A critical cross-validation of high throughput structural binding prediction methods for pMHC

T-cells recognize antigens via their T-cell receptors. The major histocompatibility complex (MHC) binds antigens in a specific way, transports them to the surface and presents the peptides to the TCR. Many in silico approaches have been developed to predict the binding characteristics of potential T-cell epitopes (peptides), with most of them being based solely on the amino acid sequence. We present a structural approach which provides insights into the spatial binding geometry. We combine different tools for side chain substitution (threading), energy minimization, as well as scoring methods for protein/peptide interfaces. The focus of this study is on high data throughput in combination with accurate results. These methods are not meant to predict the accurate binding free energy but to give a certain direction for the classification of peptides into peptides that are potential binders and peptides that definitely do not bind to a given MHC structure. In total we performed approximately 83,000 binding affinity prediction runs to evaluate interactions between peptides and MHCs, using different combinations of tools. Depending on the tools used, the prediction quality ranged from almost random to around 75% of accuracy for correctly predicting a peptide to be either a binder or a non-binder. The prediction quality strongly depends on all three evaluation steps, namely, the threading of the peptide, energy minimization and scoring.     

Capillary electrophoresis and herbicide analysis: Present and future perspectives

The aim of this article is to provide an overview on the potential of the CE–herbicides binomial. To this end, the methods proposed so far are discussed: their characteristics, types of samples and analytes to which the methods have been applied, sample preparation steps, if required (e.g. cleanup–preconcentration, derivatization steps), and type of detection in each case. Also, the methods are compared with counterparts based on LC, when appropriate. The role of MS detection in present and future analytical research in this field (both for identification and quantitation) are commented. The foreseeable and desirable trends in analysis of herbicides are also outlined in the light of the present trends in metabolomics as a way of knowing the pathways, the intermediate and final degradation products that can influence the crops and the food chain of humans and other animals, as a result.     

Protein identification methods in proteomics

A combination of high-resolution two-dimensional (2-D) polyacrylamide gel electrophoresis, highly sensitive biological mass spectrometry, and the rapidly growing protein and DNA databases has paved the way for high-throughput proteomics. This review concentrates on protein identification. We first discuss the use of protein electroblotting and Edman sequencing as tools for de novo sequencing and protein identification. In the second part, we highlight matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) as one of the main contemporary analytical methods for linking gel-separated proteins to entries in sequence databases. In this context we describe the two main MALDI-MS-based identification methods: (i) peptide mass fingerprinting, and (ii) post-source decay (PSD) analysis. In the last part, we briefly emphasize the importance of sample preparation for obtaining highly sensitive and high-quality MALDI-MS spectra.     

Proteomics and peptidomics in neuroscience. Experience of capabilities and limitations in a neurochemical laboratory

The increasing use of proteomics has created a basis for new strategies to develop methodologies for rapid identification of protein patterns in living organisms. It has also become evident that proteomics has other potential applications than protein and peptide identification, e.g. protein characterization, with the aim of revealing their structure, function(s) and interactions of proteins. In comparative proteomics studies, the protein expression of a certain biological system is compared with another system or the same system under perturbed conditions. Global identification of proteins in neuroscience is extremely complex, owing to the limited availability of biological material and very low concentrations of the molecules. Moreover, in addition to proteins, there are number of peptides that must also be considered in global studies on the central nervous system. In this overview, we focus on and discuss problems related to the different sources of biological material and sample handling, which are part of all preparatory and analytical steps. Straightforward protocols are desirable to avoid excessive purification steps, since loss of material at each step is inevitable. We would like to merge the two worlds of proteomics/peptidomics and neuroscience, and finally we consider different practical and technical aspects, illustrated with examples from our laboratory.     

