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More than a half century ago it became feasible to simulate, using classical-mechanical equations of motion, the dynamics of molecular systems on a computer. Since then classical-physical molecular simulation has become an integral part of chemical research. It is widely applied in a variety of branches of chemistry and has significantly contributed to the development of chemical knowledge. It offers understanding and interpretation of experimental results, semiquantitative predictions for measurable and nonmeasurable properties of substances, and allows the calculation of properties of molecular systems under conditions that are experimentally inaccessible. Yet, molecular simulation is built on a number of assumptions, approximations, and simplifications which limit its range of applicability and its accuracy. These concern the potential-energy function used, adequate sampling of the vast statistical-mechanical configurational space of a molecular system and the methods used to compute particular properties of chemical systems from statistical-mechanical ensembles. During the past half century various methodological ideas to improve the efficiency and accuracy of classical-physical molecular simulation have been proposed, investigated, evaluated, implemented in general simulation software or were abandoned. The latter because of fundamental flaws or, while being physically sound, computational inefficiency. Some of these methodological ideas are briefly reviewed and the most effective methods are highlighted. Limitations of classical-physical simulation are discussed and perspectives are sketched.

The main cysteine protease (M pro ) of coronavirus SARS-CoV-2 has become a promising target for computational development in anti-COVID-19 treatments. Here, we benchmarked the performance of six biomolecular molecular dynamics (MD) force fields (OPLS-AA, CHARMM27, CHARMM36, AMBER03, AMBER14SB and GROMOS G54A7) and three water models (TIP3P, TIP4P and SPC) for reproducing the native fold and the enzymatic activity of M pro as monomeric and dimeric units. The MD sampling up to 1 μs suggested that the proper choice of the force fields and water models plays an essential role in reproducing the tertiary structure and the inter-residue distance between the catalytic dyad His41-Cys145. We found that while most benchmarked all-atom force fields reproduce well the native fold of M pro , the CHARMM27/TIP3P and OPLS-AA/TIP4P setups revealed a good performance in reproducing the structure of the catalytic domain. In addition, these FF setups were also well-adopted for MD sampling of M pro at the physiologic conditions by mimicking the presence of 100 mM NaCl and the elevated temperature of 310 K. Finally, both FFs were also performed well in reproducing the native fold of M pro in a dimeric form. Therefore, comparing the preservation of the native fold of M pro and the stability of its catalytic site architecture, our MD benchmarking suggests that the OPLS-AA/TIP4P and CHARMM27/TIP3P MD setups at the physiologic conditions may be well-suited for rapid in silico screening and developing broad-spectrum anti-coronaviral therapeutic agents., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Damage induced by transient disruption and mechanoporation in an intact cell membrane is a vital nanoscale biomechanical mechanism that critically affects cell viability. To complement experimental studies of mechanical membrane damage and disruption, molecular dynamics (MD) simulations have been performed at different force field resolutions, each of which follows different parameterization strategies and thus may influence the properties and dynamics of membrane systems. Therefore, the current study performed tensile deformation MD simulations of bilayer membranes using all-atom (AA), united-atom (UA), and coarse-grained Martini (CG-M) models to investigate how the damage biomechanics differs across atomistic and coarse-grained (CG) simulations. The mechanical response and mechanoporation damage were qualitatively similar but quantitatively different in the three models, including some progressive changes based on the coarse-graining level. The membranes yielded and reached ultimate strength at similar strains; however, the coarser systems exhibited lower average yield stresses and failure strains. The average failure strain in the UA model was approximately 7% lower than the AA, and the CG-M was 20% lower than UA and 27% lower than AA. The CG systems also nucleated a higher number of pores and larger pores, which resulted in higher damage during the deformation process. Overall, the study provides insight on the impact of force field-a critical factor in modeling biomolecular systems and their interactions-in inspecting membrane mechanosensitive responses and serves as a reference for justifying the appropriate force field for future studies of more complex membranes and more diverse biomolecular assemblies., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The relevance of motions in biological macromolecules has been clear since the early structural analyses of proteins by X-ray crystallography. Computer simulations have been applied to provide a deeper understanding of the dynamics of biological macromolecules since 1976, and are now a standard tool in many labs working on the structure and function of biomolecules. In this mini-review we highlight some areas of current interest and active development for simulations, in particular all-atom molecular dynamics simulations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Charles L. Brooks III reports financial support was provided by NIH grant GM130587. Alexander D MacKerell Jr reports financial support was provided by NIH grant GM131710. Carol B. Post reports financial support was provided by NIH grant RO1GM039478., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Machine learning potentials, particularly the deep potential (DP) model, have revolutionized molecular dynamics (MD) simulations, striking a balance between accuracy and computational efficiency. To facilitate the DP model's integration with the popular MD engine OpenMM, we have developed a versatile OpenMM plugin. This plugin supports a range of applications, from conventional MD simulations to alchemical free energy calculations and hybrid DP/MM simulations. Our extensive validation tests encompassed energy conservation in microcanonical ensemble simulations, fidelity in canonical ensemble generation, and the evaluation of the structural, transport, and thermodynamic properties of bulk water. The introduction of this plugin is expected to significantly expand the application scope of DP models within the MD simulation community, representing a major advancement in the field.

Many self-assembling peptides can form amyloid like structures with different sizes and morphologies. Driven by non-covalent interactions, their aggregation can occur through distinct pathways. Additionally, they can bind metal ions to create enzyme like active sites that allow them to catalyze diverse reactions. Due to the non-crystalline nature of amyloids, it is quite challenging to elucidate their structures using experimental spectroscopic techniques. In this aspect, molecular dynamics (MD) simulations provide a useful tool to derive structures of these macromolecules in solution. They can be further validated by comparing with experimentally measured structural parameters. However, these simulations require a multi-step process starting from the selection of the initial structure to the analysis of MD trajectories. There are multiple force fields, parametrization protocols, equilibration processes, software and analysis tools available for this process. Therefore, it is complicated for non-experts to select the most relevant tools and perform these simulations effectively. In this chapter, a systematic methodology that covers all major aspects of modeling of catalytic peptides is provided in a user-friendly manner. It will be helpful for researchers in this critical area of research., (Copyright © 2024. Published by Elsevier Inc.)

