Evaluation of the Coupled Two-Dimensional Main Chain Torsional

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Evaluation of the Coupled Two-dimensional Main Chain Torsional Potential in Modeling Intrinsically Disordered Proteins Ya Gao, Chaomin Zhang, John Z.H. Zhang, and Ye Mei J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.6b00589 • Publication Date (Web): 18 Jan 2017 Downloaded from http://pubs.acs.org on January 20, 2017

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Journal of Chemical Information and Modeling

Evaluation of the Coupled Two-dimensional Main Chain Torsional Potential in Modeling Intrinsically Disordered Proteins Ya Gao,∗,† Chaomin Zhang,† John Z. H. Zhang,‡,¶,§ and Ye Mei∗,k,¶,§ College of Fundamental Studies, Shanghai University of Engineering Science, Shanghai 201620, China, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China, NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China, Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, People’s Republic of China, and State Key Laboratory of Precision Spectroscopy, School of Physics and Materials Science, East China Normal University, Shanghai 200062, China E-mail: [email protected]; [email protected]

Abstract Intrinsically Disordered Proteins (IDPs) carry out crucial biological functions in essential biological processes of life. Due to the highly dynamic and conformationally heterogeneous nature of the disordered states of IDPs, molecular dynamics simulations are becoming an indispensable tool for the investigation of the conformational ∗

To whom correspondence should be addressed College of Fundamental Studies, Shanghai University of Engineering Science, Shanghai 201620, China ‡ School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China ¶ NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China § Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, People’s Republic of China k State Key Laboratory of Precision Spectroscopy, School of Physics and Materials Science, East China Normal University, Shanghai 200062, China †

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ensembles and dynamic properties of IDPs. Nevertheless, there is still no consensus on the most reliable force field in molecular dynamics simulations for IDPs hitherto. In this work, a recently proposed AMBER99SB2D force field is evaluated in modeling some disordered polypeptides and proteins by checking its ability in reproducing NMR experimental data. The results highlight that, by including the -ildn sidechain corrections, AMBER99SB2D -ildn exhibits the reliable results which agree with experiments comparing with its predecessors, AMBER14SB, AMBER99SB, AMBER99SB-ildn, and AMBER99SB2D force fields, and decreasing the overall magnitude of protein-protein interactions, in favor of protein-water interactions, is a key ingredient behind the improvement.

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Introduction

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) in structured proteins are the proteins or regions that lack well-defined secondary or tertiary structure under standard physiological conditions, which make up more than 50% fraction of eukaryotic proteins, 1,2 and have recently gained wide attention due to their crucial functions in biological processes, such as signaling, regulation, and post-translational modifications. 3–6 However, highly flexible and conformationally heterogeneous nature of IDPs or IDRs makes it difficult to investigate with most of the experimental structural biology techniques. For instance, X-ray crystallography is limited by the fact that systems are disordered and flexible, and cannot provide stable atomistic positions. While for Nuclear Magnetic Resonance (NMR) methods, limitations on the accessible timescale hinder its application to IDPs despite of recent advances. 7 Generally, experimental methods suffer either in the limitations of the temporal scale or the resolution, and are not capable of providing detailed information about the states or transitions of IDPs or IDRs. Molecular dynamics (MD) simulation is a powerful tool in characterizing protein disordered states, which utilizes classical potential energy functions, or ”force fields”, to describe 2


