Electrostatics in proteins and protein-ligand complexes

Hydroxamic acid derivatives as selective HDAC3 inhibitors: computer-aided drug design strategies

Histone deacetylases (HDACs) are critical epigenetic drug targets that have gained significant attention in the scientific community for the treatment of cancer. The currently marketed HDAC inhibitors lack selectivity for the various HDAC isoenzymes. Here, we describe our protocol for the discovery of novel potential hydroxamic acid based HDAC3 inhibitors through pharmacophore modeling, virtual screening, docking, molecular dynamics (MD) simulation and toxicity studies. The ten pharmacophore hypotheses were established, and their reliability was validated by different ROC (receiving operator curve) analysis. Among them, the best model (Hypothesis 9 or RRRA) was employed for searching SCHEMBL, ZINC and MolPort database to screen out hit molecules as selective HDAC3 inhibitors, followed by different docking stages. MD simulation (50 ns) and MMGBSA study were performed to study the stability of ligand binding modes and with the help of trajectory analysis, to calculate the ligand-receptor complex RMSD (root-mean-square deviation), RMSF (root-mean-square fluctuation) and H-bond distance, etc. Finally, in-silico toxicity studies were performed on top screened molecules and compared with reference drug SAHA and established structure-activity relationship (SAR). The results indicated that compound 31, with high inhibitory potency and less toxicity (probability value 0.418), is suitable for further experimental analysis.Communicated by Ramaswamy H. Sarma.

Molecular Dynamics with Conformationally Dependent, Distributed Charges

Accounting for geometry-induced changes in the electronic distribution in molecular simulation is important for capturing effects such as charge flow, charge anisotropy, and polarization. Multipolar force fields have demonstrated their ability to correctly represent chemically significant features such as anisotropy and sigma holes. It has also been shown that off-center point charges offer a compact alternative with similar accuracy. Here, it is demonstrated that allowing relocation of charges within a minimally distributed charge model (MDCM) with respect to their reference atoms is a viable route to capture changes in the molecular charge distribution depending on geometry, i.e., intramolecular polarization. The approach, referred to as "flexible MDCM" (fMDCM), is validated on a number of small molecules and provides accuracies in the electrostatic potential (ESP) of 0.5 kcal/mol on average compared with reference data from electronic structure calculations, whereas MDCM and point charges have root mean squared errors of a factor of 2 to 5 higher. In addition, MD simulations in the NVE ensemble using fMDCM for a box of flexible water molecules with periodic boundary conditions show a width of 0.1 kcal/mol for the fluctuation around the mean at 300 K on the 10 ns time scale. For water, the equilibrium valence angle in the gas phase is found to increase by 2° for simulations in the condensed phase which is consistent with experiment. The accuracy in capturing the geometry dependence of the ESP together with the long-time stability in energy conserving simulations makes fMDCM a promising tool to introduce advanced electrostatics into atomistic simulations.

Interactions of Apigenin and Safranal with the 5HT1A and 5HT2A Receptors and Behavioral Effects in Depression and Anxiety: A Molecular Docking, Lipid-Mediated Molecular Dynamics, and In Vivo Analysis

BACKGROUND

The current study utilizes in silico molecular docking/molecular dynamics to evaluate the binding affinity of apigenin and safranal with 5HT1AR/5HT2AR, followed by assessment of in vivo effects of these compounds on depressive and anxious behavior.

METHODS

The docking between apigenin and safranal and the 5HT1A and 5HT2A receptors was performed utilizing AutoDock Vina software, while MD and protein-lipid molecular dynamics simulations were executed by AMBER16 software. For in vivo analysis, healthy control (HC), disease control (DC), fluoxetine-, and apigenin-safranal-treated rats were tested for changes in depression and anxiety using the forced swim test (FST) and the elevated plus-maze test (EPMT), respectively.

RESULTS

The binding affinity estimations identified the superior interacting capacity of apigenin over safranal for 5HT1A/5HT2A receptors over 200 ns MD simulations. Both compounds exhibit oral bioavailability and absorbance. In the rodent model, there was a significant increase in the overall mobility time in the FST, while in the EPMT, there was a decrease in latency and an increase in the number of entries for the treated and HC rats compared with the DC rats, suggesting a reduction in depressive/anxiety symptoms after treatment.

CONCLUSIONS

Our analyses suggest apigenin and safranal as prospective medication options to treat depression and anxiety.

