scholarly journals Extensive Evaluation of Force Fields for G-Quadruplexes

2021 ◽  
Author(s):  
Na Li ◽  
Tong Zhu
Keyword(s):  
Force Field ◽  
Md Simulation ◽  
Comprehensive Evaluation ◽  
Force Fields ◽  
Md Simulations ◽  
Extensive Evaluation ◽  
Explicit Solvent ◽  
Quantum Chemistry Calculations ◽  
Anti Cancer ◽  
Sticking Point

<p><a></a><a></a><a>G-Quadruplexes</a> (GQs), folded by guanine-rich <a></a><a></a><a>nucleic acid</a> <a></a><a></a><a></a><a>sequences</a>, involve in gene expression processes and closely associated with the formation of tumors. So far, GQ has drawn widespread attention for its notable application of serving as potential anti-cancer target. Recently, theoretical studies for GQs have achieved significant progress, most of which are inseparable from molecular dynamics (MD) simulation. As a necessary tool to explore <a></a><a></a><a>dynamics behavior</a> of molecules, MD simulations strictly depend on force field parameters, which is a sticking point to obtain accurate results. Currently, many force fields for nucleic acids have been developed, but none of them have been accepted widely for GQs. In this paper, we selected five popular force fields, which are parmbsc0, parmbsc1, OL15, Drude2017 and AMOEBANUC17, and conducted explicit-solvent MD simulations on two DNA GQs respectively. We evaluated these force fields from many aspects in detail. Meanwhile, we compared conformational energy using quantum chemistry calculations. With the comprehensive evaluation, Drude2017 achieved better description for GQs, which we suggest that using Drude2017 force field should <a></a><a></a><a>be taken into account</a> first when investigating GQs by MD simulation<a></a><a></a><a></a><a></a><a>.</a></p>

scholarly journals Extensive Evaluation of Force Fields for G-Quadruplexes

2021 ◽  
Author(s):  
Na Li ◽  
Tong Zhu
Keyword(s):  
Force Field ◽  
Md Simulation ◽  
Comprehensive Evaluation ◽  
Force Fields ◽  
Md Simulations ◽  
Extensive Evaluation ◽  
Explicit Solvent ◽  
Quantum Chemistry Calculations ◽  
Anti Cancer ◽  
Sticking Point

<p><a></a><a></a><a>G-Quadruplexes</a> (GQs), folded by guanine-rich <a></a><a></a><a>nucleic acid</a> <a></a><a></a><a></a><a>sequences</a>, involve in gene expression processes and closely associated with the formation of tumors. So far, GQ has drawn widespread attention for its notable application of serving as potential anti-cancer target. Recently, theoretical studies for GQs have achieved significant progress, most of which are inseparable from molecular dynamics (MD) simulation. As a necessary tool to explore <a></a><a></a><a>dynamics behavior</a> of molecules, MD simulations strictly depend on force field parameters, which is a sticking point to obtain accurate results. Currently, many force fields for nucleic acids have been developed, but none of them have been accepted widely for GQs. In this paper, we selected five popular force fields, which are parmbsc0, parmbsc1, OL15, Drude2017 and AMOEBANUC17, and conducted explicit-solvent MD simulations on two DNA GQs respectively. We evaluated these force fields from many aspects in detail. Meanwhile, we compared conformational energy using quantum chemistry calculations. With the comprehensive evaluation, Drude2017 achieved better description for GQs, which we suggest that using Drude2017 force field should <a></a><a></a><a>be taken into account</a> first when investigating GQs by MD simulation<a></a><a></a><a></a><a></a><a>.</a></p>

scholarly journals Developing a molecular dynamics force field for both folded and disordered protein states

2018 ◽  
Vol 115 (21) ◽  
pp. E4758-E4766 ◽  
Author(s):  
Paul Robustelli ◽  
Stefano Piana ◽  
David E. Shaw
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Md Simulation ◽  
State Of The Art ◽  
Force Fields ◽  
Md Simulations ◽  
Disordered Proteins ◽  
Disordered Protein ◽  
Data Points ◽  
Folded Proteins

