We describe an interactive module that can be used to teach basic concepts in electrochemistry and thermodynamics to first year natural science students. The module is used together with an experimental laboratory and improves the students’ understanding of thermodynamic quantities such as Δ_rG, Δ_rH, and Δ_rS that are calculated but not directly measured in the lab. We also discuss how new technologies can substitute some parts of experimental chemistry courses, and improve accessibility to course material. Cloud computing platforms such as CoCalc facilitate the distribution of computer codes and allow students to access and apply interactive course tools beyond the course's scope. Despite some limitations imposed by cloud computing, the students appreciated the approach and the enhanced opportunities to discuss study questions with their classmates and instructor as facilitated by the interactive tools. 

Zinc plays important roles in structural stabilization of proteins, eniyine catalysis, and signal transduction. Many Zn binding sites are located at the interface between the protein and the cellular fluid. In aqueous solutions, Zn ions adopt an octahedral coordination, while in proteins zinc can have different coordinations, with a tetrahedral conformation found most frequently. The dynainics of Zn binding to proteins and the formation of complexes that involve Zn are dictated by interactions between Zn and its binding partners. We calculated the interaction energies between Zn and its ligands in complexes that mimic protein binding sites and in Zn complexes of water and one or two amino acid moieties, using quantum mechanics (QM) and molecular mechanics (MM). It was found that MM calculations that neglect or only approximate polarizability did not reproduce even the relative order of the QM interaction energies in these complexes. Interaction energies calculated with the CHARMM-Diode polarizable force field agreed better with the ab initio results,:although the deviations between QM and MM were still rather large (40-96 kcallmol). In order to gain further insight into Zn ligand interactions, the free energies of interaction were estimated by QM calculations with continuum solvent representation, and we performed energy decomposition analysis calculations to examine the characteristics of the different complexes. The ligand-types were found to have high impact on the relative strength of polarization and electrostatic interactions. Interestingly, ligand ligand interactions did not play a significant role in the binding of Zn. Finally) analysis of ligand exchange energies suggests that carboxylates could be exchanged with water molecules, which explains the flexibility in Zn:binding dynamics. An exchange between earboxylate (Asp/Glii) and imidazole (His) is less likely.

Interactions between the group XII metals Zn²⁺ and Cd²⁺ and amino acid residues play an important role in biology due to the prevalence of the first and the toxicity of the second. Estimates of the interaction energies between the ions and relevant residues in proteins are however difficult to obtain. This study reports on calculated interaction energy curves for small complexes of Zn²⁺ or Cd²⁺ and amino acid mimics (acetate, methanethiolate, and imidazole) or water. Given that many applications and models (e.g., force fields, solvation models, etc.) begin with and rely on an accurate description of gas-phase interaction energies, this is where our focus lies in this study. Four density functional theory (DFT)-functionals and MP2 were used to calculate the interaction energies not only at the respective equilibrium distances but also at a relevant range of ion–ligand separation distances. The calculated values were compared with those obtained by CCSD(T). All DFT-methods are found to overestimate the magnitude of the interaction energy compared to the CCSD(T) reference values. The deviation was analyzed in terms of energy components from localized molecular orbital energy decomposition analysis scheme and is mostly attributed to overestimation of the polarization energy. MP2 shows good agreement with CCSD(T) [root mean square error (RMSE) = 1.2 kcal/mol] for the eight studied complexes at equilibrium distance. Dispersion energy differences at longer separation give rise to increased deviations between MP2 and CCSD(T) (RMSE = 6.4 kcal/mol at 3.0 Å). Overall, the results call for caution in applying DFT methods to metalloprotein model complexes even with closed-shell metal ions such as Zn²⁺ and Cd²⁺, in particular at ion–ligand separations that are longer than the equilibrium distances.

When proteins are solvated in electrolyte solutions that contain alkali ions, the ions interact mostlywith carboxylates on the protein surface. Correctly accounting for alkali-carboxylate interactionsis thus important for realistic simulations of proteins. Acetates are the simplest carboxylates thatare amphipathic, and experimental data for alkali acetate solutions are available and can be comparedwith observables obtained from simulations. We carried out molecular dynamics simulations of alkali acetate solutions using polarizable and non-polarizable forcefields and examined the ionacetateinteractions. In particular, activity coefficients and association constants were studied in a range of concentrations (0.03, 0.1, and 1M). In addition, quantum-mechanics (QM) based energy decomposition analysis was performed in order to estimate the contribution of polarization, electrostatics, dispersion, and QM (non-classical) effects on the cation-acetate and cation-water interactions. Simulations of Li-acetate solutions in general overestimated the binding of Li⁺ and acetates. In lower concentrations, the activity coefficients of alkali-acetate solutions were too high, which is suggested to be due to the simulation protocol and not the forcefields. Energy decomposition analysis suggested that improvement of the forcefield parameters to enable accurate simulations of Li-acetate solution scan be achieved but may require the use of a polarizable forcefield. Importantly, simulations with some ion parameters could not reproduce the correct ion-oxygen distances, which calls for caution in thechoice of ion parameters when protein simulations are performed in electrolyte solutions.

Specific interactions that depend on the nature of electrolytes are observed when proteins and other molecules are studied by potentiometric, spectroscopic and theoretical methods at high salt concentrations. More recently, it became clear that such interactions may also be observed in solutions that can be described by the Debye-Hückel theory, i.e., at physiological (0.1 mol dm⁻³) and lower concentrations. We carried out molecular dynamics simulations of bovine serum albumin in physiological solutions at T=300 and 350 K. Analysis of the simulations revealed some differences between LiCl solutions and those of NaCl and KCl. The binding of Li⁺ ions to the protein was associated with a negative free energy of interaction whereas much fewer Na⁺ and K⁺ ions were associated with the protein surface. Interestingly, unlike other proteins BSA does not show a preference to Na⁺ over K⁺. Quantum chemical calculations identified a significant contribution from polarisation to the hydration of Li⁺ and (to a lesser degree) Na⁺, which may indicate that polarisable force-fields will provide more accurate results for such systems.