Electron paramagnetic resonance (EPR) has become a powerful probe of conformational heterogeneity and dynamics of biomolecules. In this Review, we discuss different computational modeling techniques that enrich the interpretation of EPR measurements of dynamics or distance restraints. A variety of spin labels are surveyed to provide a background for the discussion of modeling tools. Molecular dynamics (MD) simulations of models containing spin labels provide dynamical properties of biomolecules and their labels. These simulations can be used to predict EPR spectra, sample stable conformations and sample rotameric preferences of label sidechains. For molecular motions longer than milliseconds, enhanced sampling strategies and de novo prediction software incorporating or validated by EPR measurements are able to efficiently refine or predict protein conformations, respectively. To sample large-amplitude conformational transition, a coarse-grained or an atomistic weighted ensemble (WE) strategy can be guided with EPR insights. Looking forward, we anticipate an integrative strategy for efficient sampling of alternate conformations by de novo predictions, followed by validations by systematic EPR measurements and MD simulations. Continuous pathways between alternate states can be further sampled by WE-MD including all intermediate states., (© 2023 Wiley-VCH GmbH.)

The amyloid-forming Aβ peptide is able to interact with metal cations to form very stable complexes that influence fibril formation and contribute to the onset of Alzheimer's disease. Multiple structures of peptides derived from Aβ in complex with different metals have been resolved experimentally to provide an atomic-level description of the metal-protein interactions. However, Aβ is intrinsically disordered, and hence more amenable to an ensemble description. Molecular dynamics simulations can now reach the timescales needed to generate ensembles for these type of complexes. However, this requires accurate force fields both for the protein and the protein-metal interactions. Here we use state-of-the-art methods to generate force field parameters for the Zn(II) cations in a set of complexes formed by different Aβ variants and combine them with the Amber99SB*-ILDN optimized force field. Upon comparison of NMR experiments with the simulation results, further optimized with a Bayesian/Maximum entropy approach, we provide an accurate description of the molecular ensembles for most Aβ-metal complexes. We find that the resulting conformational ensembles are more heterogeneous than the NMR models deposited in the Protein Data Bank., (© 2023 The Authors. Proteins: Structure, Function, and Bioinformatics published by Wiley Periodicals LLC.)

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure-function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Lastly, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs., (© 2023 The Protein Society.)

Molecular dynamics (MD) simulation is used increasingly to explore mechanisms of interactions and conformational relationships between food proteins and other food compounds. MD simulation can better understand the structural changes between food proteins and small molecules at the molecular level. Based on the current extensive literature, visualization models established by MD simulation in the future may provide greater insight into finer molecular changes in food proteins and small molecules interactions. Development and software as well as force fields used in MD simulation for food protein and interaction systems were reviewed. The review focuses on the status of MD simulation technology in exploring food protein functional mechanisms, and bioactive substance delivery, food processing and storage, and food additives. Finally, current trends and applications were considered. This review provides an overview for the wider application of MD simulation technology in the food protein research field., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Recent advances in data science are impacting the development of classical force fields. Here we review some ideas and techniques from data science that have been used in force field development, including database construction, atom typing, and machine learning potentials. We highlight how new tools such as active learning and automatic differentiation are facilitating the generation of target data and the direct fitting with macroscopic observables. Philosophical changes on how force field models should be built and used are also discussed. It's inspiring that more accurate biomolecular force fields can be developed with the aid of data science techniques., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The 3D-reference interaction site model (3D-RISM) molecular solvation theory in combination with the Kovalenko-Hirata (KH) closure is extended to seven heterocyclic liquids to understand their liquid states and to test the performance of the theory in solvation free energy (SFE) calculations of solutes in select solvents. The computed solvent site distribution profiles were compared with the all-atom molecular dynamics (MD) simulations, showing comparable performances. The computational results were compared against the structural parameters for liquids, whenever available, as well as against the experimental SFEs. The liquids are found to have local ordered structures held together via weak interactions in both the RISM and MD simulations. The 3D-RISM-KH computed SFEs are in good agreement with the benchmark values for the tetrahydrothiophene-S,S-dioxide, and showed comparatively larger deviations in the case of the SFEs in the tetrahydrofuran continuum.

Molecular dynamics (MD) simulation has been widely used in the field of biomedicine to study the conformational transition of proteins caused by mutation or ligand binding/unbinding. It provides some perspectives those are difficult to find in traditional biochemical or pathological experiments, for example, detailed effects of mutations on protein structure and protein-protein/ligand interaction at the atomic level. In this review, a broad overview on conformation changes and drug discovery by MD simulation is given. We first discuss the preparation of protein structure for MD simulation, which is a key step that determines the accuracy of the simulation. Then, we summarize the applications of commonly used force fields and MD simulations in scientific research. Finally, enhanced sampling methods and common applications of these methods are introduced. In brief, MD simulation is a powerful tool and it can be used to guide experimental study. The combination of MD simulation and experimental techniques is an a priori means to solve the biomedical problems and give a deep understanding on the relationship between protein structure and function., (© 2022 John Wiley & Sons A/S.)

