http://www.springerprotocols.com/cdp/access/showWebFeedListing This feed provides the latest 25 protocols in the given category. http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_1 Molecular simulation is a very powerful toolbox in modern molecular modeling, and enables us to follow and understand structure and dynamics with extreme detail—literally on scales where motion of individual atoms can be tracked. This chapter focuses on the two most commonly used methods, namely, energy minimization and molecular dynamics, that, respectively, optimize structure and simulate the natural motion of biological macromolecules. The common theoretical framework based on statistical mechanics is covered briefly as well as limitations of the computational approach, for instance, the lack of quantum effects and limited timescales accessible. As a practical example, a full simulation of the protein lysozyme in water is described step by step, including examples of necessary hardware and software, how to obtain suitable starting molecular structures, immersing it in a solvent, choosing good simulation parameters, and energy minimization. The chapter also describes how to analyze the simulation in terms of potential energies, structural fluctuations, coordinate stability, geometrical features, and, finally, how to create beautiful ray-traced movies that can be used in presentations. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_10 Implicit models of membrane environments offer computational advantages in simulations of membrane-interacting proteins and peptides. Such methods are especially useful for studies of long time scale processes, such as folding and aggregation, or very large complexes that are otherwise intractable with explicit lipid environments. Implicit models replace explicit solute—solvent interactions with a mean-field approach. In the most physical models, continuum dielectric electrostatics is combined with empirical formulations for the nonpolar components of the free energy of solvation. The practical use of a number of implicit membrane models ranging from the empirical IMM1 method to generalized Born-based methods with two-dielectric and multidielectric representations of biological membrane characteristics is presented. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_11 Three-dimensional analysis of protein structures is proving to be one of the most fruitful modes of biological and medical discovery in the early 21st century, providing fundamental insight into many (perhaps most) biochemical functions of relevance to the cause and treatment of diseases. Fully realizing such insight, however, would require analysis of too many distinct proteins for thorough laboratory analysis of all proteins to be feasible, thus, any method capable of accurate, efficient in silico structure prediction should prove highly expeditious. The technique generally acknowledged to provide the most accurate protein structure predictions, called comparative modeling, has, thus, attracted substantial attention and is the focus of this chapter. Although other reviews have reported on the method development and research history of comparative modeling, our discussion herein focuses on the general philosophy of the method and specific strategies for successfully achieving reliable and accurate models. The chapter, thus, relates aspects of template selection, sequence alignment, spatial alignment, loop and gap modeling, side chain modeling, structural refinement, and validation. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_12 Elucidating the structure of transmembrane proteins domains with high-resolution methods is a difficult and sometimes impossible task. Here, we explain the method of combining a limited amount of experimental data with automated high-throughput molecular dynamics (MD) simulations of α-helical transmembrane bundles in an explicit lipid bilayer/water environment. The procedure uses a systematic conformational search of the helix rotation with experimentally constrained MDs simulations. The experimentally determined helix tilt and rotational angle of a labeled residue with site-specific infrared dichroism allows us to select a unique high-resolution model from a number of possible energy minima encountered in the systematic conformational search. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_13 Nuclear magnetic resonance (NMR) has become a well-established method to characterize the structures of biomolecules in solution. High-quality structures are now produced, thanks to both experimental and computational developments, allowing the use of new NMR parameters and improved protocols and force fields in structure calculation and refinement. In this chapter, we give a short overview of the various types of NMR data that can provide structural information, and then focus on the structure calculation methodology itself. We discuss and illustrate with tutorial examples both “classical” structure calculation and refinement approaches as well as more recently developed protocols for modeling biomolecular complexes. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_14 Conformational changes are the hallmarks of protein dynamics and are often intimately related to protein functions. Molecular dynamics (MD) simulation is a powerful tool to study the time-resolved properties of protein structure in atomic details. In this chapter, we discuss the various applications of MD simulation to the study of protein conformational changes, and introduce several selected advanced techniques that may significantly increase the sampling efficiencies, including locally enhanced sampling (LES), and grow-to-fit molecular dynamics (G2FMD). 