Molecular dynamics (MD) simulation is a valuable tool for characterizing the structural dynamics of folded proteins and should be similarly applicable to disordered proteins and proteins with both folded and disordered regions. It has been unclear, however, whether any physical model (force field) used in MD simulations accurately describes both folded and disordered proteins. Here, we select a benchmark set of 21 systems, including folded and disordered proteins, simulate these systems with six state-of-the-art force fields, and compare the results to over 9,000 available experimental data points. We find that none of the tested force fields simultaneously provided accurate descriptions of folded proteins, of the dimensions of disordered proteins, and of the secondary structure propensities of disordered proteins. Guided by simulation results on a subset of our benchmark, however, we modified parameters of one force field, achieving excellent agreement with experiment for disordered proteins, while maintaining state-of-the-art accuracy for folded proteins. The resulting force field, a99SB-disp, should thus greatly expand the range of biological systems amenable to MD simulation. A similar approach could be taken to improve other force fields.

scholarly journals RNA force field with accuracy comparable to state-of-the-art protein force fields

2018 ◽  
Vol 115 (7) ◽  
pp. E1346-E1355 ◽  
Author(s):  
Dazhi Tan ◽  
Stefano Piana ◽  
Robert M. Dirks ◽  
David E. Shaw
Keyword(s):  
Force Field ◽  
Md Simulation ◽  
State Of The Art ◽  
Force Fields ◽  
Md Simulations ◽  
Physical Models ◽  
Rna Structures ◽  
Rna Molecules ◽  
Wide Range ◽  
Protein Force Fields

Molecular dynamics (MD) simulation has become a powerful tool for characterizing at an atomic level of detail the conformational changes undergone by proteins. The application of such simulations to RNA structures, however, has proven more challenging, due in large part to the fact that the physical models (“force fields”) available for MD simulations of RNA molecules are substantially less accurate in many respects than those currently available for proteins. Here, we introduce an extensive revision of a widely used RNA force field in which the parameters have been modified, based on quantum mechanical calculations and existing experimental information, to more accurately reflect the fundamental forces that stabilize RNA structures. We evaluate these revised parameters through long-timescale MD simulations of a set of RNA molecules that covers a wide range of structural complexity, including single-stranded RNAs, RNA duplexes, RNA hairpins, and riboswitches. The structural and thermodynamic properties measured in these simulations exhibited dramatically improved agreement with experimentally determined values. Based on the comparisons we performed, this RNA force field appears to achieve a level of accuracy comparable to that of state-of-the-art protein force fields, thus significantly advancing the utility of MD simulation as a tool for elucidating the structural dynamics and function of RNA molecules and RNA-containing biological assemblies.

scholarly journals Different force fields give rise to different amyloid aggregation pathways in molecular dynamics simulations

2020 ◽  
Author(s):  
Suman Samantray ◽  
Feng Yin ◽  
Batuhan Kav ◽  
Birgit Strodel
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Intrinsically Disordered Proteins ◽  
Force Fields ◽  
Md Simulations ◽  
Peptide Sequence ◽  
Disordered Proteins ◽  
Test Case ◽  
Peptide Aggregation ◽  
Intrinsically Disordered

AbstractThe progress towards understanding the molecular basis of Alzheimers’s disease is strongly connected to elucidating the early aggregation events of the amyloid-β (Aβ) peptide. Molecular dynamics (MD) simulations provide a viable technique to study the aggregation of Aβ into oligomers with high spatial and temporal resolution. However, the results of an MD simulation can only be as good as the underlying force field. A recent study by our group showed that none of the force fields tested can distinguish between aggregation-prone and non-aggregating peptide sequences, producing the same and in most cases too fast aggregation kinetics for all peptides. Since then, new force fields specially designed for intrinsically disordered proteins such as Aβ were developed. Here, we assess the applicability of these new force fields to studying peptide aggregation using the Aβ16−22 peptide and mutations of it as test case. We investigate their performance in modeling the monomeric state, the aggregation into oligomers, and the stability of the aggregation end product, i.e., the fibrillar state. A main finding is that changing the force field has a stronger effect on the simulated aggregation pathway than changing the peptide sequence. Also the new force fields are not able to reproduce the experimental aggregation propensity order of the peptides. Dissecting the various energy contributions shows that AMBER99SB-disp overestimates the interactions between the peptides and water, thereby inhibiting peptide aggregation. More promising results are obtained with CHARMM36m and especially its version with increased protein–water interactions. It is thus recommended to use this force field for peptide aggregation simulations and base future reparameterizations on it.

