Study of correlations between protein peptide plane dynamics and side chain dynamics

Yanzhen Hou; Jiaojiao Liu; Jianfeng He; Xubiao Peng; Antti J. Niemi

doi:10.1371/journal.pone.0215141

Abstract

Protein dynamics is pivotal to biological processes. However, experiments are very demanding and difficult to perform, and all-atom molecular dynamics simulations can still not provide all the answers. This motivates us to analyze protein dynamics in terms of different reduced coordinate representations. We then need to resolve how to reconstruct the full all-atom dynamics from its coarse grained approximation. Accordingly we scrutinize all-atom molecular dynamics trajectories in terms of crystallographic Protein Data Bank (PDB) structures, and inquire to what extent is it possible to predict the dynamics of side chain Cβ atoms in terms of the static properties of backbone Cα and O atoms. Here we find that simulated Cβ dynamics at near physiological conditions can be reconstructed with very high precision, using the knowledge of the crystallographic backbone Cα and O positions. The precision we can reach with our PDB-based Statistical Method reconstruction exceeds that of popular all-atom reconstruction methods such as Remo and Pulchra, and is fully comparable with the precision of the highly elaborate Scwrl4 all-atom reconstruction method that we have enhanced with the knowledge of the backbone Cα and O atom positions. We then conclude that in a dynamical protein that moves around at physiological conditions, the relative positions of its Cβ atoms with respect to the backbone Cα and O atoms, deviate very little from their relative positions in static crystallographic PDB structures. This proposes that the dynamics of a biologically active protein could remain subject to very similar, stringent stereochemical constraints that dictate the structure of a folded crystallographic protein. Thus, our results provide a strong impetus to the development of coarse grained techniques that are based on reduced coordinate representations.

Citation: Hou Y, Liu J, He J, Peng X, Niemi AJ (2019) Study of correlations between protein peptide plane dynamics and side chain dynamics. PLoS ONE 14(4): e0215141. https://doi.org/10.1371/journal.pone.0215141

Editor: L. Michel Espinoza-Fonseca, University of Michigan, UNITED STATES

Received: January 24, 2019; Accepted: March 27, 2019; Published: April 12, 2019

Copyright: © 2019 Hou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data can be found from Protein Data Bank https://www.wwpdb.org/ by selecting structures with better than 1.0 A resolution.

Funding: The research of A.J. Niemi has been funded by Swedish Research Council under Contract No. 2018-04411.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The Cα atoms are located at the branch points of a protein, they connect the backbone and the side chains. As a consequence their positions are subject to relatively tight stereochemical constraints. Indeed, in the case of static crystallographic proteins, the all-atom structure can be often determined with a good precision from the knowledge of the Cα positions [1–9]. This motivates the so-called Cα trace problem where the goal is to construct an accurate all-atom model of a crystallographic folded protein structure solely from the knowledge of the positions of its Cα atoms [10–15].

Protein dynamics is pivotal to biological function and many proteins are presumed to be flexible under physiological conditions. Unlike in the case of static crystallographic protein structures, the relative positions of the various atoms can then become variable. There is no a priori reason why the dynamical all-atom structure could be determined solely from the knowledge of the Cα backbone. Instead the peptide planes and the side chains are expected to twist and bend with respect to the Cα backbone, presumably in a complicated fashion. However, high precision experiments in the case of a dynamical protein are very difficult to perform, and our knowledge of atomic level protein dynamics remains limited [16–19]. Presently, the best sources of information are all-atom molecular dynamics simulations. These are probably best exemplified by the very long Anton folding trajectories [20, 21].

In [22] a dynamical variant of the Cα trace problem is addressed. For example, two all-atom Anton trajectories were analyzed in detail, with focus on the peptide plane O atoms and side chain Cβ atoms: The O atom is the only heavy peptide plane atom that is not covalently connected to the Cα and the Cβ defines the direction of the side chain. Thus, these two atoms have a particularly important structural information content. Unexpectedly, it was found that the motions of the peptide plane O atoms and the side chain Cβ atoms could be reconstructed with high precision solely from the knowledge of the Cα atom dynamics, using a statistical analysis of static crystallographic protein structures in Protein Data Bank (PDB) [23]. The results suggest that in an isolated dynamical protein, at near physiological conditions, the motions of its O and Cβ atoms remain strongly slaved to the Cα dynamics, to the extent that they are more or less only subject to modest thermal fluctuations. If this is indeed the case, the problem of protein dynamics at physiological conditions could be much simpler than expected: If the motions of different atoms are strongly correlated and in a systematic manner, many aspects of protein dynamics could be modelled by an effective coarse grained energy function, formulated in terms of a reduced sets of coordinates that describe only the Cα atoms [24–26] or a group of atoms including the Cα as effective interaction centers [27–29].

Here we present an analysis of simulated all-atom protein dynamics in terms of reduced coordinate sets. We focus on the interrelations between the Cα, peptide plane O and side chain Cβ atoms. This choice is partly motivated by a series of articles [30–32] that propose to investigate the interrelations between the Cα, the peptide plane O and the side chain Cβ atoms in a Hamiltonian context. Specifically, we inquire how accurately can the dynamics of the Cβ atoms be determined from the knowledge of the backbone Cα and O atom dynamics, using solely a statistical analysis of static crystallographic PDB structures. We select these three groups of atoms for their pivotal structural role: Alongside the Cα atoms the peptide plane O atoms and side chain Cβ atoms are both structurally highly important. The O atoms are the only backbone heavy atoms that have no covalent bond with Cα atoms, but they have many very important formative functions. For example, in combination with the peptide plane H atoms they forge and stabilize regular secondary structures including α-helices and β-strands. Similarly, the Cβ atoms determine the orientations of the side chains, strongly affecting the positions of all the higher level side chain atoms. Indeed, in the case of crystallographic protein structures, the knowledge of the peptide planes and the Cβ atom positions is quite sufficient to determine the other side chain atom positions in terms of rotamer libraries [33–37]. This is amply demonstrated by the success of reconstruction programs such as Pulchra [13], Remo [14] and Scwrl4 [15].

Our aim is to understand how protein dynamics could be modelled in an effective manner. For this we propose to investigate and develop different coordinate descriptions that can adapt to the dynamics. Previously, several different coordinate systems have been proposed in the literature. Particularly notable are the Ramachandran angles [38, 39], widely used to validate crystallographic protein structures. But these angles provide only local information, they are not sufficient to describe the three dimensional shape, and thus, the dynamics, of a protein [40]. For this alternative coordinate systems have been developed, often starting by envisioning the Cα backbone as a piecewise linear polygonal chain for which the Cα coordinates define vertices; here N is the number of Cα atoms and we start the indexing from the N terminus. As a linear chain the Cα backbone can be framed, and one way is to employ a discrete version of the Frenet frames [41, 42]. The Frenet frames are purely geometric, they do not make any reference to the other heavy atoms of the protein. Nevertheless, discrete Frenet frames provide a very convenient description of a crystallographic protein structure, as it turns out that the Cβ and peptide plane heavy atoms are all highly localized in terms of Frenet frames, quite independently of the proteins amino acid content.