Extremely acidophilic microorganisms (optimum growth pH of ≤3) maintain a near neutral cytoplasmic pH via several homeostatic mechanisms, including an inside positive membrane potential created by potassium ions. Transcriptomic responses to pH stress in the thermoacidophilic archaeon, Sulfolobus acidocaldarius were investigated by growing cells without added sodium and/or potassium ions at both optimal and sub-optimal pH. Culturing the cells in the absence of added sodium or potassium ions resulted in a reduced growth rate compared to full-salt conditions as well as 43 and 75 significantly different RNA transcript ratios, respectively. Differentially expressed RNA transcripts during growth in the absence of added sodium ions included genes coding for permeases, a sodium/proline transporter and electron transport proteins. In contrast, culturing without added potassium ions resulted in higher RNA transcripts for similar genes as a lack of sodium ions plus genes related to spermidine that has a general role in response to stress and a decarboxylase that potentially consumes protons. The greatest RNA transcript response occurred when S. acidocaldarius cells were grown in the absence of potassium and/or sodium at a sub-optimal pH. These adaptations included those listed above plus osmoregulated glucans and mechanosensitive channels that have previously been shown to respond to osmotic stress. In addition, data analyses revealed two co-expressed IclR family transcriptional regulator genes with a previously unknown role in the S. acidocaldarius pH stress response. Our study provides additional evidence towards the importance of potassium in acidophile growth at acidic pH.

Background

Mutations in the FMS-like tyrosine kinase 3 (FLT3) are associated with uncontrolled cellular functions that contribute to the development of acute myeloid leukaemia (AML). We performed computer simulations of the FLT3-dependent signalling network in order to study the pathways that are involved in AML development and resistance to targeted therapies.

Results

Analysis of the simulations revealed the presence of alternative pathways through phosphoinositide 3 kinase (PI3K) and SH2-containing sequence proteins (SHC), that could overcome inhibition of FLT3. Inhibition of cyclin dependent kinase 6 (CDK6), a related molecular target, was also tested in the simulation but was not found to yield sufficient benefits alone.

Conclusions

The PI3K pathway provided a basis for resistance to treatments. Alternative signalling pathways could not, however, restore cancer growth signals (proliferation and loss of apoptosis) to the same levels as prior to treatment, which may explain why FLT3 resistance mutations are the most common resistance mechanism. Finally, sensitivity analysis suggested the existence of optimal doses of FLT3 and CDK6 inhibitors in terms of efficacy and toxicity.

Background: Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge. It consists of reverse engineering gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through computational methods. This approach allows to identify signalling pathways involved in specific biological functions. The ability to infer causality in gene regulatory networks, in addition to correlation, is crucial for several modelling approaches and allows targeted control in biotechnology applications. Methods: We performed simulations according to the approximate Bayesian computation method, where the core model consisted of a steady-state simulation algorithm used to study gene regulatory networks in systems for which a limited level of details is available. The simulations outcome was compared to experimentally measured transcriptomics and proteomics data through approximate Bayesian computation. Results: The structure of small gene regulatory networks responsible for the regulation of biological functions involved in biomining were inferred from multi OMICs data of mixed bacterial cultures. Several causal inter- and intraspecies interactions were inferred between genes coding for proteins involved in the biomining process, such as heavy metal transport, DNA damage, replication and repair, and membrane biogenesis. The method also provided indications for the role of several uncharacterized proteins by the inferred connection in their network context. Conclusions: The combination of fast algorithms with high-performance computing allowed the simulation of a multitude of gene regulatory networks and their comparison to experimentally measured OMICs data through approximate Bayesian computation, enabling the probabilistic inference of causality in gene regulatory networks of a multispecies bacterial system involved in biomining without need of single-cell or multiple perturbation experiments. This information can be used to influence biological functions and control specific processes in biotechnology applications.

Background: The smoothened (SMO) receptor is an essential component of the Sonic hedgehog (SHH) signalling, which is associated with the development of skin basal cell carcinoma (BCC). SMO inhibitors are indicated for BCC patients when surgical treatment or radiation therapy are not possible. Unfortunately, SMO inhibitors are not always well tolerated due to severe side effects, and their therapeutical success is limited by resistance mutations. Methods: We investigated how common are resistance-causing mutations in two genomic databases which are not linked to BCC or other cancers, namely 1000 Genomes and ExAC. To examine the potential for combination therapy or other treatments, we further performed knowledge-based simulations of SHH signalling, in the presence or absence of SMO and PI3K/Akt inhibitors. Results: The database analysis revealed that of 18 known mutations associated with Vismodegib-resistance, three were identified in the databases. Treatment of individuals carrying such mutations is thus liable to fail a priori. Analysis of the simulations suggested that a combined inhibition of SMO and the PI3K/Akt signalling pathway may provide an effective reduction in tumour proliferation. However, the inhibition dosage of SMO and PI3K/Akt depended on the activity of phosphodiesterases (PDEs). Under high PDEs activities, SMO became the most important control node of the network. By applying PDEs inhibition, the control potential of SMO decreased and P13K appeared as a significant factor in controlling tumour proliferation. Conclusions: Our systems biology approach employs knowledge-based computer simulations to help interpret the large amount of data available in public databases, and provides application-oriented solutions for improved cancer resistance treatments.

