Force-Field Induced Bias in the Structure of Aβ21-30: A Comparison of OPLS, AMBER, CHARMM, and GROMOS Force Fields

Structural characterization of DNA-binding domain of essential mammalian protein TTF 1

Transcription Termination Factor 1 (TTF1) is a multifunctional mammalian protein with vital roles in various cellular processes, including Pol I-mediated transcription initiation and termination, pre-rRNA processing, chromatin remodelling, DNA damage repair, and polar replication fork arrest. It comprises two distinct functional regions; the N-terminal regulatory region (1-445 aa), and the C-terminal catalytic region (445-859 aa). The Myb domain located at the C-terminal region is a conserved DNA binding domain spanning from 550 to 732 aa (183 residues). Despite its critical role in various cellular processes, the physical structure of TTF1 remains unsolved. Attempts to purify the functional TTF1 protein have been unsuccessful till date. Therefore, we focused on characterizing the Myb domain of this essential protein. We started with predicting a 3-D model of the Myb domain using homology modelling, and ab-initio method. We then determined its stability through MD simulation in an explicit solvent. The model predicted is highly stable, which stabilizes at 200ns. To experimentally validate the computational model, we cloned and expressed the codon optimized Myb domain into a bacterial expression vector and purified the protein to homogeneity. Further, characterization of the protein shows that, Myb domain is predominantly helical (65%) and is alone sufficient to bind the Sal Box DNA. This is the first-ever study to report a complete in silico model of the Myb domain, which is physically characterized. The above study will pave the way towards solving the atomic structure of this essential mammalian protein.

Critical assessment of popular biomolecular force fields for molecular dynamics simulations of folding and enzymatic activity of main protease of coronavirus SARS-CoV-2

The main cysteine protease (Mpro) 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 Mpro 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 Mpro, 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 Mpro 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 Mpro in a dimeric form. Therefore, comparing the preservation of the native fold of Mpro 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.

Tips and Tricks in the Modeling of Supramolecular Peptide Assemblies

Supramolecular peptide assemblies (SPAs) hold promise as materials for nanotechnology and biomedicine. Although their investigation often entails adapting experimental techniques from their protein counterparts, SPAs are fundamentally distinct from proteins, posing unique challenges for their study. Computational methods have emerged as indispensable tools for gaining deeper insights into SPA structures at the molecular level, surpassing the limitations of experimental techniques, and as screening tools to reduce the experimental search space. However, computational studies have grappled with issues stemming from the absence of standardized procedures and relevant crystal structures. Fundamental disparities between SPAs and protein simulations, such as the absence of experimentally validated initial structures and the importance of the simulation size, number of molecules, and concentration, have compounded these challenges. Understanding the roles of various parameters and the capabilities of different models and simulation setups remains an ongoing endeavor. In this review, we aim to provide readers with guidance on the parameters to consider when conducting SPA simulations, elucidating their potential impact on outcomes and validity.

Identification of small molecular inhibitors of SIRT3 by computational and biochemical approaches a potential target of breast cancer

Sirtuin 3 (SIRT3) belongs to the Sirtuin protein family, which consists of NAD+-dependent lysine deacylase, involved in the regulation of various cellular activities. Dysregulation of SIRT3 activity has been linked to several types of cancer, including breast cancer. Because of its ability to stimulate adaptive metabolic pathways, it can aid in the survival and proliferation of breast cancer cells. Finding new chemical compounds targeted towards SIRT3 was the primary goal of the current investigation. Virtual screening of ~ 800 compounds using molecular docking techniques yielded 8 active hits with favorable binding affinities and poses. Docking studies verified that the final eight compounds formed stable contacts with the catalytic domain of SIRT3. Those compounds have good pharmacokinetic/dynamic properties and gastrointestinal absorption. Based on excellent pharmacokinetic and pharmacodynamic properties, two compounds (MI-44 and MI-217) were subjected to MD simulation. Upon drug interaction, molecular dynamics simulations demonstrate mild alterations in the structure of proteins and stability. Binding free energy calculations revealed that compounds MI-44 (- 45.61 ± 0.064 kcal/mol) and MI-217 (- 41.65 ± 0.089 kcal/mol) showed the maximum energy, suggesting an intense preference for the SIRT3 catalytic site for attachment. The in-vitro MTT assay on breast cancer cell line (MDA-MB-231) and an apoptotic assay for these potential compounds (MI-44/MI-217) was also performed, with flow cytometry to determine the compound's ability to cause apoptosis in breast cancer cells. The percentage of apoptotic cells (including early and late apoptotic cells) increased from 1.94% in control to 79.37% for MI-44 and 85.37% for MI-217 at 15 μM. Apoptotic cell death was effectively induced by these two compounds in a flow cytometry assay indicating them as a good inhibitor of human SIRT3. Based on our findings, MI-44 and MI-217 merit additional investigation as possible breast cancer therapeutics.

Predicting anti-COVID-19 potential: in silico analysis of Mauritine compound from Ziziphus-spina christi as a promising papain-like protease (PLpro) inhibitor

The COVID-19 pandemic caused by the SARS-CoV-2 virus, recognized by the World Health Organization (WHO), has led to 164,523,894 confirmed cases and 3,412,032 deaths globally as of May 20, 2021. SARS-CoV-2 encodes crucial proteases for its replication cycle, including the papain-like protease (PLpro), presenting a potential target for developing COVID-19 treatments. Mauritine, a cyclopeptide alkaloid found in the Ziziphus-spina christi plant, exhibits antiviral properties and was investigated for its affinity and toxicity towards PLpro using molecular docking through MGLTools 1.5.6 with Autodock Tools 4.2. Preceding this, toxicity and ADME prediction were performed via Toxtree 3.1.0 software and SwissADME servers. Results from molecular docking revealed free binding energy values of -8.58; -7.73; -8.36; -6.07; -6.67; -7.83; -7.67; -7.40; and -6.87 Kcal/mol for Mauritine-A, Mauritine-B, Mauritine-C, Mauritine-D, Mauritine-F, Mauritine-H, Mauritine-J, Mauritine-L, and Mauritine-M, respectively. Correspondingly, inhibition constants were 0.51724; 2.14; 0.7398; 35.43; 12.95; 1.83; 2.38; 3.80; and 9.17 µM, respectively. Interactions observed included hydrogen bonds, hydrophobic interactions, and electrostatic interactions between the Mauritine compounds and the receptor. Mauritine-A and Mauritine-C emerged as a promising anti-COVID-19 candidate due to its superior affinity compared to other derivatives, as indicated by research findings. Interestingly, Mauritine-A and Mauritine-C exhibits notable stability as depicted by the RMSD and RMSF graphs, along with a considerable MM-PBSA binding free energy value of -162.431 and -137.500 kJ/mol, respectively.Communicated by Ramaswamy H. Sarma.

