De novo sequencing of proteins by mass spectrometry.

INTRODUCTION: Proteins are crucial for every cellular activity and unraveling their sequence and structure is a crucial step to fully understand their biology. Early methods of protein sequencing were mainly based on the use of enzymatic or chemical degradation of peptide chains. With the completion of the human genome project and with the expansion of the information available for each protein, various databases containing this sequence information were formed. AREAS COVERED: De novo protein sequencing, shotgun proteomics and other mass-spectrometric techniques, along with the various software are currently available for proteogenomic analysis. Emphasis is placed on the methods for de novo sequencing, together with potential and shortcomings using databases for interpretation of protein sequence data. EXPERT OPINION: As mass-spectrometry sequencing performance is improving with better software and hardware optimizations, combined with user-friendly interfaces, de-novo protein sequencing becomes imperative in shotgun proteomic studies. Issues regarding unknown or mutated peptide sequences, as well as, unexpected post-translational modifications (PTMs) and their identification through false discovery rate searches using the target/decoy strategy need to be addressed. Ideally, it should become integrated in standard proteomic workflows as an add-on to conventional database search engines, which then would be able to provide improved identification.

Strategies for Development of a Next-Generation Protein Sequencing Platform.

Proteomic analysis can be a critical bottleneck in cellular characterization. The current paradigm relies primarily on mass spectrometry of peptides and affinity reagents (i.e., antibodies), both of which require a priori knowledge of the sample. An unbiased protein sequencing method, with a dynamic range that covers the full range of protein concentrations in proteomes, would revolutionize the field of proteomics, allowing a more facile characterization of novel gene products and subcellular complexes. To this end, several new platforms based on single-molecule protein-sequencing approaches have been proposed. This review summarizes four of these approaches, highlighting advantages, limitations, and challenges for each method towards advancing as a core technology for next-generation protein sequencing.

The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis.

The MPI Bioinformatics Toolkit (http://toolkit.tuebingen.mpg.de) is an open, interactive web service for comprehensive and collaborative protein bioinformatic analysis. It offers a wide array of interconnected, state-of-the-art bioinformatics tools to experts and non-experts alike, developed both externally (e.g. BLAST+, HMMER3, MUSCLE) and internally (e.g. HHpred, HHblits, PCOILS). While a beta version of the Toolkit was released 10 years ago, the current production-level release has been available since 2008 and has serviced more than 1.6 million external user queries. The usage of the Toolkit has continued to increase linearly over the years, reaching more than 400 000 queries in 2015. In fact, through the breadth of its tools and their tight interconnection, the Toolkit has become an excellent platform for experimental scientists as well as a useful resource for teaching bioinformatic inquiry to students in the life sciences. In this article, we report on the evolution of the Toolkit over the last ten years, focusing on the expansion of the tool repertoire (e.g. CS-BLAST, HHblits) and on infrastructural work needed to remain operative in a changing web environment.

A dynamic look backward and forward.

The 2015 Gunther Laukien Prize recognized solution NMR studies of protein dynamics and thermodynamics. This Perspective surveys aspects of the development and application of NMR spin relaxation for investigations of protein flexibility and function over multiple time scales in solution. Methods highlighted include analysis of overall rotational diffusion, theoretical descriptions of R1ρ relaxation, and molecular dynamics simulations to interpret NMR spin relaxation. Applications are illustrated for the zinc-finger domain Xfin-31, the calcium-binding proteins calbindin D9k and calmodulin, and the bZip transcription factor of GCN4.

Algorithms for the de novo sequencing of peptides from tandem mass spectra.

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field.

Collisions or electrons? Protein sequence analysis in the 21st century.

How dissociation is effected determines the upstream sample handling whereas the spectral features it produces regulate the downstream informatics approach. (To listen to a podcast about this feature, please go to the Analytical Chemistry website at pubs.acs.org/journal/ancham.

ProfileGrids as a new visual representation of large multiple sequence alignments: a case study of the RecA protein family.

