Protein Sequence Search Research Articles

Rapid multiple protein sequence search by parallel and heterogeneous computation.

Protein sequence database search and multiple sequence alignment generation is a fundamental task in many bioinformatics analyses. As the data volume of sequences continues to grow rapidly, there is an increasing need for efficient and scalable multiple sequence query algorithms for super-large databases without expensive time and computational costs. We introduce Chorus, a novel protein sequence query system that leverages parallel model and heterogeneous computation architecture to enable users to query thousands of protein sequences concurrently against large protein databases on a desktop workstation. Chorus achieves over 100× speedup over BLASTP without sacrificing sensitivity. We demonstrate the utility of Chorus through a case study of analyzing a ∼1.5-TB large-scale metagenomic datasets for novel CRISPR-Cas protein discovery within 30 min. Chorus is open-source and its code repository is available at https://github.com/Bio-Acc/Chorus.

Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes.

Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.

AODB: A comprehensive database for antioxidants including small molecules, peptides and proteins

Antioxidants are widely used in the fields of food, medicine, nutraceuticals, and cosmetics. Given their important roles in promoting and maintaining human health, a large number of antioxidants have been reported. Some antioxidant-related databases have been developed; however, the annotation of antioxidants and related information stored in existing databases is incomplete and requires more efficient retrieval methods. This study aimed to develop a manually curated comprehensive antioxidant database (AODB). Currently, it stores 56,666 small molecules tested for antioxidant activity, 1480 antioxidant peptides, and 998 antioxidant proteins, including their structures, names, antioxidant assay records, computable physicochemical and ADMET properties, and sources. AODB supports text search and mining, 2D and 3D chemical structure search, and BLAST-based protein sequence search, enabling users to retrieve antioxidant data quickly and easily. AODB, as a one-stop antioxidant database, can facilitate the exploration of antioxidants and potential applications. AODB is publicly available and updated annually at https://aodb.idruglab.cn/.

Genome Mining and Molecular Networking-Based Metabolomics of the Marine Facultative Aspergillus sp. MEXU 27854.

The marine-facultative Aspergillus sp. MEXU 27854, isolated from the Caleta Bay in Acapulco, Guerrero, Mexico, has provided an interesting diversity of secondary metabolites, including a series of rare dioxomorpholines, peptides, and butyrolactones. Here, we report on the genomic data, which consists of 11 contigs (N50~3.95 Mb) with a ~30.75 Mb total length of assembly. Genome annotation resulted in the prediction of 10,822 putative genes. Functional annotation was accomplished by BLAST searching protein sequences with different public databases. Of the predicted genes, 75% were assigned gene ontology terms. From the 67 BGCs identified, ~60% belong to the NRPS and NRPS-like classes. Putative BGCs for the dioxomorpholines and other metabolites were predicted by extensive genome mining. In addition, metabolomic molecular networking analysis allowed the annotation of all isolated compounds and revealed the biosynthetic potential of this fungus. This work represents the first report of whole-genome sequencing and annotation from a marine-facultative fungal strain isolated from Mexico.

Biochemical and Structural Characterization of an Unusual and Naturally Split Class 3 Intein.

Split inteins are indispensable tools for protein engineering because their ligation and cleavage reactions enable unique modifications of the polypeptide backbone. Three different classes of inteins have been identified according to the nature of the covalent intermediates resulting from the acyl rearrangements in the multistep protein‐splicing pathway. Class 3 inteins employ a characteristic internal cysteine for a branched thioester intermediate. A bioinformatic database search of non‐redundant protein sequences revealed the absence of split variants in 1701 class 3 inteins. We have discovered the first reported split class 3 intein in a metagenomics data set and report its biochemical, mechanistic and structural analysis. The AceL NrdHF intein exhibits low sequence conservation with other inteins and marked deviations in residues at conserved key positions, including a variation of the typical class‐3 WCT triplet motif. Nevertheless, functional analysis confirmed the class 3 mechanism of the intein and revealed excellent splicing yields within a few minutes over a wide range of conditions and with barely detectable cleavage side reactions. A high‐resolution crystal structure of the AceL NrdHF precursor and a mutagenesis study explained the importance and roles of several residues at the key positions. Tolerated substitutions in the flanking extein residues and a high affinity between the split intein fragments further underline the intein's future potential as a ligation tool.