NANOGOLD decorated by pHLIP peptide: comparative force field study

2015 ◽  
Vol 17 (19) ◽  
pp. 12648-12660 ◽  
Author(s):  
A. Kyrychenko
Keyword(s):  
Force Field ◽  
Field Study ◽  
Force Fields ◽  
Md Simulations ◽  
Computational Studies ◽  
Peptide Interactions

Structure of Au135 nanoparticle functionalized by pH low insertion peptide (pHLIP) compared by MD simulations based on six popular biomolecular force fields, suggesting OPLS-AA and CHARMM36 as a tool of choice for the computational studies of NANOGOLD–peptide interactions.

scholarly journals Predicting NMR Relaxation of Proteins from Molecular Dynamics Simulations with Accurate Methyl Rotation Barriers

2020 ◽  
Author(s):  
Falk Hoffmann ◽  
Frans Mulder ◽  
Lars V. Schäfer
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Force Fields ◽  
Nmr Relaxation ◽  
Md Simulations ◽  
Quantitative Agreement ◽  
Relaxation Rates ◽  
Protein Force Fields ◽  
Dynamics Simulations ◽  
Methyl Rotation

The internal dynamics of proteins occurring on time scales from picoseconds to nanoseconds can be sensitively probed by nuclear magnetic resonance (NMR) spin relaxation experiments, as well as by molecular dynamics (MD) simulations. This complementarity offers unique opportunities, provided that the two methods are compared at a suitable level. Recently, several groups have used MD simulations to compute the spectral density of backbone and side-chain molecular motions, and to predict NMR relaxation rates from these. Unfortunately, in the case of methyl groups in protein side-chains, inaccurate energy barriers to methyl rotation were responsible for a systematic discrepancy in the computed relaxation rates, as demonstrated for the AMBER ff99SB*-ILDN force field (and related parameter sets), impairing quantitative agreement between simulations and experiments. However, correspondence could be regained by emending the MD force field with accurate coupled cluster quantum chemical calculations. Spurred by this positive result, we tested whether this approach could be generally applicable, in spite of the fact that different MD force fields employ different water models. Improved methyl group rotation barriers for the CHARMM36 and AMBER ff15ipq protein force fields were derived, such that the NMR relaxation data obtained from the MD simulations now also display very good agreement with experiment. Results herein showcase the performance of present-day MD force fields, and manifest their refined ability to accurately describe internal protein dynamics.

scholarly journals Predicting NMR Relaxation of Proteins from Molecular Dynamics Simulations with Accurate Methyl Rotation Barriers

2019 ◽  
Author(s):  
Falk Hoffmann ◽  
Frans Mulder ◽  
Lars V. Schäfer
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Force Fields ◽  
Nmr Relaxation ◽  
Md Simulations ◽  
Quantitative Agreement ◽  
Relaxation Rates ◽  
Protein Force Fields ◽  
Dynamics Simulations ◽  
Methyl Rotation

The internal dynamics of proteins occurring on time scales from picoseconds to nanoseconds can be sensitively probed by nuclear magnetic resonance (NMR) spin relaxation experiments, as well as by molecular dynamics (MD) simulations. This complementarity offers unique opportunities, provided that the two methods are compared at a suitable level. Recently, several groups have used MD simulations to compute the spectral density of backbone and side-chain molecular motions, and to predict NMR relaxation rates from these. Unfortunately, in the case of methyl groups in protein side-chains, inaccurate energy barriers to methyl rotation were responsible for a systematic discrepancy in the computed relaxation rates, as demonstrated for the AMBER ff99SB*-ILDN force field (and related parameter sets), impairing quantitative agreement between simulations and experiments. However, correspondence could be regained by emending the MD force field with accurate coupled cluster quantum chemical calculations. Spurred by this positive result, we tested whether this approach could be generally applicable, in spite of the fact that different MD force fields employ different water models. Improved methyl group rotation barriers for the CHARMM36 and AMBER ff15ipq protein force fields were derived, such that the NMR relaxation data obtained from the MD simulations now also display very good agreement with experiment. Results herein showcase the performance of present-day MD force fields, and manifest their refined ability to accurately describe internal protein dynamics.

scholarly journals Benchmark Force Fields for the Molecular Dynamic Simulation of G-Quadruplexes