BACKGROUND: Multiple sequence alignments are a fundamental tool for the comparative analysis of proteins and nucleic acids. However, large data sets are no longer manageable for visualization and investigation using the traditional stacked sequence alignment representation. RESULTS: We introduce ProfileGrids that represent a multiple sequence alignment as a matrix color-coded according to the residue frequency occurring at each column position. JProfileGrid is a Java application for computing and analyzing ProfileGrids. A dynamic interaction with the alignment information is achieved by changing the ProfileGrid color scheme, by extracting sequence subsets at selected residues of interest, and by relating alignment information to residue physical properties. Conserved family motifs can be identified by the overlay of similarity plot calculations on a ProfileGrid. Figures suitable for publication can be generated from the saved spreadsheet output of the colored matrices as well as by the export of conservation information for use in the PyMOL molecular visualization program.We demonstrate the utility of ProfileGrids on 300 bacterial homologs of the RecA family - a universally conserved protein involved in DNA recombination and repair. Careful attention was paid to curating the collected RecA sequences since ProfileGrids allow the easy identification of rare residues in an alignment. We relate the RecA alignment sequence conservation to the following three topics: the recently identified DNA binding residues, the unexplored MAW motif, and a unique Bacillus subtilis RecA homolog sequence feature. CONCLUSION: ProfileGrids allow large protein families to be visualized more effectively than the traditional stacked sequence alignment form. This new graphical representation facilitates the determination of the sequence conservation at residue positions of interest, enables the examination of structural patterns by using residue physical properties, and permits the display of rare sequence features within the context of an entire alignment. JProfileGrid is free for non-commercial use and is available from http://www.profilegrid.org. Furthermore, we present a curated RecA protein collection that is more diverse than previous data sets; and, therefore, this RecA ProfileGrid is a rich source of information for nanoanatomy analysis.

Pfam 10 years on: 10,000 families and still growing.

Classifications of proteins into groups of related sequences are in some respects like a periodic table for biology, allowing us to understand the underlying molecular biology of any organism. Pfam is a large collection of protein domains and families. Its scientific goal is to provide a complete and accurate classification of protein families and domains. The next release of the database will contain over 10,000 entries, which leads us to reflect on how far we are from completing this work. Currently Pfam matches 72% of known protein sequences, but for proteins with known structure Pfam matches 95%, which we believe represents the likely upper bound. Based on our analysis a further 28,000 families would be required to achieve this level of coverage for the current sequence database. We also show that as more sequences are added to the sequence databases the fraction of sequences that Pfam matches is reduced, suggesting that continued addition of new families is essential to maintain its relevance.

The VirusBanker database uses a Java program to allow flexible searching through Bunyaviridae sequences.

BACKGROUND: Viruses of the Bunyaviridae have segmented negative-stranded RNA genomes and several of them cause significant disease. Many partial sequences have been obtained from the segments so that GenBank searches give complex results. Sequence databases usually use HTML pages to mediate remote sorting, but this approach can be limiting and may discourage a user from exploring a database. RESULTS: The VirusBanker database contains Bunyaviridae sequences and alignments and is presented as two spreadsheets generated by a Java program that interacts with a MySQL database on a server. Sequences are displayed in rows and may be sorted using information that is displayed in columns and includes data relating to the segment, gene, protein, species, strain, sequence length, terminal sequence and date and country of isolation. Bunyaviridae sequences and alignments may be downloaded from the second spreadsheet with titles defined by the user from the columns, or viewed when passed directly to the sequence editor, Jalview. CONCLUSION: VirusBanker allows large datasets of aligned nucleotide and protein sequences from the Bunyaviridae to be compiled and winnowed rapidly using criteria that are formulated heuristically.

Statistical measures of uncertainty for branches in phylogenetic trees inferred from molecular sequences by using model-based methods.

In recent years, the emphasis of theoretical work on phylogenetic inference has shifted from the development of new tree inference methods to the development of methods to measure the statistical support for the topologies. This paper reviews 3 approaches to assign support values to branches in trees obtained in the analysis of molecular sequences: the bootstrap, the Bayesian posterior probabilities for clades, and the interior branch tests. In some circumstances, these methods give different answers. It should not be surprising: their assumptions are different. Thus the interior branch tests assume that a given topology is true and only consider if a particular branch length is longer than zero. If a tree is incorrect, a wrong branch (a low bootstrap or Bayesian support may be an indication) may have a non-zero length. If the substitution model is oversimplified, the length of a branch may be overestimated, and the Bayesian support for the branch may be inflated. The bootstrap, on the other hand, approximates the variance of the data under the real model of sequence evolution, because it involves direct resampling from this data. Thus the discrepancy between the Bayesian support and the bootstrap support may signal model inaccuracy. In practical application, use of all 3 methods is recommended, and if discrepancies are observed, then a careful analysis of their potential origins should be made.