A large subset of anaplastic large cell lymphoma (ALCL) patients harbour a somatic aberration in which anaplastic lymphoma kinase (ALK) is fused to nucleophosmin (NPM) resulting in a constitutively active signalling fusion protein, NPM-ALK. We computationally simulated the signalling network which mediates pathological cell survival and proliferation through NPM-ALK to identify therapeutically targetable nodes through which it may be possible to regain control of the tumourigenic process. The simulations reveal the predominant role of the VAV1-CDC42 (cell division control protein 42) pathway in NPM-ALK-driven cellular proliferation and of the Ras / mitogen-activated ERK kinase (MEK) / extracellular signal-regulated kinase (ERK) cascade in controlling cell survival. Our results also highlight the importance of a group of interleukins together with the Janus kinase 3 (JAK3) / signal transducer and activator of transcription 3 (STAT3) signalling in the development of NPM-ALK derived ALCL. Depending on the activity of JAK3 and STAT3, the system may also be sensitive to activation of protein tyrosine phosphatase-1 (SHP1), which has an inhibitory effect on cell survival and proliferation. The identification of signalling pathways active in tumourigenic processes is of fundamental importance for effective therapies. The prediction of alternative pathways that circumvent classical therapeutic targets opens the way to preventive approaches for countering the emergence of cancer resistance.

Characterising signal transduction networks is fundamental to our understanding of biology. However, redundancy and different types of feedback mechanisms make it difficult to understand how variations of the network components contribute to a biological process. In silico modelling of signalling interactions therefore becomes increasingly useful for the development of successful therapeutic approaches. Unfortunately, quantitative information cannot be obtained for all of the proteins or complexes that comprise the network, which limits the usability of computational models. We developed a flexible computational framework for the analysis of biological signalling networks. We demonstrate our approach by studying the mechanism of metastasis promotion by the S100A4 protein, and suggest therapeutic strategies. The advantage of the proposed method is that only limited information (interaction type between species) is required to set up a steady-state network model. This permits a straightforward integration of experimental information where the lack of details are compensated by efficient sampling of the parameter space. We investigated regulatory properties of the S100A4 network and the role of different key components. The results show that S100A4 enhances the activity of matrix metalloproteinases (MMPs), causing higher cell dissociation. Moreover, it leads to an increased stability of the pathological state. Thus, avoiding metastasis in S100A4-expressing tumours requires multiple target inhibition. Moreover, the analysis could explain the previous failure of MMP inhibitors in clinical trials. Finally, our method is applicable to a wide range of biological questions that can be represented as directional networks.

The calcium-binding signalling protein S100A4 enhances metastasis in a variety of cancers. Despite a wealth of data available, the molecular mechanism by which S100A4 drives metastasis is unknown. Integration of the current knowledge defies straightforward intuitive interpretation and requires computer-aided approaches to represent the complexity emerging from cross-regulating species. Here we carried out a systematic sensitivity analysis of the S100A4 signalling network in order to identify key control parameters for efficient therapeutic intervention. Our approach only requires limited details of the molecular interactions and permits a straightforward integration of the available experimental information. By integrating the available knowledge, we investigated the effects of combined inhibition of signalling pathways. Through selective knockout or inhibition of the network components, we show that the interaction between epidermal growth factor receptor (EGFR) and S100A4 modulates the sensitivity of angiogenesis development to matrix metalloproteinases (MMPs) activity. We also show that, in cells that express high EGFR, MMP inhibitors are not expected to be useful in tumours if high activity of S100A4 is present.

A k-nearest neighbor (k-NN) classification model was constructed for 118 RDT NEDO (Repeated Dose Toxicity New Energy and industrial technology Development Organization; currently known as the Hazard Evaluation Support System (HESS)) database chemicals, employing two acute toxicity (LD50)-based classes as a response and using a series of eight PaDEL software-derived fingerprints as predictor variables. A model developed using Estate type fingerprints correctly predicted the LD50 classes for 70 of 94 training set chemicals and 19 of 24 test set chemicals. An individual category was formed for each of the chemicals by extracting its corresponding k-analogs that were identified by k-NN classification. These categories were used to perform the read-across study for prediction of the chronic toxicity, i.e., Lowest Observed Effect Levels (LOEL). We have successfully predicted the LOELs of 54 of 70 training set chemicals (77%) and 14 of 19 test set chemicals (74%) to within an order of magnitude from their experimental LOEL values. Given the success thus far, we conclude that if the k-NN model predicts LD50classes correctly for a certain chemical, then the k-analogs of such a chemical can be successfully used for data gap filling for the LOEL. This model should support the in silico prediction of repeated dose toxicity.

Membrane proteins mediate processes that are fundamental for the flourishing of biological cells. Membrane-embedded transporters move ions and larger solutes across membranes; receptors mediate communication between the cell and its environment and membrane-embedded enzymes catalyze chemical reactions. Understanding these mechanisms of action requires knowledge of how the proteins couple to their fluid, hydrated lipid membrane environment. We present here current studies in computational and experimental membrane protein biophysics, and show how they address outstanding challenges in understanding the complex environmental effects on the structure, function, and dynamics of membrane proteins.

Drug resistance is a serious problem in cancer, viral, bacterial, fungal and parasitic diseases. Examination of crystal structures of protein-drug complexes is often not enough to explain why a certain mutation leads to drug resistance. As an example, the crystal structure of the kinase inhibitor dasatinib bound to the Abl1 kinase shows a hydrogen bond between the drug and residue Thr(315)and very few contacts between the drug and residues Val(299)and Phe(317), yet mutations in those residues lead to drug resistance. In the first case, it is tempting to suggest that the loss of a hydrogen bond leads to drug resistance, whereas in the other two cases it is not known why mutations lead to drug resistance in the first place. We carried out extensive molecular dynamics (MD) simulations and free energy calculations to explain drug resistance to dasatinib from a molecular point of view and show that resistance is due to a multitude of subtle effects. Importantly, our calculations could reproduce the experimental values for the binding energies upon mutations in all three cases and shed light on their origin.

