cα atom i due to mode k, 
2 2 /ik B ik k ir k Ta mλΔ = , the total fluctuation of Cα atom i due to all modes, 
i ikk
r rΔ = Δ∑ , correlations in positional fluctuations of Cα atoms i and j, 
/ij i j i i j jC r r r r r r= Δ ⋅Δ Δ ⋅Δ Δ ⋅Δ , where ( )i j B ik jk k i jkr r k T a a m mλΔ ⋅Δ = ∑ , 
and the overlap, ijR , between normal modes i and j, defined by the inner product of the 
modes, ij i j i jR = ⋅a a a a , where (1 1)ijR≤ ≤ − .
6 As with elastic network models, the 
protein stiffness-scale (E) is unknown. Accordingly, the acoustic wave speed, E ρ , 
which is the relevant physical unit in the free-vibration problem, is adjusted to best-fit the 
pertinent Cα fluctuation data, which is either experimental or that from the all-atom 
NMA. In the case of F-actin, the average mass density, ρ, is set explicitly and the 
Young’s modulus is determined by matching its stretching stiffness to experiment,51 as 
also performed by ben-Avraham and Tirion52 and described in more detail below. The 
Poisson ratio is taken to be 0.3 for T4 lysozyme and F-actin, which is typical of 
crystalline solids. While the choice of 0.3ν =  has, to the best of the author’s knowledge, 
no rigorous justification, it is noted that its precise value does not affect the computed 
results within the range of (0.3 0.5)ν≤ ≤ . This is typical of response calculations such as 
those performed here, in which material compressibility does not play an important role. 
 Two important considerations in generating the FE model are the choice of the 
probe-sphere radius used to define the protein volume and the degree of surface 
simplification performed. Regarding the choice of probe-sphere radius, two approaches 
were deliberated here. In the first, the probe-sphere radius is treated as an adjustable 
parameter, akin to the cut-off radius used in elastic network models. In this case, as the 
radius of the probe-sphere is increased, protein cavities in which solvent would normally 
be present become part of the effective elastic medium constituting the protein. 
Accordingly, the shape of the protein becomes a function of the probe-sphere radius, 
which will affect its mechanical response. In the second approach, the probe-sphere 
radius is treated as a fixed, physically-based parameter that is approximately equal to the 
size of a water molecule, as in electrostatic field calculations.53-55 The homogeneous 
elastic medium of the protein is then strictly applied to those volumetric regions in which 
dense intramolecular packing involving close-ranged van der Waals, hydrogen-bond, and 
bonded interactions are present, and the molecular surface is a well-defined physical 
feature of the protein. The latter approach was taken here in order to retain the physical 
connection to atomic packing in solids. 
 An important theoretical property of the FEM is that it guarantees convergence to 
the exact solution of the underlying mathematical model as the FE mesh is refined, where 
the mathematical model is defined by the protein’s analytical SES, constitutive behavior, 
and boundary conditions.15 Thus, any normal mode or mechanical response calculation 
performed using the proposed FE-based procedure should in principle systematically 
refine the discretized representation in order to ensure convergence of the computed 
model property to its exact result. In practice, however, the permissible degree of surface 
simplification using QSLIM or similar algorithm will depend on the sensitivity of the 
computed observable to details of molecular shape, which must be evaluated on a case-
by-case basis, as addressed below for T4 lysozyme and F-actin. 
T4 lysozyme 
 The initial structure of the 164 residue (18.7 kDa) mutant T4 phage lysozyme is 
taken from Matsumura et al.,56 (Protein Data Bank ID 3LZM).57 CHARMM ver. 33a158 
is used with the implicit solvation model EEF123 to build in coordinates missing in the 
crystal structure and to perform energy minimization and NMA. Steepest descent 
minimization followed by adopted-basis Newton–Raphson minimization is performed in 
the presence of successively reduced harmonic constraints on backbone atoms to achieve 
a final root-mean-square (RMS) energy gradient of 5×10–4 kcal/(mol Å) with 
corresponding RMS deviation between the x-ray and energy-minimized structures of 1.3 
Å (Fig. 1a). All-atom (ATM) and RTB NMA21 are used as implemented in CHARMM,22 
using one-block per residue for the RTB calculations. 
Fig. 1 T4 lysozyme (a) crystal structure (Protein Data Bank ID 3lmz), (b) MSMS-
triangulated SES, and (c) QSLIM-decimated SES used for the FE computation. Atomic 
structure rendered with VMD ver. 1.8.559 and triangulated models rendered with ADINA 
ver. 8.4. 
 To define the FE model, MSMS is used to compute the SES of the energy-
minimized structure of T4 lysozyme using the MSMS-default 1.5 Å radius probe 
ignoring hydrogens. As noted previously, the FE model may be defined directly from the 
atomic structure without initial energy minimization, however, the energy-minimized 
structure is used here to be consistent with the ATM model, which requires minimization. 
MSMS generates a triangulated approximation to the analytical SES that consists of 
17,300 triangular faces (Fig. 1b). This model is decimated using QSLIM to a reduced 
model consisting of 2,000 faces (Fig. 1c). The decimated surface-mesh is read into 
ADINA ver. 8.4 and used as a template to generate 6,843 4-node tetrahedral finite 
elements consisting of 1,627 nodes. Calculation of the 100 lowest non-rigid-body modes 
using an accelerated subspace-iteration method15,48 required 27 MB of RAM and about 
10 seconds on a 2.1 GHz Intel Core2Duo processor. Refining drastically the surface 
representation from 2,000 faces to 17,300 faces (and associated volume discretization) or 
computing more than 100 normal modes did not alter the Cα fluctuations significantly. 