The electrostatic potential inside and around α-lactalbumin: Fluctuations and mean-field models

The electrostatic potential (EP) generated by the protein α-lactoalbumin in the presence of added salt is computed as a thermal average at a given point in space. With this aim, constant pH Monte Carlo (MC) simulations are performed within the primitive model, namely, the solvent is treated as a continuum dielectric. The study of the thermal and spatial fluctuations of the EP reveals that they are remarkably high inside the protein. The calculations indicate that fluctuations inside the protein are mainly due to the asymmetric distribution of the charge groups, while the charge fluctuations of the titratable groups play a minor role. The computed EP matches very well with the one obtained from the Poisson equation for the average charge density in spherical symmetry. The Tanford–Kirkwood multipole expansion reproduces the simulated angular-averaged potential rather accurately. Surprisingly, two of the simplest mean-field models, the linear Poisson–Boltzmann (PB) equation and Donnan potential, provide good estimations of the average EP in the effective protein surface (surface EP). The linear PB equation predicts a linear relationship between charge and surface EP, which is numerically reproduced only if the small ions within the protein are taken into account. On the other hand, the partition coefficients of the small ions inside and outside the protein predicted by Donnan theory reproduce reasonably well the simulation results.

Computational details of molecular structure, spectroscopic properties, topological studies and SARS-Cov-2 enzyme molecular docking simulation of substituted triazolo pyrimidine thione heterocycles

Investigation the molecular structure of the system requires a detailed experience in dealing with theoretical computational guides to highlight its important role. Molecular structure of three heterocyclic compounds 8,10-diphenylpyrido[3,2-e][1,2,4]triazolo[4,3-c]pyrimidine-3(2H)-thione (HL), 8-phenyl-10-(p-tolyl)pyrido[3,2-e][1,2,4]triazolo[4,3-c]pyrimidine-3(2H)-thione (CH3L) and10-(4-nitrophenyl)-8-phenylpyrido[3,2-e][1,2,4]triazolo[4,3-c]pyrimidine-3(2H)-thione (NO2L) was studied at DFT/B3LYP/6-31G (d,p) level in ethanol solvent. Spectroscopic properties such Infrared (IR, 1H NMR and 13C NMR) and ultraviolet-visible (UV-VIS) analyses were computed. Some quantum and reactivity parameters (HOMO energy, LUMO energy, energy gap, ionization potential, electron affinity, chemical potential, global softness, lipophelicity) were studied, also molecular electrostatic potential (MEP) was performed to indicate the reactive nucleophilic and electrophilic sites. The effects of H-, CH3- and NO2- substituents on heterocyclic ligands were studied and it was found that the electron donation sites concerned with hydrogen and methyl substituents over nitro substituent. Topological analysis using reduced density gradient (RDG) was discussed in details. To predict the relevant antiviral activity of the reported heterocyclic compounds, molecular docking simulation was applied to the crystal structure of SARS-Cov-2 viral Mpro enzyme with 6WTT code and PLpro with 7JRN code. The enzymatic viral protein gives an image about the binding affinity between the target protein receptor and the heterocyclic ligands entitled. The hydrogen bonding interactions were evaluated from molecular docking with different strength for each ligand compound to discuss the efficiency of heterocyclic ligands toward viral inhibition.

Docking Screens of Noncovalent Interaction Motifs of the Human Subtype-D2 Receptor-75 Schizophrenia Antipsychotic Complexes with Physicochemical Appraisal of Antipsychotics

Chemoinformatics appraisal and molecular docking were employed to investigate 225 complexes of 75 schizophrenia antipsychotics with the dopamine receptor subtypes D2R, D3R, and D4R. Considering the effective noncovalent interactions in the subtype-D2 receptor selectivity of antipsychotics, this study evaluated the possible physicochemical properties of ligands underlying the design of safer and more effective antipsychotics. The pan-assay interference compounds (PAINs) include about 25% of typical antipsychotics and 5% of atypicals. Popular antipsychotics like haloperidol, clozapine, risperidone, and aripiprazole are not PAINs. They have stronger interactions with D2R and D4R, but their interactions with D3R are slightly weaker, which is similar to the behavior of dopamine. In contrast to typical antipsychotics, atypical antipsychotics exhibit more noncovalent interactions with D4R than with D2R. These results suggest that selectivity to D2R and D4R comes from the synergy between hydrophobic and hydrogen-bonding interactions through their concomitant occurrence in the form of a hydrogen-bonding site adorned with hydrophobic contacts in antipsychotic-receptor complexes. All the antipsychotics had more synergic interactions with D2R and D4R in comparison with D3R. The atypical antipsychotics made a good distinction between the subtype D2 receptors with high selectivity to D4R. Among the popular antipsychotics, haloperidol, clozapine, and risperidone have hydrophobic-hydrogen-bonding synergy with D4R, while aripiprazole profits with D2R. The most important residue participating in the synergic interactions was threonine for D2R and cysteine for D4R. This work could be useful in informing and guiding future drug discovery and development studies aimed at receptor-specific antipsychotics.