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics.

BACKGROUND: High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usually require large amounts of training data. RESULTS: We introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems. We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC). Furthermore, the predicted retention times are used to improve spectrum identifications by a p-value-based filtering approach. The approach was tested on a number of different datasets and shows excellent performance while requiring only very small training sets (about 40 peptides instead of thousands). Using the retention time predictor in our retention time filter improves the fraction of correctly identified peptide mass spectra significantly. CONCLUSION: The proposed kernel function is well-suited for the prediction of chromatographic separation in computational proteomics and requires only a limited amount of training data. The performance of this new method is demonstrated by applying it to peptide retention time prediction in IP-RP-HPLC and prediction of peptide sample fractionation in SAX-SPE. Finally, we incorporate the predicted chromatographic behavior in a p-value based filter to improve peptide identifications based on liquid chromatography-tandem mass spectrometry.

Analysis of proteins and peptides on a chromatographic timescale by electron-transfer dissociation MS.

Peptide and protein sequence analysis using a combination of gas-phase ion-ion chemistry and tandem MS is described. Samples are converted to multiply charged ions by ESI and then allowed to react with fluoranthene radical anions in a quadrupole linear ion trap mass spectrometer. Electron transfer from the radical anion to the multiply charged peptide or protein promotes random fragmentation along the amide backbone that is independent of peptide or protein size, sequence, or the presence of post-translational modifications. Examples are provided that demonstrate the utility of electron-transfer dissociation for characterizing post-translational modifications and for identifying proteins in mixtures on a chromatographic timescale (500 ms/protein).

Clustered sequence representation for fast homology search.

We present a novel approach to managing redundancy in sequence databanks such as GenBank. We store clusters of near-identical sequences as a representative union-sequence and a set of corresponding edits to that sequence. During search, the query is compared to only the union-sequences representing each cluster; cluster members are then only reconstructed and aligned if the union-sequence achieves a sufficiently high score. Using this approach with BLAST results in a 27% reduction in collection size and a corresponding 22% decrease in search time with no significant change in accuracy. We also describe our method for clustering that uses fingerprinting, an approach that has been successfully applied to collections of text and web documents in Information Retrieval. Our clustering approach is ten times faster on the GenBank nonredundant protein database than the fastest existing approach, CD-HIT. We have integrated our approach into FSA-BLAST, our new Open Source version of BLAST (available from http://www.fsa-blast.org/). As a result, FSA-BLAST is twice as fast as NCBI-BLAST with no significant change in accuracy.

A reduction-based exact algorithm for the contact map overlap problem.

Aligning proteins based on their structural similarity is a fundamental problem in molecular biology with applications in many settings, including structure classification, database search, function prediction, and assessment of folding prediction methods. Structural alignment can be done via several methods, including contact map overlap (CMO) maximization that aligns proteins in a way that maximizes the number of common residue contacts. In this paper, we develop a reduction-based exact algorithm for the CMO problem. Our approach solves CMO directly rather than after transformation to other combinatorial optimization problems. We exploit the mathematical structure of the problem in order to develop a number of efficient lower bounding, upper bounding, and reduction schemes. Computational experiments demonstrate that our algorithm runs significantly faster than existing exact algorithms and solves some hard CMO instances that were not solved in the past. In addition, the algorithm produces protein clusters that are in excellent agreement with the SCOP classification. An implementation of our algorithm is accessible as an on-line server at http://eudoxus.scs.uiuc.edu/cmos/cmos.html.

Current trends in computational inference from mass spectrometry-based proteomics.

Mass spectrometry offers a high-throughput approach to quantifying the proteome associated with a biological sample and hence has become the primary approach of proteomic analyses. Computation is tightly coupled to this advanced technological platform as a required component of not only peptide and protein identification, but quantification and functional inference, such as protein modifications and interactions. Proteomics faces several key computational challenges such as identification of proteins and peptides from tandem mass spectra as well as their quantitation. In addition, the application of proteomics to systems biology requires understanding the functional proteome, including how the dynamics of the cell change in response to protein modifications and complex interactions between biomolecules. This review presents an overview of recently developed methods and their impact on these core computational challenges currently facing proteomics.

Towards fully automated structure-based function prediction in structural genomics: a case study.