Identification of new pharmacophore against SARS-CoV-2 spike protein by multi-fold computational and biochemical techniques

COVID-19 appeared as a highly contagious disease after its outbreak in December 2019 by the virus, named SARS-CoV-2. The threat, which originated in Wuhan, China, swiftly became an international emergency. Among different genomic products, spike protein of virus plays a crucial role in the initiation of the infection by binding to the human lung cells, therefore, SARS-CoV-2's spike protein is a promising therapeutic target. Using a combination of a structure-based virtual screening and biochemical assay, this study seeks possible therapeutic candidates that specifically target the viral spike protein. A database of ~ 850 naturally derived compounds was screened against SARS-CoV-2 spike protein to find natural inhibitors. Using virtual screening and inhibitory experiments, we identified acetyl 11-keto-boswellic acid (AKBA) as a promising molecule for spike protein, which encouraged us to scan the rest of AKBA derivatives in our in-house database via 2D-similarity searching. Later 19 compounds with > 85% similarity with AKBA were selected and docked with receptor binding domain (RBD) of spike protein. Those hits declared significant interactions at the RBD interface, best possess and excellent drug-likeness and pharmacokinetics properties with high gastrointestinal absorption (GIA) without toxicity and allergenicity. Our in-silico observations were eventually validated by in vitro bioassay, interestingly, 10 compounds (A3, A4, C3, C6A, C6B, C6C, C6E, C6H, C6I, and C6J) displayed significant inhibitory ability with good percent inhibition (range: > 72-90). The compounds C3 (90.00%), C6E (91.00%), C6C (87.20%), and C6D (86.23%) demonstrated excellent anti-SARS CoV-2 spike protein activities. The docking interaction of high percent inhibition of inhibitor compounds C3 and C6E was confirmed by MD Simulation. In the molecular dynamics simulation, we observed the stable dynamics of spike protein inhibitor complexes and the influence of inhibitor binding on the protein's conformational arrangements. The binding free energy ΔGTOTAL of C3 (-38.0 ± 0.08 kcal/mol) and C6E (-41.98 ± 0.08 kcal/mol) respectively indicate a strong binding affinity to Spike protein active pocket. These findings demonstrate that these molecules particularly inhibit the function of spike protein and, therefore have the potential to be evaluated as drug candidates against SARS-CoV-2.

Molecular Insights into Cu/Zn Metal Response to the Amyloid β-Peptide (1-42)

Aβ1-40 peptide and Aβ1-42 peptide are the building units of beta-amyloid plaques present in Alzheimer's disease (AD)-affected brain. The binding affinity of various divalent metal ions such as Cu and Zn present in AD-affected brain with different amino acids available in Aβ-peptide became the focus to explore their role in soluble neurotoxic oligomer formation. Cu2+ metal ions are known to enhance the neurotoxicity of the Aβ1-42 peptide by catalyzing the formation of soluble neurotoxic oligomers. The competitive preference of both Cu2+ and Zn2+ simultaneously to interact with the Aβ-peptide is unknown. The divalent Cu and Zn ions were inserted in explicit aqueous Aβ1-42 peptide configurations to get insights into the binding competence of these metal ions with peptides using classical molecular dynamics (MD) simulations. The metal-ion interactions reveal that competitive binding preferences of various peptide sites become metal-ion-specific and differ significantly. For Cu2+, interactions are found to be more significant with respect to those of Asp-7, His-6, Glu-11, and His-14. Asp-1, Glu-3, Asp-7, His-6, Glu-11, and His-13 amino acid residues show higher affinity toward Zn2+ ions. MD simulations show notable variation in the solvent-accessible surface area in the hydrophobic region of the peptide. Infinitesimal mobility was obtained for Zn2+ compared to Cu2+ in an aqueous solution and Cu2+ diffusivity deviated significantly at different time scales, proving its labile features in aqueous Aβ1-42 peptides.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Sirtuin 1 inhibition: a promising avenue to suppress cancer progression through small inhibitors design

SIRT1 is a protein associated with vital cell functions such as gene regulation, metabolism, ageing, and cellular energy restoration. Its association with the tumor suppressor protein p53 is essential for controlling the growth of cells, apoptosis, and response to DNA damage. By raising p53 acetylation, encouraging apoptosis, and reducing cell proliferation, inhibiting SIRT1's catalytic domain, which interacts with p53, shows potential as a cancer treatment. The aim of the study is to find compounds that could inhibit SIRT1 and thus lower the proliferation of cancer cells. Employing molecular docking techniques, a virtual screening of ∼900 compounds (isolated from medicinal plants and derivatives) gave us 13 active compounds with good binding affinity. Additional evaluation of pharmacokinetic and pharmacodynamic properties led to the selection of eight compounds with desirable properties. Docking analysis confirmed stable interactions between the final eight compounds (C1-C8) and the SIRT1 catalytic domain. Molecular dynamics simulations show overall stability and moderate changes in protein structure upon compound binding. The compactness of the protein indicated the protein's tight packing upon the inhibitors binding. Binding free energy calculations revealed that compounds C2 (-49.96 ± 0.073 kcal/mol and C1 (-44.79 ± 0.077 kcal/mol) exhibited the highest energy, indicating strong binding affinity to the SIRT1 catalytic domain. These compounds, along with C8, C5, C6, C3, C4 and C7, showed promising potential as SIRT1 inhibitors. Based on their ability to reduce SIRT1 activity and increase apoptosis, the eight chemicals discovered in this work may be useful in treating cancer.Communicated by Ramaswamy H. Sarma.

Molecularly imprinted polymer-based sensors for identification volatile compounds in pharmaceutical products: in silico rational design

The present study aimed to strategically design a Molecularly Imprinted Polymer (MIP) with selective extraction capabilities for volatile compounds found in pork. These specific volatile compounds, such as 3-methyl-1-butanol, 1-nonanal, octanal, hexanal, 2-pentyl-furan, 1-penten-3-one, N-morpholinomethyl-isopropyl-sulfide, methyl butyrate, and (E,E)-2,4-decadienal, are primarily responsible for the distinctive aroma and flavor characteristics associated with pork. Molecular dynamics simulations were employed to investigate the stability of the pre-polymerization system, simulating the interactions between the volatile compounds as templates, 4-hydroxyethyl methacrylate (HEMA) as monomers, and ethylene glycol dimethacrylate (EGDMA) as crosslinkers. Computational simulations revealed that the optimal mole ratio of 1:4:20 for templates, monomers, and crosslinkers resulted in the most favorable functional radial distribution and exhibited the strongest interactions. To validate the computational findings, additional analyses were performed utilizing Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA), radial distribution function (RDF), and hydrogen bond (HBond) occupancy. The calculated binding free energy demonstrated that all template molecules were capable to bind with both the monomers and crosslinkers, including 1-penten-3-one and N-morpholinomethyl-isopropyl-sulfide displaying the strongest interactions, with values of -12,674 kJ/mol and -11,646 kJ/mol, respectively. The congruence between the results obtained from the molecular simulation analyses highlights the crucial role of molecular dynamics simulations in the study and development of MIP for the analysis of marker compounds present in pork.Communicated by Ramaswamy H. Sarma.