The Role of Structure and Biophysical Properties in the Pleiotropic Effects of Statins

Statins are a class of drugs used to lower low-density lipoprotein cholesterol and are amongst the most prescribed medications worldwide. Most statins work as a competitive inhibitor of 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGR), but statin intolerance from pleiotropic effects have been proposed to arise from non-specific binding due to poor enzyme-ligand sensitivity. Yet, research into the physicochemical properties of statins, and their interactions with off-target sites, has not progressed much over the past few decades. Here, we present a concise perspective on the role of statins in lowering serum cholesterol levels, and how their reported interactions with phospholipid membranes offer a crucial insight into the mechanism of some of the more commonly observed pleiotropic effects of statin administration. Lipophilicity, which governs hepatoselectivity, is directly related to the molecular structure of statins, which dictates interaction with and transport through membranes. The structure of statins is therefore a clinically important consideration in the treatment of hypercholesterolaemia. This review integrates the recent biophysical studies of statins with the literature on the physiological effects and provides new insights into the mechanistic cause of statin pleiotropy, and prospective means of understanding the cholesterol-independent effects of statins.

In silico drug design and molecular docking studies targeting Akt1 (RAC-alpha serine/threonine-protein kinase) and Akt2 (RAC-beta serine/threonine-protein kinase) proteins and investigation of CYP (cytochrome P450) inhibitors against MAOB (monoamine oxidase B) for OSCC (oral squamous cell carcinoma) treatment

The overexpression of Akt1 (RAC-alpha serine/threonine-protein Kinase) and Akt2 (RAC-beta serine/threonine-protein Kinase) is a hallmark of Oral Squamous Cell Carcinoma (OSCC). Because of the elevated frequency of OSCC occurrence in South Asian countries, novel therapeutic approaches are indispensable. Drugs that inhibit the overexpression of Akt1 and Akt2 proteins in Akt pathway and do not cause reduced expression of MAOB can be leads for OSCC treatment. In this study, Akt1, Akt2 and MAOB were targeted and 100 CYP inhibitors were screened through several in silico approaches and Galuteolin and Linarin were identified as potential leads for OSCC treatment as they inhibited Akt1 proteins with strong binding affinities of -12.3 and -11.5 kcal/mol respectively and also Akt2 proteins with strong binding affinities of -11.4 and -11.1 kcal/mol respectively, but they did not inhibit MAOB. Decreased expression of MAOB in tissues causes OSCC but overexpression is also responsible for other types of diseases and cancers. From the investigation of CYP inhibitors against MAOB, five CYP inhibitors- Diosmetin, Acacetin, Epicatechin, Eriodictyol and Capillin have expressed inhibitory action against MAOB without any interference with Akt1 and Akt2. This study mainly represents that Galuteolin and Linarin in the Akt pathway can be perceived for OSCC treatment and other five CYP inhibitors - Diosmetin, Acacetin, Epicatechin, Eriodictyol and Capillin for the treatment of other diseases and cancers caused by overexpression of MAOB. ADMET properties of CYP inhibitors obtained from admetSAR 2.0 and were compared with reference drugs for validation. Communicated by Ramaswamy H. Sarma.

Identification of a Potential Zika Virus Inhibitor Targeting NS5 Methyltransferase Using Virtual Screening and Molecular Dynamics Simulations

The NS5 methyltransferase (MTase) has been reported as an attractive molecular target for antivirals discovery against the Zika virus (ZIKV). Here, we report structure-based virtual screening of 42 390 structures from the Development Therapeutics Program (DTP) AIDS Antiviral Screen Database. Among the docked compounds, ZINC1652386 stood out due to its high affinity for MTase in comparison to the cocrystallized ligand MS2042, which interacts with the Asp146 residue in the MTase binding site by hydrogen bonding. Subsequent molecular dynamics simulations predicted that this compound forms a stable complex with MTase within 50 ns. Thus, ZINC1652386 may represent a promising ZIKV methyltransferase inhibitor.

SmoPSI: Analysis and Prediction of Small Molecule Binding Sites Based on Protein Sequence Information

The analysis and prediction of small molecule binding sites is very important for drug discovery and drug design. The traditional experimental methods for detecting small molecule binding sites are usually expensive and time consuming, and the tools for single species small molecule research are equally inefficient. In recent years, some algorithms for predicting binding sites of protein-small molecules have been developed based on the geometric and sequence characteristics of proteins. In this paper, we have proposed SmoPSI, a classification model based on the XGBoost algorithm for predicting the binding sites of small molecules, using protein sequence information. The model achieved better results with an AUC of 0.918 and an ACC of 0.913. The experimental results demonstrate that our method achieves high performances and outperforms many existing predictors. In addition, we also analyzed the binding residues and nonbinding residues and finally found the PSSM; hydrophilicity, hydrophobicity, charge, and hydrogen bonding have obviously different effects on the binding-site predictions.

