PBEQ-Solver for online visualization of electrostatic potential of biomolecules

PBEQ-Solver provides a web-based graphical user interface to read biomolecular structures, solve the Poisson-Boltzmann (PB) equations and interactively visualize the electrostatic potential. PBEQ-Solver calculates (i) electrostatic potential and solvation free energy, (ii) protein–protein (DNA or RNA) electrostatic interaction energy and (iii) pKa of a selected titratable residue. All the calculations can be performed in both aqueous solvent and membrane environments (with a cylindrical pore in the case of membrane). PBEQ-Solver uses the PBEQ module in the biomolecular simulation program CHARMM to solve the finite-difference PB equation of molecules specified by users. Users can interactively inspect the calculated electrostatic potential on the solvent-accessible surface as well as iso-electrostatic potential contours using a novel online visualization tool based on MarvinSpace molecular visualization software, a Java applet integrated within CHARMM-GUI (http://www.charmm-gui.org). To reduce the computational time on the server, and to increase the efficiency in visualization, all the PB calculations are performed with coarse grid spacing (1.5 Å before and 1 Å after focusing). PBEQ-Solver suggests various physical parameters for PB calculations and users can modify them if necessary. PBEQ-Solver is available at http://www.charmm-gui.org/input/pbeqsolver

APBSmem: A Graphical Interface for Electrostatic Calculations at the Membrane

Electrostatic forces are one of the primary determinants of molecular interactions. They help guide the folding of proteins, increase the binding of one protein to another and facilitate protein-DNA and protein-ligand binding. A popular method for computing the electrostatic properties of biological systems is to numerically solve the Poisson-Boltzmann (PB) equation, and there are several easy-to-use software packages available that solve the PB equation for soluble proteins. Here we present a freely available program, called APBSmem, for carrying out these calculations in the presence of a membrane. The Adaptive Poisson-Boltzmann Solver (APBS) is used as a back-end for solving the PB equation, and a Java-based graphical user interface (GUI) coordinates a set of routines that introduce the influence of the membrane, determine its placement relative to the protein, and set the membrane potential. The software Jmol is embedded in the GUI to visualize the protein inserted in the membrane before the calculation and the electrostatic potential after completing the computation. We expect that the ease with which the GUI allows one to carry out these calculations will make this software a useful resource for experimenters and computational researchers alike. Three examples of membrane protein electrostatic calculations are carried out to illustrate how to use APBSmem and to highlight the different quantities of interest that can be calculated

DelPhi Web Server: A comprehensive online suite for electrostatic calculations of biological macromolecules and their complexes.

Here we report a web server, the DelPhi web server, which utilizes DelPhi program to calculate electrostatic energies and the corresponding electrostatic potential and ionic distributions, and dielectric map. The server provides extra services to fix structural defects, as missing atoms in the structural file and allows for generation of missing hydrogen atoms. The hydrogen placement and the corresponding DelPhi calculations can be done with user selected force field parameters being either Charmm22, Amber98 or OPLS. Upon completion of the calculations, the user is given option to download fixed and protonated structural file, together with the parameter and Delphi output files for further analysis. Utilizing Jmol viewer, the user can see the corresponding structural file, to manipulate it and to change the presentation. In addition, if the potential map is requested to be calculated, the potential can be mapped onto the molecule surface. The DelPhi web server is available from http://compbio.clemson.edu/delphi_webserver

FL118, a novel camptothecin derivative, is insensitive to ABCG2 expression and shows improved efficacy in comparison with irinotecan in colon and lung cancer models with ABCG2-induced resistance

International audienceAbstractBackgroundIrinotecan is a camptothecin analogue currently used in clinical practice to treat advanced colorectal cancer. However, acquired resistance mediated by the drug efflux pump ABCG2 is a recognized problem. We reported on a novel camptothecin analogue, FL118, which shows anticancer activity superior to irinotecan. In this study, we sought to investigate the potency of FL118 versus irinotecan or its active metabolite, SN-38, in both in vitro and in vivo models of human cancer with high ABCG2 activity. We also sought to assess the potency and ABCG2 affinity of several FL118 analogues with B-ring substitutions.MethodsColon and lung cancer cells with and without ABCG2 overexpression were treated with FL118 in the presence and absence of Ko143, an ABCG2-selective inhibitor, or alternatively by genetically modulating ABCG2 expression. Using two distinct in vivo human tumor animal models, we further assessed whether FL118 could extend time to progression in comparison with irinotecan. Lastly, we investigated a series of FL118 analogues with B-ring substitutions for ABCG2 sensitivity.ResultsBoth pharmacological inhibition and genetic modulation of ABCG2 demonstrated that, in contrast to SN-38, FL118 was able to bypass ABCG2-mediated drug resistance. FL118 also extended time to progression in both in vivo models by more than 50% compared with irinotecan. Lastly, we observed that FL118 analogues with polar substitutions had higher affinity for ABCG2, suggesting that the nonpolar nature of FL118 plays a role in bypassing ABCG2-mediated resistance.ConclusionsOur results suggest that in contrast to SN-38 and topotecan, FL118 is a poor substrate for ABCG2 and can effectively overcome ABCG2-mediated drug resistance. Our findings expand the uniqueness of FL118 and support continued development of FL118 as an attractive therapeutic option for patients with drug-refractory cancers resulting from high expression of ABCG2

Targeting a cross-reactive Gly m 5 soy peptide as responsible for hypersensitivity reactions in a milk allergy mouse model