Other purely geometric ways to frame the Cα backbone can also be introduced. An example is the parallel transport (Bishop) framing [43]. But in terms of parallel transport frames the Cβ and peptide plane atoms fail to localize [41]. Thus, these frames do not seem to be very convenient, in the study of protein structure. We also note that reconstruction programs such as Pulchra, Remo and others, commonly use their own, specially designed framings that are built on the Cα backbone geometry.

Besides geometry based frames, proteins can also be framed using their structure; the Ramachandran angles are an example. In particular, the combination of Cα and Cβ atoms defines such a structure based framing [41, 42]. It employs the covalent bond that connects these two atoms, in addition of the virtual bonds that connect neighboring Cα atoms, to define the local frame. Such a Cβ framing adapts well to all amino acids except for glycine that has no Cβ atom. Since the Cβ atoms are highly inert in the discrete Frenet frames, such a CαCβ based framing yields a description of the protein structure that is very similar to the discrete Frenet framing description.

Here we develop an alternative structure based framing, using the peptide planes: The normal vector of a peptide plane, in combination with the virtual bonds between the Cα atoms, can be employed to define such a framing. Our aim is to study how such a peptide plane based framing describes protein dynamics. For this we analyze data from two extended Anton simulations [20] that are performed with Charmm22^⋆ force field [21]. The trajectories that we consider are the villin and the ww-domain, reported in [20]. Villin is α-helical and ww-domain is β-stranded in a folded state. The Anton simulations observe several transitions between structures that are (apparently) unfolded and that are (apparently) folded. Thus, the combination of these two trajectories cover many local structures, with all the biologically relevant amino acids appearing, except CYS with its unique potential to form sulphur bridges. Moreover, the villin in [20] involves a NLE mutant and the HIS in [20] is protonated. Thus, our analysis includes the effects of mutations and pH variations. All this ensures that there is a good diversity of dynamical details for us to analyze, in terms of these two trajectories.

The length of the villin trajectory is 120μs, and we have selected every 20th simulated structure for our prediction analysis, for a total of 31395 structures. The length of the ww-domain trajectory is 651 μs and we have chosen every 40th simulated structure for our prediction analysis, for a total of 60814 structures. We use the coordinates of the backbone Cα and O atoms along the Anton trajectories, to try and reconstruct the coordinates of the Cβ atoms using a variety of methods. We then compare the results with the Cβ coordinates along the Anton trajectories. Besides a Cα-Frenet frame based reconstruction [22], and the peptide plane based Statistical Method reconstruction that we develop here, we also analyze the results that we obtain using commonly available all-atom structural reconstruction programs Remo and Scwrl4; here we use and enhanced variant of Scwrl4 that accounts for the positions of the backbone Cα and O atoms. We have also performed the analyses using Pulchra, with results that comparable to those we obtain with Remo. For clarity of representation, we do not present the Pulchra results explicitely.

Finally, we remind that the Anton simulations provide us with dynamical data at physiologically relevant temperature values while all the reconstruction algoritms that we use, are based on static crystallographic structures that are commonly measured at very low (liquid nitrogen) temperatures. Thus, our results should also reveal how the structure deforms due to temperature effects in addition of dynamics.

Methods

Framing a protein backbone

Purely geometric discrete Frenet frames.

The discrete Frenet frames are purely geometric frames, they are built solely in terms of the Cα atoms of a protein backbone. Thus, these frames can be useful e.g. for reconstruction of the atomic structure when only the Cα atom positions are known. We define the discrete Frenet frames as follows: We assign to each Cα position r_i an orthonormal triplet (n_i, b_i, t_i) [41, 42]. We construct these vectors by first identifying t_i with the vector that points from the center of the i^th Cα towards the center of the (i + 1)^st Cα, (1)

The binormal vector is normal to the plane formed by three consecutive Cα carbons, (2) and the normal vector is (3)

For visualization we use a color coding with n ∼ x ∼ red (r), b ∼ y ∼ green (g) and t ∼ z ∼ blue (b) in terms of a right-handed Cartesian (xyz)∼(rgb) coordinate system; see Fig 1.

Download:

Fig 1. Discrete Frenet frame vectors (1), (2) and (3).

https://doi.org/10.1371/journal.pone.0215141.g001

We use the discrete Frenet frame vectors to define the (virtual) Cα backbone bond (κ) and torsion (τ) angles as follows, (4)

These angles are shown in Fig 2 We identify them as the canonical latitude (κ) and longitude (τ) angles on the surface of a unit radius (Frenet) sphere that is centered at the Cα atom; the sphere is oriented so that the north pole is in the direction κ = 0. Thus, the discrete Frenet frame vector t points to the direction of the north pole; it coincides with the z-direction in a Cα centered Cartesian coordinate system. The torsion angle τ coincides with the longitude angle and takes values τ ∈ [−π, π), increasing in the counterclockwise direction around the positive z-axis i.e. around vector t. The great circle τ = 0 passes through the north pole and the tip of the normal vector n that lies at the equator.

Download:

Fig 2. Definition of (virtual) Cα backbone bond (κ) and torsion (τ) angles.

https://doi.org/10.1371/journal.pone.0215141.g002

Structure based peptide plane CαO frames.

When in addition of the Cα atom positions also e.g. the peptide plane O atom positions are all known, we can proceed and frame the protein backbone in a structure dependent fashion: The Cα atoms Cα(i) and Cα(i + 1) that are located at the i^th and (i + 1)^st sites define two corners of the (virtual) peptide plane. The third corner coincides with the i^th O atom. Accordingly, when we continue to denote the Cα(i) coordinates by r_i and denote the O(i) coordinates by , the O_i centered CαO frames are defined by the following right-handed orthonormal triplet ( (5) (6) (7)

In Fig 3 we show these frames. In our reconstruction we shall assume that the peptide planes are ideal. The vector is then a normal vector of the (ideal) peptide plane and we can estimate the coordinates of the remaining peptide plane atoms C_i, N_i+1 and H_i+1, starting from the ideal peptide plane (IPP) geometry that we display in Fig 4.

Download:

Fig 3. The CαO frames are centered at the O atom of the peptide plane.

https://doi.org/10.1371/journal.pone.0215141.g003

Download:

Fig 4. The detailed structure of the ideal peptide plane (IPP) that we use.

https://doi.org/10.1371/journal.pone.0215141.g004

Visualizing a protein structure

Cα atom visualization.

The PDB data pool that we use is the same as used in [22]; see also [40]. It is updated continuously, to consist of all contemporary crystallographic protein structures that have been measured with better than 1.0 Å resolution. We trust that such ultra high resolution structures have been subjected to (at most) very minimal refinement.