Molecular mechanics force field calculations have historically shown significant limitations in modeling the energetic and conformational interconversions of highly substituted furanose rings. This is primarily due to the gauche effect that is not easily captured using pairwise energy potentials. In this study, we present a refinement to the set of torsional parameters in the General Amber Force Field ( gaff ) used to calculate the potential energy of mono , di- , and gem -fluorinated nucleosides. The parameters were optimized to reproduce the pseudorotation phase angle and relative energies of a diverse set of mono- and di fluoro substituted furanose ring systems using quantum mechanics umbrella sampling techniques available in the IpolQ engine in the Amber suite of programs. The parameters were developed to be internally consistent with the gaff force field and the TIP3P water model. The new set of angle and dihedral parameters and partial charges were validated by comparing the calculated phase angle probability to those obtained from experimental nuclear magnetic resonance experiments.

While the construction of a dependable force field for performing classical molecular dynamics (MD) simulation is crucial for elucidating the structure and function of biomolecular systems, the attempts to do this for glycans are relatively sparse compared to those for proteins and nucleic acids. Currently, the use of GLYCAM06 force field is the most popular, but there have been a number of concerns about its accuracy in the systematic description of structural changes. In the present work, we focus on the improvement of the GLYCAM06 force field for β-d-glucose, a simple and the most abundant monosaccharide molecule, with the aid of machine learning techniques implemented with the TensorFlow library. Following the pre-sampling over a wide range of configuration space generated by MD simulation, the atomic charge and dihedral angle parameters in the GLYCAM06 force field were re-optimized to accurately reproduce the relative energies of β-d-glucose obtained by the density functional theory (DFT) calculations according to the structural changes. The validation for the newly proposed force-field parameters was then carried out by verifying that the relative energy errors compared to the DFT value were significantly reduced and that some inconsistencies with experimental (e.g., NMR) results observed in the GLYCAM06 force field were resolved relevantly.

The spreading of antibiotic-resistant bacteria strains is one of the most serious problem in medicine to struggle nowadays. This triggered the development of alternative antimicrobial agents in recent years. One of such group is Gemini surfactants which are massively synthesised in various structural configurations to obtain the most effective antibacterial properties. Unfortunately, the comparison of antimicrobial effectiveness among different types of Gemini agents is unfeasible since various protocols for the determination of Minimum Inhibitory Concentration are used. In this work, we proposed alternative, computational, approach for such comparison. We designed a comprehensive database of 250 Gemini surfactants. Description of structure parameters, for instance spacer type and length, are included in the database. We parametrised modelled molecules to obtain force fields for the entire Gemini database. This was used to conduct in silico studies using the molecular dynamics to investigate the incorporation of these agents into model E. coli inner membrane system. We evaluated the effect of Gemini surfactants on structural, stress and mechanical parameters of the membrane after the agent incorporation. This enabled us to select four most likely membrane properties that could correspond to Gemini's antimicrobial effect. Based on our results we selected several types of Gemini spacers which could demonstrate a particularly strong effect on the bacterial membranes.

Organophosphate hydrolases are promising as potential biotherapeutic agents to treat poisoning with pesticides or nerve gases. However, these enzymes often need to be further engineered in order to become useful in practice. One example of such enhancement is the alteration of enantioselectivity of diisopropyl fluorophosphatase (DFPase). Molecular modeling techniques offer a unique opportunity to address this task rationally by providing a physical description of the substrate-binding process. However, DFPase is a metalloenzyme, and correct modeling of metal cations is a challenging task generally coming with a tradeoff between simulation speed and accuracy. Here, we probe several molecular mechanical parameter combinations for their ability to empower long simulations needed to achieve a quantitative description of substrate binding. We demonstrate that a combination of the Amber19sb force field with the recently developed 12-6 Ca 2+ models allows us to both correctly model DFPase and obtain new insights into the DFP binding process.

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.

Molecular dynamics (MD) simulation has proved to be a powerful tool in the study of proteins, nucleic acids, lipids, and carbohydrates et al. in fields of health, nutrition, and food science. In particular, MD simulation has been employed in the investigation of various lipid systems such as triglycerides, phospholipid membranes, etc. Due to the continuous updating of computing resources and the development of new MD simulation methods and force field parameters, the simulation's time and size scale of lipids system has increased by several orders of magnitude. However, MD simulation cannot be used for systems invovle chemical reactions. These greatly limit its further application in the field of lipid research. This paper reviews the progress and development of MD simulation, especially for the application of MD simulation in different lipid systems. In this paper, MD simulation and its general workflow was briefly introduced firstly. Subsequently, the application of MD simulation in various lipid systems was reviewed in-depth. Finally, the limitation and future prospects of MD simulation in lipid research were also discussed. This review provided new insights into the investigation of MD simulation, and a novel thought for lipid study. We believe that MD simulation will exhibit more and more great advantages in the investigation of lipids in the future due to the development of novlel methods., (Copyright © 2021. Published by Elsevier Ltd.)

The MCPB.py program greatly facilitates force field parameterization for metal sites in metalloproteins and organometallic compounds. Herein we present an example of MCPB.py to the parameterization of the dioxygen binding metal site of peptidylglycine-alphahydroxylating monooxygenase (PHM), which contains a copper ion. In this example, we also extend the functionality of MCPB.py to support molecular dynamics (MD) simulations in GROMACS through a python script. Illustrative MD simulations were performed using GROMACS and the results were analyzed. Notes about the program were also provided in this chapter, to assist MCPB.py users for metal site parameterizations.

Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing., Competing Interests: Competing interests: DLM serves on the scientific advisory board for OpenEye Scientific Software and is an Open Science Fellow with Silicon Therapeutics., (Copyright: © 2020 Lim VT et al.)