PEGylation of Insulin and Lysozyme To Stabilize against Thermal Denaturation: A Molecular Dynamics Simulation Study

Biologic drugs or "biologics" (proteins derived from living organisms) are one of the fastest-growing classes of FDA-approved therapeutics. These compounds are often fragile and require conjugation to polymers for stabilization, with many proteins too ephemeral for therapeutic use. During storage or administration, proteins tend to unravel and lose their secondary structure due to changes in solution temperature, pH, and other external stressors. To enhance their lifetime, protein drugs currently in the market are conjugated with polyethylene glycol (PEG), owing to its ability to increase the stability, solubility, and pharmacokinetics of protein drugs. Here, we perform all-atom molecular dynamics simulations to study the unfolding process of egg-white lysozyme and insulin at elevated temperatures. We test the validity of two force fields─CHARMM36 and Amber ff99SB-ILDN─in the unfolding process. By calculating global and local properties, we capture residues that deteriorate first─these are the "weak links" in the proteins. Next, we conjugate both proteins with PEG and find that PEG preserves the native structure of the proteins at elevated temperatures by blocking water molecules from entering the hydrophobic core, thereby causing the secondary structure to stabilize.

Macromolecular crowding impact on anti-CRISPR AcrIIC3/NmeCas9 complex: Insights from scaled particle theory, molecular dynamics, and elastic networks models

The coupling of Cas9 and its inhibitor AcrIIC3, both from the bacterium Neisseria meningitidis (Nme), form a homodimer of the (NmeCas9/AcrIIC3)₂ type. This coupling was studied to assess the impact of their interaction with the crowders in the following environments: (1) homogeneous crowded, (2) heterogeneous, and (3) microheterogeneous cytoplasmic. For this, statistical thermodynamic models based on the scaled particle theory (SPT) were used, considering the attractive and repulsive protein-crowders contributions and the stability of the formation of spherocylindrical homodimers and the effects of changes in the size of spherical dimers were estimated. Studies based on models of dynamics, elastic networks, and statistical potentials to the formation of complexes NmeCas9/AcrIIC3 using PEG as the crowding agent support the predictions from SPT. Macromolecular crowding stabilizes the formation of the dimers, being more significant when the attractive protein-crowder interactions are weaker and the crowders are smaller. The coupling is favored towards the formation of spherical and compact dimers due to crowding addition (excluded-volume effects) and the thermodynamic stability of the dimers is markedly dependent on the size of the crowders. These results support the experimental mechanistic proposal of inhibition of NmeCas9 mediated by AcrIIC3.

A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water

Computational studies of liquid water and its phase transition into vapor have traditionally been performed using classical water models. Here, we utilize the Deep Potential methodology-a machine learning approach-to study this ubiquitous phase transition, starting from the phase diagram in the liquid-vapor coexistence regime. The machine learning model is trained on ab initio energies and forces based on the SCAN density functional, which has been previously shown to reproduce solid phases and other properties of water. Here, we compute the surface tension, saturation pressure, and enthalpy of vaporization for a range of temperatures spanning from 300 to 600 K and evaluate the Deep Potential model performance against experimental results and the semiempirical TIP4P/2005 classical model. Moreover, by employing the seeding technique, we evaluate the free energy barrier and nucleation rate at negative pressures for the isotherm of 296.4 K. We find that the nucleation rates obtained from the Deep Potential model deviate from those computed for the TIP4P/2005 water model due to an underestimation in the surface tension from the Deep Potential model. From analysis of the seeding simulations, we also evaluate the Tolman length for the Deep Potential water model, which is (0.091 ± 0.008) nm at 296.4 K. Finally, we identify that water molecules display a preferential orientation in the liquid-vapor interface, in which H atoms tend to point toward the vapor phase to maximize the enthalpic gain of interfacial molecules. We find that this behavior is more pronounced for planar interfaces than for the curved interfaces in bubbles. This work represents the first application of Deep Potential models to the study of liquid-vapor coexistence and water cavitation.

Analysis of conformational stability of interacting residues in protein binding interfaces

After approximately 60 years of work, the protein folding problem has recently seen rapid advancement thanks to the inventions of AlphaFold and RoseTTAFold, which are machine-learning algorithms capable of reliably predicting protein structures from their sequences. A key component in their success was the inclusion of pairwise interaction information between residues. As research focus shifts towards developing algorithms to design and engineer binding proteins, it is likely that knowledge of interaction features at protein interfaces can improve predictions. Here, 574 protein complexes were analyzed to identify the stability features of their pairwise interactions, revealing that interactions between pre-stabilized residues are a selected feature in protein binding interfaces. In a retrospective analysis of 475 de novo designed binding proteins with an experimental success rate of 19%, inclusion of pairwise interaction pre-stabilization parameters increased the frequency of identifying experimentally successful binders to 40%.

Modeling structural interconversion in Alzheimers' amyloid beta peptide with classical and intrinsically disordered protein force fields

A comprehensive understanding of the aggregation mechanism in amyloid beta 42 (Aβ42) peptide is imperative for developing therapeutic drugs to prevent or treat Alzheimer's disease. Because of the high flexibility and lack of native tertiary structures of Aβ42, molecular dynamics (MD) simulations may help elucidate the peptide's dynamics with atomic details and collectively improve ensembles not seen in experiments. We applied microsecond-timescale MD simulations to investigate the dynamics and conformational changes of Aβ42 by using a newly developed Amber force field (ff14IDPSFF). We compared the ff14IDPSFF and the regular ff14SB force field by examining the conformational changes of two distinct Aβ42 monomers in explicit solvent. Conformational ensembles obtained by simulations depend on the force field and initial structure, Aβ42^α-helix or Aβ42^β-strand. The ff14IDPSFF sampled a high ratio of disordered structures and diverse β-strand secondary structures; in contrast, ff14SB favored helicity during the Aβ42^α-helix simulations. The conformations obtained from Aβ42^β-strand simulations maintained a balanced content in the disordered and helical structures when simulated by ff14SB, but the conformers clearly favored disordered and β-sheet structures simulated by ff14IDPSFF. The results obtained with ff14IDPSFF qualitatively reproduced the NMR chemical shifts well. In-depth peptide and cluster analysis revealed some characteristic features that may be linked to early onset of the fibril-like structure. The C-terminal region (mainly M35-V40) featured in-registered anti-parallel β-strand (β-hairpin) conformations with tested systems. Our work should expand the knowledge of force field and structure dependency in MD simulations and reveals the underlying structural mechanism-function relationship in Aβ42 peptides. Communicated by Ramaswamy H. Sarma.

Specific Ion and Electric Field Controlled Diverse Ion Distribution and Electroosmotic Transport in a Polyelectrolyte Brush Grafted Nanochannel