Free resources to assist structure-based virtual ligand screening experiments

In today's research environment, a wealth of experimental/theoretical structural data is available and the number of therapeutically relevant macromolecular structures is growing rapidly. This, coupled with the huge number of small non-peptide potential drug candidates easily available (over 7 million compounds), highlight the need of using computer-aided techniques for the efficient identification and optimization of novel hit compounds. Virtual (or in silico) ligand screening based on the three-dimensional structure of macromolecular targets (SB-VLS) is firmly established as an important approach to identify chemical entities that have a high likelihood of binding to a target molecule to elicit desired biological responses. A myriad of free applications and services facilitating the drug discovery process have been posted on the Web. In this review, we cite over 350 URLs that are useful for SB-VLS projects and essentially free for academic groups. We attempt to provide links for in silico ADME/tox prediction tools, compound collections, some ligand-based methods, characterization/simulation of 3D targets and homology modeling tools, druggable pocket predictions, active site comparisons, analysis of macromolecular interfaces, protein docking tools to help identify binding pockets and protein-ligand docking/scoring methods. As such, we aim at providing both, methods pertaining to the field of Structural Bioinformatics (defined here as tools to study macromolecules) and methods pertaining to the field of Chemoinformatics (defined here as tools to make better decisions faster in the arena of drug/lead identification and optimization). We also report several recent success stories using these free computer methods. This review should help readers finding free computer tools useful for their projects. Overall, we are confident that these tools will facilitate rapid and cost-effective identification of new hit compounds. The URLs presented in this review will be updated regularly at www.vls3d.com in the coming months, "Links" section.     

An efficient algorithm for multistate protein design based on FASTER

Most of the methods that have been developed for computational protein design involve the selection of side‐chain conformations in the context of a single, fixed main‐chain structure. In contrast, multistate design (MSD) methods allow sequence selection to be driven by the energetic contributions of multiple structural or chemical states simultaneously. This methodology is expected to be useful when the design target is an ensemble of related states rather than a single structure, or when a protein sequence must assume several distinct conformations to function. MSD can also be used with explicit negative design to suggest sequences with altered structural, binding, or catalytic specificity. We report implementation details of an efficient multistate design optimization algorithm based on FASTER (MSD‐FASTER). We subjected the algorithm to a battery of computational tests and found it to be generally applicable to various multistate design problems; designs with a large number of states and many designed positions are completely feasible. A direct comparison of MSD‐FASTER and multistate design Monte Carlo indicated that MSD‐FASTER discovers low‐energy sequences much more consistently. MSD‐FASTER likely performs better because amino acid substitutions are chosen on an energetic basis rather than randomly, and because multiple substitutions are applied together. Through its greater efficiency, MSD‐FASTER should allow protein designers to test experimentally better‐scoring sequences, and thus accelerate progress in the development of improved scoring functions and models for computational protein design. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Levels in the interpretive optimisation of selectivity in high-performance liquid chromatography: a magical mystery tour

Interpretive approaches for selectivity optimisation, which are those supported by retention models, are able to exploit efficiently the capabilities of the chromatographic system. The resolution of a mixture is usually faced in a first trial by looking for a unique experimental condition, able to resolve all compounds in the sample. If this is not possible, the problem can be outlined with less ambitious aims, focusing on only some compounds. In an extreme case, a single analyte can be individually optimised. Current strategies that give answer to the different goals pursued in the analysis, which are classified as total, partial and specific, are reviewed. Optimisation oriented to deconvolution, useful in case of partial coelution, and robust measurements of resolution, are also outlined. The steps recognised in any chromatographic optimisation procedure, and some fundamentals and tools used in optimisation approaches for isocratic and gradient elution are commented to explain different strategies. Examples of increasing complexity are supplied to explain the problematic arose, and the convenience in applying a certain methodology. Details on the mathematical treatment for each particular optimisation strategy are also given.     