Glycosaminoglycans (GAGs) are the linear carbohydrate components of proteoglycans (PGs) and are key mediators in the bioactivity of PGs in animal tissue. GAGs are heterogeneous, conformationally complex, and polydisperse, containing up to 200 monosaccharide units. These complexities make studying GAG conformation a challenge for existing experimental and computational methods. We previously described an algorithm we developed that applies conformational parameters (i.e., all bond lengths, bond angles, and dihedral angles) from molecular dynamics (MD) simulations of nonsulfated chondroitin GAG 20-mers to construct 3-D atomic-resolution models of nonsulfated chondroitin GAGs of arbitrary length. In the current study, we applied our algorithm to other GAGs, including hyaluronan and nonsulfated forms of dermatan, keratan, and heparan and expanded our database of MD-generated GAG conformations. Here, we show that individual glycosidic linkages and monosaccharide rings in 10- and 20-mers of hyaluronan and nonsulfated dermatan, keratan, and heparan behave randomly and independently in MD simulation and, therefore, using a database of MD-generated 20-mer conformations, that our algorithm can construct conformational ensembles of 10- and 20-mers of various GAG types that accurately represent the backbone flexibility seen in MD simulations. Furthermore, our algorithm efficiently constructs conformational ensembles of GAG 200-mers that we would reasonably expect from MD simulations., Competing Interests: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Six empirical force fields were tested for applicability to calculations for automated carbohydrate database filling. They were probed on eleven disaccharide molecules containing representative structural features from widespread classes of carbohydrates. The accuracy of each method was queried by predictions of nuclear Overhauser effects (NOEs) from conformational ensembles obtained from 50 to 100 ns molecular dynamics (MD) trajectories and their comparison to the published experimental data. Using various ranking schemes, it was concluded that explicit solvent MM3 MD yielded non-inferior NOE accuracy with newer GLYCAM-06, and ultimately PBE0-D3/def2-TZVP (Triple-Zeta Valence Polarized) Density Functional Theory (DFT) simulations. For seven of eleven molecules, at least one empirical force field with explicit solvent outperformed DFT in NOE prediction. The aggregate of characteristics (accuracy, speed, and compatibility) made MM3 dynamics with explicit solvent at 300 K the most favorable method for bulk generation of disaccharide conformation maps for massive database filling.

This work deals with molecular dynamics analysis of properties of systems composed of carbon nanotubes and short telomeric DNA strands able to fold into i-motif structures at slightly acidic pH conditions. The studies are focused on possible application of such constructs as pH-controlled drug delivery and release systems. We study two different approaches. The first assumes that folding/unfolding property of these DNA strands might realize a gate closing/opening mechanism with carbon nanotube as a container for drug molecules. The second approach assumes that these DNA strands can modulate the drug intercalating property as a function of pH. As a model drug molecule we used doxorubicin. We found that the first approach is impossible to realize because doxorubicin is not effectively locked in the nanotube interior by DNA oligonuceotides. The second approach is more promising though direct drug release was not observed in unbiased molecular dynamics simulations. However, by applying detailed analysis of pair interaction energies, mobilities and potential of mean force we can show that doxorubicin can be released when the DNA strands fold into i-motifs. Carbon nanotube in that latter case acts mainly as a carrier for active phase which is composed of DNA fragments able to fold into noncanonical tetraplexes (i-motif)., Competing Interests: The authors declare no conflict of interest.

Boric acid, borate esters, and hydroxy derivatives are biologically active molecules. Thus, performing molecular dynamics simulations of these molecules is vital in terms of drug design, but it is difficult to find directly generated Amber parameters based on an ab initio method for these kinds of molecules in the literature. In this study, Amber parameters for such molecules containing boron were generated based on ab initio calculations using the paramfit program, which applies a combination of genetic and simplex algorithms, and the Visual Force Field Derivation Toolkit (VFFDT) program containing the Seminario method. The minimized structure, after obtaining novel parameters and the sander program, was compared with the experimental crystallographic structures, and it was observed that the root-mean-square deviation (RMSD) value between the experimental structure and minimized structure agreed reasonably well. In addition, the molecule was heated, and the molecular dynamics simulation was successfully obtained with the novel parameters.

Glycosaminoglycans (GAGs) are linear, structurally diverse, conformationally complex carbohydrate polymers that may contain up to 200 monosaccharides. These characteristics present a challenge for studying GAG conformational thermodynamics at atomic resolution using existing experimental methods. Molecular dynamics (MD) simulations can overcome this challenge but are only feasible for short GAG polymers. To address this problem, we developed an algorithm that applies all conformational parameters contributing to GAG backbone flexibility (i.e., bond lengths, bond angles, and dihedral angles) from unbiased all-atom explicit-solvent MD simulations of short GAG polymers to rapidly construct models of GAGs of arbitrary length. The algorithm was used to generate non-sulfated chondroitin 10- and 20-mer ensembles which were compared to MD-generated ensembles for internal validation. End-to-end distance distributions in constructed and MD-generated ensembles have minimal differences, suggesting that our algorithm produces conformational ensembles that mimic the backbone flexibility seen in simulation. Non-sulfated chondroitin 100- and 200-mer ensembles were constructed within a day, demonstrating the efficiency of the algorithm and reduction in time and computational cost compared to simulation.

Molecular dynamics simulations allow the conformational motion of a molecule such as a protein to be followed over time at atomic-level detail. Several choices need to be made prior to running a simulation, including the software, which molecules to include in the simulation, and the force field used to describe their behavior. Guidance on making these choices and other important aspects of running MD simulations is outlined here.