Controlling ion distribution inside a charged nanochannel is central to using such channels in diverse applications. Here, we show the possibility of using a charged polyelectrolyte (PE) brush-grafted nanochannel for triggering diverse nanoscopic ion distribution and nanofluidic electroosmotic transport by controlling the valence and size of the counterions (that screen the charges of the PE brushes) and the strength of an externally applied axial electric field. We atomistically simulate separate cases of fully charged polyacrylic acid (PAA) brush functionalized nanochannels with Na⁺, Cs⁺, Ca²⁺, Ba²⁺, and Y³⁺ counterions screening the PE charges. Four key findings emerge from our simulations. First, we find that the counterions with a greater valence and a smaller size prefer to remain localized inside the brush layer. Second, for the case where there is an added chloride salt with the same cation (as the screening counterions), there are more coions (Cl^- ions) in the brush-free bulk than counterions (for counterions Na⁺, Ca²⁺, Ba²⁺, Y³⁺): this is a manifestation of the overscreening (OS) of the PE brush layer. Contrastingly, the number of Cs⁺ ions remain higher than the Cl^- ions inside the brush-free bulk, ensuring that there is no OS effect for this case. Third, large applied electric field enables a few Na⁺, Cs⁺, and Ba²⁺ counterions to leave the brush layer and to go to the bulk: this makes the OS of the PE brush layer disappear for the cases of PE brushes being screened by the Na⁺ and Ba²⁺ ions. On the other hand, no such electric-field-mediated disappearance of OS is observed for the cases of Ca²⁺ and Y³⁺ screening counterions; we attribute this to the firm attachment of these counterions to the negatively charged monomers. Free energy associated with a counterion binding to a PE chain corroborates this diversity in the counterion-specific response to the applied electric field. Finally, we demonstrate that such diverse ion distributions, along with specific electric-field-strength-dependent ion properties, lead to (1) electroosmotic (EOS) transport in nanochannels grafted with PAA brushes screened with Cs⁺ ions to be always counterion dominated, (2) EOS transport in nanochannels grafted with PAA brushes screened with Ca²⁺ and Y³⁺ ions to be always coion-dominated, and (3) EOS transport in nanochannels grafted with PAA brushes screened with Na⁺ and Ba²⁺ ions to be coion dominated for smaller electric fields and counterion dominated for larger electric fields.

Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation

Machine learning is becoming increasingly more important in the field of force field development. Never has it been more vital to have chemically accurate machine learning potentials because force fields become more sophisticated and their applications expand. In this study a method for developing chemically accurate Gaussian process regression models is demonstrated for an increasingly complex set of molecules. This work is an extension to previous work showing the progression of the active learning technique in producing more accurate models in much less CPU time than ever before. The per-atom active learning approach has unlocked the potential to generate chemically accurate models for molecules such as peptide-capped glycine.

Exploiting Sequence-Dependent Rotamer Information in Global Optimization of Proteins

Rotamers, namely amino acid side chain conformations common to many different peptides, can be compiled into libraries. These rotamer libraries are used in protein modeling, where the limited conformational space occupied by amino acid side chains is exploited. Here, we construct a sequence-dependent rotamer library from simulations of all possible tripeptides, which provides rotameric states dependent on adjacent amino acids. We observe significant sensitivity of rotamer populations to sequence and find that the library is successful in locating side chain conformations present in crystal structures. The library is designed for applications with basin-hopping global optimization, where we use it to propose moves in conformational space. The addition of rotamer moves significantly increases the efficiency of protein structure prediction within this framework, and we determine parameters to optimize efficiency.

Allosteric Binding Sites of Ab Peptides on the Acetylcholine Synthesizing Enzyme ChAT as Deduced by In Silico Molecular Modeling

The native function of amyloid-β (Aβ) peptides is still unexplored. However, several recent reports suggest a prominent role of Aβ peptides in acetylcholine homeostasis. To clarify this role of Aβ, we have reported that Aβ peptides at physiological concentrations can directly enhance the catalytic efficiency of the key cholinergic enzyme, choline acetyltransferase (ChAT), via an allosteric interaction. In the current study, we further aimed to elucidate the underlying ChAT-Aβ interaction mechanism using in silico molecular docking and dynamics analysis. Docking analysis suggested two most probable binding clusters on ChAT for Aβ40 and three for Aβ42. Most importantly, the docking results were challenged with molecular dynamic studies of 100 ns long simulation in triplicates (100 ns × 3 = 300 ns) and were analyzed for RMSD, RMSF, RoG, H-bond number and distance, SASA, and secondary structure assessment performed together with principal component analysis and the free-energy landscape diagram, which indicated that the ChAT-Aβ complex system was stable throughout the simulation time period with no abrupt motion during the evolution of the simulation across the triplicates, which also validated the robustness of the simulation study. Finally, the free-energy landscape analysis confirmed the docking results and demonstrated that the ChAT-Aβ complexes were energetically stable despite the unstructured nature of C- and N-terminals in Aβ peptides. Overall, this study supports the reported in vitro findings that Aβ peptides, particularly Aβ42, act as endogenous ChAT-Potentiating-Ligand (CPL), and thereby supports the hypothesis that one of the native biological functions of Aβ peptides is the regulation of acetylcholine homeostasis.

Notch ankyrin domain: evolutionary rise of a thermodynamic sensor

Notch signalling pathway plays a key role in metazoan biology by contributing to resolution of binary decisions in the life cycle of cells during development. Outcomes such as proliferation/differentiation dichotomy are resolved by transcriptional remodelling that follows a switch from Notch^on to Notch^off state, characterised by dissociation of Notch intracellular domain (NICD) from DNA-bound RBPJ. Here we provide evidence that transitioning to the Notch^off state is regulated by heat flux, a phenomenon that aligns resolution of fate dichotomies to mitochondrial activity. A combination of phylogenetic analysis and computational biochemistry was utilised to disclose structural adaptations of Notch1 ankyrin domain that enabled function as a sensor of heat flux. We then employed DNA-based micro-thermography to measure heat flux during brain development, followed by analysis in vitro of the temperature-dependent behaviour of Notch1 in mouse neural progenitor cells. The structural capacity of NICD to operate as a thermodynamic sensor in metazoans stems from characteristic enrichment of charged acidic amino acids in β-hairpins of the ankyrin domain that amplify destabilising inter-residue electrostatic interactions and render the domain thermolabile. The instability emerges upon mitochondrial activity which raises the perinuclear and nuclear temperatures to 50 °C and 39 °C, respectively, leading to destabilization of Notch1 transcriptional complex and transitioning to the Notch^off state. Notch1 functions a metazoan thermodynamic sensor that is switched on by intercellular contacts, inputs heat flux as a proxy for mitochondrial activity in the Notch^on state via the ankyrin domain and is eventually switched off in a temperature-dependent manner.

Structural Ensemble of the Insulin Monomer

Experimental evidence suggests that monomeric insulin exhibits significant conformational heterogeneity, and modifications of apparently disordered regions affect both biological activity and the longevity of pharmaceutical formulations, presumably through receptor binding and fibrillation/degradation, respectively. However, a microscopic understanding of conformational heterogeneity has been lacking. Here, we integrate all-atom molecular dynamics simulations with an analysis pipeline to investigate the structural ensemble of human insulin monomers. We find that 60% of the structures present at least one of the following elements of disorder: melting of the A-chain N-terminal helix, detachment of the B-chain N-terminus, and detachment of the B-chain C-terminus. We also observe partial melting and extension of the B-chain helix and significant conformational heterogeneity in the region containing the B-chain β-turn. We then estimate hydrogen-exchange protection factors for the sampled ensemble and find them in line with experimental results for KP-insulin, although the simulations underestimate the importance of unfolded states. Our results help explain the ready exchange of specific amide sites that appear to be protected in crystal structures. Finally, we discuss the implications for insulin function and stability.