Urine sample preparation for proteomic analysis

Sample preparation for both environmental and more importantly biological matrices is a bottleneck of all kinds of analytical processes. In the case of proteomic analysis this element is even more important due to the amount of cross‐reactions that should be taken into consideration. The incorporation of new post‐translational modifications, protein hydrolysis, or even its degradation is possible as side effects of proteins sample processing. If protocols are evaluated appropriately, then identification of such proteins does not bring difficulties. However, if structural changes are provided without sufficient attention then protein sequence coverage will be reduced or even identification of such proteins could be impossible. This review summarizes obstacles and achievements in protein sample preparation of urine for proteome analysis using different tools for mass spectrometry analysis. The main aim is to present comprehensively the idea of urine application as a valuable matrix. This article is dedicated to sample preparation and application of urine mainly in novel cancer biomarkers discovery.     

Modulating Protein–Protein Interactions by Cyclic and Macrocyclic Peptides. Prominent Strategies and Examples

Cyclic and macrocyclic peptides constitute advanced molecules for modulating protein–protein interactions (PPIs). Although still peptide derivatives, they are metabolically more stable than linear counterparts, and should have a lower degree of flexibility, with more defined secondary structure conformations that can be adapted to imitate protein interfaces. In this review, we analyze recent progress on the main methods to access cyclic/macrocyclic peptide derivatives, with emphasis in a few selected examples designed to interfere within PPIs. These types of peptides can be from natural origin, or prepared by biochemical or synthetic methodologies, and their design could be aided by computational approaches. Some advances to facilitate the permeability of these quite big molecules by conjugation with cell penetrating peptides, and the incorporation of β-amino acid and peptoid structures to improve metabolic stability, are also commented. It is predicted that this field of research could have an important future mission, running in parallel to the discovery of new, relevant PPIs involved in pathological processes.     

A Water‐Soluble Conjugated Polymer for Protein Identification and Denaturation Detection

Rapid and sensitive methods to detect proteins and protein denaturation have become increasingly needful in the field of proteomics, medical diagnostics, and biology. In this paper, we have reported the synthesis of a new cationic water‐soluble conjugated polymer that contains fluorene and diene moieties in the backbone ( PFDE ) for protein identification by sensing an array of PFDE solutions in different ionic strengths using the linear discriminant analysis technique (LDA). The PFDE can form complexes with proteins by electrostatic and/or hydrophobic interactions and exhibits different fluorescence response. Three main factors contribute to the fluorescence response of PFDE , namely, the net charge density on the protein surface, the hydrophobic nature of the protein, and the metalloprotein characteristics. The denaturation of proteins can also be detected using PFDE as a fluorescent probe. The interactions between PFDE and proteins were also studied by dynamic light scattering (DLS) and isothermal titration microcalorimetry (ITC) techniques. In contrast to other methods based on conjugated polymers, the synthesis of a series of quencher or dye‐labeled acceptors or protein substrates has been avoided in our method, which significantly reduces the cost and the synthetic complexity. Our method provides promising applications on protein identification and denaturation detection in a simple, fast, and label‐free manner based on non‐specific interaction‐induced perturbation of PFDE fluorescence response.     

Data processing for mass spectrometry-based metabolomics

Modern analytical technologies afford comprehensive and quantitative investigation of a multitude of different metabolites. Typical metabolomic experiments can therefore produce large amounts of data. Handling such complex datasets is an important step that has big impact on extent and quality at which the metabolite identification and quantification can be made, and thus on the ultimate biological interpretation of results. Increasing interest in metabolomics thus led to resurgence of interest in related data processing. A wide variety of methods and software tools have been developed for metabolomics during recent years, and this trend is likely to continue. In this paper we overview the key steps of metabolomic data processing and focus on reviewing recent literature related to this topic, particularly on methods for handling data from liquid chromatography mass spectrometry (LC-MS) experiments.     