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

BACKGROUND

Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

OBJECTIVE

Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

METHODS

Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

RESULTS

In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

CONCLUSION

A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

IgG Charge: Practical and Biological Implications

Practically, IgG charge can contribute significantly to thermodynamic nonideality, and hence to solubility and viscosity. Biologically, IgG charge isomers exhibit differences in clearance and potency. It has been known since the 1930s that all immunoglobulins carry a weak negative charge in physiological solvents. However, there has been no systematic exploration of this fundamental property. Accurate charge measurements have been made using membrane confined electrophoresis in two solvents (pH 5.0 and pH 7.4) on a panel of twelve mAb IgGs, as well as their F(ab’)₂ and Fc fragments. The following observations were made at pH 5.0: (1) the measured charge differs from the calculated charge by ~40 for the intact IgGs, and by ~20 for the Fcs; (2) the intact IgG charge depends on both Fv and Fc sequences, but does not equal the sum of the F(ab)’₂ and Fc charge; (3) the Fc charge is consistent within a class. In phosphate buffered saline, pH 7.4: (1) the intact IgG charges ranged from 0 to −13; (2) the F(ab’)₂ fragments are nearly neutral for IgG1s and IgG2s, and about −5 for some of the IgG4s; (3) all Fc fragments are weakly anionic, with IgG1 < IgG2 < IgG4; (4) the charge on the intact IgGs does not equal the sum of the F(ab’)₂ and Fc charge. In no case is the calculated charge, based solely on H⁺ binding, remotely close to the measured charge. Some mAbs carried a charge in physiological salt that was outside the range observed for serum-purified human poly IgG. To best match physiological properties, a therapeutic mAb should have a measured charge that falls within the range observed for serum-derived human IgGs. A thermodynamically rigorous, concentration-dependent protein–protein interaction parameter is introduced. Based on readily measured properties, interaction curves may be generated to aid in the selection of proteins and solvent conditions. Example curves are provided.

Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein-Ligand Complexes

Electrostatic interactions between small molecules and their respective receptors are essential for molecular recognition and are also key contributors to the binding free energy. Assessing the electrostatic match of protein-ligand complexes therefore provides important insights into why ligands bind and what can be changed to improve binding. Ideally, the ligand and protein electrostatic potentials at the protein-ligand interaction interface should maximize their complementarity while minimizing desolvation penalties. In this work, we present a fast and efficient tool to calculate and visualize the electrostatic complementarity (EC) of protein-ligand complexes. We compiled benchmark sets demonstrating electrostatically driven structure-activity relationships (SAR) from literature data, including kinase, protein-protein interaction, and GPCR targets, and used these to demonstrate that the EC method can visualize, rationalize, and predict electrostatically driven ligand affinity changes and help to predict compound selectivity. The methodology presented here for the analysis of EC is a powerful and versatile tool for drug design.

Skeletal Muscle Dystrophy mutant of lamin A alters the structure and dynamics of the Ig fold domain

Mutations in the different domains of A-type lamin proteins cause a diverse plethora of diseases collectively termed as laminopathies which can affect multiple organs. Ig fold is one such domain of lamin A which is implicated in numerous nuclear interactions wherein the mutations lead to different laminopathies. W514R is one such mutation in the Ig fold which leads to severe phenotypes in Skeletal Muscle Dystrophy (SMD) which is a class of laminopathies. In this report, we elucidated gross alterations in structure and dynamics at the level of individual amino acids. These studies indicate altered conformational features of residues in the close vicinity of W514. Imaging of mammalian cells transfected with the mutant have shown distinct perturbation of the nuclear meshwork with concomitant alteration in nuclear interactions as a result of increased oligomerization of Ig W514R. Hence, this novel approach of amalgamating theoretical and experimental procedures to predict the severity of a mutant in the context of laminopathies could be extended for numerous lamin A mutants.

Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions

The ability to design and fine-tune non-covalent interactions between organic ligands and proteins is indispensable to rational drug development. Aromatic stacking has long been recognized as one of the key constituents of ligand-protein interfaces. In this communication, we employ a two-parameter geometric model to conduct a large-scale statistical analysis of aromatic contacts in the experimental and computer-generated structures of ligand-protein complexes, considering various combinations of aromatic amino acid residues and ligand rings. The geometry of interfacial π-π stacking in crystal structures accords with experimental and theoretical data collected for simple systems, such as the benzene dimer. Many contemporary ligand docking programs implicitly treat aromatic stacking with van der Waals and Coulombic potentials. Although this approach generally provides a sufficient specificity to model aromatic interactions, the geometry of π-π contacts in high-scoring docking conformations could still be improved. The comprehensive analysis of aromatic geometries at ligand-protein interfaces lies the foundation for the development of type-specific statistical potentials to more accurately describe aromatic interactions in molecular docking. A Perl script to detect and calculate the geometric parameters of aromatic interactions in ligand-protein complexes is available at https://github.com/michal-brylinski/earomatic. The dataset comprising experimental complex structures and computer-generated models is available at https://osf.io/rztha/.

Micro-Environmental Signature of The Interactions between Druggable Target Protein, Dipeptidyl Peptidase-IV, and Anti-Diabetic Drugs

OBJECTIVE

Druggability of a target protein depends on the interacting micro-environment between the target protein and drugs. Therefore, a precise knowledge of the interacting micro-environment between the target protein and drugs is requisite for drug discovery process. To understand such micro-environment, we performed in silico interaction analysis between a human target protein, Dipeptidyl Peptidase-IV (DPP-4), and three anti-diabetic drugs (saxagliptin, linagliptin and vildagliptin).

MATERIALS AND METHODS

During the theoretical and bioinformatics analysis of micro-environmental properties, we performed drug-likeness study, protein active site predictions, docking analysis and residual interactions with the protein-drug interface. Micro-environmental landscape properties were evaluated through various parameters such as binding energy, intermolecular energy, electrostatic energy, van der Waals'+H-bond+desolvo energy (EVHD) and ligand efficiency (LE) using different in silico methods. For this study, we have used several servers and software, such as Molsoft prediction server, CASTp server, AutoDock software and LIGPLOT server.

RESULTS

Through micro-environmental study, highest log P value was observed for linagliptin (1.07). Lowest binding energy was also observed for linagliptin with DPP-4 in the binding plot. We also identified the number of H-bonds and residues involved in the hydrophobic interactions between the DPP-4 and the anti-diabetic drugs. During interaction, two H-bonds and nine residues, two H-bonds and eleven residues as well as four H-bonds and nine residues were found between the saxagliptin, linagliptin as well as vildagliptin cases and DPP-4, respectively.

CONCLUSION

Our in silico data obtained for drug-target interactions and micro-environmental signature demonstrates linagliptin as the most stable interacting drug among the tested anti-diabetic medicines.

Mapping of the continuous epitopes displayed on the Clostridium perfringens type D epsilon-toxin

The epsilon toxin, produced by Clostridium perfringens, is responsible for enterotoxemia in ruminants and is a potential bioterrorism agent. In the present study, 15 regions of the toxin were recognized by antibodies present in the serum, with different immunodominance scales, and may be antigen determinants that can be used to formulate subunit vaccines.

An improved quantum biochemistry description of the glutamate–GluA2 receptor binding within an inhomogeneous dielectric function framework

A new methodology to define the inhomogeneous dielectric constant of protein residues, to apply to the calculation of protein–ligand properties such as the electrostatic interaction.

Electrostatic component of binding energy: Interpreting predictions from poisson-boltzmann equation and modeling protocols

Macromolecular interactions are essential for understanding numerous biological processes and are typically characterized by the binding free energy. Important component of the binding free energy is the electrostatics, which is frequently modeled via the solutions of the Poisson-Boltzmann Equations (PBE). However, numerous works have shown that the electrostatic component (ΔΔGelec ) of binding free energy is very sensitive to the parameters used and modeling protocol. This prompted some researchers to question the robustness of PBE in predicting ΔΔGelec . We argue that the sensitivity of the absolute ΔΔGelec calculated with PBE using different input parameters and definitions does not indicate PBE deficiency, rather this is what should be expected. We show how the apparent sensitivity should be interpreted in terms of the underlying changes in several numerous and physical parameters. We demonstrate that PBE approach is robust within each considered force field (CHARMM-27, AMBER-94, and OPLS-AA) once the corresponding structures are energy minimized. This observation holds despite of using two different molecular surface definitions, pointing again that PBE delivers consistent results within particular force field. The fact that PBE delivered ΔΔGelec values may differ if calculated with different modeling protocols is not a deficiency of PBE, but natural results of the differences of the force field parameters and potential functions for energy minimization. In addition, while the absolute ΔΔGelec values calculated with different force field differ, their ordering remains practically the same allowing for consistent ranking despite of the force field used. © 2016 Wiley Periodicals, Inc.