F-actin 
 The atomic structure of F-actin (52 protomers, 2.2 MDa molecular weight) is 
generated using FilaSitus ver. 1.460 based on the Holmes fiber model61 and the structure 
of G-actin:ADP:Ca2+ from the actin-gelsolin segment-1 complex.62,63 This structure of F-
actin-ADP models the filament in its “young” state when the DNase I binding region of 
subdomain 2 of G-actin (residues 40–48) is in its disordered loop conformation as 
opposed to its ordered α-helix conformation.63-65 Importantly, in its disordered loop 
conformation this region forms intramolecular contacts in F-actin that stabilize the 
filament and have direct consequences on its mechanical properties.66-68 
 Calculation of the SES using MSMS and a 3 Å radius probea results in a model 
with 1,248,038 triangular faces, which is subsequently decimated in several seconds 
using QSLIM to a reduced model with 40,000 triangular faces. The decimated surface-
mesh is read into ADINA ver. 8.4 and used as a template to generate 134,883 4-node 
tetrahedral finite elements consisting of 31,881 nodes (Fig. 2b). Planar axial stretching is 
used to determine the effective Young’s modulus of F-actin, E = 2.69 GPa, by fitting its 
computed value to its experimentally-measured value in the absence of tropomyosin, 43.7 
nN.51 The homogeneous mass density, ρ = 1,170 kg/m3, is based on the 42 kDa molecular 
weight of G-actin and the calculated molecular volume of F-actin, which is equal to 
3.1×106 Å3 for the 52-mer considered. Normal mode analysis using the accelerated 
subspace-iteration procedure in ADINA requires 22 MB and less than 10 seconds to 
calculate the lowest 10 modes on a 2.1 GHz Intel Core2Duo processor. To test 
                                                 
a Use of the MSMS-default 1.5 Å radius probe resulted in QSLIM-decimated surface models that were 
poorly formed with multiple intersecting and degenerate triangles due to re-entrant surfaces of F-actin. Use 
of a 3 Å radius probe resolved this problem of SES-representation and is not expected to affect significantly 
the large length-scale normal modes of F-actin, which has relatively large minor and major diameters of 
~40 and 80 Å, respectively. 
convergence of the FE solution to the exact solution, the FE mesh was coarsened 
considerably to a model consisting of only 7,558 4-node tetrahedral volume elements 
(4,000 surface triangles), for which the lowest four eigen-frequencies increased by at 
most 15% with respect to the more detailed model. Further mesh refinement beyond 
40,000 surface elements was precluded by the problematic surface mesh generated by the 
proposed procedure, in which substantial element intersections were present. 
Fig. 2 (a) Atomic structure of the 52-monomer F-actin filament analyzed and (b) the 
triangulated SES used to define the FE model. Atomic structure is rendered with VMD 
ver. 1.8.5 59 and the FEM model rendered using ADINA ver. 8.4. 
Results 
T4 lysozyme 
 Equilibrium thermal fluctuations of Cα atoms aid in understanding protein 
function as mediated by local conformational flexibility and provide a first quantitative 
test for the proposed coarse-grained procedure. Experimental fluctuations are related to 
the experimental temperature- or B-factor by, 2 28 / 3i iB rπ= Δ , where 
irΔ  is the 
mean-squared fluctuation of atom i. While both coarse-grained models capture well the 
overall experimental variation in flexibility of T4 lysozyme (Fig. 3a and Table 1),56 local 
differences are evident in disordered loop regions where conformational flexibility is 
overestimated significantly by both the RTB and FEM procedures (e.g., residue numbers 
35–40). Comparison with the all-atom model indicates that these discrepancies are 
inherent to the protein structure, however, and not artifacts of the RTB and FEM 
procedures (Fig. 3b). Indeed, Cα fluctuations calculated with the RTB and FEM models 
correlate notably better with fluctuations calculated with the all-atom model than with 
experiment (Table 1). 
residue index
RMSF (Å)
0 20 40 60 80 100 120 140 160
residue index
RMSF (Å)
0 20 40 60 80 100 120 140 160
(a) (b)residue index residue index
Fig. 3. Coarse-grained RMSF of Cα atoms in T4 lysozyme compared with (a) experiment 
and (b) all-atom NMA. 100 modes are used to compute the all-atom, RTB, and FEM 
fluctuations. Correlation coefficients provided in Table 1. 
Table 1 Correlation coefficients corresponding to Cα atom RMSF in Figure 3. 
 Experiment ATM 
RTB 0.73 0.95 
FEM 0.68 0.89 
 Inter-residue spatial correlations measured at Cα atoms provide additional insight 
into protein function,69,70 as well as a further test of the proposed coarse-grained 
procedure. Interestingly, the RTB and FEM procedures provide similar information with 
respect to the all-atom model, as measured over either the lowest 10 or 100 modes (Fig. 4 
top and bottom, respectively). The fact that the correlation maps are largely determined 
with as few as ten modes reconfirms numerous previous findings that the lowest modes 
of proteins dominate their free vibration response.14,71,72 The similarity in the FEM and 
ATM correlation maps provides additional evidence that T4 lysozyme behaves 
remarkably similar to a homogeneous isotropic elastic solid in free vibration. 
Fig. 4 T4 lysozyme inter-Cα correlations computed using (top) 10 modes and (bottom) 
100 modes for the (a) ATM, (b) RTB, and (c) FEM models. 
 The lowest four mode shapes computed using the FEM may be projected onto the 
ground-state (energy-minimized) structure of T4 lysozyme to visualize their nature (Fig. 
5). Similar to the native hen egg lysozyme, the lowest mode is a hinge-bending mode,1,73 
whereas the three higher modes are a combination of hinge- and twist-deformations. 
Quantitative comparison between the coarse-grained and all-atom models is made in 
Table 2 for the lowest four mode shapes, and the lowest 200 frequencies in Figure 6. 
Fig. 5 Lowest four eigenmodes computed by the FEM superimposed on the minimized 
structure of T4 lysozyme. Overlap with the all-atom model is given in Table 2. Images 
rendered using VMD ver. 1.8.5 59. 
Table 2 Overlap of coarse-grained model and all-atom normal modes as measured at Cα 
positions. 
 Mode 1 Mode 2 Mode 3 Mode 4 
RTB 0.97 0.93 0.82 0.28 
FEM 0.91 0.86 0.76 0.71 
 The modal frequency distributions provide a final quantitative evaluation of the 
FEM and RTB approach for T4 lysozyme (Fig. 6). While the overall correlation between 
the FEM and all-atom frequencies is reasonable, particularly for low mode-numbers, the 
FEM tends to underestimate the “exact” frequency computed using the all-atom model at 
high mode-numbers. This suggests that the FEM models the protein as overly compliant 
in this regime, which is to be expected because higher modes excite shorter wavelength, 
stiffer degrees of freedom in the all-atom protein resulting from chain connectivity, 
whereas the elastic solid approximation assumes a compliance that is length-scale 
invariant. Backbone Cα fluctuations as well as Cα correlations are apparently unaffected 
by this approximation because the low modes dominate these observables. Interestingly, 
the opposite tendency was observed by Tama et al.,21 for the RTB-approach with 
successively larger blocks. This is also to be expected because the assumption of rigid 
blocks in the protein renders the structure overly stiff on short length scales (high 
frequency modes), and the length-scale at which this deviation from the all-atom model 
becomes significant increases with increasing block-size. 