SMI-BLAST: a novel supervised search framework based on PSI-BLAST for protein remote homology detection.

As one of the most important and widely used mainstream iterative search tool for protein sequence search, an accurate Position-Specific Scoring Matrix (PSSM) is the key of PSI-BLAST. However, PSSMs containing non-homologous information obviously reduce the performance of PSI-BLAST for protein remote homology. To further study this problem, we summarize three types of Incorrectly Selected Homology (ISH) errors in PSSMs. A new search tool Supervised-Manner-based Iterative BLAST (SMI-BLAST) is proposed based on PSI-BLAST for solving these errors. SMI-BLAST obviously outperforms PSI-BLAST on the Structural Classification of Proteins-extended (SCOPe) dataset. Compared with PSI-BLAST on the ISH error subsets of SCOPe dataset, SMI-BLAST detects 1.6-2.87 folds more remote homologous sequences, and outperforms PSI-BLAST by 35.66% in terms of ROC1 scores. Furthermore, this framework is applied to JackHMMER, DELTA-BLAST and PSI-BLASTexB, and their performance is further improved. User-friendly webservers for SMI-BLAST, JackHMMER, DELTA-BLAST and PSI-BLASTexB are established at http://bliulab.net/SMI-BLAST/, by which the users can easily get the results without the need to go through the mathematical details. Supplementary data are available at Bioinformatics online.

Experimental Approaches in Delineating mTOR Signaling.

The mTOR signaling controls essential biological functions including proliferation, growth, metabolism, autophagy, ageing, and others. Hyperactivation of mTOR signaling leads to a plethora of human disorders; thus, mTOR is an attractive drug target. The discovery of mTOR signaling started from isolation of rapamycin in 1975 and cloning of TOR genes in 1993. In the past 27 years, numerous research groups have contributed significantly to advancing our understanding of mTOR signaling and mTOR biology. Notably, a variety of experimental approaches have been employed in these studies to identify key mTOR pathway members that shape up the mTOR signaling we know today. Technique development drives mTOR research, while canonical biochemical and yeast genetics lay the foundation for mTOR studies. Here in this review, we summarize major experimental approaches used in the past in delineating mTOR signaling, including biochemical immunoprecipitation approaches, genetic approaches, immunofluorescence microscopic approaches, hypothesis-driven studies, protein sequence or motif search driven approaches, and bioinformatic approaches. We hope that revisiting these distinct types of experimental approaches will provide a blueprint for major techniques driving mTOR research. More importantly, we hope that thinking and reasonings behind these experimental designs will inspire future mTOR research as well as studies of other protein kinases beyond mTOR.

ProtPCV: A Fixed Dimensional Numerical Representation of Protein Sequence to Significantly Reduce Sequence Search Time.

Protein sequence is a wealth of experimental information which is yet to be exploited to extract information on protein homologues. Consequently, it is observed from publications that dynamic programming, heuristics and HMM profile-based alignment techniques along with the alignment free techniques do not directly utilize ordered profile of physicochemical properties of a protein to identify its homologue. Also, it is found that these works lack crucial bench-marking or validation in absence of which their incorporation in search engines may appears to be questionable. In this direction this research approach offers fixed dimensional numerical representation of protein sequences extending the concept of periodicity count value of nucleotide types (2017) to accommodate Euclidean distance as direct similarity measure between two proteins. Instead of bench-marking with BLAST and PSI-BLAST only, this new similarity measure was also compared with Needleman-Wunsch and Smith-Waterman. For enhancing the strength of comparison, this work for the first time introduces two novel benchmarking methods based on correlation of "similarity scores" and "proximity of ranked outputs from a standard sequence alignment method" between all possible pairs of search techniques including the new one presented in this paper. It is found that the novel and unique numerical representation of a protein can reduce computational complexity of protein sequence search to the tune of O(log(n)). It may also help implementation of various other similarity-based operation possible, such as clustering, phylogenetic analysis and classification of proteins on the basis of the properties used to build this numerical representation of protein.