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the interactions on the atomistic level. 8–10 Fabritiies et. al. determined the kinetics and energetics of a disordered protein domain using high-throughput molecular dynamics simulations. 11 Huang et. al. identified the metastable conformational states of the intrinsically disordered monomer of the human islet amyloid polypeptide (hIAPP), and the transition among them utilizing MD simulations. 12 Diffusion of hydration water around IDPs was further investigated and compared with the globular proteins via MD simulations, revealing the less restricted motions of water molecules at the surface of IDPs. 13 In recent years, MD simulations are increasingly employed to unravel the biological importance of IDPs. 14–19 However, a growing number of studies utilizing varied force fields draw the same conclusion that the resulting structures in explicit solvent are too collapsed relative to experiment. 20–22 Ensembles obtained with different force fields exhibit notable differences in chain dimensions, hydrogen bonding, secondary structure content, etc. 23 To alleviate this deviation, Chen et. al. corrected the main chain dihedral terms for eight disorder-promoting residues with CMAP method and proposed ff99IDPs force field, 24 which showed an improved agreement with NMR experimental measurement relative to its predecessor AMBER99SB-ildn force field. Barducci et. al. systematically evaluated the quality of three commonly used force fields, AMBER99SB∗ -ildn, CHARMM22∗ , and AMBER03w, combining with enhanced sampling MD simulations in modeling disordered states of proteins, and AMBER03w with the -ildn corrections outperformed the others. 25 Nevertheless, CHARMM22∗ by Grubm¨ uller et. al. gave the most consistent ensembles of IDPs by comparing against small-angle X-ray scattering (SAXS) and NMR data. 26 As a consequence of these developments, the community is increasingly focusing on improving current force fields or the creation of more accurate force fields. Additionally, the significance of balanced protein-water interactions is drawing more attention, which has been proposed to improve the properties of IDPs, 21,27 as the protein-protein interactions are generally overestimated in many ”standard” force fields. 28 Lately, a new water model, TIP4P-D, was proposed, 27 and excellent agreement with experiment was obtained for Histatin 5 IDP model with AMBER99SB-ildn

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force field when it is used. 29 In our previous work, a new modification to AMBER force field by incorporating the coupled two-dimensional (2D) main chain torsion energy has been examined for balanced representation of secondary structures. 30 The detailed explanation of the implementation and parameterization of this coupled 2D torsion potential can be found in our previous work. 30 Here we give a brief introduction. The alanine dipeptide (AD) was chosen as a model system for parameterization. The fitting of main chain torsion energy at a molecular mechanical (MM) level aims to reproduce the total energy of AD at a quantum mechanical (QM) level. Energy calculations are conducted on 24×24 grid points in the 2-dimensional space of main chain torsions with a 15◦ interval. All quantum mechanical calculations are performed at the M06 2X/aug-cc-pvtz//HF/6-31G** level using Gaussian 09. 31,32 At the QM level, the solvation model based on solute electron density developed in Truhlar’s group is included in both optimization and single point calculations. 33,34 In the AMBER force field, the torsion energy is expressed as a series of separate Fourier expansions for φ and ψ. However, a more rational method involves decomposing the main chain torsion energy map with a double Fourier series, after which the expansion coefficients can be shown in matrix form. At any value (φ,ψ), the potential energy can be calculated as

E(φ, ψ) =

Nφ X

Nψ X

C(m, n)e−imφ e−inψ

(1)

m=−Nφ n=−Nψ

Previous studies have shown AMBER2D force fields can give an excellent agreement with experiments in J coupling, chemical shifts, and secondary structure population, and also successfully study the folding of two model peptides adopting either α-helix or β-hairpin structures. 35,36 However, main chain torsion parameters are not very transferable among different residues, indicating that more types of residues with different sidechains should be taken into calculation. In this work, the effect of the -ildn sidechain dihedral corrections, 37 originally devel-

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oped for AMBER99SB, 38 is studied. And the performance of AMBER14SB, AMBER99SB, AMBER99SB-ildn, AMBER99SB2D , and AMBER99SB2D -ildn force fields, is evaluated in modeling a set of disordered peptides that were previously characterized by NMR spectroscopy. By calculating 3 JHN HA couplings, Cα chemical shift, radius of gyration, hydrogen bond number, and secondary structure distribution of model systems and disordered proteins, ensembles under AMBER99SB2D -ildn force field give the reliable optimal agreement with the experiments.