ωCG [cm−1]
ωATM [cm−1]
0 5 10 15 20 25 30 35 40
ωATM [cm–1]
ωCG [cm–1]
Fig. 6 Correlation between coarse-grained (CG) and all-atom (ATM) model frequencies 
for the lowest 200 modes. 
F-actin 
 F-actin is a highly dynamic biopolymer with a considerable degree of internal 
plasticity in the state of tilt and twist of its constituent protomers, which depends on the 
bound nucleotide-state (ATP/ADP), bound actin-binding protein, and solvent 
conditions.74-78 Additionally, the bending stiffness of F-actin has been shown to increase 
by a factor of two in the presence of phalloidin, by 50% in the F-ADP-P versus F-ADP 
state, and to be regulated by tropomyosin in a Ca2+-dependent fashion.66 Thus, any 
modeling attempt to predict the mechanical properties of F-actin and investigate their 
relation to its detailed internal structure and composition must consider such variations. 
 Modeling attempts to investigate the structure-function relation of F-actin include 
an early study by ben-Avraham and Tirion,68 who treated G-actin monomers as internally 
rigid and connected to their nearest neighbor monomers by compliant springs, a more 
recent study by Ming et al.,79 in which conventional eNMA is used together with 
substructure-synthesis to calculate the large wavelength normal modes of a micron-long 
F-actin molecule, and most recently an all-atom MD study by Chu and Voth,67 who found 
that the loop-helix transition of the DNase I binding region of subdomain 2 of G-actin 
plays a central role in respectively stabilizing-destabilizing F-actin by disrupting inter-
monomer interactions. Chu and Voth67 also calculated the apparent persistence length of 
F-actin and found that the loop-to-helix transition between the ATP- and ADP-bound 
states accounted for the approximately 50% decrease in associated bending stiffness 
observed experimentally.66 
 The normal modes of F-actin (52-mer, 0.14 μm length) computed here in free 
planar-vibration yield four bending modes as the lowest modes (Fig. 7). Association of F-
actin with a homogeneous elastic rod in free vibration80 results in an apparent bending 
stiffness, κ = 6.8×10–26 Nm2, for the lowest mode, which is near the upper limit of 
bending stiffness typically reported experimentally.66,81,82 
 Subjecting F-actin to an axially compressive load and performing a linearized 
buckling analysis yields the lowest critical Euler buckling load, Pcrit = 33 pN. Association 
of the filament again with a homogeneous elastic Euler–Bernoulli beam yields the 
effective bending stiffness, 2 2 26 2/ 6.9 10  NmcritP Lκ π
−= = × , which is similar to the 
bending stiffness calculated from the lowest bending mode because that mode of 
deformation is the same as the lowest Euler buckling mode. 
Mode 1
Mode 4
Mode 3
Mode 2
Fig. 7 Four lowest free vibration modes of F-actin (52-mer, 0.144 μm length) in planar 
deformation. The corresponding angular frequencies are, 0.18×10–2, 0.48×10–2, 0.92×10–
2, and 0.16×10–1 rad/psec. 
 The bending stiffness calculated here for F-actin is consistent with experimental 
measurements of the ATP-bound-state in which the DNase binding region (residues 40–
48) in subdomain 2 of G-actin is in its disordered loop conformation, thereby stabilizing 
inter-monomer interactions.66,67 While this is to be expected given the structure of G-
actin:ADP:Ca2+ employed, in which the DNase binding region is also in its disordered 
loop conformation, a similar coarse-grained analysis of F-actin must be performed in 
which the DNase binding region of G-actin is in its ordered α-helical structure. Only then 
may it be stated definitively whether the observed mechanical behavior is due solely to 
this detailed structural difference or to some other source, such as a lack of modeling 
resolution. 
 While a more detailed investigation of this type is of direct interest in evaluating 
the full utility of the proposed procedure, it is also of interest fundamentally to investigate 
the respective roles of molecular shape versus molecular interactions on determining the 
mechanical properties of supramolecular assemblies such as F-actin, MTs, and viral 
capsids. In particular, an intriguing hypothesis is that mechanical response is determined 
solely by molecular shape, in which case the mechanical properties of supramolecular 
assemblies would be robust to amino acid mutations that do not alter molecular shape. A 
competing hypothesis is that mechanical response is sensitive to both molecular shape 
and detailed molecular interactions, in which case amino acid mutations would be more 
tightly constrained. In either case, investigation of the respective roles of molecular shape 
versus specific interactions on protein mechanics clearly requires that all-atom models be 
considered, either directly or via incorporation into coarse-grained models. Such 
investigations are currently underway and are expected to provide fundamental insight 
into the origin and robustness of the mechanical properties of supramolecular assemblies. 
Concluding discussion 
 A coarse-grained FE-based procedure is proposed to compute the normal modes 
and mechanical response of proteins and their supramolecular assemblies. The procedure 
takes as input the atomic structure to define uniquely the volume associated with the SES, 
mass density, and elastic stiffness of the protein. The initial, high resolution SES 
discretized at atomic resolution is simplified using a quadric simplification algorithm to 
obtain a molecular surface representation of arbitrary prescribed spatial resolution. While 
the proposed procedure is applied to proteins with known atomic structure, the molecular 
volume could equally be defined from electron density data, rendering the procedure 
applicable to a broad class of biomolecules and biomolecular complexes for which only a 
rough approximation to the molecular volume is known. As with existing coarse-grained 
elastic network models, energy minimization is not required prior to the NMA because 
the initial structure is assumed to be the ground-state structure. 
 Ongoing development of the proposed procedure is directed towards three areas 
of improvement. First, the atomic-based Hessian from all-atom force-fields such as 
CHARMM58 will be projected onto the FE-space such that the model optimally 
converges to the “exact” all-atom solution as the FE mesh is refined to atomic length-
scales. Such a procedure will enable the systematic coarsening of protein structure and 
interactions without the a priori assumption of elastic response. Indeed, an intriguing and 
as of yet unresolved question regards the relative effects of molecular shape versus 
specific molecular interactions on the mechanical response of supramolecular assemblies 
such as F-actin, MTs, and viral capsids. Second, the Poisson–Boltzmann equation used to 
model aqueous electrolyte-mediated electrostatic interactions in proteins may be coupled 
directly to the elastic-based FE model, so that it may be included in computations of 
normal modes and mechanical response. Langevin dynamics may also be incorporated 
into the model by coupling the protein-domain to the Stokes equations to model solvent 
damping.41,83 Finally, the proposed surface discretization and simplification procedure 
requires improvement because it often results in surface meshes with intersecting or 
degenerate triangles, as encountered here for F-actin. 