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5379
Author(s):  
Na Li ◽  
Ya Gao ◽  
Feng Qiu ◽  
Tong Zhu
Keyword(s):  
Md Simulation ◽  
Main Chain ◽  
Force Fields ◽  
Hydrogen Bond Network ◽  
Channel Stability ◽  
Anti Cancer ◽  
G Quadruplex ◽  
Overall Performance ◽  
Bond Network ◽  
Function Relationship

G-quadruplexes have drawn widespread attention for serving as a potential anti-cancer target and their application in material science. Molecular dynamics (MD) simulation is the key theoretical tool in the study of GQ’s structure-function relationship. In this article, we systematically benchmarked the five force fields of parmbsc0, parmbsc1, OL15, AMOEBA, and Drude2017 on the MD simulation of G-quadruplex from four aspects: structural stability, central ion channel stability, description of Hoogsteen hydrogen bond network, and description of the main chain dihedral angle. The results show that the overall performance of the Drude force field is the best. Although there may be a certain over-polarization effect, it is still the best choice for the MD simulation of G-quadruplexes.

scholarly journals Predicting NMR Relaxation of Proteins from Molecular Dynamics Simulations with Accurate Methyl Rotation Barriers

2019 ◽  
Author(s):  
Falk Hoffmann ◽  
Frans Mulder ◽  
Lars V. Schäfer
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Force Fields ◽  
Nmr Relaxation ◽  
Md Simulations ◽  
Quantitative Agreement ◽  
Relaxation Rates ◽  
Protein Force Fields ◽  
Dynamics Simulations ◽  
Methyl Rotation

The internal dynamics of proteins occurring on time scales from picoseconds to nanoseconds can be sensitively probed by nuclear magnetic resonance (NMR) spin relaxation experiments, as well as by molecular dynamics (MD) simulations. This complementarity offers unique opportunities, provided that the two methods are compared at a suitable level. Recently, several groups have used MD simulations to compute the spectral density of backbone and side-chain molecular motions, and to predict NMR relaxation rates from these. Unfortunately, in the case of methyl groups in protein side-chains, inaccurate energy barriers to methyl rotation were responsible for a systematic discrepancy in the computed relaxation rates, as demonstrated for the AMBER ff99SB*-ILDN force field (and related parameter sets), impairing quantitative agreement between simulations and experiments. However, correspondence could be regained by emending the MD force field with accurate coupled cluster quantum chemical calculations. Spurred by this positive result, we tested whether this approach could be generally applicable, in spite of the fact that different MD force fields employ different water models. Improved methyl group rotation barriers for the CHARMM36 and AMBER ff15ipq protein force fields were derived, such that the NMR relaxation data obtained from the MD simulations now also display very good agreement with experiment. Results herein showcase the performance of present-day MD force fields, and manifest their refined ability to accurately describe internal protein dynamics.

scholarly journals Identifying the Hot Spot Residues of the SARS-CoV-2 Main Protease Using MM-PBSA and Multiple Force Fields

2021 ◽  
Author(s):  
Jinyoung Byun ◽  
Juyong Lee
Keyword(s):  
Md Simulation ◽  
Force Fields ◽  
Hot Spot ◽  
Md Simulations ◽  
Solvation Energy ◽  
Binding Energies ◽  
Binding Affinities ◽  
Main Protease ◽  
Single Force ◽  
Simulation Time

Abstract In this study, we investigated the binding affinities between the main protease of SARS-CoV-2 virus and its various ligand to identify the hot spot residues of the protease. To investigate the effect of various force fields, we performed MD simulations with three different force fields: GROMOS54a7, Amber99-SB, and CHARMM36. The total amount of MD simulation time was 1.1 µs. To investigate how known ligands interact with Mpro of SARS-CoV-2, the binding affinities were calculated by using the MMPBSA approach. It is identified that no single force field succeeded in predicting the relative rankings of experimental binding affinities. When compared between different force fields, Amber99-SB and GROMOS54a7 results are fairly correlated while CHARMM36 results show weak or almost no correlations with the others. Additionally, we identified specific residues of Mpro, which contribute more importantly to the binding energies with ligands. It is identified that the residues of the S4 subsite of the binding site, N142, M165, and R188, contribute strongly to ligand binding. In addition, the terminal residues, D295, R298, and Q299 are identified to have attractive interactions with ligands via electrostatic and solvation energy. We believe that our findings will help facilitate develop novel inhibitors of SARS-CoV-2.

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