Free Energy Predictions of Ligand Binding to an α-Helix Using Steered Molecular Dynamics and Umbrella Sampling Simulations

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Fabrication of phosphatidylcholine-EGCG nanoparticles with sustained release in simulated gastrointestinal digestion and their transcellular permeability in a Caco-2 monolayer model

In this study, we prepared phosphatidylcholine (PC)-EGCG complex nanoparticles (P-E NPs) by solvent reflux method. The physicochemical properties, in vitro digestion, uptake in Caco-2 cells, and bidirectional permeability of P-E NPs were systematically investigated. The constructed P-E1.5:1 NPs had an average particle size of 118 nm, a ζ-potential of -37.8 mV, and a polymerization dispersion index (PDI) of 0.16. The encapsulation efficiency (EE) of EGCG was 85.0% and the loading capacity (LC) was 24.4%. UV spectra, FTIR, XRD and intermolecular force results indicated that hydrophobic, electrostatic and hydrogen bonding interactions contributed to formate P-E1.5:1 NPs. P-E1.5:1 NPs exhibited first-order kinetics sustained release properties in simulated gastrointestinal digestion. Furthermore, P-E1.5:1 NPs were able to enhance absorptive transport and inhibit efflux transport mediated by MRP2 and P-gp compared to EGCG. These results indicated that P-E1.5:1 NPs may be a potential strategy to ameliorate EGCG bioavailability.

Steered Molecular Dynamics Simulations Study on FABP4 Inhibitors

Ordinary small molecule de novo drug design is time-consuming and expensive. Recently, computational tools were employed and proved their efficacy in accelerating the overall drug design process. Molecular dynamics (MD) simulations and a derivative of MD, steered molecular dynamics (SMD), turned out to be promising rational drug design tools. In this paper, we report the first application of SMD to evaluate the binding properties of small molecules toward FABP4, considering our recent interest in inhibiting fatty acid binding protein 4 (FABP4). FABP4 inhibitors (FABP4is) are small molecules of therapeutic interest, and ongoing clinical studies indicate that they are promising for treating cancer and other diseases such as metabolic syndrome and diabetes.

Enhanced sampling and free energy calculations for protein simulations

Molecular dynamics simulation is a powerful computational technique to study biomolecular systems, which complements experiments by providing insights into the structural dynamics relevant to biological functions at atomic scale. It can also be used to calculate the free energy landscapes of the conformational transitions to better understand the functions of the biomolecules. However, the sampling of biomolecular configurations is limited by the free energy barriers that need to be overcome, leading to considerable gaps between the timescales reached by MD simulation and those governing biological processes. To address this issue, many enhanced sampling methodologies have been developed to increase the sampling efficiency of molecular dynamics simulations and free energy calculations. Usually, enhanced sampling algorithms can be classified into methods based on collective variables (CV-based) and approaches which do not require predefined CVs (CV-free). In this chapter, the theoretical basis of free energy estimation is briefly reviewed first, followed by the reviews of the most common CV-based and CV-free methods including the presentation of some examples and recent developments. Finally, the combination of different enhanced sampling methods is discussed.

Calculating the absolute binding free energy of the insulin dimer in an explicit solvent

Insulin is a significant hormone in the regulation of glucose level in the blood. Its monomers bind to each other to form dimers or hexamers through a complex process. To study the binding of the insulin dimer, we first calculate its absolute binding free energy by the steered molecular dynamics method and the confinement method based on a fictitious thermodynamic cycle. After considering some special correction terms, the final calculated binding free energy at 298 K is −8.97 ± 1.41 kcal mol⁻¹, which is close to the experimental value of −7.2 ± 0.8 kcal mol⁻¹. Furthermore, we discuss the important residue–residue interactions between the insulin monomers, including hydrophobic interactions, π–π interactions and hydrogen bond interactions. The analysis reveals five key residues, Vla^B12, Tyr^B16, Phe^B24, Phe^B25, and Tyr^B26, for the dimerization of the insulin. We also perform MM-PBSA calculations for the wild-type dimer and some mutants and study the roles of the key residues by the change of the binding energy of the insulin dimer.

In this paper, we calculate the absolute binding free energy of an insulin dimer by steered MD method. The result of −8.97 kcal mol⁻¹ is close to the experimental value −7.2 kcal mol⁻¹. We also analyze the residue–residue interactions.