 The utility of the proposed FE-based procedure is explored here for one specific 
globular protein and supramolecular assembly, namely T4 lysozyme and F-actin. Clearly, 
in order to evaluate the utility of the proposed procedure thoroughly, a set of proteins of 
drastically varying structure must be analyzed, as well as additional supramolecular 
assemblies. Additional response variables and the effects of internal structural variations 
of the molecules examined should also be investigated. Notwithstanding these additional 
analyses and the foregoing model improvements, the current communication establishes 
an effective theoretical framework for the computation of the normal modes and 
generalized mechanical response of proteins and their supramolecular assemblies based 
on the elastic medium theory of proteins. 
Acknowledgements 
Discussions with Marco Cecchini, Martin Karplus, Klaus–Jürgen Bathe, and Michael 
Garland are gratefully acknowledged, as is funding from the Alexander von Humboldt 
Foundation in the form of a post-doctoral fellowship. The author additionally thanks 
Michael Sanner for bringing QSLIM to his attention. 
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  A coarse-grained computational procedure based on the Finite Element Method
is proposed to calculate the normal modes and mechanical response of proteins
and their supramolecular assemblies. Motivated by the elastic network model,
proteins are modeled as homogeneous isotropic elastic solids with volume
defined by their solvent-excluded surface. The discretized Finite Element
representation is obtained using a surface simplification algorithm that
facilitates the generation of models of arbitrary prescribed spatial
resolution. The procedure is applied to compute the normal modes of a mutant of
T4 phage lysozyme and of filamentous actin, as well as the critical Euler
buckling load of the latter when subject to axial compression. Results compare
favorably with all-atom normal mode analysis, the Rotation Translation Blocks
procedure, and experiment. The proposed methodology establishes a computational
framework for the calculation of protein mechanical response that facilitates
the incorporation of specific atomic-level interactions into the model,
including aqueous-electrolyte-mediated electrostatic effects. The procedure is
equally applicable to proteins with known atomic coordinates as it is to
electron density maps of proteins, protein complexes, and supramolecular
assemblies of unknown atomic structure.

Introduction 
 Equilibrium conformational fluctuations of proteins about their folded, native 
structure play an important role in their biological function.1-4 Three prominent 
approaches used to compute conformational fluctuations of proteins are, in order of 
increasing computational efficiency and decreasing modeling resolution, molecular 
dynamics (MD), all-atom normal mode analysis (NMA), and coarse-grained elastic NMA 
(eNMA). MD attempts to sample the equilibrium distribution of states in the vicinity of 
the native structure via time-integration of Newton’s equations of motion, typically 
modeling solvent explicitly.5 All-atom NMA assumes harmonic fluctuations about the 
native state in solving the free vibration problem for the protein while treating the solvent 
implicitly.2,3,6 Finally, eNMA employs a coarse-grained elastic description of the protein 
in which specific atomic interactions are replaced by a simple network of linear elastic 
springs, typically connecting Cα atoms within an arbitrary cut-off radius.7,8 Successively 
coarser and thus computationally more efficient eNMA descriptions are obtained by 
reducing the total number of interaction sites in the system.9-12 The idea of treating 
proteins as effective elastic media in calculating their normal modes dates back at least to 
Suezaki and Go.13 
 Despite their relative simplicity, elastic coarse-grained models have proven 
remarkably successful in calculating the slow, large length-scale vibrational modes of 
proteins and their supramolecular assemblies. As shown recently by Lu and Ma,14 their 
success may partially be attributed to the fact that biomolecular shape plays a dominant 
role in determining the lowest normal modes of proteins. Indeed, large length-scale 
modes naturally average over heterogeneous interactions present at atomic length-scales, 
thereby rendering elastic descriptions valid in this regime. Global structural averages 
such as backbone fluctuations and inter-residue correlations are in turn also successfully 
predicted because they are dominated by these low frequency modes. 
 The success of eNMA motivates the current work, in which the elastic network 
model for proteins is cast in the framework of the well established Finite Element Method 
(FEM).15,16 In formulating the model, the protein is defined by its mass density, ρ, 
isotropic elastic modulus, E, and solvent-excluded surface (SES), which is obtained by 
rolling a water molecule-size probe-sphere over its van der Waals surface.17-20 As an 
initial exploration of the utility of the FEM in analyzing protein mechanical response, the 
normal modes of a mutant of T4 lysozyme and of F-actin are computed, as well as the 
critical Euler buckling load of F-actin when subject to axial compression. NMA results 
for T4 lysozyme are compared with all-atom NMA, the Rotation Translation Blocks 
(RTB) procedure,21,22 which treats residues as rigid but retains atomic-level interactions 
as modeled by the implicit solvent force-field EEF1,23 and experiment. 
 Similar to eNMA, the proposed FE-based procedure offers several advantages 
over all-atom NMA, including the elimination of costly energy minimization that may 
distort the initial protein structure, direct applicability to x-ray data of proteins with 
unknown atomic structure,24-26 and a significant speed-up of the NMA itself due to a 
drastic reduction in the number of degrees of freedom simulated. 
 Additionally, the FEM offers several distinct advantages over existing elastic 
network models that provide the primary motivation for the current work. Principal 
among these is the suitability of the FEM to calculate the mechanical response of proteins 
and their supramolecular assemblies to applied bending, buckling, and other generalized 
loading scenarios, which is needed to probe the structure-function relation of 
supramolecular assemblies such as viral capsids,27,28 microtubules,29,30 F-actin 
bundles,31,32 and molecular motors.33,34 Moreover, casting the coarse-grained elastic 
model in the framework of the FEM opens two important avenues of model refinement 
that are currently being pursued. First, the atomic Hessian can be projected onto the FE-
space in order to incorporate atomic-level interactions into the model, thereby eliminating 
the a priori assumption of homogeneous isotropic elastic response. This idea is similar to 
the initial version of the Rotation Translation Blocks (RTB) procedure proposed in 
Durand et al.,35, as well as related works in modeling crystals.36,37 The incorporation of 
atomic-level interactions may be particularly important in modeling binding interfaces 
present between constituent monomers in supramolecular assemblies such as F-actin, 
MTs, and viral coat protein subunits, particularly near the onset of mechanical failure. 