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Materials and Methods

We chose unstructured peptides of sequence EGAAXAASS (X=G, W, I, D, V) as model systems of protein disordered states, which have been extensively investigated by Grzesiek and co-workers by means of NMR spectroscopy. 39 These five systems allowed us to compare diverse conformational preferences of amino acids. Besides, the aspartic proteinase inhibitor IA3 (PDB code 1DP5) was also selected. 40 Expression of aspartic proteinase has been confirmed in the life cycles of many human infectious agents. IA3 can fold into a rigid α-helix in the bounded state, and present disordered state when it is free. For model systems, replica exchange molecular dynamics (REMD) simulations were employed, 41 in which, high temperature replicas can easily surmount the energy barrier and explore phase space more efficiently than standard molecular dynamics at room temperature. Each replica of REMD simulations was extended to 50 ns to ensure convergence. Mittal et. al. has shown that REMD and bias exchange metadynamics yield similar ensemble results. 42 For the free IA3 , direct MD simulations at experimental temperature 298 K were utilized, and extended to 1000 ns under AMBER14SB, AMBER99SB, AMBER99SB-ildn, and AMBER99SB2D -ildn force fields, and 1200 ns under AMBER99SB2D force field, to guarantee convergence. All the simulations were conducted in explicit water. The φ and ψ distribution of the central amino acid X of EGAAXAASS peptides during different time periods under the five force

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fields is shown in Fig. S1-S5. The distribution shows convergence is reached during the 50 ns simulations. For IA3, the average helix fraction of the last 400 ns is calculated and depicted in Fig. S6, in which average helix fraction all reaches its plateau under the five force fields respectively. The details of the setup for the simulations are available in the supporting information. 3

J(HN , Hα ), an indicator of secondary structure distribution, was calculated via the

Karplus equation 43 3

J(ϑ) = Acos2 (ϑ + ∆) + Bcos(ϑ + ∆) + C

(2)

The parameters used in this work were taken from Br¨ uschweiler et. al., 44 which was fitted using density functional theory (DFT) and enhances the power of J-coupling analysis. The Cα chemical shift was calculated with SPARTA version 2.60, which has been proved to be reliable by a cross-validation procedure. 45 STRIDE was utilized to identify secondary structure content. 46 The definition of helix, and β-sheet was the same as Barducci et. al.. 25

3 3.1

Discussion and Results Model Systems

The distributions of the radius of gyration (Rg) and secondary structure content are reported in Fig. 1 to characterize the conformational ensemble of each peptide under each force field. In general, the five polypeptides all adopted the most compact conformations under the recently developed force field AMBER14SB with the smallest average Rg value. The distribution of Rg under AMBER99SB2D force field is broader than that under AMBER99SB, indicating that the five peptides under AMBER99SB2D adopt more flexible and extended conformations. For pepI and pepD, the ildn sidechain dihedral correction for Isoleucine and Aspartic amino acids is also included. However, under AMBER99SB-ildn force field, pepI and pepD both adopted a more tight conformation with the decrease of Rg comparing