Effect of the Force Field on Molecular Dynamics Simulations of the Multidrug Efflux Protein P-Glycoprotein

Molecular dynamics (MD) simulations have been used extensively to study P-glycoprotein (P-gp), a flexible multidrug transporter that is a key player in the development of multidrug resistance to chemotherapeutics. A substantial body of literature has grown from simulation studies that have employed various simulation conditions and parameters, including AMBER, CHARMM, OPLS, GROMOS, and coarse-grained force fields, drawing conclusions from simulations spanning hundreds of nanoseconds. Each force field is typically parametrized and validated on different data and observables, usually of small molecules and peptides; there have been few comparisons of force field performance on large protein-membrane systems. Here we compare the conformational ensembles of P-gp embedded in a POPC/cholesterol bilayer generated over 500 ns of replicate simulation with five force fields from popular biomolecular families: AMBER 99SB-ILDN, CHARMM 36, OPLS-AA/L, GROMOS 54A7, and MARTINI. We find considerable differences among the ensembles with little conformational overlap, although they correspond to similar extents to structural data obtained from electron paramagnetic resonance and cross-linking studies. Moreover, each trajectory was still sampling new conformations at a high rate after 500 ns of simulation, suggesting the need for more sampling. This work highlights the need to consider known limitations of the force field used (e.g., biases toward certain secondary structures) and the simulation itself (e.g., whether sufficient sampling has been achieved) when interpreting accumulated results of simulation studies of P-gp and other transport proteins.

Energetics and structure of alanine-rich α-helices via adaptive steered molecular dynamics

The energetics and hydrogen bonding profiles of the helix-to-coil transition were found to be an additive property and to increase linearly with chain length, respectively, in alanine-rich α-helical peptides. A model system of polyalanine repeats was used to establish this hypothesis for the energetic trends and hydrogen bonding profiles. Numerical measurements of a synthesized polypeptide Ac-Y(AEAAKA)kF-NH2 and a natural α-helical peptide a2N (1-17) provide evidence of the hypothesis's generality. Adaptive steered molecular dynamics was employed to investigate the mechanical unfolding of all of these alanine-rich polypeptides. We found that the helix-to-coil transition is primarily dependent on the breaking of the intramolecular backbone hydrogen bonds and independent of specific side-chain interactions and chain length. The mechanical unfolding of the α-helical peptides results in a turnover mechanism in which a 310-helical structure forms during the unfolding, remaining at a near constant population and thereby maintaining additivity in the free energy. The intermediate partially unfolded structures exhibited polyproline II helical structure as previously seen by others. In summary, we found that the average force required to pull alanine-rich α-helical peptides in between the endpoints-namely the native structure and free coil-is nearly independent of the length or the specific primary structure.

Polarizable and non-polarizable force fields: Protein folding, unfolding, and misfolding

Molecular dynamics simulations are an invaluable tool to characterize the dynamic motions of proteins in atomistic detail. However, the accuracy of models derived from simulations inevitably relies on the quality of the underlying force field. Here, we present an evaluation of current non-polarizable and polarizable force fields (AMBER ff14SB, CHARMM 36m, GROMOS 54A7, and Drude 2013) based on the long-standing biophysical challenge of protein folding. We quantify the thermodynamics and kinetics of the β-hairpin formation using Markov state models of the fast-folding mini-protein CLN025. Furthermore, we study the (partial) folding dynamics of two more complex systems, a villin headpiece variant and a WW domain. Surprisingly, the polarizable force field in our set, Drude 2013, consistently leads to destabilization of the native state, regardless of the secondary structure element present. All non-polarizable force fields, on the other hand, stably characterize the native state ensembles in most cases even when starting from a partially unfolded conformation. Focusing on CLN025, we find that the conformational space captured with AMBER ff14SB and CHARMM 36m is comparable, but the ensembles from CHARMM 36m simulations are clearly shifted toward disordered conformations. While the AMBER ff14SB ensemble overstabilizes the native fold, CHARMM 36m and GROMOS 54A7 ensembles both agree remarkably well with experimental state populations. In addition, GROMOS 54A7 also reproduces experimental folding times most accurately. Our results further indicate an over-stabilization of helical structures with AMBER ff14SB. Nevertheless, the presented investigations strongly imply that reliable (un)folding dynamics of small proteins can be captured in feasible computational time with current additive force fields.

Overscreening, Co-Ion-Dominated Electroosmosis, and Electric Field Strength Mediated Flow Reversal in Polyelectrolyte Brush Functionalized Nanochannels

Controlling the direction and strength of nanofluidic electrohydrodyanmic transport in the presence of an externally applied electric field is extremely important in a number of nanotechnological applications. Here, we employ all-atom molecular dynamics simulations to discover the possibility of changing the direction of electroosmotic (EOS) liquid flows by merely changing the electric field strength in a nanochannel functionalized with polyelectrolyte (PE) brushes. In exploring this, we have uncovered three facets of nanoconfined PE brush behavior and resulting EOS transport. First, we identify the onset of an overscreening effect: such overscreening refers to the presence of more counterions (Na⁺) within the brush layer than needed to neutralize the negative brush charges. Accordingly, as a consequence of the overscreening, in the bulk liquid outside the brush layer, there is a greater number of co-ions (Cl^-) than counterions in the presence of an added salt (NaCl). Second, this specific ion distribution ensures that the overall EOS flow is along the direction of motion of the co-ions. Such co-ion-dictated EOS transport directly contradicts the notion that EOS flow is always dictated by the motion of the counterions. Finally, for large-enough electric fields, the brush height reduces significantly, causing some of the excess overscreening-inducing counterions to squeeze out of the PE brush layer into the brush-free bulk. As a result, the overscreening effect disappears and the number of co-ions and counterions outside the PE brush layer become similar. Despite that there is an EOS transport, this EOS transport, unlike the standard EOS transport that occurs due to the imbalance of the co-ions and counterions, occurs since a larger residence time of the water molecules in the first solvation shell of the counterions (Na⁺) ensures a water transport in the direction of motion of the counterions. The net effect is the reversal of the direction of the EOS transport by merely changing the strength of the electric field.

Structural and thermodynamic analysis of factors governing the stability and thermal folding/unfolding of SazCA

Molecular basis of protein stability at different temperatures is a fundamental problem in protein science that is substantially far from being accurately and quantitatively solved as it requires an explicit knowledge of the temperature dependence of folding free energy of amino acid residues. In the present study, we attempted to gain insights into the thermodynamic stability of SazCA and its implications on protein folding/unfolding. We report molecular dynamics simulations of water solvated SazCA in a temperature range of 293-393 K to study the relationship between the thermostability and flexibility. Our structural analysis shows that the protein maintains the highest structural stability at 353 K and the protein conformations are highly flexible at temperatures above 353 K. Larger exposure of hydrophobic surface residues to the solvent medium for conformations beyond 353 K were identified from H-bond analysis. Higher number of secondary structure contents exhibited by SazCA at 353 K corroborated the conformations at 353 K to exhibit the highest thermal stability. The analysis of thermodynamics of protein stability revealed that the conformations that denature at higher melting temperatures tend to have greater maximum thermal stability. Our analysis shows that 353 K conformations have the highest melting temperature, which was found to be close to the experimental optimum temperature. The enhanced protein stability at 353 K due the least value of heat capacity at unfolding suggested an increase in folding. Comparative Gibbs free energy analysis and funnel shaped energy landscape confirmed a transition in folding/unfolding pathway of SazCA at 353 K.