Second, the FE-based protein model may be coupled directly to field calculations 
including the Poisson–Boltzmann Equation to model solvent-mediated electrostatic 
interactions38-40 and the Stokes Equations to model solvent-damping in dynamic response 
calculations.41,42 
Methods 
 The FEM is a mature field that is discussed in detail in references such as Bathe15 
and Zienkiewicz and Taylor.16 Accordingly, the focus here is on its application to 
proteins and readers are kindly referred to the above-referenced books for details on its 
theoretical foundations. 
 Generation of the FE model requires three steps: (1) definition and discretization 
of the protein volume; (2) definition of the local effective mass density and constitutive 
behavior of the protein; and (3) application of boundary conditions such as displacement- 
or force-based loading. The protein volume is defined by its bounding SES, which is also 
called the Richards Molecular Surface or simply the Molecular Surface. This surface is 
defined by the closest point of contact of a solvent-sized probe-sphere that is rolled over 
the van der Waals surface of the protein, which defines the molecular volume that is 
never penetrated by any part of the solvent probe-sphere.17-19 The SES is computed using 
MSMS ver. 2.6.1, which generates a high density triangulated approximation (one 
triangular vertex per Å2) to the exact SES.20 The MSMS-discretized SES is subsequently 
decimated to arbitrary prescribed spatial resolution using the surface simplification 
algorithm QSLIM.43-45 The QSLIM algorithm employs iterative vertex-pair contraction 
together with a quadric error metric to retain a near-optimal representation of the original 
surface while reducing the total number of faces by an arbitrary, user-specified amount.43 
The protein volume that is bounded by the closed SES is subsequently discretized with 
3D tetrahedral finite elements via automatic mesh generation using the commercial Finite 
Element program ADINA ver. 8.4 (Watertown, MA, USA). Application of the proposed 
FE-based procedure directly to x-ray data would require definition of the molecular 
volume from the electron density map using Voronoi tessellation or a similar procedure, 
as proposed by Wriggers et al.,46, and performed by Ming et al.24 
 The protein constitutive response is modeled using the standard Hooke’s law, 
which treats the protein as a homogeneous, isotropic, elastic continuum with Young’s 
modulus E and Poisson ratio ν.47 While this is conceptually similar to elastic-network 
based models, it is rigorously distinct: Elastic network models typically connect Cα 
atoms by springs of equal stiffness, which results in general in a locally anisotropic and 
inhomogeneous elastic material with length-scale dependent mechanical properties. In 
contrast, the FE-model defined here treats the protein as strictly homogeneous, with an 
isotropic elastic material response that is length-scale invariant. The mass density of the 
protein is taken to be homogeneous, although it could equally be defined as a spatially-
varying function from the underlying atomic constitution or from electron density data. 
 Finally, arbitrary boundary conditions consisting of displacement- or force-based 
loading may be applied to the molecule, modeling the effects of the protein environment. 
In the current application, the free vibration problem is solved for T4 lysozyme and F-
actin in the absence of any boundary condition and the linearized buckling problem for F-
actin is solved by applying co-axial compressive point loads to the ends of the molecule. 
 Given the protein volume, constitutive behavior, and boundary conditions, the 
FEM uses numerical volume-integration to derive a set of algebraic equations that is 
linear in the finite element nodal displacement degrees of freedom, u , 
 + =Mu Ku R  (1) 
where M is the diagonal mass-matrix, K is the elastic stiffness matrix, and R is a forcing 
vector that results from natural (force-based) boundary conditions.15 In the case of the 
free vibration problem relevant to the NMA of proteins, 0=R . Substitution of the 
oscillatory solution, cos( )tω γ= +u y , into the free-vibration form of Eq. (1) results in 
the generalized eigenvalue problem, 
 2 0ω− =Ky My  (2) 
which after definition of the eigenvalues, 2:λ ω= , may be written in secular form, 
 det 0λ− =K M . (3) 
Various efficient FE procedures exist to obtain the solution to the generalized eigenvalue 
problem, yielding the eigenvalues and eigenvectors, ( , )i iλ y . In the present application an 
accelerated subspace-iteration method15,48 is used for T4 lysozyme and F-actin. The 
substructure synthesis procedure49 commonly available in structural mechanics FE 
programs could also be applied to calculate the normal modes of F-actin, as recently 
proposed by Ming et al.50 
 The eigenvectors corresponding to the FE nodal degrees of freedom are linearly 
interpolated to the Cα positions given by the atomic coordinates that were used to define 
the FE model. Standard equilibrium thermal averages may then be computed in the 
standard way, including the fluctuation of Cα atom i due to mode k, 
2 2 /ik B ik k ir k Ta mλΔ = , the total fluctuation of Cα atom i due to all modes, 
i ikk
r rΔ = Δ∑ , correlations in positional fluctuations of Cα atoms i and j, 
/ij i j i i j jC r r r r r r= Δ ⋅Δ Δ ⋅Δ Δ ⋅Δ , where ( )i j B ik jk k i jkr r k T a a m mλΔ ⋅Δ = ∑ , 
and the overlap, ijR , between normal modes i and j, defined by the inner product of the 
modes, ij i j i jR = ⋅a a a a , where (1 1)ijR≤ ≤ − .
6 As with elastic network models, the 
protein stiffness-scale (E) is unknown. Accordingly, the acoustic wave speed, E ρ , 
which is the relevant physical unit in the free-vibration problem, is adjusted to best-fit the 
pertinent Cα fluctuation data, which is either experimental or that from the all-atom 
NMA. In the case of F-actin, the average mass density, ρ, is set explicitly and the 
Young’s modulus is determined by matching its stretching stiffness to experiment,51 as 
also performed by ben-Avraham and Tirion52 and described in more detail below. The 
Poisson ratio is taken to be 0.3 for T4 lysozyme and F-actin, which is typical of 
crystalline solids. While the choice of 0.3ν =  has, to the best of the author’s knowledge, 
no rigorous justification, it is noted that its precise value does not affect the computed 
results within the range of (0.3 0.5)ν≤ ≤ . This is typical of response calculations such as 
those performed here, in which material compressibility does not play an important role. 