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with that under AMBER99SB force field. When the ildn sidechain correction is included in AMBER99SB2D force field, pepI adopted a more extended ensemble, while for pepD, a slight decrease of the average Rg, indicating a slight more compact conformation, is observed. The ensemble under each force field is also characterized by the number of hydrogen bonds (H-bonds) as listed in Table 1 and secondary structure content shown in Table 2. In AMBER99SB2D , the conformations are more flexible, as suggested by an increased content of coil structure as compared with that under AMBER14SB, AMBER99SB, and AMBER99SBildn. The number of intrapeptide H-bonds is also lessening along with the increasing of the H-bonds between peptide and water molecules, indicating the magnitude of protein-protein interactions decreased, in favor of protein-water interactions under AMBER99SB2D . After including the ildn sidechain corrections, more flexible and extended conformations are sampled for pepI under AMBER99SB2D -ildn force field, along with the increase of coil population and more P-W hydrogen bonds. Yet for pepD under AMBER99SB2D -ildn the decrease of coil population is observed, which is similar to the result of A03-ildn force field for pepD in Barducci et. al.. 25 To uncover the mechanism of the improvement for pepI and the deterioration for pepD under AMBER99SB2D -ildn force field, molecular dynamics simulations for the five dipeptides were carried out. Simulated average populations of α-helix, β-sheet, and polyproline helix type II (ppII) are listed in Table S1. For Isoleucine, comparing with AMBER99SB, the population of ppII is largely increased and turned into the dominant conformation under AMBER99SB2D , which is consistent with the NMR and vibrational spectroscopy study. 47 This scenario is not altered by introducing the ildn correction to AMBER99SB2D for Isoleucine dipeptide. While for Aspartic dipeptide, a slight decrease of ppII population is detected under AMBER99SB2D -ildn. A further molecular dynamics simulation under AMBER99SB-ildn force field for Aspartic dipeptide revealed the decrease of the population of ppII under AMBER99SB2D -ildn directly results from the small number of ppII population under AMBER99SB-ildn, which disagrees with the experimental value, 49%. 47 The

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potential energy curves of χ1 under AMBER99SB and AMBER99SB-ildn force field for Isoleucine and Aspartic amino acids are depicted in Fig. S7 and Fig. S8 respectively. For Isoleucine amino acid, it is shown that the structure flows from 60◦ to 180◦ region after ildn side chain correction. Simulations with χ1 confined at 180◦ and 60◦ are respectively performed to investigate the variation of the secondary structure population along with χ1. It is indicated that after the ildn side chain correction the ppII population increased about 22%, shown in Table S2. While for Aspartic amino acid, the structure flows from 180◦ to 60◦ region after ildn side chain correction, and the ppII population decreased about 10% correspondingly, shown in Table S3. It is inconsistent with the fact that the dominant conformation adopted by the dipeptides is ppII. The side chain parameters fitting procedure of AMBER99SB-ildn, in which the coupling between φ or ψ and χ1 is not considered with φ and ψ kept fixed to the extended conformation, is one likely reason. Despite the side chain correction of Aspartic amino acid under AMBER99SB-ildn improves its NMR properties, the secondary structure distribution contradicts with the experiment, which accounts for the decrease in coil population under AMBER99SB-ildn force field. Therefore, comparing with AMBER14SB, AMBER99SB, and AMBER99SB-ildn force fields, more flexible and extended conformations sampled for the five unstructured peptides of sequence EGAAXAASS under AMBER99SB2D force field is a result of the increase of ppII conformation for the dipeptides, and meanwhile protein-protein interaction is decreased in favor of the protein-water interaction. For Isoleucine amino acid, the ildn side chain correction further improves the ppII conformational sampling and is consistent with the experiment. However, to determine which force field generates the most reliable conformational ensemble, 3 JHN HA coupling and Cα chemical shifts are also calculated and compared with available experimental data. The 3 JHN HA coupling, related to the φ backbone dihedral angle, is an excellent indicator of secondary structure distribution. Root-Mean Square Deviation (RMSD), which is listed in Table 3, is calculated to quantify the agreement with experiments for each peptide associated with each force field. AMBER99SB2D -ildn force field provides the smallest RMSD compar-

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ing with experiment. Introducing the ildn sidechain corrections improves the quality of AMBER99SB2D force field in a whole. Chemical shift, which can be measured directly from experiment and is sensitive to diverse structural properties, can also serve as a good metric to characterize disordered proteins. The Cα chemical shifts for each peptide and RMSDs for each force field are listed in Table 3. The poorest average agreement with experimental measurements is noted in AMBER14SB force field, while AMBER99SB2D -ildn provides the best estimates. Thus, the more extended ensembles generated from AMBER99SB2D -ildn force field, the more coincident ensembles with experiment obtained under AMBER99SB2D -ildn force field.