For visualization purposes, we use the statistical distribution of the Cα backbone bond and torsion angles on a stereographically projected two-sphere , shown in Fig 5 and constructed as follows: Let be a (unit radius) sphere that is centered at the i^th Cα atom of a given PDB structure. The vector t_i then has its tail at the location of Cα(i) and its head lies at the north pole of , pointing towards the Cα(i + 1) atom. The following atom Cα(i + 2) is located similarly, in the direction of the discrete Frenet frame vector t_i+1 that points from the center of towards its north pole. We parallel transport t_i+1, with no rotation, until its tail coincides with the origin of . Let (κ_i, τ_i) be the discrete Frenet frame coordinates of the parallel transported head of t_i+1 on the surface of . These coordinates describe how a miniature observer at the position of Cα(i) atom, with head oriented towards the north pole of , finds the backbone twisting and bending when she proceeds along the chain to the position of Cα(i + 1).

Download:

Fig 5.

Top: Stereographic projection of two sphere onto plane, with the projection taken from the south pole. Bottom: Statistical distribution of all Cα atoms in our pool of PDB structures, on stereographically projected sphere.

https://doi.org/10.1371/journal.pone.0215141.g005

After we repeat this construction for all the Cα atoms of all PDB structures in our PDB data pool, we obtain a statistical distribution of coordinates (κ, τ). We visualize this distribution by projecting the sphere stereographically onto the complex plane from the south pole as shown in Fig 5. The projection is defined by

In Fig 5 we also show the (κ, τ) distribution of all the Cα atom coordinates in our PDB data set, on the stereographically projected Frenet sphere. The distribution is largely concentrated inside an annulus, with inner circle κ_in ≈ 1 and outer circle κ_out ≈ π/2. In the Figure we have identified the regular secondary structure regions corresponding to α-helices, β-strands and left-handed α-helices.

Visualization of peptide plane O atoms and side chain Cβ atoms.

To visualize the peptide plane O atom and side chain Cβ atom distributions in our PDB data set, we use the Cα centered Frenet spheres but with no stereographic projection. We depict the direction of these atoms on the surface of , exactly as they are seen by a discrete Frenet frame observer who stands at the position of the Cα atom. In Fig 6 we show the ensuing statistical distributions that we obtain for the O and Cβ atoms in our statistical pool of PDB structures. Again, the major regular secondary structures have been identified.

Download:

Fig 6.

Fig a): The distribution of peptide plane O atom coordinates (ϑ, ψ) in our PDB pool of structures on the surface of the Frenet sphere . Fig b): The distribution of Cβ atom coordinates (θ, ϕ) in our PDB pool of structures on the surface of the Frenet sphere . In both Figures we have identified the α-helices (α), β-strands (β), left-handed α-helices (α_L) and cis-peptide planes (cis).

https://doi.org/10.1371/journal.pone.0215141.g006

The Statistical Method reconstruction algorithm

Our Statistical Method reconstruction algorithm is an extension of the algorithm developed in [22]. It builds on the CαO framing, it aims to use the known positions of Cα and O atoms to predict the positions of the remaining atoms along a protein chain. As an example, we here predict the spherical coordinates (θ, ϕ) i.e. directions of Cβ atoms on the surface of a proper two-sphere , using the knowledge of the Cα coordinates (κ, τ) and O coordinates that we denote (ϑ, ψ) in the sequel.

We do not address fluctuations in the covalent bond lengths and other distance scales. By construction, any reconstruction that builds on crystallographic data can only reproduce (at most) very long time scale average values of these quantities. Besides, our analyses confirm that the radial distance variations in Anton trajectories are minor in comparison to the directional (angular) variations. Thus, for the Cα-Cβ covalent bond length we use the Anton average value 1.55 Å and for the other distances we refer to Fig 4.

We start as follows [22]: We divide the statistical distribution of our PDB data shown in Fig 5 (bottom) into 60 equal sectors with angular size Δτ = π/30 radians. We then divide each of these sectors into two radial sets, one corresponding to bond angle values κ > 1.2 (rad) and the other to κ ≤ 1.2 (rad): The value κ = 1.2 divides the annulus in Fig 5 (roughly) into two annuli corresponding to α-helix-like (κ > 1.2) and β-strand-like (κ < 1.2) structures.

Next, we observe that the statistical distribution of peptide plane O atoms shown in Fig 6 (top) forms a very narrow circular strip with latitude ϑ ≈ π/4. Accordingly, we further divide the PDB data in terms of the O atom coordinate values, by longitudinally dividing the statistical O distribution of Fig 6 into 90 equal length Δψ segments with length Δψ = π/45.

We use our division of PDB structures according to the (κ, τ, ψ) values, to predict the Cβ coordinates (θ, ϕ) of a given structure as follows:

Step 1. We start from the known τ_i value of the given Cα(i) atom, to select the pertinent sector τ_i ∈ Δτ. We then use the κ_i value of Cα(i) together with κ_i+1 of the Cα(i + 1) atom, to further assign Cα(i) to one of the four sets
We then use the ψ_i value of O(i) to select the Δψ segment. These steps identify a subset of [Δκ;Δτ;Δψ] to which we putatively assign the Cβ(i) atom.
Step 2. We proceed to consider those PDB structures for which the (κ, τ, ψ) values are in the same subset [Δκ;Δτ;Δψ]. Among these we search for a PDB structure that has equal amino acids at two consecutive sites, as the Cβ atom of interest.
- If we find only one such matching PDB structure we use the coordinates of its Cβ atom for our prediction.
- If we have more than one pair of matching amino acids in the PDB subset, we use the average values of the ensuing Cβ coordinates for our prediction.
- If there are no PDB entries with matching amino acid pairs, we use the average value of Cβ coordinates (θ, ϕ) of all PDB structures in the given subset [Δκ;Δτ;Δψ] for our prediction.
- Finally, if the PDB subset [Δκ;Δτ;Δψ] has no entries, we use a neighboring subset for our prediction. In selecting the neighboring subset, we move clockwise in along the strip of O atoms, then clockwise in terms of the torsion angle.

In Step 2, the average latitude value κ_ave is simply where the summation is over all elements in the given subset [Δκ;Δτ;Δψ]. For the average longitude value τ_ave we first define and

The average value is then obtained from

We compute the average longitude value ψ_ave in the same way.

The present Statistical Method reconstruction algorithm is extremely simple and computationally very fast, in fact much faster than any of the other reconstruction programs that we have considered even though we have not optimized the search algorithm, but use a straightforward MatLab code. We note that the sizes of the subsets Δκ, Δτ, Δψ can be changed and optimized, the choice we have made here is only to exemplify the method.