Folding and self-assembly of short intrinsically disordered peptides and protein regions

Proteins and peptide fragments are highly relevant building blocks in self-assembly for nanostructures with plenty of applications. Intrinsically disordered proteins (IDPs) and protein regions (IDRs) are defined by the absence of a well-defined secondary structure, yet IDPs/IDRs show a significant biological activity. Experimental techniques and computational modelling procedures for the characterization of IDPs/IDRs are discussed. Directed self-assembly of IDPs/IDRs allows reaching a large variety of nanostructures. Hybrid materials based on the derivatives of IDPs/IDRs show a promising performance as alternative biocides and nanodrugs. Cell mimicking, in vivo compartmentalization, and bone regeneration are demonstrated for IDPs/IDRs in biotechnological applications. The exciting possibilities of IDPs/IDRs in nanotechnology with relevant biological applications are shown.

Physicochemical characterization of paclitaxel prodrugs with cytochrome 3A4 to correlate solubility and bioavailability implementing molecular docking and simulation studies

Prodrugs are biologically inactive drug molecules that may be developed through rational drug design with an objective to improve a drug's pharmaceutical and pharmacokinetic properties. Paclitaxel, a highly potent anticancer drug, is directed against many cancers like breast cancer, ovarian cancer, lung cancer, head and neck tumors, non-small cell lung cancer, and Kaposi's sarcoma, etc. Along with its excellent antitumor activity the drug had a major limitation of low water solubility. To overcome this limitation of this nanomolar active drug many prodrugs were formed in the past. Though increase in the solubility of the drug was obtained but that may or may not account for its increase in bioavailability. CYP3A4 liver enzymes are responsible for the metabolism of fifty percent of the drugs and are major metabolizing enzyme for paclitaxel. Phosphate prodrugs are well known to account the insolubility of many drugs and thus increasing their bioavailability also. In this study, we calculated the ADMET properties of a dataset of twenty phosphate prodrugs of paclitaxel. On the basis of reflection of three favourable properties, ten prodrugs were chosen for further docking studies against CYP3A4. Finally, three prodrugs showing unfavourable binding affinities were selected for Molecular Dynamics Simulations and from this in-silico study we identified that all the three selected prodrugs were unstable as compared to the paclitaxel. The instability of these prodrugs showed their lesser interaction with the CYP3A4 and hence contributing more towards its bioavailability. Thus the three suggested prodrugs those were studied in-silico for oral bioavailability can be further validated for gastrointestinal cancer.Communicated by Ramaswamy H. Sarma.

Including residual contact information into replica-exchange MD simulations significantly enriches native-like conformations

Proteins are complex biomolecules which perform critical tasks in living organisms. Knowledge of a protein's structure is essential for understanding its physiological function in detail. Despite the incredible progress in experimental techniques, protein structure determination is still expensive, time-consuming, and arduous. That is why computer simulations are often used to complement or interpret experimental data. Here, we explore how in silico protein structure determination based on replica-exchange molecular dynamics (REMD) can benefit from including contact information derived from theoretical and experimental sources, such as direct coupling analysis or NMR spectroscopy. To reflect the influence from erroneous and noisy data we probe how false-positive contacts influence the simulated ensemble. Specifically, we integrate varying numbers of randomly selected native and non-native contacts and explore how such a bias can guide simulations towards the native state. We investigate the number of contacts needed for a significant enrichment of native-like conformations and show the capabilities and limitations of this method. Adhering to a threshold of approximately 75% true-positive contacts within a simulation, we obtain an ensemble with native-like conformations of high quality. We find that contact-guided REMD is capable of delivering physically reasonable models of a protein's structure.

Molecular dynamics simulations identify the regions of compromised thermostability in SazCA

The present study examined the structure and dynamics of the most active and thermostable carbonic anhydrase, SazCA, probed using molecular dynamics simulations. The molecular system was described by widely used biological force-fields (AMBER, CHARMM22, CHARMM36, and OPLS-AA) in conjunction with TIP3P water model. The comparison of molecular dynamics simulation results suggested AMBER to be a suitable choice to describe the structure and dynamics of SazCA. In addition to this, we also addressed the effect of temperature on the stability of SazCA. We performed molecular dynamics simulations at 313, 333, 353, 373, and 393 K to study the relationship between thermostability and flexibility in SazCA. The amino acid residues VAL98, ASN99, GLY100, LYS101, GLU145, and HIS207 were identified as the most flexible residues from root-mean-square fluctuations. The salt bridge analysis showed that ion-pairs ASP113-LYS81, ASP115-LYS81, ASP115-LYS114, GLU144-LYS143, and GLU144-LYS206, were responsible for the compromised thermal stability of SazCA.

Assessing the Direct Binding of Ark-Like E3 RING Ligases to Ubiquitin and Its Implication on Their Protein Interaction Network

The ubiquitin pathway required for most proteins’ targeted degradation involves three classes of enzymes: E1-activating enzyme, E2-conjugating enzyme, and E3-ligases. The human Ark2C is the single known E3 ligase that adopts an alternative, Ub-dependent mechanism for the activation of Ub transfer in the pathway. Its RING domain binds both E2-Ub and free Ub with high affinity, resulting in a catalytic active Ub_R-RING-E2-Ub_D complex formation. We examined potential changes in the conformational plasticity of the Ark2C RING domain and its ligands in their complexed form within the ubiquitin pathway through molecular dynamics (MD). Three molecular mechanics force fields compared to previous NMR relaxation studies of RING domain of Arkadia were used for effective and accurate assessment of MDs. Our results suggest the Ark2C Ub-RING docking site has a substantial impact on maintaining the conformational rigidity of E2-E3 assembly, necessary for the E3’s catalytic activity. In the Ub_R-RING-E2-Ub_D catalytic complex, the Ub_R molecule was found to have greater mobility than the other Ub, bound to E2. Furthermore, network-based bioinformatics helped us identify E3 RING ligase candidates which potentially exhibit similar structural modules as Ark2C, along with predicted substrates targeted by the Ub-binding RING Ark2C. Our findings could trigger a further exploration of related unrevealed functions of various other E3 RING ligases.

Experimentally Consistent Simulation of Aβ21-30 Peptides with a Minimal NMR Bias

Misfolded amyloid peptides are neurotoxic molecules associated with Alzheimer's disease. The Aβ_21-30 peptide fragment is a decapeptide fragment of the complete Aβ42 peptide which is a hypothesized cause of Alzheimer's disease via amyloid fibrillogenesis. Aβ_21-30 is investigated here with a combination of NMR (nuclear magnetic resonance) spectroscopy experiments and molecular dynamics simulations with experiment directed simulation (EDS). EDS is a maximum entropy biasing method that augments a molecular dynamics simulation with experimental data (NMR chemical shifts) to improve agreement with experiments and thus accuracy. EDS molecular dynamics shows that the Aβ_21-30 monomer has a β turn stabilized by the following interactions: S26-K28, D23-S26, and D23-K28. NMR, total correlation spectroscopy, and rotating frame Overhauser effect spectroscopy experiments provide independent agreement. Subsequent two- and four-monomer EDS simulations show aggregation. Diffusion coefficients calculated from molecular simulation also agreed with experimentally measured values only after using EDS, providing independent assessment of accuracy. This work demonstrates how accuracy can be improved by directly using experimental data in molecular dynamics of complex processes like self-assembly.