Gilteritinib is a highly selective and effective inhibitor of the FLT3/ITD mutated protein, and is used successfully in treating acute myeloid leukaemia (AML). Unfortunately, tumour cells gradually develop resistance to gilteritinib due to mutations in the molecular drug target. The atomistic details behind this observed resistance are not clear, since the protein structure of the complex is only available in the inactive state, while the drug binds better to the active state. To overcome this limitation, we used a computer-aided approach where we docked gilteritinib to the active site of FLT3/ITD and calculated the Gibbs free energy difference between the binding energies of the parental and mutant enzymes. These calculations agreed with experimental estimations for one mutation (F691L) but not the other (D698N). To further understand how these mutations operate, we used metadynamics simulations to study the conformational landscape of the activation process. Both mutants show a lower activation energy barrier which suggests that they are more likely to adopt an active state until inhibited, making the mutant enzymes more active. This suggests that a higher efficiency of tyrosine kinases contributes to resistance not only against type 2 but also against type 1 kinase inhibitors.

Human serum albumin (HSA) is the most prominent protein in blood plasma, responsible for the maintenance of blood viscosity and transport of endogenous and exogenous molecules. Fatty acids (FA) are the most common ligands of HSA and their binding can modify the protein's structure. The protein can assume two well-defined conformations, referred to as 'Neutral' and 'Basic'. The Neutral (N) state occurs at pH close to 7.0 and in the absence of bound FA. The Basic (B) state occurs at pH higher than 8.0 or when the protein is bound to long-chain FA. HSA's allosteric behaviour is dependent on the number on FA bound to the structure. However, the mechanism of this allosteric regulation is not clear. To understand how albumin changes its conformation, we compared a series of HSA structures deposited in the protein data bank to identify the minimum amount of FA bound to albumin, which is enough to drive the allosteric transition. Thereafter, non-biased molecular dynamics (MD) simulations were used to track protein's dynamics. Surprisingly, running an ensemble of relatively short MD simulations, we observed rapid transition from the B to the N state. These simulations revealed differences in the mobilities of the protein's subdomains, with one domain unable to fully complete its transition. To track the transition dynamics in full, we used these results to choose good geometrical collective variables for running metadynamics simulations. The metadynamics calculations showed that there was a low energy barrier for the transition from the B to the N state, while a higher energy barrier was observed for the N to the B transition. These calculations also offered valuable insights into the transition process. Human serum albumin (HSA) is an allosteric protein that can change conformation state through low energy barriers, being the most prominent protein in blood plasma, responsible for the maintenance of blood viscosity and transport of endogenous and exogenous molecules.

Amyloid-related diseases are a group of illnesses in which an abnormal accumulation of proteins into fibrillar structures is evident. Results from a wide range of studies, ranging from identification of amyloid-β dimers in the brain to biophysical characterization of the interactions between amyloidogenic peptides and lipid membranes during fibril growth shed light on the initial events which take place during amyloid aggregation. Accounts of fibril disaggregation and formation of globular aggregates due to interactions with lipids or fatty acids further demonstrate the complexity of the aggregation process and the difficulty to treat amyloid-related diseases. There is an inherent difficulty in generalizing from studies of aggregation in vitro, but the involvement of too many cellular components limits the ability to follow amyloid aggregation in a cellular (or extracellular) context. Fortunately, the development of experimental methods to generate stable globular aggregates suggests new means of studying the molecular events associated with amyloid aggregation. Furthermore, simulation studies enable deeper understanding of the experimental results and provide useful predictions that can be tested in the laboratory. Computer simulations can nowadays provide molecular or even atomistic details that are experimentally not available or very difficult to obtain. In the present review, recent developments on modelling and experiments of amyloid aggregation are reviewed, and an integrative account on how isolated interactions (as observed in vitro and in silico) combine during the course of amyloid-related diseases is presented. Finally, it is argued that an integrative approach is necessary to get a better understanding of the protein aggregation process.

Mutations that lead to drug resistance limit the efficacy of antibiotics, antiviral drugs, targeted cancer therapies, and other treatments. Accurately calculating protein–drug binding affinity changes upon mutations in the drug target is of high interest as this can yield a better understanding into how such mutations drive drug-resistance, especially when the mutation in question does not directly interfere with binding of the drug. The main aim of this article is to provide an up-to-date reference on the computational tools that are available for the calculation of Gibbs energy (free energy) changes upon mutation, their strengths, and limitations. The methods that are discussed include free energy calculations (free energy perturbation, thermodynamic integration, multistate Bennett acceptance ratio), analysis of molecular dynamics simulations (linear interaction energy, molecular mechanics [MM]/Poisson–Boltzmann solvated area, and MM/generalized Born solvated area), and methods that involve quantum mechanical calculations (including QM/MM). The possibility to use machine learning is also introduced. Given that the benefit of accurately calculating binding affinity changes upon mutation depends on comparing calculated values with experimental measurements, a brief survey on experimental methods and observables is provided. Examples of computational studies that go beyond calculating the Gibbs energy changes are given. Factors that need to be addressed by the computational chemist and potential pitfalls are discussed at length.

Targeted therapies have revolutionized cancer treatment. Unfortunately, their success is limited due to the development of drug resistance within the tumor, which is an evolutionary process. Understanding how drug resistance evolves is a prerequisite to a better success of targeted therapies. Resistance is usually explained as a response to evolutionary pressure imposed by treatment. Thus, evolutionary understanding can and should be used in the design and treatment of cancer. In this article, drug-resistance to targeted therapies is reviewed from an evolutionary standpoint. The concept of apoptosis-induced compensatory proliferation (AICP) is developed. It is shown that AICP helps to explain some of the phenomena that are observed experimentally in cancers. Finally, potential drug targets are suggested in light of AICP.