Results

We report on results that we have obtained using our Statistical Method reconstruction algorithm, and the publicly available reconstruction algorithms Remo and Scwrl4. Since Scwrl4 requires that the peptide planes are known, we first construct the peptide planes using the Cα and O atom coordinates from the Anton simulation. We then estimate the positions of peptide plane C, N and H atoms using the ideal peptide plane structure shown in Fig 4. Accordingly we denote this enhanced variant ScwrIPP in the sequel. The present Statistical Method reconstruction algorithm will be denoted Stat in the sequel. Note that we do not use the full Anton peptide planes in ScwrIPP. This is so that the information content in ScwrIPP and Stat are comparable.

Description of Anton data distributions

We start by displaying the various data distributions in the Anton simulations; for the bond and torsion angles of the Cα backbone, for the peptide plane structures and for the Cβ atoms on the surface of a two sphere.

Bond and torsion angle distributions in Anton simulations.

In Fig 7 we show the distributions of the Cα bond and torsion angles on the stereographically projected two sphere of Fig 5, for the villin and ww-domain trajectories in the Anton data.

Download:

Fig 7.

a) Statistical distribution of Cα atoms in villin structures of Anton. b) Statistical distribution of Cα atoms in ww-domain structures of Anton.

https://doi.org/10.1371/journal.pone.0215141.g007

Both distributions in Fig 7 are somewhat different from the PDB distribution in Fig 5. Specifically, the villin distribution is largely concentrated in the vicinity of the α-helical region while the ww-domain distribution has a wider spread. The data distributions in Fig 7 both confirm that the Anton data has a substantial dynamical content.

Peptide plane structure in Anton simulations.

We proceed to confirm that the ideal, crystallographic peptide plane structure of Fig 4 is indeed preserved, in the course of Anton simulations. For this we plot the statistical distributions of the C(i) and N(i + 1) directions as seen in the O(i) centered CαO frames, in the Anton trajectories of villin and ww-domain. The results are shown in Fig 8: We observe that all four distributions are consistent with the ideal peptide plane structure of Fig 4, to the extent that any (minor) deviation could be attributed to a thermal fluctuation.

Download:

Fig 8. Statistical distribution of peptide plane C(i) and N(i + 1) atoms in Anton simulations, as seen in the O(i) centered CαO frames.

a) C(i) in villin, b) N(i + 1) in villin, c) C(i) in ww-domain, d) N(i + 1) in ww-domain.

https://doi.org/10.1371/journal.pone.0215141.g008

Cβ distributions in Anton simulations.

In Fig 9 we show the Cβ density distributions of Fig 6, along the Anton trajectories. In each case the distributions have a support which is very similar to that in Fig 6; the villin distribution has a higher population in the vicinity of the α-helical region of Fig 6 and the ww-domain has a higher population in the vicinity of the β-stranded region, as expected.

Download:

Fig 9. Statistical distribution of side chain Cβ(i) atoms as seen from the position of Cα(i) atoms along the Anton trajectory; a) villin and b) ww-domain.

https://doi.org/10.1371/journal.pone.0215141.g009

Reconstruction of side chain Cβ(i) coordinates

Comparison of direction with Frenet frame based reconstructions.

In [22] a Frenet frame based method has been developed to reconstruct the positions of atoms along a protein chain, from the knowledge of the Cα atom positions. The present Statistical Method builds on that method. Thus, by comparing the results from these two methods we obtain an understanding how the knowledge of O atom positions influences the quality of reconstruction. For this we consider the vectors that point from the Cα atom to the ensuing Cβ atom. We evaluate these vectors from Anton simulations, and we reconstruct them using the two methods. We then compute the statistical (probability) distributions of the angles between the Anton vectors and the reconstructed vectors. The results are shown in Fig 10.

Download:

Fig 10. Comparison of the angle between the

directions in original Anton data and reconstructed data using the present CαO based Statistical Method and the Frenet frames based method of [22].

a) for the villin data and b) for the ww-domain data.

https://doi.org/10.1371/journal.pone.0215141.g010

We find that in both methods, the predicted Cβ positions are extremely close to the original Anton results: The peaks of all four distributions in Fig 10 are very strongly peaked in the range of 0.08—0.11 radians; the values are slightly larger in the Frenet frames based reconstruction. With the Cα-Cβ average covalent bond length ∼1.55 Å this corresponds to a distance in the range of 0.12—0.15 Å, which is hardly observable with presently available atomic level x-ray spectroscopy. For example, in our pool of ultra high resolution PDB structures, the Debye-Waller B-factor fluctuation distances all have values that are larger than 0.15 Å [22].

We observe from Fig 10 that the present, Statistical Method reconstructed Cβ atoms are systematically slightly closer to the Cβ atoms that are reconstructed in the Frenet frame based method. Thus, the inclusion of the peptide plane O atoms does appear to improve the reconstruction precision.

Comparison of direction with ScwrIPP and Remo reconstruction.

In Fig 11 we compare the angles between the present Statistical Method and ScrwIPP. For comparison, we also compare with results from the Remo reconstruction program. In that case the only input is the Cα positions of the Anton trajectory, the O(i) positions are determined by the program. The results shown in Fig 11 display the statistical (probability) distributions of the angles between the Anton vectors and the reconstructed vectors . The results are very good in all three methods, those obtained with ScwrIPP are slightly better than those obtained with Statistical Method. The comparison to Remo reconstruction confirms that the inclusion of O atom positions leads to a visible and systematic improvement in the precision.

Download:

Fig 11. Comparison of the angle between the

directions in original Anton data and reconstructed data using the present Statistical Method (Stat), ScwrIPP and Remo.

a) for the villin data and b) for the ww-domain data.

https://doi.org/10.1371/journal.pone.0215141.g011

Statistical distributions of on the two-sphere.

In Figs 12 and 13 we show various statistical distributions of Cβ(i) atoms, as they are seen in the CαO frames on the surface of a O(i) centered two-sphere. We show the results for the original Anton data, and the results from Statistical Method, ScwrIPP and Remo reconstructions. The α-helical and β-stranded regions are clearly identifiable in all distributions. We observe that Statistical method and ScwrIPP reproduce properly the original villin and ww-domain distributions. But in the case of Remo the distributions shows fragmentation and are also slightly, but systematically, shifted to the left, both in the case of villin and ww-domain. There is also an apparent small dislocated region in the villin distribution of Remo, pointed out in Fig 12d).

Download:

Fig 12. Statistical distribution of villin Cβ(i) atoms, when viewed from the position of O(i) atoms in CαO frames.

a) Original Anton data, b) Statistical Method reconstructed data c) ScwrIPP reconstructed data and d) Remo reconstructed data. In a) we have identified the α-helical and β-stranded regions. In d) we have pointed out a dislocated region in Remo reconstruction.

https://doi.org/10.1371/journal.pone.0215141.g012

Download:

Fig 13. Statistical distribution of ww-domain Cβ(i) atoms, when viewed from the position of O(i) atoms in CαO frames.

a) Original Anton data, b) Statistical Method reconstructed data c) ScwrIPP reconstructed data and d) Remo reconstructed data.

https://doi.org/10.1371/journal.pone.0215141.g013

Comparison of direction with ScwrIPP and Remo reconstruction.