As the global Structural Genomics projects have picked up pace, the number of structures annotated in the Protein Data Bank as hypothetical protein or unknown function has grown significantly. A major challenge now involves the development of computational methods to assign functions to these proteins accurately and automatically. As part of the Midwest Center for Structural Genomics (MCSG) we have developed a fully automated functional analysis server, ProFunc, which performs a battery of analyses on a submitted structure. The analyses combine a number of sequence-based and structure-based methods to identify functional clues. After the first stage of the Protein Structure Initiative (PSI), we review the success of the pipeline and the importance of structure-based function prediction. As a dataset, we have chosen all structures solved by the MCSG during the 5 years of the first PSI. Our analysis suggests that two of the structure-based methods are particularly successful and provide examples of local similarity that is difficult to identify using current sequence-based methods. No one method is successful in all cases, so, through the use of a number of complementary sequence and structural approaches, the ProFunc server increases the chances that at least one method will find a significant hit that can help elucidate function. Manual assessment of the results is a time-consuming process and subject to individual interpretation and human error. We present a method based on the Gene Ontology (GO) schema using GO-slims that can allow the automated assessment of hits with a success rate approaching that of expert manual assessment.

Personalized genomic medicine with a patchwork, partially owned genome.

"His book was known as the Book of Sand, because neither the book nor the sand have any beginning or end." - Jorge Luis BorgesThe human genome is a three billion-letter recipe for the genesis of a human being, directing development from a single-celled embryo to the trillions of adult cells. Since the sequencing of the human genome was announced in 2001, researchers have an increased ability to discern the genetic basis for diseases. This reference genome has opened the door to genomic medicine, aimed at detecting and understanding all genetic variations of the human genome that contribute to the manifestation and progression of disease. The overarching vision of genomic (or "personalized") medicine is to custom-tailor each treatment for maximum effectiveness in an individual patient. Detecting the variation in a patient's deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein structures is no longer an insurmountable hurdle. Today, the challenge for genomic medicine lies in contextualizing those myriad genetic variations in terms of their functional consequences for a person's health and development throughout life and in terms of that patient's susceptibility to disease and differential clinical responses to medication. Additionally, several recent developments have complicated our understanding of the nominal human genome and, thereby, altered the progression of genomic medicine. In this brief review, we shall focus on these developments and examine how they are changing our understanding of our genome.

ABRF ESRG 2006 study: Edman sequencing as a method for polypeptide quantitation.

The Edman Sequencing Research Group (ESRG) designs studies on the use of Edman degradation for protein and peptide analysis. These studies provide a means for participating laboratories to compare their analyses against a benchmark of those from other laboratories that provide this valuable service. The main purpose of the 2006 study was to determine how accurate Edman sequencing is for quantitative analysis of polypeptides. Secondarily, participants were asked to identify a modified amino acid residue, N-epsilon-acetyl lysine [Lys(Ac)], present within one of the peptides. The ESRG 2006 peptide mixture consisted of three synthetic peptides. The Peptide Standards Research Group (PSRG) provided two peptides, with the following sequences: KAQYARSVLLEKDAEPDILELATGYR (peptide B), and RQAKVLLYSGR (peptide C). The third peptide, peptide C*, synthesized and characterized by ESRG, was identical to peptide C but with acetyl lysine in position 4. The mixture consisted of 20% peptide B and 40% each of peptide C and its acetylated form, peptide C*. Participating laboratories were provided with two tubes, each containing 100 picomoles of the peptide mixture (as determined by quantitative amino acid analysis) and were asked to provide amino acid assignments, peak areas, retention times at each cycle, as well as initial and repetitive yield estimates for each peptide in the mixture. Details about instruments and parameters used in the analysis were also collected. Participants in the study with access to a mass spectrometer (MALDI-TOF or ESI) were asked to provide information about the relative peak areas of the peptides in the mixture as a comparison with the peptide quantitation results from Edman sequencing. Positive amino acid assignments were 88% correct for peptide C and 93% correct for peptide B. The absolute initial sequencing yields were an average of 67% for peptide (C+C*) and 65.6 % for peptide B. The relative molar ratios determined by Edman sequencing were an average of 4.27 (expected ratio of 4) for peptides (C+C*)/B, and 1.49 for peptide C*/C (expected ratio of 1); the seemingly high 49% error in quantification of Lys(Ac) in peptide C* can be attributed to commercial unavailability of its PTH standard. These values compare very favorably with the values obtained by mass spectrometry.