 Two important considerations in generating the FE model are the choice of the 
probe-sphere radius used to define the protein volume and the degree of surface 
simplification performed. Regarding the choice of probe-sphere radius, two approaches 
were deliberated here. In the first, the probe-sphere radius is treated as an adjustable 
parameter, akin to the cut-off radius used in elastic network models. In this case, as the 
radius of the probe-sphere is increased, protein cavities in which solvent would normally 
be present become part of the effective elastic medium constituting the protein. 
Accordingly, the shape of the protein becomes a function of the probe-sphere radius, 
which will affect its mechanical response. In the second approach, the probe-sphere 
radius is treated as a fixed, physically-based parameter that is approximately equal to the 
size of a water molecule, as in electrostatic field calculations.53-55 The homogeneous 
elastic medium of the protein is then strictly applied to those volumetric regions in which 
dense intramolecular packing involving close-ranged van der Waals, hydrogen-bond, and 
bonded interactions are present, and the molecular surface is a well-defined physical 
feature of the protein. The latter approach was taken here in order to retain the physical 
connection to atomic packing in solids. 
 An important theoretical property of the FEM is that it guarantees convergence to 
the exact solution of the underlying mathematical model as the FE mesh is refined, where 
the mathematical model is defined by the protein’s analytical SES, constitutive behavior, 
and boundary conditions.15 Thus, any normal mode or mechanical response calculation 
performed using the proposed FE-based procedure should in principle systematically 
refine the discretized representation in order to ensure convergence of the computed 
model property to its exact result. In practice, however, the permissible degree of surface 
simplification using QSLIM or similar algorithm will depend on the sensitivity of the 
computed observable to details of molecular shape, which must be evaluated on a case-
by-case basis, as addressed below for T4 lysozyme and F-actin. 
T4 lysozyme 
 The initial structure of the 164 residue (18.7 kDa) mutant T4 phage lysozyme is 
taken from Matsumura et al.,56 (Protein Data Bank ID 3LZM).57 CHARMM ver. 33a158 
is used with the implicit solvation model EEF123 to build in coordinates missing in the 
crystal structure and to perform energy minimization and NMA. Steepest descent 
minimization followed by adopted-basis Newton–Raphson minimization is performed in 
the presence of successively reduced harmonic constraints on backbone atoms to achieve 
a final root-mean-square (RMS) energy gradient of 5×10–4 kcal/(mol Å) with 
corresponding RMS deviation between the x-ray and energy-minimized structures of 1.3 
Å (Fig. 1a). All-atom (ATM) and RTB NMA21 are used as implemented in CHARMM,22 
using one-block per residue for the RTB calculations. 
Fig. 1 T4 lysozyme (a) crystal structure (Protein Data Bank ID 3lmz), (b) MSMS-
triangulated SES, and (c) QSLIM-decimated SES used for the FE computation. Atomic 
structure rendered with VMD ver. 1.8.559 and triangulated models rendered with ADINA 
ver. 8.4. 
 To define the FE model, MSMS is used to compute the SES of the energy-
minimized structure of T4 lysozyme using the MSMS-default 1.5 Å radius probe 
ignoring hydrogens. As noted previously, the FE model may be defined directly from the 
atomic structure without initial energy minimization, however, the energy-minimized 
structure is used here to be consistent with the ATM model, which requires minimization. 
MSMS generates a triangulated approximation to the analytical SES that consists of 
17,300 triangular faces (Fig. 1b). This model is decimated using QSLIM to a reduced 
model consisting of 2,000 faces (Fig. 1c). The decimated surface-mesh is read into 
ADINA ver. 8.4 and used as a template to generate 6,843 4-node tetrahedral finite 
elements consisting of 1,627 nodes. Calculation of the 100 lowest non-rigid-body modes 
using an accelerated subspace-iteration method15,48 required 27 MB of RAM and about 
10 seconds on a 2.1 GHz Intel Core2Duo processor. Refining drastically the surface 
representation from 2,000 faces to 17,300 faces (and associated volume discretization) or 
computing more than 100 normal modes did not alter the Cα fluctuations significantly. 
F-actin 
 The atomic structure of F-actin (52 protomers, 2.2 MDa molecular weight) is 
generated using FilaSitus ver. 1.460 based on the Holmes fiber model61 and the structure 
of G-actin:ADP:Ca2+ from the actin-gelsolin segment-1 complex.62,63 This structure of F-
actin-ADP models the filament in its “young” state when the DNase I binding region of 
subdomain 2 of G-actin (residues 40–48) is in its disordered loop conformation as 
opposed to its ordered α-helix conformation.63-65 Importantly, in its disordered loop 
conformation this region forms intramolecular contacts in F-actin that stabilize the 
filament and have direct consequences on its mechanical properties.66-68 
 Calculation of the SES using MSMS and a 3 Å radius probea results in a model 
with 1,248,038 triangular faces, which is subsequently decimated in several seconds 
using QSLIM to a reduced model with 40,000 triangular faces. The decimated surface-
mesh is read into ADINA ver. 8.4 and used as a template to generate 134,883 4-node 
tetrahedral finite elements consisting of 31,881 nodes (Fig. 2b). Planar axial stretching is 
used to determine the effective Young’s modulus of F-actin, E = 2.69 GPa, by fitting its 
computed value to its experimentally-measured value in the absence of tropomyosin, 43.7 
nN.51 The homogeneous mass density, ρ = 1,170 kg/m3, is based on the 42 kDa molecular 
weight of G-actin and the calculated molecular volume of F-actin, which is equal to 
3.1×106 Å3 for the 52-mer considered. Normal mode analysis using the accelerated 
subspace-iteration procedure in ADINA requires 22 MB and less than 10 seconds to 
calculate the lowest 10 modes on a 2.1 GHz Intel Core2Duo processor. To test 
                                                 
a Use of the MSMS-default 1.5 Å radius probe resulted in QSLIM-decimated surface models that were 
poorly formed with multiple intersecting and degenerate triangles due to re-entrant surfaces of F-actin. Use 
of a 3 Å radius probe resolved this problem of SES-representation and is not expected to affect significantly 
the large length-scale normal modes of F-actin, which has relatively large minor and major diameters of 
~40 and 80 Å, respectively. 
convergence of the FE solution to the exact solution, the FE mesh was coarsened 
considerably to a model consisting of only 7,558 4-node tetrahedral volume elements 
(4,000 surface triangles), for which the lowest four eigen-frequencies increased by at 
most 15% with respect to the more detailed model. Further mesh refinement beyond 
40,000 surface elements was precluded by the problematic surface mesh generated by the 
proposed procedure, in which substantial element intersections were present. 