In Fig 14 we compare the deviations in the statistical (probability) distributions for the reconstructed angles in the case of Statistical Method, ScwrIPP and Remo. The reference structures are the angles along the Anton trajectories, for villin and ww-domain. The deviations for Statistical Method and ScwrIPP are again very small and strongly peaked, at around 0.04 radians; for Remo the deviation is also small, but visibly less so. The distance between the Cα(i) and the O(i) atom is around 2.40 Å. Thus, a peak at 0.04 radians in angular deviation corresponds to a distance around 0.05 Å. This is hardly observable with present day x-ray protein crystallography techniques.

Download:

Fig 14. Statistical probability distributions for the angular difference between the Anton trajectory vectors

and the reconstructed vectors in the Statistical Method, ScwrIPP and Remo.

a) villin, b) ww-domain.

https://doi.org/10.1371/journal.pone.0215141.g014

Statistical distributions of on a two-sphere.

In Figs 15 and 16 we compare the distributions of Cβ(i + 1) atoms, as they are seen on the CαO frames at the surface of O(i) centered two-spheres, for villin and ww-domain. We show the results for the original Anton data, and from Statistical Method, ScwrIPP and Remo reconstructions. We observe that the Statistical Method and ScwrIPP reconstructions are remarkably close to the original Anton distributions. The Remo reconstruction shown in the Fig 16d) displays more dispersion than the other two, shown in Fig 16b) and 16c).

Download:

Fig 15. Statistical distribution of villin Cβ(i + 1) atoms viewed from position of O(i) atoms in CαO frames.

a) Original Anton data b) Statistical Method c) ScwrIPP d) Remo. Note the isolated region that has been identified in Fig a.

https://doi.org/10.1371/journal.pone.0215141.g015

Download:

Fig 16. Statistical distribution of ww-domain Cβ(i + 1) atoms viewed from position of O(i) atoms in CαO frames.

a) Original Anton data b) Statistical Method c) ScwrIPP d) Remo Note the isolated region that has been identified in Fig a.

https://doi.org/10.1371/journal.pone.0215141.g016

Comparison of the Cβ(i)-O(i)-Cβ(i + 1) angle.

We combine the results in Figs 12 and 15, respectively in Figs 13 and 16 into analysis of the statistical distributions of the angle ∠Cβ(i)O(i)Cβ(i + 1) between the directions of the vectors and . This measures the quality in the reconstruction of the entire Cα-Cβ-peptide plane complex. In Fig 17 we show the results for both villin and ww-domain, in the four cases of original Anton, and Statistical Method, ScwrIPP and Remo reconstruction.

Download:

Fig 17. Statistical distribution of the angle ∠Cβ(i)O(i)Cβ(i + 1) for original Anton data and its reconstruction using Statistical Method, ScwrIPP and Remo.

a) for villin, b) for ww-domain.

https://doi.org/10.1371/journal.pone.0215141.g017

A comparison shows that the Statistical Method provides slight improvement over ScwrIPP. In the case of villin the peak in the Statistical Method is slightly closer to the original Anton peak, and in the case of ww-domain Statistical Method succeeds to reproduce the double peak profile of Anton. The difference between these two methods and Remo reconstruction is apparent, the latter displays visibly larger deviations from the original Anton data. This confirms that the knowledge of the O(i) atom positions brings about clear improvement in the reconstruction.

Isolated region in Cβ(i + 1) distributions.

In both Figs 15 and 16a), 16b) and 16c) we observe a very similar isolated region; in Fig d) this region becomes connected. In Fig 18 we show the locations of the corresponding regions on the stereographically projected two-sphere of Fig 5. The Fig 18 show that the isolated regions correspond to residues that are positioned either in the left-handed α-helical region or in a region that is the mirror image of the β-stranded region i.e. in both cases the torsion angle has become shifted by τ ∼ π with respect to the τ-values in the conventional α-helical and β-stranded regions. Note that the π-shifted β-stranded region is very rare among the crystallographic structures shown in Fig 5 which proposes that it might correspond to a higher energy, intermediate dynamical transition state.

Download:

Fig 18. Statistical distribution of the (κ, τ) values that correspond to the isolated regions marked in Figs 15 and 16a).

In Fig a) villin and in b) ww-domain.

https://doi.org/10.1371/journal.pone.0215141.g018

Discussion

We have investigated dynamical proteins using results from all-atom molecular dynamics simulation, with Anton supercomputer. In particular, we have inspected the very long time period Anton simulation trajectories of villin and ww-domain. We have found that the Cβ positions along these trajectories can be accurately reconstructed, solely from the knowledge of the backbone Cα and O atom dynamics in combination with a statistical analysis of static crystallographic PDB structures.

We have compared the results that we obtain from our Statistical Method reconstruction, with publicly available all-atom reconstruction programs Remo and Scwrl4. Remo starts from the Cα trace, then reconstructs the peptide planes using elaborate optimization protocols. It then proceeds with Scwrl to add the side chain atoms for the full all-atom structure. Scwrl4 can also be used in a stand-alone mode, and for this we have enhanced it using the knowledge of the Cα and O atom coordinates along the Anton trajectories, with N, C and H in ideal peptide plane positions. Both Remo and Scwrlt4 need to reconstruct the entire all-atom structure, to evaluate the Cβ coordinates. This provides added precision to the reconstruction, as the removal of higher level steric clashes serves as a constraint. On the other hand, the present Statistical Method places the Cβ atoms with no regard to higher level side chain structures, or potential steric clashes between side chains and peptide planes. Only the backbone Cα and O atoms time evolution is employed in the reconstruction. Nevertheless, the results that we get using the Statistical Method are a clear improvement over those obtained with Remo, and are fully comparable with those obtained by our enhanced Scwrl4. Thus, at the present level of scrutiny, side chain dynamics and in particular that of Cβ atoms, appears to be fully slaved to that of the backbone Cα and O atom dynamics. The scrutiny of the higher level side chain atoms does not seem to bring about much improvement in reconstructing the Cβ dynamics.

All the three methods that we have employed are, by their construction, designed to reconstruct static low temperature crystallographic protein structures. They are not intended to model the all-atom structure of a dynamical and biologically active protein at physiological temperatures. Nevertheless, each of them succeeds in the reconstruction of the Cβ dynamics in Anton simulations, with an impressive precision. We find this to be remarkable and very surprising. It motivates the development of coarse grained approaches to model large time scale protein dynamics, in terms of reduced coordinates that describe the dynamics of Cα, O and Cβ atoms only.

Acknowledgments

We thank D.E. Shaw Research for providing us with the Anton data for our analysis. AJN acknowledges support from Vetenskapsrådet and Carl Trygger’s Stiftelse för vetenskaplig forskning.