Classification of the biological activities of chemical substances is important for developing new medicines efficiently. Various machine learning methods are often employed to screen large libraries of compounds and predict the activities of new substances by training the molecular structure-activity relationships. One such method is graph classification, in which a molecular structure can be represented in terms of a labeled graph with nodes that correspond to atoms and edges that correspond to the bonds between these atoms. In a conventional graph definition, atomic symbols and bond orders are employed as node and edge labels, respectively. In this study, we developed new graph definitions using the assignment of atom and bond types in the force fields of molecular dynamics methods as node and edge labels, respectively. We found that these graph definitions improved the accuracies of activity classifications for chemical substances using graph kernels with support vector machines and deep neural networks. The higher accuracies obtained using our proposed definitions can enhance the development of the materials informatics using graph-based machine learning methods., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We present a rare comparison of structures of the same protein but generated by different potentials. We used four popular water potentials (SPC, TIP3P, TIP4P, TIP5P) in conjunction with the equally popular ff99SB. However, the ff12SB protein potential was used with TI3P only. Simulations (60 ns) were run on the catabolite activator protein (CAP), which is a textbook case of allosteric interaction. Overall, all potentials generated largely similar structures but failed to reproduce a crucial structural feature determined by NMR experiment. This example shows the need to develop next-generation potentials. Graphical abstract Catabolite activator protein.

Molecular dynamics simulations can be applied to explore the complex liquid phase behavior of lipid-based formulations and the gastrointestinal tract lumen. In order for the results from these simulations to be of value, the manner in which molecules interact with both aqueous and oil phases present needs to be as correct as possible. An existing molecular dynamics force field, GROMOS 53a6, was demonstrated to poorly reproduce the partitioning of straight-chain alcohol and short-chain polyethylene glycol (PEG) molecules between octanol and water phase (logP), with the molecules too hydrophobic. Force field parameters for Lennard-Jones interactions between CH2 and CH3 with water oxygen were adjusted to reproduce the experimental octanol logP, with all other Lennard-Jones and force field parameters left untouched. This parameter set, called 53a6 DBW , was subsequently used to recalculate straight-chain alcohol and short-chain PEG molecules, with significant improvement in the values obtained. Simulations of a nonionic surfactant in water, octaethylene glycol monocaprylate, were also performed to observe the aggregation behavior. 53a6 DBW demonstrated significant improvements in water interactions with the PEG chains, well hydrating the PEG groups, and allowing the formation of micelles. Further improvements and evaluation of the improved parameter set are ongoing., (Copyright © 2019 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Refinement is the last step in protein structure prediction pipelines to convert approximate homology models to experimental accuracy. Protocols based on molecular dynamics (MD) simulations have shown promise, but current methods are limited to moderate levels of consistent refinement. To explore the energy landscape between homology models and native structures and analyze the challenges of MD-based refinement, eight test cases were studied via extensive simulations followed by Markov state modeling. In all cases, native states were found very close to the experimental structures and at the lowest free energies, but refinement was hindered by a rough energy landscape. Transitions from the homology model to the native states require the crossing of significant kinetic barriers on at least microsecond time scales. A significant energetic driving force toward the native state was lacking until its immediate vicinity, and there was significant sampling of off-pathway states competing for productive refinement. The role of recent force field improvements is discussed and transition paths are analyzed in detail to inform which key transitions have to be overcome to achieve successful refinement., Competing Interests: The authors declare no conflict of interest.

The study on the polymerization of fibrinogen molecules into fibrin monomers and eventually a stable, mechanically robust fibrin clot is a persistent and enduring topic in the field of thrombosis and hemostasis. Despite many research advances in fibrin polymerization, the change in the structure of fibrin clots and its influence on the formation of a fibrous protein network are still poorly understood. In this paper, we develop a new computational method to simulate fibrin clot polymerization using dissipative particle dynamics simulations. With an effective combination of reactive molecular dynamics formularies and many body dissipative particle dynamics principles, we constructed the reactive dissipative particle dynamics (RDPD) model to predict the complex network formation of fibrin clots and branching of the fibrin network. The 340 kDa fibrinogen molecule is converted into a spring-bead coarse-grain system with 11 beads using a topology representing network algorithm, and using RDPD, we simulated polymerization and formation of the fibrin clot. The final polymerized structure of the fibrin clot qualitatively agrees with experimental results from the literature, and to the best of our knowledge this is the first molecular-based study that simulates polymerization and structure of fibrin clots.

Computational modeling involving Quantum Mechanics (QM) and Molecular Mechanics (MM) calculations are widely utilized to unveil the atomic-molecular properties that underpin their inherent characteristic features. The choice over the either of the QM and MM methods or a multiscale composite approach is driven by the target property of interest, and of course, the molecular size. Often, tailor-made schemes need to be devised to match the specific study purpose. Herein, we provide a perspective of these approaches addressing their effectiveness in terms of the delicate balance between the accuracy and computational feasibility. We focus on representative examples to highlight how different approaches can be fruitfully exploited for modeling the conformational landscape, and possibly, the spectroscopic behavior of biochemical molecules, especially amino acids building blocks., (© 2018 Wiley Periodicals, Inc.)

Studies suggest that patients with deep vein thrombosis and diabetes often have hypercoagulable blood plasma, leading to a higher risk of thromboembolism formation through the rupture of blood clots, which may lead to stroke and death. Despite many advances in the field of blood clot formation and thrombosis, the influence of mechanical properties of fibrin in the formation of thromboembolisms in platelet-poor plasma is poorly understood. In this paper, we combine the concepts of reactive molecular dynamics and coarse-grained molecular modeling to predict the complex network formation of fibrin clots and the branching of fibrin monomers. The 340-kDa fibrinogen molecule was converted into a coarse-grained molecule with nine beads, and using our customized reactive potentials, we simulated the formation and polymerization process of a fibrin clot. The results show that higher concentrations of thrombin result in higher branch-point formation in the fibrin clot structure. Our results also highlight many interesting properties, such as the formation of thicker or thinner fibers depending on the thrombin concentration. To the best of our knowledge, this is the first successful molecular polymerization study of fibrin clots to focus on thrombin concentration.