Predicting stabilizing mutations in proteins using Poisson-Boltzmann based models: study of unfolded state ensemble models and development of a successful binary classifier based on residue interaction energies

In many cases the stability of a protein has to be increased to permit its biotechnological use. Rational methods of protein stabilization based on optimizing electrostatic interactions have provided some fine successful predictions. However, the precise calculation of stabilization energies remains challenging, one reason being that the electrostatic effects on the unfolded state are often neglected. We have explored here the feasibility of incorporating Poisson-Boltzmann model electrostatic calculations performed on representations of the unfolded state as large ensembles of geometrically optimized conformations calculated using the ProtSA server. Using a data set of 80 electrostatic mutations experimentally tested in two-state proteins, the predictive performance of several such models has been compared to that of a simple one that considers an unfolded structure of non-interacting residues. The unfolded ensemble models, while showing correlation between the predicted stabilization values and the experimental ones, are worse than the simple model, suggesting that the ensembles do not capture well the energetics of the unfolded state. A more attainable goal is classifying potential mutations as either stabilizing or non-stabilizing, rather than accurately calculating their stabilization energies. To implement a fast classification method that can assist in selecting stabilizing mutations, we have used a much simpler electrostatic model based only on the native structure and have determined its precision using different stabilizing energy thresholds. The binary classifier developed finds 7 true stabilizing mutants out of every 10 proposed candidates and can be used as a robust tool to propose stabilizing mutations.

Binding Interactions of Keratin-Based Hair Fiber Extract to Gold, Keratin, and BMP-2

Hair-derived keratin biomaterials composed mostly of reduced keratin proteins (kerateines) have demonstrated their utility as carriers of biologics and drugs for tissue engineering. Electrostatic forces between negatively-charged keratins and biologic macromolecules allow for effective drug retention; attraction to positively-charged growth factors like bone morphogenetic protein 2 (BMP-2) has been used as a strategy for osteoinduction. In this study, the intermolecular surface and bulk interaction properties of kerateines were investigated. Thiol-rich kerateines were chemisorbed onto gold substrates to form an irreversible 2-nm rigid layer for surface plasmon resonance analysis. Kerateine-to-kerateine cohesion was observed in pH-neutral water with an equilibrium dissociation constant (KD) of 1.8 × 10(-4) M, indicating that non-coulombic attractive forces (i.e. hydrophobic and van der Waals) were at work. The association of BMP-2 to kerateine was found to be greater (KD = 1.1 × 10(-7) M), within the range of specific binding. Addition of salts (phosphate-buffered saline; PBS) shortened the Debye length or the electrostatic field influence which weakened the kerateine-BMP-2 binding (KD = 3.2 × 10(-5) M). BMP-2 in bulk kerateine gels provided a limited release in PBS (~ 10% dissociation in 4 weeks), suggesting that electrostatic intermolecular attraction was significant to retain BMP-2 within the keratin matrix. Complete dissociation between kerateine and BMP-2 occurred when the PBS pH was lowered (to 4.5), below the keratin isoelectric point of 5.3. This phenomenon can be attributed to the protonation of keratin at a lower pH, leading to positive-positive repulsion. Therefore, the dynamics of kerateine-BMP-2 binding is highly dependent on pH and salt concentration, as well as on BMP-2 solubility at different pH and molarity. The study findings may contribute to our understanding of the release kinetics of drugs from keratin biomaterials and allow for the development of better, more clinically relevant BMP-2-conjugated systems for bone repair and regeneration.

Binding energies of tyrosine kinase inhibitors: Error assessment of computational methods for imatinib and nilotinib binding

The binding energies of imatinib and nilotinib to tyrosine kinase have been determined by quantum mechanical (QM) computations, and compared with literature binding energy studies using molecular mechanics (MM). The potential errors in the computational methods include these critical factors. Errors in X-ray structures such as structural distortions and steric clashes give unrealistically high van der Waals energies, and erroneous binding energies.MM optimization gives a very different configuration to the QM optimization for nilotinib, whereas the imatinib ion gives similar configurations. Solvation energies are a major component of the overall binding energy. The QM based solvent model (PCM/SMD) gives different values from those used in the implicit PBSA solvent MM models. A major error in inhibitor—kinase binding lies in the non-polar solvation terms. Solvent transfer free energies and the required empirical solvent accessible surface area factors for nilotinib and imatinib ion to give the transfer free energies have been reverse calculated. These values differ from those used in the MM PBSA studies.An intertwined desolvation—conformational binding selectivity process is a balance of thermodynamic desolvation and intramolecular conformational kinetic control.The configurational entropies (TΔS) are minor error sources.