References

1. Jones TA, Zou JY, Cowan SW, Kjeldgaard M. Improved methods for building protein models in electron density maps and the location of errors in these models. Acta Cryst. A47 110–119 (1991)
- View Article
- Google Scholar
2. Sillitoe I, Cuff AL, Dessailly BH, Dawson NL, Furnham N, Lee D, et al. CATH: comprehensive structural and functional annotations for genome sequences. Nucleic Acids Res. 43(D1), D376–D381 (2015)
- View Article
- Google Scholar
3. Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247 536–540 (1995) pmid:7723011
- View Article
- PubMed/NCBI
- Google Scholar
4. Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols 5 725–738 (2010) pmid:20360767
- View Article
- PubMed/NCBI
- Google Scholar
5. Arnold K, Bordoli L, Kopp J, Schwede T. Bioinformatics 22 195–201 (2006) pmid:16301204
- View Article
- PubMed/NCBI
- Google Scholar
6. Zhang Y. Protein structure prediction: when is it useful? Curr. Opin. Struct. Biol. 19 145–155 (2009) pmid:19327982
- View Article
- PubMed/NCBI
- Google Scholar
7. Dill K, Ozkan SB, Weikl TR, Chodera JD, Voelz VA. The protein folding problem: when will it be solved? Curr. Op. Struct. Biol. 17 342–346 (2007)
- View Article
- Google Scholar
8. Scheraga HA, Khalili M, Liwo A. Protein-folding dynamics: overview of molecular simulation techniques. Ann. Rev. Phys. Chem. 58 57–83 (2007)
- View Article
- Google Scholar
9. Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A. Coarse-Grained Protein Models and Their Applications. Chem. Rev. 116, 7898–7936 (2016) pmid:27333362
- View Article
- PubMed/NCBI
- Google Scholar
10. Holm L, Sander C. Database algorithm for generating protein backbone and side-chain co-ordinates from a Cα trace: application to model building and detection of co-ordinate errors Journ. Mol. Biol. 218 183–194 (1991)
- View Article
- Google Scholar
11. DePristo MA, de Bakker PIW, Shetty RP, Blundell TL. Discrete restraint-based protein modeling and the Cα-trace problem Prot. Sci. 12 2032–2046 (2003)
- View Article
- Google Scholar
12. Lovell SC, Davis IW, Arendall WB III, de Bakker PIW, Word JM, Prisant MG, et al. Structure validation by Cα geometry: ϕ, ψ and Cβ deviation. Proteins 50 437–450 (2003) pmid:12557186
- View Article
- PubMed/NCBI
- Google Scholar
13. Rotkiewicz P, Skolnick J. Fast procedure for reconstruction of full-atom protein models from reduced representations Journ. Comp. Chem. 29 1460–1465 (2008)
- View Article
- Google Scholar
14. Li Y, Zhang Y. REMO: A new protocol to refine full atomic protein models from Cα traces by optimizing hydrogen bonding networks Proteins 76 665–676 (2009) pmid:19274737
- View Article
- PubMed/NCBI
- Google Scholar
15. Krivov GG, Shapovalov MV, Dunbrack RL Jr. Improved prediction of protein side-chain conformations with SCWRL4 Proteins 77 778–795 (2009)
- View Article
- Google Scholar
16. Henzler-Wildman K, Kern D. Dynamic personalities of proteins Nature 450 964–972 (2007) pmid:18075575
- View Article
- PubMed/NCBI
- Google Scholar
17. Frauenfelder H, Chen G, Berendzen J, Fenimore PW, Jansson H, McMahon BH, et al. A unified model of protein dynamics PNAS 106 5129–5134 (2009) pmid:19251640
- View Article
- PubMed/NCBI
- Google Scholar
18. Bu Z, Callaway DJE. Chapter 5—Proteins MOVE! Protein dynamics and long-range allostery in cell signaling Adv. Protein Chem. Struct. Biol. 83 163–221 (2011)
- View Article
- Google Scholar
19. Khodadadi S, Sokolov AP. Protein dynamics: from rattling in a cage to structural relaxation Soft Matter 11 4984–4998 (2015) pmid:26027652
- View Article
- PubMed/NCBI
- Google Scholar
20. Lindorff-Larsen K, Piana S., Dror RO, Shaw DE. How fast-folding proteins fold. Science 334 517–520 (2011) pmid:22034434
- View Article
- PubMed/NCBI
- Google Scholar
21. Piana S, Lindorff-Larsen K, Shaw DE. How robust are protein folding simulations with respect to force field parameterization? Biophys. J. 100 L47–L49 (2011) pmid:21539772
- View Article
- PubMed/NCBI
- Google Scholar
22. Liu J, Dai J, He J, Peng X, Niemi AJ. Can all-atom protein dynamics be reconstructed from the knowledge of Cα time evolution? arXiv:1901.06864
23. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. (2000) The Protein Data Bank Nucl. Acids Res. 28 235–242. (2000) pmid:10592235
- View Article
- PubMed/NCBI
- Google Scholar
24. Danielsson UH, Lundgren M, Niemi AJ, Gauge field theory of chirally folded homopolymers with applications to folded proteins Phys. Rev. E82 021910 (2010)
- View Article
- Google Scholar
25. Molkenthin N, Hu S, Niemi AJ, Discrete nonlinear Schrödinger Equation and polygonal solitons with applications to collapsed proteins Phys. Rev. Lett. 106 078102–078105 (2011) pmid:21405545
- View Article
- PubMed/NCBI
- Google Scholar
26. Niemi AJ. in Chamon C., Goerbig M.O., Moessner R., Cugliandolo L.F. (Eds.) Topological Aspects of Condensed Matter Physics: Lecture Notes of the Les Houches Summer School Vol. 103 (Oxford University Press, Oxford, 2017)
27. Liwo A, Czaplewski C, Pillardy J, Scheraga HA. Cumulant-based expressions for the multibody terms for the correlation between local and electrostatic interactions in the united-residue force field J. Chem. Phys. 115 2323 (2001)
- View Article
- Google Scholar
28. Liwo A, Khalili M, Czaplewski C, Kalinowski S, Oldziej S, Wachucik K, et al. Modification and Optimization of the United-Residue (UNRES) Potential Energy Function for Canonical Simulations. I. Temperature Dependence of the Effective Energy Function and Tests of the Optimization Method with Single Training Proteins J. Phys. Chem. B111 260–285 (2007).
- View Article
- Google Scholar
29. Liwo A, Czaplewski C, Oldziej S, Rojas AV, Kazmierkiewicz R, Makowski M, et al. in Coarse-Graining of Condensed Phase and Biomolecular Systems Voth G. ed. (CRC Press 2008)
30. Sasai M, Wolynes PG, Molecular theory of associative memory Hamiltonian models of protein folding Phys. Rev. Lett. 65 2740 (1990) pmid:10042680
- View Article
- PubMed/NCBI
- Google Scholar
31. Hardin C, Eastwood MP, Prentis MC, Luthey-Schulten Z, Wolynes PG, Associative memory Hamiltonians for structure prediction without homology: α/β proteins Proc. Nat. Acad. (USA) 100 1679–1684 (2003)
- View Article
- Google Scholar
32. Davtyan A, Schafer NP, Zheng W, Clementi C, Wolynes PG, Papoian GA. AWSEM-MD: protein structure prediction using coarse-grained physical potentials and bioinformatically based local structure biasing J. Phys. Chem. 116 8494–8503 (2012)
- View Article
- Google Scholar
33. Janin J, Wodak S, Levitt M, Maigret B, Conformation of amino acid side-chains in proteins J. Mol. Biol. 125 357–386 (1978) pmid:731698
- View Article
- PubMed/NCBI
- Google Scholar
34. Lovell SC, Word J, Richardson JS, Richardson DC The penultimate rotamer library. Proteins 40 389–408 (2000)
- View Article
- Google Scholar
35. Schrauber H, Eisenhaber F, Argos P Rotamers: to be or not to be?: an analysis of amino acid side chain conformations in globular. J. Mol. Biol. 230 592–612 (1993) pmid:8464066
- View Article
- PubMed/NCBI
- Google Scholar
36. Dunbrack RL Jr, Karplus M. Backbone-dependent Rotamer library for proteins application to side chain prediction. J. Mol. Biol. 230: 543–574. (1993) pmid:8464064
- View Article
- PubMed/NCBI
- Google Scholar
37. Shapovalov MS, Dunbrack RL Jr.. A smoothed backbone-dependent Rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure 19 844–858 (2011) pmid:21645855
- View Article
- PubMed/NCBI
- Google Scholar
38. Ramachandran G, Ramakrishnan C, Sasisekharan V. Stereochemistry of polypeptide chain configurations, Journ. Mol. Biol. 7 95–99 (1963)
- View Article
- Google Scholar
39. Ramakrishnan C, Ramachandran G. Stereochemical Criteria for Polypeptide and Protein Chain Conformations: II. Allowed Conformations for a Pair of Peptide Units, Biophys. J. 5 909–933 (1965) pmid:5884016
- View Article
- PubMed/NCBI
- Google Scholar
40. Hinsen K, Hu S, Kneller GR, Niemi AJ. A comparison of reduced coordinate sets for describing protein structure J. Chem. Phys. 139 124115 (2013) pmid:24089758
- View Article
- PubMed/NCBI
- Google Scholar
41. Hu S, Lundgren M, Niemi AJ Discrete Frenet frame, inflection point solitons, and curve visualization with applications to folded proteins Phys. Rev. E83 061908 (2011)
- View Article
- Google Scholar
42. Peng X, Chenani A, Hu S, Zhou Y, Niemi AJ, A three dimensional visualisation approach to protein heavy-atom structure reconstruction BMC Struct. Biol. 14:27 (2014) pmid:25551190
- View Article
- PubMed/NCBI
- Google Scholar
43. Bishop RL, Amer. Math. Monthly 82 246–251 (1974)
- View Article
- Google Scholar