Several tumor types are sensitive to deactivation of just one or very few genes that are constantly active in the cancer cells,a phenomenon that is termed oncogene addiction'. Drugs that target the products of those oncogenes can yield a temporary relief, and even complete remission. Unfortunately, many patients receiving oncogene-targeted therapies relapse on treatment. This often happens due to somatic mutations in the oncogene (resistance mutations"). 'Compound mutations', which in the context of cancer drug resistance are defined as two or more mutations of the drug target in the same clone may lead to enhanced resistance against the most selective inhibitors. Here, it is shown that the vast majority the resistance mutations occurring in cancer patients treated with tyrosin kinase inhibitors aimed at three different proteins follow an evolutionary pathway. Using bioinforrnatic analysis tools, found that the drug-resistance mutations in the tyrosine kinase domains of Abl1, ALK and exons 20 and 21 of EGFR favour transformations to residues that can be identified in similar positions in evolutionary related proteins. The results demonstrate that evolutionary pressure shapes the mutational landscape in the case of drug-resistance somatic mutations. The constraints on the mutational landscape suggest that it may be possible to counter single drug-resistance point mutations. The observation of relatively many resistance mutations in Abl1, but not in the other genes, is explained by the fact that mutations in Abl1 tend to be biochemically conservative, whereas mutations in EGFR and ALK tend to be radical. Analysis of Abl1 compound mutations suggests that such mutations are more prevalent than hitherto reported and may be more difficult to counter. This supports the notion that such mutations may provide an escape route for targeted cancer drug resistance.

Introductory general and physical chemistry courses often deal with colligative properties of solutions and do not discuss nonideal solutions in detail. Yet, a growing body of evidence reveals that even at physiological concentrations electrolyte solutions cannot be treated as ideal when a charged or partially charged solute (such as a protein) is present in the solution. In such cases, the interactions between the salt ions and the solute depend on the specific ions that constitute the electrolyte solution (specific ion effects). For example, the catalytic efficiency of an enzyme may be different in NaCl and KCl solutions. In this article, specific ion effects are reviewed from a historical perspective, and then the current state of knowledge is presented at a qualitative level that is appropriate for second-year or advanced undergraduate science students. Finally, the related nomenclature (Bjerrum ion pairs, Hofmeister series, lyotropic series, and specific ion effects) is analyzed, and some suggestions are made with respect to the terminology, to make it more accessible to students. The material is appropriate for courses where solution chemistry is discussed, for example, in physical chemistry. In addition, it may be included in the chemistry curriculum for life or pharmaceutical sciences.

The use of actinides for medical, scientific and technological purposes has gained momentum in the recent years. This creates a need to understand their interactions with biomolecules, both at the interface and as they become complexed. Calculation of the Gibbs binding energies of the ions to biomolecules, i. e., the Gibbs energy change associated with a transfer of an ion from the water phase to its binding site, could help to understand the actinides' toxicities and to design agents that bind them with high affinities. To this end, there is a need to obtain accurate reference values for actinide hydration, that for most actinides are not available from experiment. In this study, a set of ionic radii is developed that enables future calculations of binding energies for Pu3+ and five actinides with renewed scientific and technological interest: Ac3+, Am3+, Cm3+, Bk3+ and Cf3+. Reference hydration energies were calculated using quantum chemistry and ion solvation theory and agree well for all ions except Ac3+, where ion solvation theory seems to underestimate the magnitude of the Gibbs hydration energy. The set of radii and reference energies that are presented here provide means to calculate binding energies for actinides and biomolecules.

Proteins interact with ions in various ways. The surface of proteins has an innate capability to bind ions, and it is also influenced by the screening of the electrostatic potential owing to the presence of salts in the bulk solution. Alkali metal ions and chlorides interact with the protein surface, but such interactions are relatively weak and often transient. In this paper, computer simulations and analysis of protein structures are used to characterize the interactions between ions and the protein surface. The results show that the ion-binding properties of protein residues are highly variable. For example, alkali metal ions are more often associated with aspartate residues than with glutamates, whereas chlorides are most likely to be located near arginines. When comparing NaCl and KCl solutions, it was found that certain surface residues attract the anion more strongly in NaCl. This study demonstrates that protein–salt interactions should be accounted for in the planning and execution of experiments and simulations involving proteins, particularly if subtle structural details are sought after.

Biomembranes assemble and operate at the interface with electrolyte solutions. Interactions between ions in solutions and the lipid affect the membrane structure, dynamics and electrostatic potential. In this article, I review some of the experimental and computational methods that are used to study membrane-ions interactions. Experimental methods that account for membrane-ion interactions directly and indirectly are presented first. Then, studies in which molecular dynamics simulations were used to gain an understanding of membrane-ion interactions are surveyed. Finally, the current view on membrane-ion interactions and their significance is briefly discussed.

Myosin V moves along actin filaments by an arm-over-arm motion, known as the lever mechanism. Each of its arms is composed of six consecutive IQ peptides that bind light chain proteins, such as calmodulin or calmodulin-like proteins. We have employed a multistage approach in order to investigate the mechanochemical structural basis of the movement of myosin V from the budding yeast Saccharomyces cerevisiae. For that purpose, we previously carried out molecular dynamics simulations of the Mlc1p−IQ2 and the Mlc1p−IQ4 protein−peptide complexes, and the present study deals with the structures of the IQ peptides when stripped from the Mlc1p protein. We have found that the crystalline structure of the IQ2 peptide retains a stable rodlike configuration in solution, whereas that of the IQ4 peptide grossly deviates from its X-ray conformation exhibiting an intrinsic tendency to curve and bend. The refolding process of the IQ4 peptide is initially driven by electrostatic interactions followed by nonpolar stabilization. Its bending appears to be affected by the ionic strength, when ionic strength higher than 300 mM suppresses it from flexing. Considering that a poly-IQ sequence is the lever arm of myosin V, we suggest that the arm may harbor a joint, localized within the IQ4 sequence, enabling the elasticity of the neck of myosin V. Given that a poly-IQ sequence is present at the entire class of myosin V and the possibility that the yeast's myosin V molecule can exist either as a nonprocessive monomer or as a processive dimer depending on conditions (Krementsova, E. B., Hodges, A. R., Lu, H., and Trybus, K. M. (2006) J. Biol. Chem. 281, 6079−6086), our observations may account for a general structural feature for the myosins' arm embedded flexibility.