Development of Charge-Augmented Three-Point Water Model (CAIPi3P) for Accurate Simulations of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are molecules without a fixed tertiary structure, exerting crucial roles in cellular signalling, growth and molecular recognition events. Due to their high plasticity, IDPs are very challenging in experimental and computational structural studies. To provide detailed atomic insight in IDPs' dynamics governing their functional mechanisms, all-atom molecular dynamics (MD) simulations are widely employed. However, the current generalist force fields and solvent models are unable to generate satisfactory ensembles for IDPs when compared to existing experimental data. In this work, we present a new solvation model, denoted as the Charge-Augmented Three-Point Water Model for Intrinsically Disordered Proteins (CAIPi3P). CAIPi3P has been generated by performing a systematic scan of atomic partial charges assigned to the widely popular molecular scaffold of the three-point TIP3P water model. We found that explicit solvent MD simulations employing CAIPi3P solvation considerably improved the small-angle X-ray scattering (SAXS) scattering profiles for three different IDPs. Not surprisingly, this improvement was further enhanced by using CAIPi3P water in combination with the protein force field parametrized for IDPs. We also demonstrated the applicability of CAIPi3P to molecular systems containing structured as well as intrinsically disordered regions/domains. Our results highlight the crucial importance of solvent effects for generating molecular ensembles of IDPs which reproduce the experimental data available. Hence, we conclude that our newly developed CAIPi3P solvation model is a valuable tool for molecular simulations of intrinsically disordered proteins and assessing their molecular dynamics.

Molecular insights into the inhibitory mechanisms of gallate moiety on the Aβ1-40 amyloid aggregation: A molecular dynamics simulation study

Alzheimer's disease is the most common form of neurodegenerative disease and the formation of Aβ amyloid aggregates has been widely demonstrated to be the principal cause of Alzheimer's disease. Our previous study and other studies suggested that the gallate moiety played an obligatory role in the inhibition process of naturally occurring polyphenols on Aβ amyloid fibrils formation. However, the detailed mechanisms were still unknown. Thus, in the present study, the gallic acid (GA) was specially selected and the molecular recognition mechanisms between GA molecules and Aβ_1-40 monomer were examined and analyzed by molecular dynamics simulation. The in silico experiments revealed that GA significantly prevented the conformational changes of Aβ_1-40 monomer with no β-sheet structure during the whole 100 ns. By analyzing the binding sites of GA molecules to Aβ_1-40 monomer, we found that both hydrophilic and hydrophobic amino acid residues were participated in the binding of GA molecules to Aβ_1-40 monomer. Moreover, results from the binding free energy analysis further demonstrated that the strength of polar interactions was significantly stronger than that of nonpolar interactions. We believed that our results could help to elucidate the underlying mechanisms of gallate moiety on the anti-amyloidogenic effects of polyphenols at the atomic level.

Recent Results on Computational Molecular Modeling of The Origins of Life

In the last decade of research in the origins of life, there has been an increase in the interest on theoretical molecular modeling methods aimed to improve the accuracy and speed of the algorithms that solve the molecular mechanics and chemical reactions of the matter. Research on the scenarios of prebiotic chemistry has also advanced. The presented work attempts to discuss the latest computational techniques and trends implemented so far. Although it is difficult to cover the full extent of the current publications, we tried to orient the reader into the modern tendencies and challenges faced by those who are in the origins of life field.

Design, synthesis, biological evaluation, common feature pharmacophore model and molecular dynamics simulation studies of ethyl 4-(phenoxymethyl)-2-phenylthiazole-5-carboxylate as Src homology-2 domain containing protein tyrosine phosphatase-2 (SHP2) inhibitors

SHP2 is a non-receptor protein tyrosine phosphatase (PTP) encoded by the PTPN11 gene involved in cell death pathway (PD-1/PD-L1) and cell growth and differentiation pathway (MAPK). Moreover, mutations in SHP2 have been implicated in Leopard syndrome (LS), Noonan syndrome (NS), juvenile myelomonocytic leukemia (JMML) and several types of cancer and solid tumors. Thus, SHP2 inhibitors are much needed reagents for evaluation of SHP2 as a therapeutic target. A series of novel ethyl 4-(phenoxymethyl)-2-phenylthiazole-5-carboxylate derivatives were designed and synthesized, and their SHP2 inhibitory activities (IC₅₀) were determined. Among the desired compounds, 1d shares the highest inhibitory activity (IC₅₀ = 0.99 μM) against SHP2. Additionally, a common feature pharmacophore model was established to explain the structure activity relationship of the desired compounds. Finally, molecular dynamics simulation was carried out to explore the most likely binding mode of compound 1d with SHP2. In brief, the findings reported here may at least provide a new strategy or useful insights in discovering novel effective SHP2 inhibitors.Communicated by Ramaswamy H. Sarma.

Pairwise-additive and polarizable atomistic force fields for molecular dynamics simulations of proteins

Protein force fields have been undergoing continual development since the first complete parameter sets were introduced nearly four decades ago. The functional forms that underlie these models have many common elements for the treatment of bonded and nonbonded forces, which are reviewed here. The most widely used force fields to date use a fixed-charge convention in which electronic polarization effects are treated via a mean-field approximation during partial charge assignment. Despite success in modeling folded proteins over many years, the fixed-charge assumption has limitations that cannot necessarily be overcome within their potential energy equations. To overcome these limitations, several force fields have recently been derived that explicitly treat electronic polarization effects with straightforward extensions of the potential energy functions used by nonpolarizable force fields. Here, we review the history of the most popular nonpolarizable force fields (AMBER, CHARMM, OPLS, and GROMOS) as well as studies that have validated them and applied them to studies of protein folding and misfolding. Building upon these force fields are more recent polarizable interaction potentials, including fluctuating charge models, POSSIM, AMOEBA, and the classical Drude oscillator. These force fields differ in their implementations but all attempt to model electronic polarization in a computationally tractable manner. Despite their recent emergence in the field of protein folding, several studies have already applied these polarizable models to challenging problems in this domain, including the role of polarization in folding free energies and sequence-specific effects on the stability of α-helical structures.

Combining molecular dynamics simulations and experimental analyses in protein misfolding

The fold of a protein determines its function and its misfolding can result in loss-of-function defects. In addition, for certain proteins their misfolding can lead to gain-of-function toxicities resulting in protein misfolding diseases such as Alzheimer's, Parkinson's, or the prion diseases. In all of these diseases one or more proteins misfold and aggregate into disease-specific assemblies, often in the form of fibrillar amyloid deposits. Most, if not all, protein misfolding diseases share a fundamental molecular mechanism that governs the misfolding and subsequent aggregation. A wide variety of experimental methods have contributed to our knowledge about misfolded protein aggregates, some of which are briefly described in this review. The misfolding mechanism itself is difficult to investigate, as the necessary timescale and resolution of the misfolding events often lie outside of the observable parameter space. Molecular dynamics simulations fill this gap by virtue of their intrinsic, molecular perspective and the step-by-step iterative process that forms the basis of the simulations. This review focuses on molecular dynamics simulations and how they combine with experimental analyses to provide detailed insights into protein misfolding and the ensuing diseases.