Novel ENAM and LAMB3 mutations in Chinese families with hypoplastic amelogenesis imperfecta

Amelogenesis imperfecta is a group of inherited diseases affecting the quality and quantity of dental enamel. To date, mutations in more than ten genes have been associated with non-syndromic amelogenesis imperfecta (AI). Among these, ENAM and LAMB3 mutations are known to be parts of the etiology of hypoplastic AI in human cases. When both alleles of LAMB3 are defective, it could cause junctional epidermolysis bullosa (JEB), while with only one mutant allele in the C-terminus of LAMB3, it could result in severe hypoplastic AI without skin fragility. We enrolled three Chinese families with hypoplastic autosomal-dominant AI. Despite the diagnosis falling into the same type, the characteristics of their enamel hypoplasia were different. Screening of ENAM and LAMB3 genes was performed by direct sequencing of genomic DNA from blood samples. Disease-causing mutations were identified and perfectly segregated with the enamel defects in three families: a 19-bp insertion mutation in the exon 7 of ENAM (c.406_407insTCAAAAAAGCCGACCACAA, p.K136Ifs*16) in Family 1, a single-base deletion mutation in the exon 5 of ENAM (c. 139delA, p. M47Cfs*11) in Family 2, and a LAMB3 nonsense mutation in the last exon (c.3466C>T, p.Q1156X) in Family 3. Our results suggest that heterozygous mutations in ENAM and LAMB3 genes can cause hypoplastic AI with markedly different phenotypes in Chinese patients. And these findings extend the mutation spectrum of both genes and can be used for mutation screening of AI in the Chinese population.

Targeting the cyclin-binding groove site to inhibit the catalytic activity of CDK2/cyclin A complex using p27(KIP1)-derived peptidomimetic inhibitors

Functionally activated cyclin-dependent kinase 2 (CDK2)/cyclin A complex has been validated as an interesting therapeutic target to develop the efficient antineoplastic drug based on the cell cycle arrest. Cyclin A binds to CDK2 and activates the kinases as well as recruits the substrate and inhibitors using a hydrophobic cyclin-binding groove (CBG). Blocking the cyclin substrate recruitment on CBG is an alternative approach to override the specificity hurdle of the currently available ATP site targeting CDK2 inhibitors. Greater understanding of the interaction of CDK2/cyclin A complex with p27 (negative regulator) reveals that the Leu-Phe-Gly (LFG) motif region of p27 binds with the CBG site of cyclin A to arrest the malignant cell proliferation that induces apoptosis. In the present study, Replacement with Partial Ligand Alternatives through Computational Enrichment (REPLACE) drug design strategies have been applied to acquire LFG peptide-derived peptidomimetics library. The peptidomimetics function is equivalent with respect to substrate p27 protein fashion but does not act as an ATP antagonist. The combined approach of molecular docking, molecular dynamics (MD), and molecular electrostatic potential and ADME/T prediction were carried out to evaluate the peptidomimetics. Resultant interaction and electrostatic potential maps suggested that smaller substituent is desirable at the position of phenyl ring to interact with Trp217, Arg250, and Gln254 residues in the active site. The best docked poses were refined by the MD simulations which resulted in conformational changes. After equilibration, the structure of the peptidomimetic and receptor complex was stable. The results revealed that the various substrate protein-derived peptidomimetics could serve as perfect leads against CDK2 protein.

Biological applications of classical electrostatics methods

Continuum electrostatics modeling of solvation based on the Poisson–Boltzmann (PB) equation has gained wide acceptance in biomolecular applications such as energetic analysis and structural visualization. Successful application of the PB solvent models requires careful calibration of the solvation parameters. Extensive testing and validation is also important to ensure accuracy in their applications. Limitation in the continuum modeling of solvation is also a known issue in certain biomolecular applications. Growing interest in membrane systems has further spurred developmental efforts to allow inclusion of membrane in the PB solvent models. Despite their past successes due to careful parameterization, algorithm development and parallel implementation, there is still much to be done to improve their transferability from the small molecular systems upon which they were developed and validated to complex macromolecular systems as advances in technology continue to push forward, providing ever greater computational resources to researchers to study more interesting biological systems of higher complexity.

On the electrostatic properties of homodimeric proteins

A large fraction of proteins function as homodimers, but it is not always clear why the dimerization is important for functionality since frequently each monomer possesses a distinctive active site. Recent work (PLoS Computational Biology, 9(2), e1002924) indicates that homodimerization may be important for forming an electrostatic funnel in the spermine synthase homodimer which guides changed substrates toward the active centers. This prompted us to investigate the electrostatic properties of a large set of homodimeric proteins and resulted in an observation that in a vast majority of the cases the dimerization indeed results in specific electrostatic features, although not necessarily in an electrostatic funnel. It is demonstrated that the electrostatic dipole moment of the dimer is predominantly perpendicular to the axis connecting the centers of the mass of the monomers. In addition, the surface points with highest potential are located in the proximity of the interfacial plane of the homodimeric complexes. These findings indicate that frequently homodimerization provides specific electrostatic features needed for the function of proteins.