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., Competing Interests: The authors declare no conflict of interest., (Copyright © 2018 the Author(s). Published by PNAS.)

Anesthetic drug molecules are being increasingly studied through the use of computational methods such as molecular dynamics (MD). Molecular mechanics force fields require the investigator to supply parameters for the force field equation, which are not available for novel molecules. Careful selection of these parameters is critical for simulations to produce meaningful results. Therefore, this chapter presents a state-of-the-art method for determining these parameters by comparison to quantum mechanics calculations and experimental quantities. Ketamine is used as an example to demonstrate the process., (© 2018 Elsevier Inc. All rights reserved.)

Molecular dynamics (MD) is a computational technique which is used to study biomolecules in virtual environment. Each of the constituent atoms represents a particle and hence the biomolecule embodies a multi-particle mechanical system analyzed within a simulation box during MD analysis. The potential energies of the atoms are explained by a mathematical expression consisting of different forces and space parameters. There are various software and force fields that have been developed for MD studies of the biomolecules. MD analysis has unravelled the various biological mechanisms (protein folding/unfolding, protein-small molecule interactions, protein-protein interactions, DNA/RNA-protein interactions, proteins embedded in membrane, lipid-lipid interactions, drug transport etc.) operating at the atomic and molecular levels. However, there are still some parameters including torsions in amino acids, carbohydrates (whose structure is extended and not well defined like that of proteins) and single stranded nucleic acids for which the force fields need further improvement, although there are several workers putting in constant efforts in these directions. The existing force fields are not efficient for studying the crowded environment inside the cells, since these interactions involve multiple factors in real time. Therefore, the improved force fields may provide the opportunities for their wider applications on the complex biosystems in diverse cellular conditions. In conclusion, the intervention of MD in the basic sciences involving interdisciplinary approaches will be helpful for understanding many fundamental biological and physiological processes at the molecular levels that may be further applied in various fields including biotechnology, fisheries, sustainable agriculture and biomedical research., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Conventional classical force fields by construction do not explicitly partition intermolecular interactions to include polarization and charge transfer effects, whereas fully quantum mechanical treatments allow a means to effect this dissection (although not uniquely due to the lack of a charge transfer operator). Considering the importance of polarization in a variety of systems, a particular class of classical models, charge equilibration models, have been extensively developed to study those systems; since these types of interaction models are inherently based on movement of charge throughout a system, they are natural platform for including polarization and charge transfer effects within the context of molecular simulations. Here, we present two bond-space charge equilibration models we term as QE2 and mixed QE2 treat charge transfer in classical molecular mechanical calculations those provide practical solutions to two major drawbacks of charge equilibration models: (a) a nonvanishing amount of charge transfer between two heteroatoms at large separations, and (b) superlinear polarizability scaling during bond dissociation due to charge transfer over unphysical, large distances. To control charge transfer during dissociation of a bond in a molecular system, we introduce a distance-dependent scaling function (QE2 model) which, controls and recovers physical behavior of the homonuclear and heteronuclear charge transfer between two atoms at small and large values of internuclear separation; and the mixed QE2 model in which we combine the QE2 model under allow and disallow charge transfer situations that describe both charge transfer and polarizability in a distance-dependent manner. We demonstrate the utility of both models in the case of a water dimer, and compare the results with other existing models, and further, we perform short molecular dynamics simulations for few water clusters with the QE2 model to show the charge transfer and internuclear separation are correlated in dynamics. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

Intrinsically disordered proteins are essential for biological processes such as cell signalling, but are also associated to devastating diseases including Alzheimer's disease, Parkinson's disease or type II diabetes. Because of their lack of a stable three-dimensional structure, molecular dynamics simulations are often used to obtain atomistic details that cannot be observed experimentally. The applicability of molecular dynamics simulations depends on the accuracy of the force field chosen to represent the underlying free energy surface of the system. Here, we use replica exchange molecular dynamics simulations to test five modern force fields, OPLS, AMBER99SB, AMBER99SB*ILDN, AMBER99SBILDN-NMR and CHARMM22*, in their ability to model Aβ 42 , an intrinsically disordered peptide associated with Alzheimer's disease, and compare our results to nuclear magnetic resonance (NMR) experimental data. We observe that all force fields except AMBER99SBILDN-NMR successfully reproduce local NMR observables, with CHARMM22* being slightly better than the other force fields., (© 2016 The Protein Society.)

The absolute binding free energies and binding enthalpies of twelve host-guest systems in the SAMPL5 blind challenge were computed using our attach-pull-release (APR) approach. This method has previously shown good correlations between experimental and calculated binding data in retrospective studies of cucurbit[7]uril (CB7) and β-cyclodextrin (βCD) systems. In the present work, the computed binding free energies for host octa acid (OA or OAH) and tetra-endo-methyl octa-acid (TEMOA or OAMe) with guests are in good agreement with prospective experimental data, with a coefficient of determination (R 2 ) of 0.8 and root-mean-squared error of 1.7 kcal/mol using the TIP3P water model. The binding enthalpy calculations achieve moderate correlations, with R 2 of 0.5 and RMSE of 2.5 kcal/mol, for TIP3P water. Calculations using the newly developed OPC water model also show good performance. Furthermore, the present calculations semi-quantitatively capture the experimental trend of enthalpy-entropy compensation observed, and successfully predict guests with the strongest and weakest binding affinity. The most populated binding poses of all twelve systems, based on clustering analysis of 750 ns molecular dynamics (MD) trajectories, were extracted and analyzed. Computational methods using MD simulations and explicit solvent models in a rigorous statistical thermodynamic framework, like APR, can generate reasonable predictions of binding thermodynamics. Especially with continuing improvement in simulation force fields, such methods hold the promise of making substantial contributions to hit identification and lead optimization in the drug discovery process.