The Associative Memory, Water Mediated, Structure and Energy Model (AWSEM)-Amylometer: Predicting Amyloid Propensity and Fibril Topology Using an Optimized Folding Landscape Model.

Amyloids are fibrillar protein aggregates with simple repeated structural motifs in their cores, usually β-strands but sometimes α-helices. Identifying the amyloid-prone regions within protein sequences is important both for understanding the mechanisms of amyloid-associated diseases and for understanding functional amyloids. Based on the crystal structures of seven cross-β amyloidogenic peptides with different topologies and one recently solved cross-α fiber structure, we have developed a computational approach for identifying amyloidogenic segments in protein sequences using the Associative memory, Water mediated, Structure and Energy Model (AWSEM). The AWSEM-Amylometer performs favorably in comparison with other predictors in predicting aggregation-prone sequences in multiple data sets. The method also predicts well the specific topologies (the relative arrangement of β-strands in the core) of the amyloid fibrils. An important advantage of the AWSEM-Amylometer over other existing methods is its direct connection with an efficient, optimized protein folding simulation model, AWSEM. This connection allows one to combine efficient and accurate search of protein sequences for amyloidogenic segments with the detailed study of the thermodynamic and kinetic roles that these segments play in folding and aggregation in the context of the entire protein sequence. We present new simulation results that highlight the free energy landscapes of peptides that can take on multiple fibril topologies. We also demonstrate how the Amylometer methodology can be straightforwardly extended to the study of functional amyloids that have the recently discovered cross-α fibril architecture.

Ensembl Genomes 2018: an integrated omics infrastructure for non-vertebrate species.

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the Ensembl project (http://www.ensembl.org). Together, the two resources provide a consistent set of programmatic and interactive interfaces to a rich range of data including genome sequence, gene models, transcript sequence, genetic variation, and comparative analysis. This paper provides an update to the previous publications about the resource, with a focus on recent developments and expansions. These include the incorporation of almost 20 000 additional genome sequences and over 35 000 tracks of RNA-Seq data, which have been aligned to genomic sequence and made available for visualization. Other advances since 2015 include the release of the database in Resource Description Framework (RDF) format, a large increase in community-derived curation, a new high-performance protein sequence search, additional cross-references, improved annotation of non-protein-coding genes, and the launch of pre-release and archival sites. Collectively, these changes are part of a continuing response to the increasing quantity of publicly-available genome-scale data, and the consequent need to archive, integrate, annotate and disseminate these using automated, scalable methods.

Protein Identification from Tandem Mass Spectra by Database Searching.

Protein identification from tandem mass spectra is one of the most versatile and widely used proteomics workflows, able to identify proteins, characterize post-translational modifications, and provide semiquantitative measurements of relative protein abundance. This manuscript describes the concepts, prerequisites, and methods required to analyze a tandem mass spectrometry dataset in order to identify its proteins, by using a tandem mass spectrometry search engine to search protein sequence databases. The discussion includes instructions for extraction, preparation, and formatting of spectral datafiles, selection of appropriate search parameter settings, and basic interpretation of the results.

MuBLASTP: database-indexed protein sequence search on multicore CPUs.

BackgroundThe Basic Local Alignment Search Tool (BLAST) is a fundamental program in the life sciences that searches databases for sequences that are most similar to a query sequence. Currently, the BLAST algorithm utilizes a query-indexed approach. Although many approaches suggest that sequence search with a database index can achieve much higher throughput (e.g., BLAT, SSAHA, and CAFE), they cannot deliver the same level of sensitivity as the query-indexed BLAST, i.e., NCBI BLAST, or they can only support nucleotide sequence search, e.g., MegaBLAST. Due to different challenges and characteristics between query indexing and database indexing, the existing techniques for query-indexed search cannot be used into database indexed search.ResultsmuBLASTP, a novel database-indexed BLAST for protein sequence search, delivers identical hits returned to NCBI BLAST. On Intel Haswell multicore CPUs, for a single query, the single-threaded muBLASTP achieves up to a 4.41-fold speedup for alignment stages, and up to a 1.75-fold end-to-end speedup over single-threaded NCBI BLAST. For a batch of queries, the multithreaded muBLASTP achieves up to a 5.7-fold speedups for alignment stages, and up to a 4.56-fold end-to-end speedup over multithreaded NCBI BLAST.ConclusionsWith a newly designed index structure for protein database and associated optimizations in BLASTP algorithm, we re-factored BLASTP algorithm for modern multicore processors that achieves much higher throughput with acceptable memory footprint for the database index.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1302-4) contains supplementary material, which is available to authorized users.

CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU.

BackgroundHMMER software suite is widely used for analysis of homologous protein and nucleotide sequences with high sensitivity. The latest version of hmmsearch in HMMER 3.x, utilizes heuristic-pipeline which consists of MSV/SSV (Multiple/Single ungapped Segment Viterbi) stage, P7Viterbi stage and the Forward scoring stage to accelerate homology detection. Since the latest version is highly optimized for performance on modern multi-core CPUs with SSE capabilities, only a few acceleration attempts report speedup. However, the most compute intensive tasks within the pipeline (viz., MSV/SSV and P7Viterbi stages) still stand to benefit from the computational capabilities of massively parallel processors.ResultsA Multi-Tiered Parallel Framework (CUDAMPF) implemented on CUDA-enabled GPUs presented here, offers a finer-grained parallelism for MSV/SSV and Viterbi algorithms. We couple SIMT (Single Instruction Multiple Threads) mechanism with SIMD (Single Instructions Multiple Data) video instructions with warp-synchronism to achieve high-throughput processing and eliminate thread idling. We also propose a hardware-aware optimal allocation scheme of scarce resources like on-chip memory and caches in order to boost performance and scalability of CUDAMPF. In addition, runtime compilation via NVRTC available with CUDA 7.0 is incorporated into the presented framework that not only helps unroll innermost loop to yield upto 2 to 3-fold speedup than static compilation but also enables dynamic loading and switching of kernels depending on the query model size, in order to achieve optimal performance.ConclusionsCUDAMPF is designed as a hardware-aware parallel framework for accelerating computational hotspots within the hmmsearch pipeline as well as other sequence alignment applications. It achieves significant speedup by exploiting hierarchical parallelism on single GPU and takes full advantage of limited resources based on their own performance features. In addition to exceeding performance of other acceleration attempts, comprehensive evaluations against high-end CPUs (Intel i5, i7 and Xeon) shows that CUDAMPF yields upto 440 GCUPS for SSV, 277 GCUPS for MSV and 14.3 GCUPS for P7Viterbi all with 100 % accuracy, which translates to a maximum speedup of 37.5, 23.1 and 11.6-fold for MSV, SSV and P7Viterbi respectively. The source code is available at https://github.com/Super-Hippo/CUDAMPF.

Computational clustering for viral reference proteomes.

The enormous number of redundant sequenced genomes has hindered efforts to analyze and functionally annotate proteins. As the taxonomy of viruses is not uniformly defined, viral proteomes pose special challenges in this regard. Grouping viruses based on the similarity of their proteins at proteome scale can normalize against potential taxonomic nomenclature anomalies. We present Viral Reference Proteomes (Viral RPs), which are computed from complete virus proteomes within UniProtKB. Viral RPs based on 95, 75, 55, 35 and 15% co-membership in proteome similarity based clusters are provided. Comparison of our computational Viral RPs with UniProt's curator-selected Reference Proteomes indicates that the two sets are consistent and complementary. Furthermore, each Viral RP represents a cluster of virus proteomes that was consistent with virus or host taxonomy. We provide BLASTP search and FTP download of Viral RP protein sequences, and a browser to facilitate the visualization of Viral RPs. http://proteininformationresource.org/rps/viruses/ chenc@udel.edu Supplementary data are available at Bioinformatics online.