[ref1] 1. Jones TA, Zou JY, Cowan SW, Kjeldgaard M. Improved methods for building protein models in electron density maps and the location of errors in these models. Acta Cryst. A47 110–119 (1991)
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Sillitoe I, Cuff AL, Dessailly BH, Dawson NL, Furnham N, Lee D, et al. CATH: comprehensive structural and functional annotations for genome sequences. Nucleic Acids Res. 43(D1), D376–D381 (2015)
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref3] 3. Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247 536–540 (1995) pmid:7723011
View Article
PubMed/NCBI
Google Scholar

[8] View Article

[9] PubMed/NCBI

[10] Google Scholar

[ref4] 4. Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols 5 725–738 (2010) pmid:20360767
View Article
PubMed/NCBI
Google Scholar

[12] View Article

[13] PubMed/NCBI

[14] Google Scholar

[ref5] 5. Arnold K, Bordoli L, Kopp J, Schwede T. Bioinformatics 22 195–201 (2006) pmid:16301204
View Article
PubMed/NCBI
Google Scholar

[16] View Article

[17] PubMed/NCBI

[18] Google Scholar

[ref6] 6. Zhang Y. Protein structure prediction: when is it useful? Curr. Opin. Struct. Biol. 19 145–155 (2009) pmid:19327982
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref7] 7. Dill K, Ozkan SB, Weikl TR, Chodera JD, Voelz VA. The protein folding problem: when will it be solved? Curr. Op. Struct. Biol. 17 342–346 (2007)
View Article
Google Scholar

[24] View Article

[25] Google Scholar

[ref8] 8. Scheraga HA, Khalili M, Liwo A. Protein-folding dynamics: overview of molecular simulation techniques. Ann. Rev. Phys. Chem. 58 57–83 (2007)
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref9] 9. Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A. Coarse-Grained Protein Models and Their Applications. Chem. Rev. 116, 7898–7936 (2016) pmid:27333362
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref10] 10. Holm L, Sander C. Database algorithm for generating protein backbone and side-chain co-ordinates from a Cα trace: application to model building and detection of co-ordinate errors Journ. Mol. Biol. 218 183–194 (1991)
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref11] 11. DePristo MA, de Bakker PIW, Shetty RP, Blundell TL. Discrete restraint-based protein modeling and the Cα-trace problem Prot. Sci. 12 2032–2046 (2003)
View Article
Google Scholar

[37] View Article

[38] Google Scholar

[ref12] 12. Lovell SC, Davis IW, Arendall WB III, de Bakker PIW, Word JM, Prisant MG, et al. Structure validation by Cα geometry: ϕ, ψ and Cβ deviation. Proteins 50 437–450 (2003) pmid:12557186
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref13] 13. Rotkiewicz P, Skolnick J. Fast procedure for reconstruction of full-atom protein models from reduced representations Journ. Comp. Chem. 29 1460–1465 (2008)
View Article
Google Scholar

[44] View Article

[45] Google Scholar

[ref14] 14. Li Y, Zhang Y. REMO: A new protocol to refine full atomic protein models from Cα traces by optimizing hydrogen bonding networks Proteins 76 665–676 (2009) pmid:19274737
View Article
PubMed/NCBI
Google Scholar

[47] View Article

[48] PubMed/NCBI

[49] Google Scholar

[ref15] 15. Krivov GG, Shapovalov MV, Dunbrack RL Jr. Improved prediction of protein side-chain conformations with SCWRL4 Proteins 77 778–795 (2009)
View Article
Google Scholar