Analysis of fast boundary-integral approximations for modeling electrostatic contributions of molecular binding

We analyze and suggest improvements to a recently developed approximate continuum-electrostatic model for proteins. The model, called BIBEE/I (boundary-integral based electrostatics estimation with interpolation), was able to estimate electrostatic solvation free energies to within a mean unsigned error of 4% on a test set of more than 600 proteins-a significant improvement over previous BIBEE models. In this work, we tested the BIBEE/I model for its capability to predict residue-by-residue interactions in protein-protein binding, using the widely studied model system of trypsin and bovine pancreatic trypsin inhibitor (BPTI). Finding that the BIBEE/I model performs surprisingly less well in this task than simpler BIBEE models, we seek to explain this behavior in terms of the models' differing spectral approximations of the exact boundary-integral operator. Calculations of analytically solvable systems (spheres and tri-axial ellipsoids) suggest two possibilities for improvement. The first is a modified BIBEE/I approach that captures the asymptotic eigenvalue limit correctly, and the second involves the dipole and quadrupole modes for ellipsoidal approximations of protein geometries. Our analysis suggests that fast, rigorous approximate models derived from reduced-basis approximation of boundary-integral equations might reach unprecedented accuracy, if the dipole and quadrupole modes can be captured quickly for general shapes.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Progress in developing Poisson-Boltzmann equation solvers

This review outlines the recent progress made in developing more accurate and efficient solutions to model electrostatics in systems comprised of bio-macromolecules and nano-objects, the last one referring to objects that do not have biological function themselves but nowadays are frequently used in biophysical and medical approaches in conjunction with bio-macromolecules. The problem of modeling macromolecular electrostatics is reviewed from two different angles: as a mathematical task provided the specific definition of the system to be modeled and as a physical problem aiming to better capture the phenomena occurring in the real experiments. In addition, specific attention is paid to methods to extend the capabilities of the existing solvers to model large systems toward applications of calculations of the electrostatic potential and energies in molecular motors, mitochondria complex, photosynthetic machinery and systems involving large nano-objects.

Poisson-Boltzmann Calculations: van der Waals or Molecular Surface?

The Poisson-Boltzmann equation is widely used for modeling the electrostatics of biomolecules, but the calculation results are sensitive to the choice of the boundary between the low solute dielectric and the high solvent dielectric. The default choice for the dielectric boundary has been the molecular surface, but the use of the van der Waals surface has also been advocated. Here we review recent studies in which the two choices are tested against experimental results and explicit-solvent calculations. The assignment of the solvent high dielectric constant to interstitial voids in the solute is often used as a criticism against the van der Waals surface. However, this assignment may not be as unrealistic as previously thought, since hydrogen exchange and other NMR experiments have firmly established that all interior parts of proteins are transiently accessible to the solvent.

Understanding the molecular mechanism of the broad and potent neutralization of HIV-1 by antibody VRC01 from the perspective of molecular dynamics simulation and binding free energy calculations

VRC01 is one of the most broadly and potently neutralizing HIV-1 antibodies known-it has been shown to neutralize 91 % of the tested primary isolate Env pseudoviruses by recognizing the viral envelope glycoprotein gp120. To explore the mechanism of HIV-1 neutralization by VRC01 and thus obtain valuable information for vaccine design, we performed molecular dynamics simulations and binding free energy calculations for apo-VRC01, apo-gp120, and the gp120-VRC01 complex. For gp120, residue energy decomposition analysis showed that the hotspot residues Asn280, Lys282, Asp368, Ile371, and Asp457 are located in three primary loops, including the CD4-binding loop, loop D, and loop V5. For VRC01, the hotspot residues Trp47, Trp50, Asn58, Arg61, Gln64, Trp100, and Tyr91 mainly come from CDR2 of the heavy chain. By decomposing the binding free energy into different components, intermolecular van der Waals interactions and nonpolar solvation were found to dominate the binding process. Principal component analysis of loops D and V5, which are related to neutralization resistance, indicated that these two areas have a larger conformational space in apo-gp120 compared to bound gp120. A comparison of three representative structures from the cluster analysis of loops D and V5 indicated that changes primarily occur at the tip of loop V5, and are caused by fluctuations in the terminal Glu1 residue of the antibody. This information can be used to guide the design of vaccines and small molecule inhibitors.

Bioinformatics and variability in drug response: a protein structural perspective

Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.