3.2

Protein: IA3

IA3 is a 31-residue and intrinsically disordered protein that can fold into a rigid α-helix when binding with aspartic proteinase. Molecular dynamics simulations, starting from the initial bounded structure, α-helix, are conducted to mimic the ordered-to-disordered transition of protein and evaluate its conformational dynamic properties under AMBER14SB, AMBER99SB, AMBER99SB-ildn, AMBER99SB2D , and AMBER99SB2D -ildn force fields respectively. To reach convergence, the simulation under each of the five force fields was extended to 1000 ns under AMBER14SB, AMBER99SB, AMBER99SB-ildn, and AMBER99SB2D -ildn force fields and 1200 ns under AMBER99SB2D force field in a TIP3P water box. The average helix fraction during the last 400 ns was calculated and depicted in Fig. S6, which shows convergence is reached. Under AMBER99SB, AMBER99SB-ildn, and AMBER99SB2D -ildn force fields, the helical structure was rapidly unfolded in the initial 100ns, and RMSDs from its bounded structure increased to about 10 ˚ A, which is shown in Fig. 2. The corresponding helix fraction also decreased to about 0.30. After that, neither RMSD nor helix fraction experienced drastic change under AMBER99SB , AMBER99SB-ildn and AMBER99SB2D -ildn force fields except for a slight increase in helix fraction during 330-550 ns for AMBER99SB2D ildn force field. Under AMBER14SB force field, the helical structure was unfolded in the 9



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initial 200 ns with RMSD increased to about 10 ˚ A and helix fraction decreased to about 0.61. Further unfolding was not observed and average helix fraction stays at about 0.58. However, under AMBER99SB2D force field, the helical structure remained folded until about 300 ns, and the helix fraction correspondingly decreased from ∼0.86 to ∼0.52. After another about 200 ns simulation, the helical structure was further unfolded, and the helix fraction decreased to about 0.28. At about 800 ns, the helix fraction further decreased to 0.17. Simulation was extended to 1200 ns, and during the last 400 ns simulation, marginally stable conformation ensemble was obtained. At the end of the simulations, the average helix fraction stayed at about 0.58, 0.31, 0.14, 0.13, and 0.28 under AMBER14SB, AMBER99SB, AMBER99SBildn, AMBER99SB2D , and AMBER99SB2D -ildn force fields respectively. It is consistent with the fact that most unfolded and disordered proteins are far from being ideal random coils, and exist with a nascent structure being a prominent aspect instead. 48,49 The mapped free energy landscapes into RMSD and Rg during the last 400 ns simulations under the five force fields are depicted in Fig. 3. Under AMBER14SB force field, the sampled structures concentrated on the region with RMSD about 6-11 ˚ A and Rg about 8-12 ˚ A. The representative four structures are presented, indicating partially folded helical structures still occupy 87.0% during the last 400 ns simulation. Under AMBER99SB force field, the dominated sampled structures concentrate on the unfolded region with RMSD about 10.5 ˚ A and Rg about 9 ˚ A, with certain percentage of helical and β-sheet conformation. While under AMBER99SB-ildn force field, helical conformation distinctly decreased along with coil conformation increased. Under AMBER99SB2D force field, the sampled area with RMSD about 12-16 ˚ A and Rg about 9-13 ˚ A is more broader than that under AMBER14SB, AMBER99SB, and AMBER99SB-ildn force field, indicating conformations are more flexible and extended. When the ildn sidechain correction is included in AMBER99SB2D force field, the population of helical fraction increased accompanying the population of β-sheet decreased, while the population of coil changed little. Actually, secondary structure content is also calculated for the last 400 ns simulations, shown in Fig. 4. The coil content is 22.7%, 36.8%, 41.2%,