Background: Cross-reactivity between soybean allergens and bovine caseins has been previously reported. In this study we aimed to map epitopes of the major soybean allergen Gly m 5 that are co-recognized by casein specific antibodies, and to identify a peptide responsible for the cross-reactivity. Methods: Cow's milk protein (CMP)-specific antibodies were used in different immunoassays (immunoblotting, ELISA, ELISA inhibition test) to evaluate the in vitro recognition of soybean proteins (SP). Recombinant Gly m 5 (α), a truncated fragment containing the C-terminal domain (α-T) and peptides of α-T were obtained and epitope mapping was performed with an overlapping peptide assay. Bioinformatics tools were used for epitope prediction by sequence alignment, and for modelling the cross-recognized soy proteins and peptides. The binding of SP to a monoclonal antibody was studied by surface Plasmon resonance (SPR). Finally, the in vivo cross-recognition of SP was assessed in a mouse model of milk allergy. Results: Both α and α-T reacted with the different CMP-specific antibodies. α-T contains IgG and IgE epitopes in several peptides, particularly in the peptide named PA. Besides, we found similar values of association and dissociation constants between the α-casein specific mAb and the different milk and soy components. The food allergy mouse model showed that SP and PA contain the cross-reactive B and T epitopes, which triggered hypersensitivity reactions and a Th2-mediated response on CMP-sensitized mice. Conclusions: Gly m 5 is a cross-reactive soy allergen and the α-T portion of the molecule contains IgG and IgE immunodominant epitopes, confined to PA, a region with enough conformation to be bound by antibodies. These findings contribute to explain the intolerance to SP observed in IgE-mediated CMA patients, primarily not sensitised to SP, as well as it sets the basis to propose a mucosal immunotherapy for milk allergy using this soy peptide.Centro de Investigación y Desarrollo en Fermentaciones IndustrialesCentro de Investigación y Desarrollo en Criotecnología de AlimentosLaboratorio de Investigaciones del Sistema InmuneFacultad de Ciencias Exacta

A topological approach for protein classification

Protein function and dynamics are closely related to its sequence and structure. However prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology. Protein classification, which is typically done through measuring the similarity be- tween proteins based on protein sequence or physical information, serves as a crucial step toward the understanding of protein function and dynamics. Persistent homology is a new branch of algebraic topology that has found its success in the topological data analysis in a variety of disciplines, including molecular biology. The present work explores the potential of using persistent homology as an indepen- dent tool for protein classification. To this end, we propose a molecular topological fingerprint based support vector machine (MTF-SVM) classifier. Specifically, we construct machine learning feature vectors solely from protein topological fingerprints, which are topological invariants generated during the filtration process. To validate the present MTF-SVM approach, we consider four types of problems. First, we study protein-drug binding by using the M2 channel protein of influenza A virus. We achieve 96% accuracy in discriminating drug bound and unbound M2 channels. Additionally, we examine the use of MTF-SVM for the classification of hemoglobin molecules in their relaxed and taut forms and obtain about 80% accuracy. The identification of all alpha, all beta, and alpha-beta protein domains is carried out in our next study using 900 proteins. We have found a 85% success in this identifica- tion. Finally, we apply the present technique to 55 classification tasks of protein superfamilies over 1357 samples. An average accuracy of 82% is attained. The present study establishes computational topology as an independent and effective alternative for protein classification

Analysis on Some Basic Ion Channel Modeling Problems

The modeling and simulation of ion channel proteins are essential to the study of many vital physiological processes within a biological cell because most ion channel properties are very difficult to address experimentally in biochemistry. They also generate a lot of new numerical issues to be addressed in applied and computational mathematics. In this dissertation, we mainly deal with some numerical issues that are arisen from the numerical solution of one important ion channel dielectric continuum model, Poisson-Nernst-Planck (PNP) ion channel model, based on the finite element approximation approach under different boundary conditions and unstructured tetrahedral meshes. In particular, we present the derivation of an improved PNP ion channel model using Dirichlet boundary value conditions and membrane surface charges, and obtain its variational formulations. To solve this PNP ion channel model numerically, we develop a fast finite element iterative method and program it as a software package by using effective numerical techniques. This work makes it possible for us to carry out numerical tests in order to study the affection of different boundary value conditions on the PNP numerical solutions. To solve a PNP ion channel model by the finite element method, one important task is to generate an interface fitted unstructured tetrahedral mesh but it is very challenging to complete since the PNP ion channel model involves three physical regions -- a protein region, a membrane region, and a solvent region, and the interfaces between these three regions are very complex. To address this mesh challenge, in this dissertation, we develop a new algorithm for generating a triangular surface mesh of a simulation box domain and a new algorithm for constructing a tetrahedral mesh of the membrane region, such that we can easily split a mesh of the simulation box domain into three submeshes --- the meshes of protein, membrane, and solvent regions in high quality. Remarkably, our membrane mesh generation algorithm works for an ion channel protein with an irregular ion channel pore provided that a triangular mesh of the interface between the membrane and protein regions does not have any hole. Furthermore, we implement these two new mesh algorithms based on the state-of-the-art package FEniCS, and then adapt them to one commonly-used ion channel mesh generation package. With our PNP ion channel program package, we study the impacts of boundary value conditions, membrane surface changes, and simulation box sizes on the quality of a PNP ion channel model. Such studies are done numerically by using crystallographic molecular structures of ion channel proteins in a solution of multiple ionic species. We visualize the three-dimensional electrostatic potential and ionic concentrations not only in color mapping on a cross-section of protein, membrane, or solvent region but also in two-dimensional curves with curve values being the average values of potential and concentration functions over a block partition of the solvent region along the membrane normal direction. %We also test whether the channel conductance and charge selectivity obtained from the PNP ion channel model could be sensitive to some mutations of an ion channel protein