In Silico Prediction of the Thermodynamic Equilibrium of Solute Partition in Multiphase Complex Fluids: A Case Study of Oil-Water Microemulsion

Multiphase complex fluids such as micelles, microemulsions, and dispersions are ubiquitous in product formulations of foods, pharmaceuticals, cosmetics, and fine chemicals. Quantifying how active solutes partition in the microstructure of such multiphase fluids is necessary for designing formulations that can optimally deliver the benefits of functional actives. In this paper, we at first predict the structure of a heptane/butanol/sodium dodecyl sulfate droplet in water that self-assembled to form a microemulsion through the molecular dynamics (MD) simulation and subsequently investigate the thermodynamic equilibrium of solute partitioning using COSMOmic. To our knowledge, this is the first time that the MD/COSMOmic approach is used for predicting solute partitioning in a microemulsion. The predicted partition coefficients are compared to experimental values derived from retention measurements of the same microemulsion. We show that the experimental data of droplet-water partition coefficients (K_droplet/w) can be reliably predicted by the method that combines MD simulations with COSMOmic.

Skin toxicology and 3Rs-Current challenges for public health protection

Driven by the fast paced development of complex test systems in vitro, mass spectrometry and omics, we finally have the tools to unravel the molecular events that underlie toxicological adversity. Yet, timely regulatory adaptation of these new tools continues to pose major challenges even for organs readily accessible such as skin. The reasons for this encompass a need for conservatism as well as the need of tests to serve an existing regulatory framework rather than to produce scientific knowledge. It is important to be aware of this in order to align regulatory skin toxicity with the 3R principles more readily. While most chemical safety testing is still based on animal data, regulatory frameworks have seen a strong push towards non-animal approaches. The endpoints corrosion, irritation, sensitisation, absorption and phototoxicity, for example, can now be covered in vitro with the corresponding test guidelines (TGs) being made available by the OECD. However, in vitro approaches tend to be more reductionist. Hence, a combination of several tests is usually preferable to achieve satisfying predictivity. Moreover, the test systems and their combined use need to be standardised and are therefore subject not only to validation but also to the ongoing development of so-called integrated approaches to testing and assessment (IATAs). Concomitantly, skin models are being refined to deliver the complexity required for increased applicability and predictivity. Given the importance of regulatory applicability for 3R-derived approaches to have a long-lasting impact, this review examines the state of regulatory implementation and perspectives, respectively.

Catechol-cation adhesion on silica surfaces: molecular dynamics simulations

Understanding the interaction mechanism between catechol-cation and inorganic surfaces is vital for controlling the interfacial adhesion behavior. In this work, molecular dynamics simulations are employed to study the adhesion of siderophore analogues (Tren-Lys-Cam, Tren-Arg-Cam and Tren-His-Cam) on silica surfaces with different degrees of ionization and the effects of cationic amino acids and ionic strength on adhesion are discussed. Simulation results indicate that adhesion of catechol-cation onto the ionized silica surface is dominated by electrostatic interactions. At different degrees of ionization, the rank of the adhesions of three siderophore analogues on silica is different. Further analysis shows that the amino acid terminus has a large influence on the adhesion process, especially histidine adhesion on negatively charged surfaces. Tren-Lys-Cam (TLC) has a larger adhesion free energy than Tren-Arg-Cam (TAC) at a higher degree of ionization (18%); both the bulkier structure and delocalized charge of Arg decreased the cation's electrostatic interaction with the charged silica. In addition, the adhesion free energy on ionized silica surfaces decreased with increasing ionic strength of aqueous solutions. A linear correlation between the potential of mean force obtained from umbrella sampling and the rupture force via steered molecular dynamics simulations for siderophore analogue adhesion on silica surfaces is also found. This work may provide some guidance for developing the next generation underwater adhesives.