Methanol dehydrogenase (MDH) is an enzyme used by certain bacteria for the oxidation of methanol to formaldehyde, which is a necessary metabolic reaction. The discovery of a lanthanide-dependent MDH reveals that lanthanide ions (Ln(3+)) have a role in biology. Two types of MDH exist in methane-utilizing bacteria: one that is Ca2+-dependent (MxaF) and another that is Ln(3+)-dependent. Given that the triply charged Ln(3+) are strongly hydrated, it is not clear how preference for Ln(3+) is manifested and if the Ca2+-dependent MxaF protein can also bind Ln(3+) ions. A computational approach was used to estimate the Gibbs energy differences between the binding of Ln(3+) and Ca2+ to MDH using density functional theory. The results show that both proteins bind La3+ with higher affinity than Ca2+, albeit with a more pronounced difference in the case of Ln(3+)-dependent MDH. Interestingly, the binding of heavier lanthanides is preferred over the binding of La3+, with Gd3+ showing the highest affinity for both proteins of all Ln(3+) ions that were tested (La3+, Sm3+, Gd3+, Dy3+, and Lu3+). Energy decomposition analysis reveals that the higher affinity of La3+ than Ca2+ to MDH is due to stronger contributions of electrostatics and polarization, which overcome the high cost of desolvating the ion.

Proton transfer (PT) reactions take place oil the molecular Surface of proteins, membranes, ionic polymers, and other molecules. The rates of the reactions can be followed experimentally, while the atomistic details can be elucidated by molecular modeling. This manuscript gives a brief overview of the use of computer simulations and molecular modeling, in conjuction with experiments, to study PT reactions oil the surface of solvated molecules. An integrative approach is discussed, where molecular dynamics simulations are performed with a protein, and quantum-mechanics-based calculations are performed oil a small molecule. The simulation results allow the identification of the necessary conditions that yield PT reactions oil the molecular surface. The reactions are efficient when they involve a donor and acceptor located a few A apart and under the influence of a negative electrostatic field. In proton-pumping proteins, it is possible to identify such conditions a priori and locate proton-attracting antenna domains without the need to mutate each potential donor and acceptor. Based on density functional theory calculations, the arrangement of water molecules that interconnect the donor and acceptor moieties is suggested as the rate-limiting step for proton transfer on the molecular surface.

Biomolecules including proteins, lipid membranes, and nucleic acids operate at an aqueous milieu that includes solvated ions. The interactions with ions affect biomolecules in different ways depending on the nature of the solute and the type of the ions. The dynamic nature of small soluble ions makes it difficult to follow them by structural methods. Consequently, theories were developed to explain how biomolecules interact in an environment that includes electrolytes. Moreover, simulations studies are often used to study such systems at the molecular or atomistic level. The status of the field, and inparticular of simulation studies, is the subject of this progress report.

How salt ions affect solutes and the water beyond the solvation shell is not well understood. Molecular dynamics simulations of alkali-acetate solutions were analysed here in order to examine if, and how, different cations and solute concentrations affect the water structure and the interactions between water and acetates. The results revealed that water structure is perturbed to more than 1 nm away from the acetates and that this effect is more pronounced in physiological than in molar electrolyte concentrations. Analysis of simulations of a soluble protein revealed that the water orientation is perturbed to at least 1.5 nm from the protein structure. Furthermore, modifications to the orientation of water around carboxylate side chains were shown to depend on the local environment on the protein surface, and could extend to well over 1 nm, which may have an effect on protein dynamics during MD simulations in small water boxes.

Cadmium is a highly toxic group XII metal, similar to zinc and mercury. Unlike zinc, which is one of the most common metal cofactors in biology, cadmium is highly toxic. Many Zn2+-binding proteins can bind Cd2+-ions without significantly affecting their structures. Here, the protein data bank is analysed with regard to protein-cadmium interactions, which shows that cadmium can bind to a variety of ion binding sites in proteins. Statistical analysis of Cd2+-side chain interactions is compared with a similar analysis of other ions. This analysis reveals that with regard to amino acid side-chain preference, Cd2+ is more similar to Mn2+ than to Zn2+ or Hg2+. Finally, the interaction energies of three native metal binding proteins are calculated where Cd2+ binds instead of Zn2+, Ca2+ or Cu2+. The interaction energies are decomposed into individual components whose contributions are discussed.

Cancers ultimately develop due to aberrations that involve proteins and modify their effects. Given that the structures of many proteinsinvolved in cancer pathogenesis are known, numerous studies have employed MD simulations in cancer research. In this chapter, somecauses and treatments for cancer are briefly introduced. Thereafter, systems where cancer development or therapy have been studied byMD simulations are described, focusing on contemporary subjects of interest. These include tumor cell metabolism, RAS proteins,driver mutations, allosteric inhibitors, kinetics of drug binding, activation of protein kinases and anticancer drug delivery. While notproviding a complete picture of the fields, these subjects allow the reader to understand what sorts of systems are studied, how, andwhich conclusions can be made with the help of MD simulations. This might help the interested reader to utilize such simulations forfurther studies in the field.