Characterization of β-turns by electronic circular dichroism spectroscopy: a coupled molecular dynamics and time-dependent density functional theory computational study

Electronic circular dichroism is one of the most used spectroscopic techniques for peptide and protein structural characterization. However, while valuable experimental spectra exist for α-helix, β-sheet and random coil secondary structures, previous studies showed important discrepancies for β-turns, limiting their use as a reference for structural studies. In this paper, we simulated circular dichroism spectra for the best-characterized β-turns in peptides, namely types I, II, I' and II'. In particular, by combining classical molecular dynamics simulations and state-of-the-art quantum time-dependent density functional theory (with the polarizable embedding multiscale model) computations, two common electronic circular dichroism patterns were found for couples of β-turn types (namely, type I/type II' and type II/type I'), at first for a minimal di-peptide model (Ace-Ala-Ala-NHMe), but also for all sequences tested with non-aromatic residues in the central positions. On the other hand, as expected, aromatic substitution causes important perturbations to the previously found ECD patterns. Finally, by applying suitable approximations, these patterns were subsequently rationalized based on the exciton chirality rule. All these results provide useful predictions and pave the way for a possible experimental characterization of β-turns based on circular dichroism spectroscopy.

Atomic Partitioning of the MPn (n = 2, 3, 4) Dynamic Electron Correlation Energy by the Interacting Quantum Atoms Method: A Fast and Accurate Electrostatic Potential Integral Approach

Recently, the quantum topological energy partitioning method called interacting quantum atoms (IQA) has been extended to MPn (n = 2, 3, 4) wave functions. This enables the extraction of chemical insight related to dynamic electron correlation. The large computational expense of the IQA-MPn approach is compensated by the advantages that IQA offers compared to older nontopological energy decomposition schemes. This expense is problematic in the construction of a machine learning training set to create kriging models for topological atoms. However, the algorithm presented here markedly accelerates the calculation of atomically partitioned electron correlation energies. Then again, the algorithm cannot calculate pairwise interatomic energies because it applies analytical integrals over whole space (rather than over atomic volumes). However, these pairwise energies are not needed in the quantum topological force field FFLUX, which only uses the energy of an atom interacting with all remaining atoms of the system that it is part of. Thus, it is now feasible to generate accurate and sizeable training sets at MPn level of theory. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

Intrinsically disordered proteins in various hypotheses on the pathogenesis of Alzheimer's and Parkinson's diseases

Amyloid-β (Aβ) and α-synuclein (αS) are two intrinsically disordered proteins (IDPs) at the centers of the pathogenesis of Alzheimer's and Parkinson's diseases, respectively. Different hypotheses have been proposed for explanation of the molecular mechanisms of the pathogenesis of these two diseases, with these two IDPs being involved in many of these hypotheses. Currently, we do not know, which of these hypothesis is more accurate. Experiments face challenges due to the rapid conformational changes, fast aggregation processes, solvent and paramagnetic effects in studying these two IDPs in detail. Furthermore, pathological modifications impact their structures and energetics. Theoretical studies using computational chemistry and computational biology have been utilized to understand the structures and energetics of Aβ and αS. In this chapter, we introduce Aβ and αS in light of various hypotheses, and discuss different experimental and theoretical techniques that are used to study these two proteins along with their weaknesses and strengths. We suggest that a promising solution for studying Aβ and αS at the center of varying hypotheses could be provided by developing new techniques that link quantum mechanics, statistical mechanics, thermodynamics, bioinformatics to machine learning. Such new developments could also lead to development in experimental techniques.

The influence of water potential in simulation: a catabolite activator protein case study

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.

Computational Insights into Compaction of Gas-Phase Protein and Protein Complex Ions in Native Ion Mobility-Mass Spectrometry

Native ion mobility-mass spectrometry (IM-MS) is a rapidly growing field for studying the composition and structure of biomolecules and biomolecular complexes using gas-phase methods. Typically, ions are formed in native IM-MS using gentle nanoelectrospray ionization conditions, which in many cases can preserve condensed-phase stoichiometry. Although much evidence shows that large-scale condensed-phase structure, such as quaternary structure and topology, can also be preserved, it is less clear to what extent smaller-scale structure is preserved in native IM-MS. This review surveys computational and experimental efforts aimed at characterizing compaction and structural rearrangements of protein and protein complex ions upon transfer to the gas phase. A brief summary of gas-phase compaction results from molecular dynamics simulations using multiple common force fields and a wide variety of protein ions is presented and compared to literature IM-MS data.

Edaravone inhibits the conformational transition of amyloid-β42: insights from molecular dynamics simulations

Previous work has shown that edaravone inhibits fibrillogenesis of amyloid-β protein (Aβ). However, the detailed mechanism by which edaravone inhibits the conformational transition of the Aβ42 monomer is not known at the molecular level. Here, explicit-solvent molecular dynamics (MD) simulations were coupled with molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) method to address the issue. MD simulations confirmed that edaravone inhibits the conformational transition of the Aβ42 monomer in a dose-dependent manner. It was found that the direct interactions between edaravone and Aβ42 are responsible for its inhibiting effects. The analysis of binding free energy using the MM-PBSA method demonstrated that the nonpolar interactions provide favourable contributions (about -71.7 kcal/mol). Conversely, the polar interactions are unfavourable for the binding process. A total of 14 residues were identified as greatly contributing to the binding free energy between edaravone and the Aβ42 monomer. In addition, the intra-peptide hydrophobic interactions were weakened and the salt bridge D23-K28 was interrupted by edaravone. Therefore, the conformational transition was inhibited. Our studies provide molecular-level insights into how edaravone molecules inhibit the conformational transition of the Aβ42 monomer, which may be useful for designing amyloid inhibitors.Communicated by Ramaswamy H. Sarma.

Femtosecond Hydration Map of Intrinsically Disordered α-Synuclein

Protein hydration water plays a fundamentally important role in protein folding, binding, assembly, and function. Little is known about the hydration water in intrinsically disordered proteins that challenge the conventional sequence-structure-function paradigm. Here, by combining experiments and simulations, we show the existence of dynamical heterogeneity of hydration water in an intrinsically disordered presynaptic protein, namely α-synuclein, implicated in Parkinson's disease. We took advantage of nonoccurrence of cysteine in the sequence and incorporated a number of cysteine residues at the N-terminal segment, the central amyloidogenic nonamyloid-β component (NAC) domain, and the C-terminal end of α-synuclein. We then labeled these cysteine variants using environment-sensitive thiol-active fluorophore and monitored the solvation dynamics using femtosecond time-resolved fluorescence. The site-specific femtosecond time-resolved experiments allowed us to construct the hydration map of α-synuclein. Our results show the presence of three dynamically distinct types of water: bulk, hydration, and confined water. The amyloidogenic NAC domain contains dynamically restrained water molecules that are strikingly different from the water molecules present in the other two domains. Atomistic molecular dynamics simulations revealed longer residence times for water molecules near the NAC domain and supported our experimental observations. Additionally, our simulations allowed us to decipher the molecular origin of the dynamical heterogeneity of water in α-synuclein. These simulations captured the quasi-bound water molecules within the NAC domain originating from a complex interplay between the local chain compaction and the sequence composition. Our findings from this synergistic experimental simulation approach suggest longer trapping of interfacial water molecules near the amyloidogenic hotspot that triggers the pathological conversion into amyloids via chain sequestration, chain desolvation, and entropic liberation of ordered water molecules.