Affinity enhancement of nanobody binding to EGFR: in silico site-directed mutagenesis and molecular dynamics simulation approaches

Epidermal growth factor receptor (EGFR), a transmembrane glycoprotein, is overexpressed in many cancers such as head-neck, breast, prostate, and skin cancers for this reason it is a good target in cancer therapy and diagnosis. In nanobody-based cancer diagnosis and treatment, nanobodies with high affinity toward receptor (e.g. EGFR) results in effective treatment or diagnosis of cancer. In this regard, the main aim of this study is to develop a method based on molecular dynamic (MD) simulations for designing of 7D12 based nanobody with high affinity compared with wild-type nanobody. By surveying electrostatic and desolvation interactions between different residues of 7D12 and EGFR, the critical residues of 7D12 that play the main role in the binding of 7D12 to EGFR were elucidated and based on these residues, five logical variants were designed. Following the 50 ns MD simulations, pull and umbrella sampling simulation were performed for 7D12 and all its variants in complex with EGFR. Binding free energy of 7D12 (and all its variants) with EGFR was obtained by weighted histogram analysis method. According to binding free energy results, GLY101 to GLU mutation showed the highest binding affinity but this variant is unstable after 50 ns MD simulations. ALA100 to GLU mutation shows suitable binding enhancement with acceptable structural stability. Suitable position and orientation of GLU in residue 100 of 7D12 against related amino acids of EGFR formed some extra hydrogen and electrostatic interactions which resulted in binding enhancement.

A New Method for Navigating Optimal Direction for Pulling Ligand from Binding Pocket: Application to Ranking Binding Affinity by Steered Molecular Dynamics

In this paper we present a new method for finding the optimal path for pulling a ligand from the binding pocket using steered molecular dynamics (SMD). Scoring function is defined as the steric hindrance caused by a receptor to ligand movement. Then the optimal path corresponds to the minimum of this scoring function. We call the new method MSH (Minimal Steric Hindrance). Contrary to existing navigation methods, our approach takes into account the geometry of the ligand while other methods including CAVER only consider the ligand as a sphere with a given radius. Using three different target + receptor sets, we have shown that the rupture force Fmax and nonequilibrium work Wpull obtained based on the MSH method show a much higher correlation with experimental data on binding free energies compared to CAVER. Furthermore, Wpull was found to be a better indicator for binding affinity than Fmax. Thus, the new MSH method is a reliable tool for obtaining the best direction for ligand exiting from the binding site. Its combination with the standard SMD technique can provide reasonable results for ranking binding affinities using Wpull as a scoring function.

Exploration of the chlorpyrifos escape pathway from acylpeptide hydrolases using steered molecular dynamics simulations

Acylpeptide hydrolases (APH) catalyze the removal of an N-acylated amino acid from blocked peptides. APH is significantly more sensitive than acetylcholinesterase, a target of Alzheimer's disease, to inhibition by organophosphorus (OP) compounds. Thus, OP compounds can be used as a tool to probe the physiological functions of APH. Here, we report the results of a computational study of molecular dynamics simulations of APH bound to the OP compounds and an exploration of the chlorpyrifos escape pathway using steered molecular dynamics (SMD) simulations. In addition, we apply SMD simulations to identify potential escape routes of chlorpyrifos from hydrolase hydrophobic cavities in the APH-inhibitor complex. Two previously proposed APH pathways were reliably identified by CAVER 3.0, with the estimated relative importance of P1 > P2 for its size. We identify the major pathway, P2, using SMD simulations, and Arg526, Glu88, Gly86, and Asn65 are identified as important residues for the ligand leaving via P2. These results may help in the design of APH-targeting drugs with improved efficacy, as well as in understanding APH selectivity of the inhibitor binding in the prolyl oligopeptidase family.

Molecular dynamics simulations of acylpeptide hydrolase bound to chlorpyrifosmethyl oxon and dichlorvos

Acylpeptide hydrolases (APHs) catalyze the removal of N-acylated amino acids from blocked peptides. Like other prolyloligopeptidase (POP) family members, APHs are believed to be important targets for drug design. To date, the binding pose of organophosphorus (OP) compounds of APH, as well as the different OP compounds binding and inducing conformational changes in two domains, namely, α/β hydrolase and β-propeller, remain poorly understood. We report a computational study of APH bound to chlorpyrifosmethyl oxon and dichlorvos. In our docking study, Val471 and Gly368 are important residues for chlorpyrifosmethyl oxon and dichlorvos binding. Molecular dynamics simulations were also performed to explore the conformational changes between the chlorpyrifosmethyl oxon and dichlorvos bound to APH, which indicated that the structural feature of chlorpyrifosmethyl oxon binding in APH permitted partial opening of the β-propeller fold and allowed the chlorpyrifosmethyl oxon to easily enter the catalytic site. These results may facilitate the design of APH-targeting drugs with improved efficacy.