New approach for rapid detection of known hemoglobin variants using LC-MS/MS combined with a peptide database

The identification of hemoglobin (Hb) variants is usually performed by means of different analytical steps and methodologies. Phenotypic methods, such as gel electrophoresis and high performance liquid chromatography, are used to detect the different electrophoretic or chromatographic behaviors of hemoglobin variants in comparison to HbA0 used as a control. These data often need to be combined with mass spectrometry analyses of intact globins and their tryptic peptide mixtures. As an alternative to a 'step-by-step' procedure, we have developed a 'single step' approach for the identification of Hb variants present in biological samples. This is based on the microHPLC-ESI-MS/MS analysis of the peptide mixture generated by a tryptic digestion of diluted Hb samples and an in-house new database containing solely the variant tryptic peptide of known human Hb variants. The experimental results (full MS and MS/MS spectra) are correlated with theoretical mass spectra generated from our in-house-built variant peptide database (Hbp) using the SEQUEST algorithm. Simple preparation of samples and an automated identification of the variant peptide are the main characteristics of this approach, making it an attractive method for the detection of Hb variants at the routine clinical level. We have analyzed 16 different samples, each containing a different known variant of hemoglobin.     

Identification of post-translationally modified proteins in proteome studies.

Proteome studies are powerful tools to solve many different problems in metabolism, signal transduction, drug discovery, and other areas of interest in life sciences. Up to now, high-sensitive methods for protein identification after two-dimensional gel electrophoresis using mass spectrometry are available. However, the identification of post-translational modifications after two-dimensional gel electrophoresis is still an unsolved problem. In this paper, we want to give several examples for the successful identification of post-translational modifications and point mutations.     

Quantification of Protein “Biomarkers” in Wheat-Based Food Systems: Dealing with Process-Related Issues

Selected food proteins may represent suitable markers for assessing either the presence/absence of specific food ingredients or the type and intensity of food processes. A fundamental step in the quantification of any protein marker is choosing a proper protocol for solubilizing the protein of interest. This step is particularly critical in the case of solid foods and when the protein analyte is prone to undergo intermolecular disulfide exchange reactions with itself or with other protein components in the system as a consequence of process-induced unfolding. In this frame, gluten-based systems represent matrices where a protein network is present and the biomarker proteins may be either linked to other components of the network or trapped into the network itself. The protein biomarkers considered here were wheat gluten toxic sequences for coeliac (QQPFP, R5), wheat germ agglutinin (WGA), and chicken egg ovalbumin (OVA). These proteins were considered here in the frame of three different cases dealing with processes different in nature and severity. Results from individual cases are commented as for: (1) the molecular basis of the observed behavior of the protein; (2) the design of procedure aimed at improving the recovery of the protein biomarker in a form suitable for reliable identification and quantification; (3) a critical analysis of the difficulties associated with the plain transfer of an analytical protocol from one product/process to another. Proper respect for the indications provided by the studies exemplified in this study may prevent coarse errors in assays and vane attempts at estimating the efficacy of a given treatment under a given set of conditions. The cases presented here also indicate that recovery of a protein analyte often does not depend in a linear fashion on the intensity of the applied treatment, so that caution must be exerted when attributing predictive value to the results of a particular study.     

A hybrid elastic band string algorithm for studies of enzymatic reactions

A common challenge in theoretical biophysics is the identification of a minimum energy path (MEP) for the rearrangement of a group of atoms from one stable configuration to another. The structure with maximum energy along the MEP approximates the transition state for the process and the energy profile itself permits estimation of the transition rates. In this work we describe a computationally efficient algorithm for the identification of minimum energy paths in complicated biosystems. The algorithm is a hybrid of the nudged elastic band (NEB) and string methods. It has been implemented in the pDynamo simulation program and tested by examining elementary steps in the reaction mechanisms of three enzymes: citrate synthase, RasGAP, and lactate dehydrogenase. Good agreement is found for the energies and geometries of the species along the reaction profiles calculated using the new algorithm and previous versions of the NEB and string techniques, and also those obtained by the common method of adiabatic exploration of the potential energy surface as a function of predefined reaction coordinates. Precisely refined structures of the saddle points along the paths may be subsequently obtained with the climbing image variant of the NEB algorithm. Directions in which the utility of the methods that we have implemented can be further improved are discussed.     