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52.6%, and 49.0% for AMBER14SB, AMBER99SB, AMBER99SB-ildn, AMBER99SB2D , and AMBER99SB2D -ildn force fields respectively. Experimental Cα chemical shift data for IA3 are available, which can be retrieved from the Biological Magnetic Resonance Bank (BMRB) with accession no. 6078. 40000 snapshots are extracted from the last 400 ns simulations and calculated with SPARTA. The RMSD between calculated Cα chemical shifts and experimental values is shown in Fig. 4, in which AMBER99SB2D force field provides the best estimate. For AMBER99SB2D -ildn force field, the helical fragmentation near the N-terminal gave rise to the large deviation. IA3 is also investigated in Chen et. al., in which simulations were only extended to 120 ns. 50 In Shaw et. al., 27 it is indicated that water models typically used significantly underestimate London dispersion interactions, which tends to produce too compact disordered-state ensembles relative to experiments. Thus, water model strongly influences the simulated structural properties of disordered protein states.

4

Conclusion

Molecular dynamics simulations are increasingly used to obtain structural ensembles and investigate the dynamic properties of intrinsically disordered proteins, which play a crucial role in many essential biological processes. However, contradictory findings are concluded from reported literatures owing to the rugged free energy landscape and the inaccuracy of empirical force field for IDP simulations. On the one hand, excellent agreement between the computed and the measured experimental observables was observed in some IDP simulations. 51–53 On the other hand, the accuracy of unfolded state and IDP structural ensembles obtained using several widely used force fields have called into question. 23,27 Consequently, there is presently no consensus on the most accurate force field or the suitability of any force field for IDP simulations. Moreover, the role of protein-water interaction is gradually recognized, which is of crucial importance in IDPs simulations. In this work, we evaluated the accuracy of AMBER14SB, AMBER99SB, AMBER99SB-

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ildn, AMBER99SB2D and AMBER99SB2D -ildn force fields in simulating model disordered systems and protein. Experimental data of 3 JHN HA coupling and Cα chemical shifts are used to evaluate the accuracy of ensembles obtained using the three force fields. For the five amino acid disordered model systems, AMBER99SB2D force field provides a more flexible and extended ensembles relative to AMBER99SB. Including the ildn sidechain corrections improves the coil conformational sampling for Isoleucine amino acid, yet for Aspartic amino acid, a slight decrease in coil content is observed. A further simulation analysis exhibits under AMBER99SB-ildn force field, the ppII population of Aspartic dipeptide drastically disagrees with experiment. However, averagely AMBER99SB2D -ildn provides a more accurate description which is further proved by J coupling and chemical shift calculation. Notably, under AMBER99SB2D -ildn force field, the number of hydrogen bonds between peptide and water molecules are increased, indicating the interaction between protein and water molecules are strengthened accompanied with the weakening of the interaction of intrapeptide. For IA3 , AMBER99SB2D force field provides the best estimate. For AMBER99SB2D -ildn force field, the helical fragmentation give rise to the large deviation. Besides, water model strongly influences the simulated structural properties of disordered protein states. This work can pave the way for future improvement of IDPs simulations of coupled two-dimensional torsional potential.

Supporting Information Available Simulation details, secondary structure population of dipeptides, and potential energy curves of χ1 under AMBER99SB and AMBER99SB-ildn force fields.

This material is available

free of charge via the Internet at http://pubs.acs.org/.

Acknowledgement This work is supported by the National Natural Science Foundation of China (Grant No. 21503132, 21403068, and 21173082), National Key R&D Program 2016YFA0501700, Shang12


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hai Municipal Education Commission (ZZGCD15055, 15CG59), Zhanchi Program of Shanghai University of Engineering Science, and the Large Instruments Open Foundation of East China Normal University. We thank Supercomputer Center of East China Normal University for CPU time support.