Molecular dynamics (MD) simulations have evolved to an increasingly reliable and accessible technique and are today implemented in many areas of biomedical sciences. We present a generally applicable method to study dehydration of hydrates based on MD simulations and apply this approach to the dehydration of ampicillin trihydrate. The crystallographic unit cell of the trihydrate is used to construct the simulation cell containing 216 ampicillin and 648 water molecules. This system is dehydrated by removing water molecules during a 2200 ps simulation, and depending on the computational dehydration rate, different dehydrated structures were observed. Removing all water molecules immediately and removing water relatively fast (10 water molecules/10 ps) resulted in an amorphous system, whereas relatively slow computational dehydration (3 water molecules/10 ps) resulted in a crystalline anhydrate. The structural changes could be followed in real time, and in addition, an intermediate amorphous phase was identified. The computationally identified dehydrated structure (anhydrate) was slightly different from the experimentally known anhydrate structure suggesting that the simulated computational structure could represent a kinetically trapped dehydration intermediate., (Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Molecular dynamics (MD) simulations have been used as one of the main research tools to study a wide range of biological systems and bridge the gap between X-ray crystallography or NMR structures and biological mechanism. In the field of RNA nanostructures, MD simulations have been used to fix steric clashes in computationally designed RNA nanostructures, characterize the dynamics, and investigate the interaction between RNA and other biomolecules such as delivery agents and membranes.In this chapter we present examples of computational protocols for molecular dynamics simulations in explicit and implicit solvent using the Amber Molecular Dynamics Package. We also show examples of post-simulation analysis steps and briefly mention selected tools beyond the Amber package. Limitations of the methods, tools, and protocols are also discussed. Most of the examples are illustrated for a small RNA duplex (helix), but the protocols are applicable to any nucleic acid structure, subject only to the computational speed and memory limitations of the hardware available to the user.

Specialized to simulate proteins in molecular dynamics (MD) simulations with explicit solvation, FF12MC is a combination of a new protein simulation protocol employing uniformly reduced atomic masses by tenfold and a revised AMBER forcefield FF99 with (i) shortened CH bonds, (ii) removal of torsions involving a nonperipheral sp(3) atom, and (iii) reduced 1-4 interaction scaling factors of torsions ϕ and ψ. This article reports that in multiple, distinct, independent, unrestricted, unbiased, isobaric-isothermal, and classical MD simulations FF12MC can (i) simulate the experimentally observed flipping between left- and right-handed configurations for C14-C38 of BPTI in solution, (ii) autonomously fold chignolin, CLN025, and Trp-cage with folding times that agree with the experimental values, (iii) simulate subsequent unfolding and refolding of these miniproteins, and (iv) achieve a robust Z score of 1.33 for refining protein models TMR01, TMR04, and TMR07. By comparison, the latest general-purpose AMBER forcefield FF14SB locks the C14-C38 bond to the right-handed configuration in solution under the same protein simulation conditions. Statistical survival analysis shows that FF12MC folds chignolin and CLN025 in isobaric-isothermal MD simulations 2-4 times faster than FF14SB under the same protein simulation conditions. These results suggest that FF12MC may be used for protein simulations to study kinetics and thermodynamics of miniprotein folding as well as protein structure and dynamics. Proteins 2016; 84:1490-1516. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc., (© 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.)

Calcium ions (Ca(2+) ) play key roles in various fundamental biological processes such as cell signaling and brain function. Molecular dynamics (MD) simulations have been used to study such interactions, however, the accuracy of the Ca(2+) models provided by the standard MD force fields has not been rigorously tested. Here, we assess the performance of the Ca(2+) models from the most popular classical force fields AMBER and CHARMM by computing the osmotic pressure of model compounds and the free energy of DNA-DNA interactions. In the simulations performed using the two standard models, Ca(2+) ions are seen to form artificial clusters with chloride, acetate, and phosphate species; the osmotic pressure of CaAc2 and CaCl2 solutions is a small fraction of the experimental values for both force fields. Using the standard parameterization of Ca(2+) ions in the simulations of Ca(2+) -mediated DNA-DNA interactions leads to qualitatively wrong outcomes: both AMBER and CHARMM simulations suggest strong inter-DNA attraction whereas, in experiment, DNA molecules repel one another. The artificial attraction of Ca(2+) to DNA phosphate is strong enough to affect the direction of the electric field-driven translocation of DNA through a solid-state nanopore. To address these shortcomings of the standard Ca(2+) model, we introduce a custom model of a hydrated Ca(2+) ion and show that using our model brings the results of the above MD simulations in quantitative agreement with experiment. Our improved model of Ca(2+) can be readily applied to MD simulations of various biomolecular systems, including nucleic acids, proteins and lipid bilayer membranes. © 2016 Wiley Periodicals, Inc. Biopolymers 105: 752-763, 2016., (© 2016 Wiley Periodicals, Inc.)

Molecular dynamics (MD) simulations have become a central tool for investigating various biophysical questions with atomistic detail. While many different proxies are used to qualify MD force fields, most are based on largely structural parameters such as the root mean square deviation from experimental coordinates or nuclear magnetic resonance (NMR) chemical shifts and residual dipolar couplings. NMR derived Lipari-Szabo squared generalized order parameter (O(2) ) values of amide NH bond vectors of the polypeptide chain were also often employed for refinement and validation. However, with a few exceptions, side chain methyl symmetry axis order parameters have not been incorporated into experimental reference sets. Using a test set of five diverse proteins, the performance of several force fields implemented in the NAMDD simulation package was examined. It was found that simulations employing explicit water implemented using the TIP3 model generally performed significantly better than those using implicit water in reproducing experimental methyl symmetry axis O(2) values. Overall the CHARMM27 force field performs nominally better than two implementations of the Amber force field. It appeared that recent quantum mechanics modifications to side chain torsional angles of leucine and isoleucine in the Amber force field have significantly hindered proper motional modeling for these residues. There remained significant room for improvement as even the best correlations of experimental and simulated methyl group Lipari-Szabo generalized order parameters fall below an R(2) of 0.8., (© 2016 The Protein Society.)