Fms-like tyrosine kinase 3 (FLT3) is a receptor tyrosine kinase that is a drug target for leukemias. Several potent inhibitors of FLT3 exist, and bind to the inactive form of the enzyme. Unfortunately, resistance due to mutations in the kinase domain of FLT3 limits the therapeutic effects of these inhibitors. As in many other cases, it is not straightforward to explain why certain mutations lead to drug resistance. Extensive fully atomistic molecular dynamics (MD) simulations of FLT3 were carried out with an inhibited form (FLT-quizartinib complex), a free (apo) form, and an active conformation. In all cases, both the wild type (wt) proteins and two resistant mutants (D835F and Y842H) were studied. Analysis of the simulations revealed that impairment of protein-drug interactions cannot explain the resistance mutations in question. Rather, it appears that the active state of the mutant forms is perturbed by the mutations. It is therefore likely that perturbation of deactivation of the protein (which is necessary for drug binding) is responsible for the reduced affinity of the drug to the mutants. Importantly, this study suggests that it is possible to explain the source of resistance by mutations in FLT3 by an analysis of unbiased MD simulations.

Acute myeloid leukemia is an aggressive cancer, which, in spite of increasingly better understanding of its genetic background remains difficult to treat. Mutations in the FLT3 gene are observed in ≈30% of the patients. Most of these mutations are internal tandem duplications (ITDs) of a sequence within the protein coding region, an activation mechanism that is almost non-existent with other genes and cancers. As patients each carry their own unique set of mutations, it is challenging to understand how ITDs activate the protein, and ascertain the risk for each individual patient. Available treatment options are limited due to development of drug resistance. Here, recent studies are reviewed that help to better understand the molecular mechanism behind activation of the FLT3 protein due to mutations. It is argued that difference in mutation sequences and especially location might be coupled to prognosis. When it comes to FLT3 inhibitors, key differences between them can be attributed to the mode of inhibition (type-1 and type-2 inhibitors), effective inhibitory coefficient in the blood plasma and off-target binding. Accounting for the position and length of insertions may in the future be used to predict prognosis and rationalise treatment. Development of new inhibitors must take into account the potential for resistance mutations. Inhibitors aimed at multiple specific targets are currently being developed. These, and as well as combination therapies will hopefully lead to longer periods during which targeted FLT3 therapy will remain effective.

Protein kinases are enzymes with partially overlapping specificities, many of which are important clinical targets. In this article, we give some background on protein kinases and discuss in more depth four such enzymes that have been studied in our labs using computer simulations. The combination of molecular dynamics simulations and enzyme or cell growth experiments was instrumental to explain why certain mutations lead (or do not lead) to resistance to targeted therapy aimed at these proteins. Stochastic network simulations were used to study protein-protein interactions and suggest points for intervention against tumour growth.

The complexity of cancer and the vast amount of experimental data available have made computer-aided approaches necessary. Biomolecular modelling techniques are becoming increasingly easier to use, whereas hardware and software are becoming better and cheaper. Cross-talk between theoretical and experimental scientists dealing with cancer-research from a molecular approach, however, is still uncommon. This is in contrast to other fields, such as amyloid-related diseases, where molecular modelling studies are widely acknowledged. The aim of this review paper is therefore to expose some of the more common approaches in molecular modelling to cancer scientists in simple terms, illustrating success stories while also revealing the limitations of computational studies at the molecular level.

Plasmepsins (PMs) are essential proteases of the plasmodia parasites and are therefore promising targets for developing drugs against malaria. We have discovered six inhibitors of PM II by high-throughput fragment-based docking of a diversity set of ∼40 000 molecules, and consensus scoring with force field energy functions. Using the common scaffold of the three most active inhibitors (IC₅₀=2–5 μM), another seven inhibitors were identified by substructure search. Furthermore, these 13 inhibitors belong to at least three different classes of compounds. The in silico approach was very effective since a total of 13 active compounds were discovered by testing only 59 molecules in an enzymatic assay. This hit rate is about one to two orders of magnitude higher than those reported for medium- and high-throughput screening techniques in vitro. Interestingly, one of the inhibitors identified by docking was halofantrine, an antimalarial drug of unknown mechanism. Explicit water molecular dynamics simulations were used to discriminate between two putative binding modes of halofantrine in PM II.

The recent re-refinement of the X-ray structure of apo plasmepsin II from Plasmodium falciparum suggests that the two carboxylate groups in the catalytic dyad are noncoplanar, (Robbins et al., Acta Crystallogr D Biol Crystallogr 2009;65: 294–296) in remarkable contrast with the vast majority of structures of aspartic proteases. Here, evidence for the noncoplanarity of the catalytic aspartates is provided by analysis of multiple explicit water molecular dynamics (MD) simulations of plasmepsin II, human β-secretase, and HIV-protease. In the MD runs of plasmepsin II, the angle between the planes of the two carboxylates of the catalytic dyad is almost always in the range 60°–120°, in agreement with the perpendicular orientation in the re-refined X-ray structure. The noncoplanar arrangement is prevalent also in the β-secretase simulations, as well as in the runs with the inhibitor-bound proteases. Quantum-mechanics calculations provide further evidence that before catalysis the noncoplanar arrangement is favored energetically in eukaryotic aspartic proteases. Remarkably, the coplanar orientation of the catalytic dyad is observed in MD simulations of HIV-protease at 100 K but not at 300 K, which indicates that the noncoplanar arrangement is favored by conformational entropy. This finding suggests that the coplanar orientation in the crystal structures of apo aspartic proteases is promoted by the very low temperature used for data collection (usually around 100 K).