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Table 1: Average of the structural measures (Rg and number of H-bonds) on the conformational ensembles generated by the different force fields for each of the 5 peptides studied. peptides PepG

PepW

PepI

PepD

PepV

force-fields A14SB A99SB A99SB2D A14SB A99SB A99SB2D A14SB A99SB A99SB-ildn A99SB2D A99SB2D -ildn A14SB A99SB A99SB-ildn A99SB2D A99SB2D -ildn A14SB A99SB A99SB2D

Rg(˚ A) 5.89±0.01 6.06±0.01 6.43±0.01 6.38±0.01 6.56±0.01 7.09±0.01 6.28±0.01 6.44±0.01 6.28±0.01 6.86±0.01 7.10±0.01 5.46±0.01 6.08±0.01 5.90±0.01 6.54±0.01 6.37±0.01 6.29±0.01 6.59±0.01 7.05±0.01

No. of hbonds(P-P) 0.63±0.01 0.59±0.01 0.44±0.01 0.54±0.01 0.45±0.01 0.36±0.01 0.78±0.01 0.46±0.01 0.66±0.01 0.44±0.01 0.34±0.01 1.27±0.01 0.64±0.01 0.77±0.01 0.52±0.01 0.61±0.01 0.73±0.01 0.37±0.01 0.32±0.00

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No. of hbonds(P-W) 12.46±0.02 12.72±0.02 13.04±0.02 12.60±0.02 12.72±0.02 13.55±0.02 12.14±0.02 13.11±0.02 12.71±0.02 12.77±0.01 13.36±0.01 11.33±0.02 12.84±0.02 12.27±0.02 13.15±0.02 12.86±0.02 12.54±0.02 13.15±0.02 13.06±0.01


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Table 2: Average of the structural measures (secondary structure distribution) on the conformational ensembles generated by the different force fields for each of the 5 peptides studied. peptides PepG

PepW

PepI

PepD

PepV

force-fields A14SB A99SB A99SB2D A14SB A99SB A99SB2D A14SB A99SB A99SB-ildn A99SB2D A99SB2D -ildn A14SB A99SB A99SB-ildn A99SB2D A99SB2D -ildn A14SB A99SB A99SB2D

helix(%) 10.4±0.3 8.7±0.3 2.4±0.2 9.1±0.3 4.3±0.2 7.8±0.3 24.0±0.5 6.2±0.3 15.8±0.4 4.9±0.2 2.9±0.2 37.7±0.5 9.1±0.3 7.3±0.3 5.8±0.3 11.1±0.3 19.1±0.4 3.0±0.2 1.0±0.1

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β-sheet(%) 0.6±0.1 1.7±0.1 0.5±0.1 0.2±0.1 0.4±0.1 0.0±0.0 0.0±0.0 0.4±0.1 0.5±0.1 0.0±0.0 0.2±0.0 0.1±0.0 3.0±0.2 0.6±0.1 4.7±0.2 2.6±0.2 0.2±0.0 0.1±0.0 0.9±0.1


turn(%) 61.9±0.6 53.1±0.6 49.9±0.6 52.5±0.6 49.7±0.6 24.5±0.5 40.7±0.6 43.8±0.6 45.8±0.6 38.6±0.6 30.6±0.5 50.6±0.5 63.7±0.6 70.4±0.5 48.4±0.6 49.8±0.6 40.0±0.6 46.4±0.6 30.7±0.5

coil(%) 27.1±0.5 36.5±0.6 47.2±0.6 38.2±0.6 45.5±0.6 67.7±0.6 35.3±0.6 49.6±0.6 37.9±0.6 56.6±0.6 66.3±0.6 11.7±0.3 24.2±0.5 21.8±0.4 41.2±0.6 36.6±0.5 40.7±0.6 50.4±0.6 67.4±0.6

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Table 3: Agreement between experimental and calculated NMR observables for each peptide and force field studied. peptides PepG

PepW

PepI

PepD

PepV

RMSD

force-fields A14SB A99SB A99SB2D A14SB A99SB A99SB2D A14SB A99SB A99SB-ildn A99SB2D A99SB2D -ildn A14SB A99SB A99SB-ildn A99SB2D A99SB2D -ildn A14SB A99SB A99SB2D A14SB A99SB A99SB-ildn A99SB2D A99SB2D -ildn method error