Options for veterinary drug analysis using mass spectrometry

Several classes of chemical compounds, exhibiting many different chemical properties, are classified under the generic term of “veterinary drugs”, among which are the antimicrobial medicines such as antibiotics or dyes, and drugs exhibiting growth promoting properties like steroids, β-agonist compounds, thyrostats or growth hormones. For food safety purposes, the resort to these substances in animal breeding has been submitted to strict regulation within the European Union for more than 15 years. Systems of control have therefore been set up within the same period of time to ensure compliance with the regulation. The current strategy relies on targeted analytical approaches focusing on the detection of residues of the administered compounds or their metabolites in different kinds of feed, food or biological matrices. If screening methods, which provide rapid discrimination between compliant and suspect samples, may be based on several techniques such as immunoassays or mass spectrometry, confirmatory methods mainly rely on the latter, which provides adequate specificity and sensitivity for unambiguous identification of the target analytes in biological matrices at trace level. The present article reviews the main mass spectrometric strategies, from the very first, nonetheless still efficient, single MS and multidimensional and high-resolution MS through to advanced isotope ratio MS. Several applications in the field of residue analysis illustrate each of these approaches and focus on the balance between issues related to the compounds of interest (chemistry, matrix, concentration, …) and the large offer of mass spectrometric-related technical possibilities, from the choice of the ionization conditions (EI, NCI, PCI, reagent gases, ESI+, ESI?), to the mass analyzers (single quadrupole, triple quadrupole, ion traps, time-of-flight, magnetic sectors, isotope ratio mass spectrometer) and corresponding acquisition modes (full scan, LR–SIM, HR–SIM, SRM, precursor scan, …). All the displayed strategies, from the importance of sample preparation to MS analysis to potential derivatization steps and chromatographic separation parameters are discussed in that context. Besides the advantages of each strategy, main issues associated to such MS approaches are commented with an emphasis not only on such critical points as ion suppression and resolution, but also on the adequacy of the current regulation regarding the evolution of the technology. Finally, future trends which may lead to strong and positive impacts in the field of residue analysis are presented, including latest developments and improvements in chromatography or software dedicated to signal acquisition and data analysis.     

Automating proteome analysis: improvements in throughput, quality and accuracy of protein identification by peptide mass fingerprinting

The use of robots has major effects on maximizing the proteomic workflow required in an increasing number of high-throughput projects and on increasing the quality of the data. In peptide mass finger printing (PMF), automation of steps downstream of two-dimensional gel electrophoresis is essential. To achieve this goal, the workflow must be fluid. We have developed tools using macros written in Microsoft Excel and Word to complete the automation of our platform. Additionally, because sample preparation is crucial for identification of proteins by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, we optimized a sandwich method usable by any robot for spotting digests on a MALDI target. This procedure enables further efficient automated washing steps directly on the MALDI target. The success rate of PMF identification was evaluated for the automated sandwich method, and for the dried-droplet method implemented on the robot as recommended by the manufacturer. Of the two methods, the sandwich method achieved the highest identification success rate and sequence coverage of proteins.     

Ultrasound in analytical chemistry

Ultrasound is a type of energy which can help analytical chemists in almost all their laboratory tasks, from cleaning to detection. A generic view of the different steps which can be assisted by ultrasound is given here. These steps include preliminary operations usually not considered in most analytical methods (e.g. cleaning, degassing, and atomization), sample preparation being the main area of application. In sample preparation ultrasound is used to assist solid-sample treatment (e.g. digestion, leaching, slurry formation) and liquid-sample preparation (e.g. liquid–liquid extraction, emulsification, homogenization) or to promote heterogeneous sample treatment (e.g. filtration, aggregation, dissolution of solids, crystallization, precipitation, defoaming, degassing). Detection techniques based on use of ultrasonic radiation, the principles on which they are based, responses, and the quantities measured are also discussed.