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_15 Computational protein folding can be classified into pathway and sampling approaches. Here, we use the AMBER simulation package as an example to illustrate the protocols for all-atom molecular simulations of protein folding, including system setup, simulation, and analysis. We introduced two traditional pathway approaches: ab inito folding and high-temperature unfolding. The popular replica exchange method was chosen to represent sampling approaches. Our emphasis is placed on the analysis of the simulation trajectories, and some in-depth discussions are provided for commonly encountered problems. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_16 A short review of the results of molecular modeling of prion disease is presented in this chapter. According to the “one-protein theory” proposed by Prusiner, prion proteins are misfolded naturally occurring proteins, which, on interaction with correctly folded proteins may induce misfolding and propagate the disease, resulting in insoluble amyloid aggregates in cells of affected specimens. Because of experimental difficulties in measurements of origin and growth of insoluble amyloid aggregations in cells, theoretical modeling is often the only one source of information regarding the molecular mechanism of the disease. Replica exchange Monte Carlo simulations presented in this chapter indicate that proteins in the native state, N, on interaction with an energetically higher structure, R, can change their conformation into R and form a dimer, R2. The addition of another protein in the N state to R2 may lead to spontaneous formation of a trimer, R3. These results reveal the molecular basis for a model of prion disease propagation or conformational diseases in general. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_17 The availability of three-dimensional (3D) models of both drug leads (small molecule ligands) and drug targets (proteins) is essential to molecular docking and computational drug discovery. This chapter describes an emerging methodology that can be used to identify both drug leads and drug targets using three newly developed web-accessible databases: 1) DrugBank; 2) The Human Metabolome Database; and 3) PubChem. Specifically, it illustrates how putative drug targets and drug leads for exogenous diseases (i.e., infectious diseases) can be readily identified and their 3D structures selected using only the genomic sequences from pathogenic bacteria or viruses as input. It also illustrates how putative drug targets and drug leads for endogenous diseases (i.e., non-infectious diseases or chronic conditions) can be identified using similar databases and similar sequence input. This chapter is intended to illustrate how bioinformatics and cheminformatics can work synergistically to help provide the necessary inputs for computer-aided drug design. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_18 An important contribution to today's computer-aided drug design is the automated screening of large compound databases against structurally resolved protein receptors targets. The introduction of ligand flexibility has, by now, become a standardized procedure. In contrast, a general approach to treat target degrees of freedom is still to be found, a consequence of the extreme increase of computational complexity, which comes along with the relaxation of protein degrees of freedom. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_19 Molecular docking is a key tool in structural molecular biology and computer-assisted drug design. The goal of ligand—protein docking is to predict the predominant binding mode(s) of a ligand with a protein of known three-dimensional structure. Successful docking methods search high-dimensional spaces effectively and use a scoring function that correctly ranks candidate dockings. Docking can be used to perform virtual screening on large libraries of compounds, rank the results, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization. The setting up of the input structures for the docking is just as important as the docking itself, and analyzing the results of stochastic search methods can sometimes be unclear. This chapter discusses the background and theory of molecular docking software, and covers the usage of some of the most-cited docking software. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_2 A description of Monte Carlo methods for simulation of proteins is given. Advantages and disadvantages of the Monte Carlo approach are presented. The theoretical basis for calculating equilibrium properties of biological molecules by the Monte Carlo method is presented. Some of the standard and some of the more recent ways of performing Monte Carlo on proteins are presented. A discussion of the estimation of errors in properties calculated by Monte Carlo is given. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_3 A method for incorporating quantum mechanics into enzyme kinetics modeling is presented. Three aspects are emphasized: 1) combined quantum mechanical and molecular mechanical methods are used to represent the potential energy surface for modeling bond forming and breaking processes, 2) instantaneous normal mode analyses are used to incorporate quantum vibrational free energies to the classical potential of mean force, and 3) multidimensional tunneling methods are used to estimate quantum effects on the reaction coordinate motion. Centroid path integral simulations are described to make quantum corrections to the classical potential of mean force. In this method, the nuclear quantum vibrational and tunneling contributions are not separable. An integrated centroid path integral—free energy perturbation and umbrella sampling (PI-FEP/UM) method along with a bisection sampling procedure was summarized, which provides an accurate, easily convergent method for computing kinetic isotope effects for chemical reactions in solution and in enzymes. In the ensemble-averaged variational transition state theory with multidimensional tunneling (EA-VTST/MT), these three aspects of quantum mechanical effects can be individually treated, providing useful insights into the mechanism of enzymatic reactions. These methods are illustrated by applications to a model process in the gas phase, the decarboxy-lation reaction of N-methyl picolinate in water, and the proton abstraction and reprotonation process catalyzed by alanine racemase. These examples show that the incorporation of quantum mechanical effects is essential for enzyme kinetics simulations. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_4 In the context of molecular dynamics simulations of proteins, the term “force field” refers to the combination of a mathematical formula and associated parameters that are used to describe the energy of the protein as a function of its atomic coordinates. In this review, we describe the functional forms and parameterization protocols of the widely used biomolecular force fields Amber, CHARMM, GROMOS, and OPLS-AA. We also summarize the ability of various readily available noncommercial molecular dynamics packages to perform simulations using these force fields, as well as to use modern methods for the generation of constant-temperature, constant-pressure ensembles and to treat long-range interactions. Finally, we finish with a discussion of the ability of these force fields to support the modeling of proteins in conjunction with nucleic acids, lipids, carbohydrates, and/or small molecules. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_5 Normal mode analysis and essential dynamics analysis are powerful methods used for the analysis of collective motions in biomolecules. Their application has led to an appreciation of the importance of protein dynamics in function and the relationship between structure and dynamical behavior. In this chapter, the methods and their implementation are introduced and recent developments such as elastic networks and advanced sampling techniques are described. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_6 Reliable first-principles calculations of protein—ligand binding constants can play important roles in the study and characterization of biological recognition processes and applications to drug discovery. A detailed procedure for such a calculation is outlined in this chapter. The methodology is computationally implemented using the molecular dynamics sampling of relevant configurational spaces and free energy perturbation techniques. The procedure is illustrated with the model system of the phosphotyrosine peptide binding to the Src SH2 domain. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_7 Selected applications of free energy calculations to the realm of membrane proteins are reviewed. The theoretical underpinnings of these calculations are described, focusing on free energy perturbation and the use of thermodynamic integration to determine free energy changes along well—delineated order parameters. Current strategies for improving the reliability of free energy calculations, while making them somewhat more affordable are outlined. Application of the free energy methodology to understand the structure and function of membrane proteins is illustrated in three concrete examples: The binding of an agonist ligand to a G protein—coupled receptor, the assisted transport of a small permeant through a membrane channel, and the recognition and association of transmembrane α—helical domains. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_8 Membrane protein structures are underrepresented in the Protein Data Bank (PDB) because of difficulties associated with expression and crystallization. As such, it is one area in which computational studies, particularly molecular dynamics (MD), can provide useful additional information. Recently, there has been substantial progress in the simulation of lipid bilayers and membrane proteins embedded within them. Initial efforts at simulating membrane proteins embedded within a lipid bilayer were relatively slow and interactive processes, but recent advances now mean that the setup and running of membrane protein simulations is somewhat more straightforward, although not without its problems. In this chapter, we outline practical methods for setting up and running MD simulations of a membrane protein embedded within a lipid bilayer and discuss methodologies that are likely to contribute future improvements. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-177-2_9 This chapter discusses the practical aspects of setting up molecular dynamics simulations for membrane-associated proteins and peptides. Special emphasis lies on the analysis of such systems. The main focus is the association between a cationic peptide and an anionic lipid bilayer—a peptide/lipid—bilayer system—but the extension onto more complicated systems is discussed. Topology files for selected lipids and several new analysis tools relevant for protein—membrane simulations are presented, the most important ones of which are: g_helixaxis, to calculate the axis of a helix and its angle with the bilayer; g_arom, to calculate aromatic order parameters; and g_under, to calculate which lipids interact with the protein. A procedure is explained to calculate properties involving peptide-interacting lipids only, as opposed to all lipids. 