3

JHN HA -coupling (Hz) 5.67±0.02 5.65±0.02 5.49±0.03 6.44±0.03 8.03±0.02 6.37±0.03 6.62±0.03 8.44±0.03 8.03±0.03 6.84±0.03 7.39±0.03 6.22±0.02 7.47±0.03 7.90±0.02 6.44±0.03 6.77±0.03 6.88±0.03 8.55±0.02 7.41±0.03 0.76 0.90 0.95 0.57 0.40 0.39

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Cα chemical shift (ppm) 44.99±0.01 44.96±0.01 44.89±0.01 57.46±0.01 57.06±0.01 57.31±0.01 61.69±0.01 61.06±0.01 61.42±0.01 61.14±0.01 61.09±0.01 55.28±0.01 54.40±0.01 54.07±0.01 54.37±0.01 54.28±0.01 62.82±0.01 62.11±0.01 62.04±0.01 0.79 0.29 0.31 0.26 0.23 0.38



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Figure captions Fig. 1. Radius of gyration (left Panel) and secondary structure distributions (right panel) calculated on the conformational ensembles generated by AMBER14SB (black), AMBER99SB (red), AMBER99SB-ildn (orange), AMBER99SB2D (green), and AMBER99SB2D ildn (blue) force fields. Fig. 2. Time evolution of the backbone RMSD (top panel) from the IA3 and helix fraction (bottom panel) calculated on the conformational ensembles generated by AMBER14SB (black), AMBER99SB (red), AMBER99SB-ildn (orange), AMBER99SB2D (green), and AMBER99SB2D -ildn (blue) force fields. Fig. 3. Fitted free energy landscape in the 2D space of RMSD and Rg and representative structures of the top 4 clusters and their occupations from the trajectory utilizing AMBER14SB (A), AMBER99SB (B), AMBER99SB-ildn (C), AMBER99SB2D (D), and AMBER99SB2D -ildn (E) force field, respectively. Fig. 4. Secondary structure distributions (top panel) calculated on the conformational ensembles and comparison of the secondary chemical shift data of IA3 from experimental measurement under AMBER14SB (black), AMBER99SB (red), AMBER99SB-ildn (orange), AMBER99SB2D (green), and AMBER99SB2D -ildn (blue) force fields.

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1 0.8 0.6 0.4 0.2 0 0.8 0.6 0.4 0.2 0 0.8 0.6 0.4 0.2 0 0.8 0.6 0.4 0.2 0 0.8 0.6 0.4 0.2 0

1 0.8 0.6 0.4 0.2 0 0.8 0.6 0.4 0.2 0 0.8 0.6 0.4 0.2 0.8 0

pepG

pepW

pepI

0.6 pepD

0.4 0.2 0 0.8 0.6 0.4 0.2 0

pepV

3

4

5

6

7

8

9

Radius of gyration (Å)

10

Helix β-Sheet Turn

Figure 1

25


Coil

A14SB A99SB A99SB-ildn 2D

A99SB

2D

A99SB -ildn

Probability

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Probability

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20 A14SB A99SB A99SB-ildn

RMSD (Å)

15

2D

A99SB

2D

A99SB -ildn

10 5 0 1

Helix Fraction

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.8 0.6 0.4 0.2 0

0

200

400

600 Time (ns)

800

Figure 2

26


1000

1200

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 ACS Paragon Plus Environment


Probability

1 0.8

A14SB A99SB A99SB-ildn

0.6

A99SB

2D 2D

A99SB -ildn

0.4 0.2 0

β-Sheet

Helix

Turn

Coil

65 Cα chemical shift (ppm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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60 55 A14SB: RMSD=1.69 A99SB: RMSD=1.17

50

A99SB-ildn: RMSD=0.69 2D

A99SB : RMSD=0.59

45

2D

A99SB -ildn: RMSD=1.38

40

0

5

10

15 Residue Figure 4

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20

25

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Journal of Chemical InformationPage and 30 Modeling of 30

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Evaluation of the Coupled Two-Dimensional Main Chain Torsional

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