There is strong experimental evidence of the influence of surfactants (e.g., fatty acids) on the kinetics of amyloid fibril formation. However, the structures of mixed assemblies and interactions between surfactants and fibril-forming peptides are still not clear. Here, coarse-grained simulations are employed to study the aggregation kinetics of amyloidogenic peptides in the presence of amphiphilic lipids. The simulations show that the lower the fibril formation propensity of the peptides, the higher the influence of the surfactants on the peptide self-assembly kinetics. In particular, the lag phase of weakly aggregating peptides increases because of the formation of mixed oligomers, which are promoted by hydrophobic interactions and favorable entropy of mixing. A transient peak in the number of surfactants attached to the growing fibril is observed before reaching the mature fibril in some of the simulations. This peak originates from transient fibrillar defects consisting of exposed hydrophobic patches on the fibril surface, which provide a possible explanation for the temporary maximum of fluorescence observed sometimes in kinetic traces of the binding of small-molecule dyes to amyloid fibrils.

The C-terminal segment (residues 218–289) of the HET-s protein of the filamentous fungus Podosporina anserina is a prion-forming domain. The structural model of the HET-s(218–289) amyloid fibril based on solid-state nuclear magnetic resonance (NMR) restraints shows a β solenoid topology which is comprised of a β-sheet core and interconnecting loops. For the single-point mutants Phe286Ala and Trp287Ala, slower aggregation rates in vitro and loss of prionic infectivity have been reported recently. Here we have used molecular dynamics to compare the flexibility of the mutants and wild type. The simulations, initiated from a trimeric aggregate extracted from the NMR structural model, show structural stability on a 100-ns time scale for wild type and mutants. Analysis of the fluctuations along the simulations reveals that the mutants are less flexible than the wild type in the C-terminal segment at only one of the two external monomers. Analysis of interaction energy and buried accessible surface indicates that residue Phe286 in particular is stabilized in the Trp287Ala mutant. The simulation results provide an atomistic explanation of the suggestion (based on indirect experimental evidence) that flexibility at the protofibril end(s) is required for fibril elongation. Moreover, they provide further evidence that the growth of the HET-s amyloid fibril is directional.

The capacity to transfer protons between surface groups is an innate property of many proteins. The transfer of a proton between donor and acceptor, located as far as 6−7 Å apart, necessitates the participation of water molecules in the process. In a previous study we investigated the mechanism of proton transfer (PT) between bulk exposed sites, a few ångströms apart, using as a model the proton exchange between the proton-binding sites of the fluorescein molecule in dilute aqueous solution.¹ The present study expands the understanding of PT reactions between adjacent sites exposed to water through the calculation the minimum energy pathways (MEPs) by the conjugate peak refinement algorithm² and a quantum-mechanical potential. The PT reaction trajectories were calculated for the fluorescein system with an increasing number of water molecules. The MEP calculations reveal that the transition state is highly strained and involves a supramolecular structure in which fluorescein and the interconnecting water molecules are covalently bonded together and the protons are shared between neighboring oxygens. These findings are in accord with the high activation energy, as measured for the reaction, and indicate that PT reactions on the surface proceed by a semi- or fully concerted rather than stepwise mechanism. A similar mechanism is assumed to be operative on the surface of proteins and renders water-mediated PT reactions as highly efficient as they are.

This is a perspective article entitled "Frontiers in computational biophysics: understanding conformational dynamics of complex lipid mixtures relevant to biology" which is following a CECAM meeting with the same name.

Time-resolved measurements indicated that protons could propagate on the surface of a protein or a membrane by a special mechanism that enhanced the shuttle of the proton toward a specific site. It was proposed that a suitable location of residues on the surface contributes to the proton shuttling function. In this study, this notion was further investigated by the use of molecular dynamics simulations, where Na⁺ and Cl⁻are the ions under study, thus avoiding the necessity for quantum mechanical calculations. Molecular dynamics simulations were carried out using as a model a few Na⁺ and Cl⁻ ions enclosed in a fully hydrated simulation box with a small globular protein (the S6 of the bacterial ribosome). Three independent 10-ns-long simulations indicated that the ions and the protein's surface were in equilibrium, with rapid passage of the ions between the protein's surface and the bulk. However, it was noted that close to some domains the ions extended their duration near the surface, thus suggesting that the local electrostatic potential hindered their diffusion to the bulk. During the time frame in which the ions were detained next to the surface, they could rapidly shuttle between various attractor sites located under the electrostatic umbrella. Statistical analysis of the molecular dynamics and electrostatic potential/entropy consideration indicated that the detainment state is an energetic compromise between attractive forces and entropy of dilution. The similarity between the motion of free ions next to a protein and the proton transfer on the protein's surface are discussed.

The adipocyte lipid binding protein (ALBP) binds fatty acids (FA) in a cavity that is inaccessible from the bulk. Therefore, the penetration of the FA necessitates conformational changes whose nature is still unknown. It was suggested that the lipid first enters through a “portal region” which consists of the αII helix and the adjacent tight turns. The initial event in the ligand binding must be the interaction of the lipid with the protein surface. To analyze this interaction, we have carried out three molecular dynamics simulations of the apo-ALBP, with a palmitate ion initially located at different positions near the protein surface. The simulation indicated that the ligand could adsorb to the protein in more than one location. Yet, in one case, the ligand managed to penetrate the protein by entering a newly formed cavity some 10 Å deep. The entry site is located near the N-terminus, at the junction between the loops connecting the β-strands. Further progression of the penetration seems to be arrested by the need for desolvation of the COOH end of the headgroup. Evolutionary analysis showed that amino acids in this entry site are well conserved. On the basis of these observations, we suggest that the ligand may enter the protein from a site other than the portal region. Furthermore, the rate-limiting step is proposed to be the desolvation of the FA polar headgroup.