2008-04-04T04:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-148-6_1 Complex diseases can involve the interaction of multiple genes and environmental factors. Discovering these genes is difficult, and in silico based strategies can significantly improve their detection. Data mining and automated tracking of new knowledge facilitate locus mapping. At the gene search stage, in silico prioritization of candidate genes plays an indispensable role in dealing with linked or associated loci. In silico analysis can also differentiate subtle consequences of coding DNA variants and remains the major method to predict functionality for non-coding DNA variants, particularly those in promoter regions. 2007-12-21T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-148-6_10 DNA sequencing is increasingly used in a range of medical activities involving DNA diagnostics and research. This is the result of improving technology and cheaper costs. Paradoxically, a greater demand for DNA sequencing has placed additional work on the laboratory because sequencing profiles must be checked visually despite the availability of informatics-based tools in interpreting DNA sequence traces. In this environment it is essential to have more sophisticated software that will allow the sites of known and unknown DNA variants to be quickly identified, as well as providing an objective assessment of quality for the DNA sequence generated. This chapter describes the Applied Biosystems SeqScape® software program (version 2.5) and how it has assisted in the interpretation of DNA sequencing in a DNA diagnostic laboratory. 2007-12-21T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-148-6_11 Increasingly, the molecular genetics laboratory has to assess the biological significance of changes (variants) in a DNA sequence. Using the large genes BRCA1 and BRCA2 as examples, some approaches used to determine the biological significance of DNA variants are described. These include the characterization of the variant through a review of the literature and the various databases to assess if it has previously been described. Potential difficulties with the various databases that are available are described. Other considerations include the co-inheritance of the variant with other DNA changes, and its evolutionary conservation. Determining the possible effect of the variant on protein function is described in terms of the Grantham assessment as well as identifying functional domains. Studies looking at the distribution of the variant in both the population and the family can also help in assessing its significance. Loss of the variant in a tumor sample would imply that it is not deleterious. Ultimately, it is not any single parameter that helps determine the DNA variants biological significance. Usually this requires multiple lines of evidence. 2007-12-21T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-148-6_12 Disease- and locus-specific variant databases have been a valuable resource to clinical and research geneticists. With the recent rapid developments in technologies, the number of DNA variants detected in a typical molecular genetics laboratory easily exceeds 1,000. To keep track of the growing inventory of DNA variants, many laboratories employ information technology to store the data as well as distributing the data and its associated information to clinicians and researchers via the Web. While it is a valuable resource, the hosting of a web-accessible database requires collaboration between bioinformaticians and biologists and careful planning to ensure its usability and availability. In this chapter, a series of tutorials on building a local DNA variant database out of a sample dataset will be provided. However, this tutorial will not include programming details on building a web interface and on constructing the web application necessary for web hosting. Instead, an introduction to the two commonly used methods for hosting web-accessible variant databases will be described. Apart from the tutorials, this chapter will also consider the resources and planning required for making a variant database project successful. 2007-12-21T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-148-6_13 A problem frequently encountered by the biological scientist is the identification of a previously unknown gene or protein sequence, where there are few or no clues as to the biochemical function, ligand specificity, gene regulation, protein–protein interactions, tissue specificity, cellular localization, developmental phase of activity, or biological role. Through the process of bioinformatics there are now many approaches for predicting answers to at least some of these questions, often then allowing the design of more insightful experiments to characterize more definitively the new protein. 2007-12-21T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-148-6_14 Peptide mass fingerprinting is a simple, quick, cheap, and relatively effective method of identifying proteins from mass spectrometry data. Proteins extracted from the complex mixture comprising the proteome of a sample are individually digested with a proteolytic enzyme into a series of peptide fragments. The set of masses of these peptides, determined by mass spectrometry, form a peptide mass fingerprint of the protein. Comparison of this experimental fingerprint with the theoretical fingerprints of all known protein sequences for this organism, derived computationally from a protein sequence database, allows the identification of the particular protein. In this chapter, I discuss the technique including preparation for the peptide mass fingerprinting analysis, the appropriate selection of computational search parameters, and the analysis and interpretation of search results in the context of identifying proteins from microbial samples. 2007-12-21T05:00:00Z