Carotenoids are important actors both in light-harvesting (LH) and in photoprotection functions of photosynthetic pigment-protein complexes. A deep theoretical investigation of this multiple role is still missing owing to the difficulty of describing the delicate interplay between electronic and nuclear degrees of freedom. A possible strategy is to combine accurate quantum mechanical (QM) methods with classical molecular dynamics. To do this, however, accurate force-fields (FF) are necessary. This article presents a new FF for the different carotenoids present in LH complexes of plants. The results show that all the important structural properties described by the new FF are in very good agreement with QM reference values. This increased accuracy in the simulation of the structural fluctuations is also reflected in the description of excited states. Both the energy order and the different nature of the lowest singlet states are preserved during the dynamics when the new FF is used, whereas an unphysical mixing is found when a standard FF is used., (© 2016 Wiley Periodicals, Inc.)

Following chemical reactions in atomistic detail is one of the most challenging aspects of current computational approaches to chemistry. In this chapter the application of adiabatic reactive MD (ARMD) and its multistate version (MS-ARMD) are discussed. Both methods allow to study bond-breaking and bond-forming processes in chemical and biological processes. Particular emphasis is put on practical aspects for applying the methods to investigate the dynamics of chemical reactions. The chapter closes with an outlook of possible generalizations of the methods discussed., (© 2016 Elsevier Inc. All rights reserved.)

S-Adenosylmethionine (AdoMet) is involved in many biological processes as cofactor in enzymes transferring its sulfonium methyl group to various substrates. Additionally, it is used as drug and nutritional supplement to reduce the pain in osteoarthritis and against depression. Due to the biological relevance of AdoMet it has been part of various computational simulation studies and will also be in the future. However, to our knowledge no rigorous force field parameter development for its simulation in biological systems has been reported. Here, we use electronic structure calculations combined with molecular dynamics simulations in explicit solvent to develop force field parameters compatible with the AMBER99 force field. Additionally, we propose new dynamic Hirshfeld-I atomic charges which are derived from the polarized electron density of AdoMet in aqueous solution to describe its electrostatic interactions in biological systems. The validation of the force field parameters and the atomic charges is performed against experimental interproton NOE distances of AdoMet in aqueous solution and crystal structures of AdoMet in the cavity of three representative proteins.

Background: Many prokaryotic genomes comprise Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) offering defense against foreign nucleic acids. These immune systems are conditioned by the production of small CRISPR-derived RNAs matured from long RNA precursors. This often requires a Csy4 endoribonuclease cleaving the RNA 3'-end., Methods: We report extended explicit solvent molecular dynamic (MD) simulations of Csy4/RNA complex in precursor and product states, based on X-ray structures of product and inactivated precursor (55 simulations; ~3.7μs in total)., Results: The simulations identify double-protonated His29 and deprotonated terminal phosphate as the likely dominant protonation states consistent with the product structure. We revealed potential substates consistent with Ser148 and His29 acting as the general base and acid, respectively. The Ser148 could be straightforwardly deprotonated through solvent and could without further structural rearrangements deprotonate the nucleophile, contrasting similar studies investigating the general base role of nucleobases in ribozymes. We could not locate geometries consistent with His29 acting as general base. However, we caution that the X-ray structures do not always capture the catalytically active geometries and then the reactive structures may be unreachable by the simulation technique., Conclusions: We identified potential catalytic arrangement of the Csy4/RNA complex but we also report limitations of the simulation technique. Even for the dominant protonation state we could not achieve full agreement between the simulations and the structural data., General Significance: Potential catalytic arrangement of the Csy4/RNA complex is found. Further, we provide unique insights into limitations of simulations of protein/RNA complexes, namely, the influence of the starting experimental structures and force field limitations. This article is part of a Special Issue entitled Recent developments of molecular dynamics., (Copyright © 2014 Elsevier B.V. All rights reserved.)

High resolution protein crystal structures resolved with X-ray diffraction data at cryogenic temperature are commonly used as experimental data to refine forcefields and evaluate protein folding simulations. However, it has been unclear hitherto whether the C-H bond lengths in cryogenic protein structures are significantly different from those defined in forcefields to affect protein folding simulations. This article reports the finding that the C-H bonds in high resolution cryogenic protein structures are 10-14% shorter than those defined in current AMBER forcefields, according to 3709 C-H bonds in the cryogenic protein structures with resolutions of 0.62-0.79 Å. Also, 20 all-atom, isothermal-isobaric, 0.5-μs molecular dynamics simulations showed that chignolin folded from a fully-extended backbone formation to the native β-hairpin conformation in the simulations using AMBER forcefield FF12SB at 300 K with an aggregated native state population including standard error of 10 ± 4%. However, the aggregated native state population with standard error reduced to 3 ± 2% in the same simulations except that C-H bonds were shortened by 10-14%. Furthermore, the aggregated native state populations with standard errors increased to 35 ± 3% and 26 ± 3% when using FF12MC, which is based on AMBER forcefield FF99, with and without the shortened C-H bonds, respectively. These results show that the 10-14% bond length differences can significantly affect protein folding simulations and suggest that re-parameterization of C-H bonds according to the cryogenic structures could improve the ability of a forcefield to fold proteins in molecular dynamics simulations., (Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.)