Fig. 2 (a) Atomic structure of the 52-monomer F-actin filament analyzed and (b) the 
triangulated SES used to define the FE model. Atomic structure is rendered with VMD 
ver. 1.8.5 59 and the FEM model rendered using ADINA ver. 8.4. 
Results 
T4 lysozyme 
 Equilibrium thermal fluctuations of Cα atoms aid in understanding protein 
function as mediated by local conformational flexibility and provide a first quantitative 
test for the proposed coarse-grained procedure. Experimental fluctuations are related to 
the experimental temperature- or B-factor by, 2 28 / 3i iB rπ= Δ , where 
irΔ  is the 
mean-squared fluctuation of atom i. While both coarse-grained models capture well the 
overall experimental variation in flexibility of T4 lysozyme (Fig. 3a and Table 1),56 local 
differences are evident in disordered loop regions where conformational flexibility is 
overestimated significantly by both the RTB and FEM procedures (e.g., residue numbers 
35–40). Comparison with the all-atom model indicates that these discrepancies are 
inherent to the protein structure, however, and not artifacts of the RTB and FEM 
procedures (Fig. 3b). Indeed, Cα fluctuations calculated with the RTB and FEM models 
correlate notably better with fluctuations calculated with the all-atom model than with 
experiment (Table 1). 
residue index
RMSF (Å)
0 20 40 60 80 100 120 140 160
residue index
RMSF (Å)
0 20 40 60 80 100 120 140 160
(a) (b)residue index residue index
Fig. 3. Coarse-grained RMSF of Cα atoms in T4 lysozyme compared with (a) experiment 
and (b) all-atom NMA. 100 modes are used to compute the all-atom, RTB, and FEM 
fluctuations. Correlation coefficients provided in Table 1. 
Table 1 Correlation coefficients corresponding to Cα atom RMSF in Figure 3. 
 Experiment ATM 
RTB 0.73 0.95 
FEM 0.68 0.89 
 Inter-residue spatial correlations measured at Cα atoms provide additional insight 
into protein function,69,70 as well as a further test of the proposed coarse-grained 
procedure. Interestingly, the RTB and FEM procedures provide similar information with 
respect to the all-atom model, as measured over either the lowest 10 or 100 modes (Fig. 4 
top and bottom, respectively). The fact that the correlation maps are largely determined 
with as few as ten modes reconfirms numerous previous findings that the lowest modes 
of proteins dominate their free vibration response.14,71,72 The similarity in the FEM and 
ATM correlation maps provides additional evidence that T4 lysozyme behaves 
remarkably similar to a homogeneous isotropic elastic solid in free vibration. 
Fig. 4 T4 lysozyme inter-Cα correlations computed using (top) 10 modes and (bottom) 
100 modes for the (a) ATM, (b) RTB, and (c) FEM models. 
 The lowest four mode shapes computed using the FEM may be projected onto the 
ground-state (energy-minimized) structure of T4 lysozyme to visualize their nature (Fig. 
5). Similar to the native hen egg lysozyme, the lowest mode is a hinge-bending mode,1,73 
whereas the three higher modes are a combination of hinge- and twist-deformations. 
Quantitative comparison between the coarse-grained and all-atom models is made in 
Table 2 for the lowest four mode shapes, and the lowest 200 frequencies in Figure 6. 
Fig. 5 Lowest four eigenmodes computed by the FEM superimposed on the minimized 
structure of T4 lysozyme. Overlap with the all-atom model is given in Table 2. Images 
rendered using VMD ver. 1.8.5 59. 
Table 2 Overlap of coarse-grained model and all-atom normal modes as measured at Cα 
positions. 
 Mode 1 Mode 2 Mode 3 Mode 4 
RTB 0.97 0.93 0.82 0.28 
FEM 0.91 0.86 0.76 0.71 
 The modal frequency distributions provide a final quantitative evaluation of the 
FEM and RTB approach for T4 lysozyme (Fig. 6). While the overall correlation between 
the FEM and all-atom frequencies is reasonable, particularly for low mode-numbers, the 
FEM tends to underestimate the “exact” frequency computed using the all-atom model at 
high mode-numbers. This suggests that the FEM models the protein as overly compliant 
in this regime, which is to be expected because higher modes excite shorter wavelength, 
stiffer degrees of freedom in the all-atom protein resulting from chain connectivity, 
whereas the elastic solid approximation assumes a compliance that is length-scale 
invariant. Backbone Cα fluctuations as well as Cα correlations are apparently unaffected 
by this approximation because the low modes dominate these observables. Interestingly, 
the opposite tendency was observed by Tama et al.,21 for the RTB-approach with 
successively larger blocks. This is also to be expected because the assumption of rigid 
blocks in the protein renders the structure overly stiff on short length scales (high 
frequency modes), and the length-scale at which this deviation from the all-atom model 
becomes significant increases with increasing block-size. 
ωCG [cm−1]
ωATM [cm−1]
0 5 10 15 20 25 30 35 40
ωATM [cm–1]
ωCG [cm–1]
Fig. 6 Correlation between coarse-grained (CG) and all-atom (ATM) model frequencies 
for the lowest 200 modes. 
F-actin 
 F-actin is a highly dynamic biopolymer with a considerable degree of internal 
plasticity in the state of tilt and twist of its constituent protomers, which depends on the 
bound nucleotide-state (ATP/ADP), bound actin-binding protein, and solvent 
conditions.74-78 Additionally, the bending stiffness of F-actin has been shown to increase 
by a factor of two in the presence of phalloidin, by 50% in the F-ADP-P versus F-ADP 
state, and to be regulated by tropomyosin in a Ca2+-dependent fashion.66 Thus, any 
modeling attempt to predict the mechanical properties of F-actin and investigate their 
relation to its detailed internal structure and composition must consider such variations. 