[51] View Article

[52] Google Scholar

[ref16] 16. Henzler-Wildman K, Kern D. Dynamic personalities of proteins Nature 450 964–972 (2007) pmid:18075575
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref17] 17. Frauenfelder H, Chen G, Berendzen J, Fenimore PW, Jansson H, McMahon BH, et al. A unified model of protein dynamics PNAS 106 5129–5134 (2009) pmid:19251640
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref18] 18. Bu Z, Callaway DJE. Chapter 5—Proteins MOVE! Protein dynamics and long-range allostery in cell signaling Adv. Protein Chem. Struct. Biol. 83 163–221 (2011)
View Article
Google Scholar

[62] View Article

[63] Google Scholar

[ref19] 19. Khodadadi S, Sokolov AP. Protein dynamics: from rattling in a cage to structural relaxation Soft Matter 11 4984–4998 (2015) pmid:26027652
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref20] 20. Lindorff-Larsen K, Piana S., Dror RO, Shaw DE. How fast-folding proteins fold. Science 334 517–520 (2011) pmid:22034434
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref21] 21. Piana S, Lindorff-Larsen K, Shaw DE. How robust are protein folding simulations with respect to force field parameterization? Biophys. J. 100 L47–L49 (2011) pmid:21539772
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref22] 22. Liu J, Dai J, He J, Peng X, Niemi AJ. Can all-atom protein dynamics be reconstructed from the knowledge of Cα time evolution? arXiv:1901.06864

[ref23] 23. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. (2000) The Protein Data Bank Nucl. Acids Res. 28 235–242. (2000) pmid:10592235
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref24] 24. Danielsson UH, Lundgren M, Niemi AJ, Gauge field theory of chirally folded homopolymers with applications to folded proteins Phys. Rev. E82 021910 (2010)
View Article
Google Scholar

[82] View Article

[83] Google Scholar

[ref25] 25. Molkenthin N, Hu S, Niemi AJ, Discrete nonlinear Schrödinger Equation and polygonal solitons with applications to collapsed proteins Phys. Rev. Lett. 106 078102–078105 (2011) pmid:21405545
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref26] 26. Niemi AJ. in Chamon C., Goerbig M.O., Moessner R., Cugliandolo L.F. (Eds.) Topological Aspects of Condensed Matter Physics: Lecture Notes of the Les Houches Summer School Vol. 103 (Oxford University Press, Oxford, 2017)

[ref27] 27. Liwo A, Czaplewski C, Pillardy J, Scheraga HA. Cumulant-based expressions for the multibody terms for the correlation between local and electrostatic interactions in the united-residue force field J. Chem. Phys. 115 2323 (2001)
View Article
Google Scholar

[90] View Article

[91] Google Scholar

[ref28] 28. Liwo A, Khalili M, Czaplewski C, Kalinowski S, Oldziej S, Wachucik K, et al. Modification and Optimization of the United-Residue (UNRES) Potential Energy Function for Canonical Simulations. I. Temperature Dependence of the Effective Energy Function and Tests of the Optimization Method with Single Training Proteins J. Phys. Chem. B111 260–285 (2007).
View Article
Google Scholar

[93] View Article

[94] Google Scholar

[ref29] 29. Liwo A, Czaplewski C, Oldziej S, Rojas AV, Kazmierkiewicz R, Makowski M, et al. in Coarse-Graining of Condensed Phase and Biomolecular Systems Voth G. ed. (CRC Press 2008)

[ref30] 30. Sasai M, Wolynes PG, Molecular theory of associative memory Hamiltonian models of protein folding Phys. Rev. Lett. 65 2740 (1990) pmid:10042680
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref31] 31. Hardin C, Eastwood MP, Prentis MC, Luthey-Schulten Z, Wolynes PG, Associative memory Hamiltonians for structure prediction without homology: α/β proteins Proc. Nat. Acad. (USA) 100 1679–1684 (2003)
View Article
Google Scholar

[101] View Article

[102] Google Scholar

[ref32] 32. Davtyan A, Schafer NP, Zheng W, Clementi C, Wolynes PG, Papoian GA. AWSEM-MD: protein structure prediction using coarse-grained physical potentials and bioinformatically based local structure biasing J. Phys. Chem. 116 8494–8503 (2012)
View Article
Google Scholar

[104] View Article

[105] Google Scholar

[ref33] 33. Janin J, Wodak S, Levitt M, Maigret B, Conformation of amino acid side-chains in proteins J. Mol. Biol. 125 357–386 (1978) pmid:731698
View Article
PubMed/NCBI
Google Scholar

[107] View Article

[108] PubMed/NCBI

[109] Google Scholar

[ref34] 34. Lovell SC, Word J, Richardson JS, Richardson DC The penultimate rotamer library. Proteins 40 389–408 (2000)
View Article
Google Scholar

[111] View Article

[112] Google Scholar

[ref35] 35. Schrauber H, Eisenhaber F, Argos P Rotamers: to be or not to be?: an analysis of amino acid side chain conformations in globular. J. Mol. Biol. 230 592–612 (1993) pmid:8464066
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref36] 36. Dunbrack RL Jr, Karplus M. Backbone-dependent Rotamer library for proteins application to side chain prediction. J. Mol. Biol. 230: 543–574. (1993) pmid:8464064
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref37] 37. Shapovalov MS, Dunbrack RL Jr.. A smoothed backbone-dependent Rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure 19 844–858 (2011) pmid:21645855
View Article
PubMed/NCBI
Google Scholar

[122] View Article

[123] PubMed/NCBI

[124] Google Scholar

[ref38] 38. Ramachandran G, Ramakrishnan C, Sasisekharan V. Stereochemistry of polypeptide chain configurations, Journ. Mol. Biol. 7 95–99 (1963)
View Article
Google Scholar

[126] View Article

[127] Google Scholar

[ref39] 39. Ramakrishnan C, Ramachandran G. Stereochemical Criteria for Polypeptide and Protein Chain Conformations: II. Allowed Conformations for a Pair of Peptide Units, Biophys. J. 5 909–933 (1965) pmid:5884016
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref40] 40. Hinsen K, Hu S, Kneller GR, Niemi AJ. A comparison of reduced coordinate sets for describing protein structure J. Chem. Phys. 139 124115 (2013) pmid:24089758
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref41] 41. Hu S, Lundgren M, Niemi AJ Discrete Frenet frame, inflection point solitons, and curve visualization with applications to folded proteins Phys. Rev. E83 061908 (2011)
View Article
Google Scholar

[137] View Article

[138] Google Scholar

[ref42] 42. Peng X, Chenani A, Hu S, Zhou Y, Niemi AJ, A three dimensional visualisation approach to protein heavy-atom structure reconstruction BMC Struct. Biol. 14:27 (2014) pmid:25551190
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref43] 43. Bishop RL, Amer. Math. Monthly 82 246–251 (1974)
View Article
Google Scholar

[144] View Article

[145] Google Scholar