 Modeling attempts to investigate the structure-function relation of F-actin include 
an early study by ben-Avraham and Tirion,68 who treated G-actin monomers as internally 
rigid and connected to their nearest neighbor monomers by compliant springs, a more 
recent study by Ming et al.,79 in which conventional eNMA is used together with 
substructure-synthesis to calculate the large wavelength normal modes of a micron-long 
F-actin molecule, and most recently an all-atom MD study by Chu and Voth,67 who found 
that the loop-helix transition of the DNase I binding region of subdomain 2 of G-actin 
plays a central role in respectively stabilizing-destabilizing F-actin by disrupting inter-
monomer interactions. Chu and Voth67 also calculated the apparent persistence length of 
F-actin and found that the loop-to-helix transition between the ATP- and ADP-bound 
states accounted for the approximately 50% decrease in associated bending stiffness 
observed experimentally.66 
 The normal modes of F-actin (52-mer, 0.14 μm length) computed here in free 
planar-vibration yield four bending modes as the lowest modes (Fig. 7). Association of F-
actin with a homogeneous elastic rod in free vibration80 results in an apparent bending 
stiffness, κ = 6.8×10–26 Nm2, for the lowest mode, which is near the upper limit of 
bending stiffness typically reported experimentally.66,81,82 
 Subjecting F-actin to an axially compressive load and performing a linearized 
buckling analysis yields the lowest critical Euler buckling load, Pcrit = 33 pN. Association 
of the filament again with a homogeneous elastic Euler–Bernoulli beam yields the 
effective bending stiffness, 2 2 26 2/ 6.9 10  NmcritP Lκ π
−= = × , which is similar to the 
bending stiffness calculated from the lowest bending mode because that mode of 
deformation is the same as the lowest Euler buckling mode. 
Mode 1
Mode 4
Mode 3
Mode 2
Fig. 7 Four lowest free vibration modes of F-actin (52-mer, 0.144 μm length) in planar 
deformation. The corresponding angular frequencies are, 0.18×10–2, 0.48×10–2, 0.92×10–
2, and 0.16×10–1 rad/psec. 
 The bending stiffness calculated here for F-actin is consistent with experimental 
measurements of the ATP-bound-state in which the DNase binding region (residues 40–
48) in subdomain 2 of G-actin is in its disordered loop conformation, thereby stabilizing 
inter-monomer interactions.66,67 While this is to be expected given the structure of G-
actin:ADP:Ca2+ employed, in which the DNase binding region is also in its disordered 
loop conformation, a similar coarse-grained analysis of F-actin must be performed in 
which the DNase binding region of G-actin is in its ordered α-helical structure. Only then 
may it be stated definitively whether the observed mechanical behavior is due solely to 
this detailed structural difference or to some other source, such as a lack of modeling 
resolution. 
 While a more detailed investigation of this type is of direct interest in evaluating 
the full utility of the proposed procedure, it is also of interest fundamentally to investigate 
the respective roles of molecular shape versus molecular interactions on determining the 
mechanical properties of supramolecular assemblies such as F-actin, MTs, and viral 
capsids. In particular, an intriguing hypothesis is that mechanical response is determined 
solely by molecular shape, in which case the mechanical properties of supramolecular 
assemblies would be robust to amino acid mutations that do not alter molecular shape. A 
competing hypothesis is that mechanical response is sensitive to both molecular shape 
and detailed molecular interactions, in which case amino acid mutations would be more 
tightly constrained. In either case, investigation of the respective roles of molecular shape 
versus specific interactions on protein mechanics clearly requires that all-atom models be 
considered, either directly or via incorporation into coarse-grained models. Such 
investigations are currently underway and are expected to provide fundamental insight 
into the origin and robustness of the mechanical properties of supramolecular assemblies. 
Concluding discussion 
 A coarse-grained FE-based procedure is proposed to compute the normal modes 
and mechanical response of proteins and their supramolecular assemblies. The procedure 
takes as input the atomic structure to define uniquely the volume associated with the SES, 
mass density, and elastic stiffness of the protein. The initial, high resolution SES 
discretized at atomic resolution is simplified using a quadric simplification algorithm to 
obtain a molecular surface representation of arbitrary prescribed spatial resolution. While 
the proposed procedure is applied to proteins with known atomic structure, the molecular 
volume could equally be defined from electron density data, rendering the procedure 
applicable to a broad class of biomolecules and biomolecular complexes for which only a 
rough approximation to the molecular volume is known. As with existing coarse-grained 
elastic network models, energy minimization is not required prior to the NMA because 
the initial structure is assumed to be the ground-state structure. 
 Ongoing development of the proposed procedure is directed towards three areas 
of improvement. First, the atomic-based Hessian from all-atom force-fields such as 
CHARMM58 will be projected onto the FE-space such that the model optimally 
converges to the “exact” all-atom solution as the FE mesh is refined to atomic length-
scales. Such a procedure will enable the systematic coarsening of protein structure and 
interactions without the a priori assumption of elastic response. Indeed, an intriguing and 
as of yet unresolved question regards the relative effects of molecular shape versus 
specific molecular interactions on the mechanical response of supramolecular assemblies 
such as F-actin, MTs, and viral capsids. Second, the Poisson–Boltzmann equation used to 
model aqueous electrolyte-mediated electrostatic interactions in proteins may be coupled 
directly to the elastic-based FE model, so that it may be included in computations of 
normal modes and mechanical response. Langevin dynamics may also be incorporated 
into the model by coupling the protein-domain to the Stokes equations to model solvent 
damping.41,83 Finally, the proposed surface discretization and simplification procedure 
requires improvement because it often results in surface meshes with intersecting or 
degenerate triangles, as encountered here for F-actin. 
 The utility of the proposed FE-based procedure is explored here for one specific 
globular protein and supramolecular assembly, namely T4 lysozyme and F-actin. Clearly, 
in order to evaluate the utility of the proposed procedure thoroughly, a set of proteins of 
drastically varying structure must be analyzed, as well as additional supramolecular 
assemblies. Additional response variables and the effects of internal structural variations 
of the molecules examined should also be investigated. Notwithstanding these additional 
analyses and the foregoing model improvements, the current communication establishes 
an effective theoretical framework for the computation of the normal modes and 
generalized mechanical response of proteins and their supramolecular assemblies based 
on the elastic medium theory of proteins. 
Acknowledgements 
Discussions with Marco Cecchini, Martin Karplus, Klaus–Jürgen Bathe, and Michael 
Garland are gratefully acknowledged, as is funding from the Alexander von Humboldt 
Foundation in the form of a post-doctoral fellowship. The author additionally thanks 
Michael Sanner for bringing QSLIM to his attention. 
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