CuBLASTP: Fine-Grained Parallelization of Protein Sequence Search on CPU+GPU

BLAST, short for Basic Local Alignment Search Tool, is a ubiquitous tool used in the life sciences for pairwise sequence search. However, with the advent of next-generation sequencing (NGS), whether at the outset or downstream from NGS, the exponential growth of sequence databases is outstripping our ability to analyze the data. While recent studies have utilized the graphics processing unit (GPU) to speedup the BLAST algorithm for searching protein sequences (i.e., BLASTP), these studies use coarse-grained parallelism, where one sequence alignment is mapped to only one thread. Such an approach does not efficiently utilize the capabilities of a GPU, particularly due to the irregularity of BLASTP in both execution paths and memory-access patterns. To address the above shortcomings, we present a fine-grained approach to parallelize BLASTP, where each individual phase of sequence search is mapped to many threads on a GPU. This approach, which we refer to as cuBLASTP, reorders data-access patterns and reduces divergent branches of the most time-consuming phases (i.e., hit detection and ungapped extension). In addition, cuBLASTP optimizes the remaining phases (i.e., gapped extension and alignment with trace back) on a multicore CPU and overlaps their execution with the phases running on the GPU.

PROTEOFORMER: deep proteome coverage through ribosome profiling and MS integration.

An increasing amount of studies integrate mRNA sequencing data into MS-based proteomics to complement the translation product search space. However, several factors, including extensive regulation of mRNA translation and the need for three- or six-frame-translation, impede the use of mRNA-seq data for the construction of a protein sequence search database. With that in mind, we developed the PROTEOFORMER tool that automatically processes data of the recently developed ribosome profiling method (sequencing of ribosome-protected mRNA fragments), resulting in genome-wide visualization of ribosome occupancy. Our tool also includes a translation initiation site calling algorithm allowing the delineation of the open reading frames (ORFs) of all translation products. A complete protein synthesis-based sequence database can thus be compiled for mass spectrometry-based identification. This approach increases the overall protein identification rates with 3% and 11% (improved and new identifications) for human and mouse, respectively, and enables proteome-wide detection of 5′-extended proteoforms, upstream ORF translation and near-cognate translation start sites. The PROTEOFORMER tool is available as a stand-alone pipeline and has been implemented in the galaxy framework for ease of use.

GRASP: guided reference-based assembly of short peptides.

Protein sequences predicted from metagenomic datasets are annotated by identifying their homologs via sequence comparisons with reference or curated proteins. However, a majority of metagenomic protein sequences are partial-length, arising as a result of identifying genes on sequencing reads or on assembled nucleotide contigs, which themselves are often very fragmented. The fragmented nature of metagenomic protein predictions adversely impacts homology detection and, therefore, the quality of the overall annotation of the dataset. Here we present a novel algorithm called GRASP that accurately identifies the homologs of a given reference protein sequence from a database consisting of partial-length metagenomic proteins. Our homology detection strategy is guided by the reference sequence, and involves the simultaneous search and assembly of overlapping database sequences. GRASP was compared to three commonly used protein sequence search programs (BLASTP, PSI-BLAST and FASTM). Our evaluations using several simulated and real datasets show that GRASP has a significantly higher sensitivity than these programs while maintaining a very high specificity. GRASP can be a very useful program for detecting and quantifying taxonomic and protein family abundances in metagenomic datasets. GRASP is implemented in GNU C++, and is freely available at http://sourceforge.net/projects/grasp-release.

A proteogenomics approach integrating proteomics and ribosome profiling increases the efficiency of protein identification and enables the discovery of alternative translation start sites.

Next-generation transcriptome sequencing is increasingly integrated with MS to enhance MS-based protein and peptide identification. Recently, a breakthrough in transcriptome analysis was achieved with the development of ribosome profiling (ribo-seq). This technology is based on the deep sequencing of ribosome-protected mRNA fragments, thereby enabling the direct observation of in vivo protein synthesis at the transcript level. In order to explore the impact of a ribo-seq-derived protein sequence search space on MS/MS spectrum identification, we performed a comprehensive proteome study on a human cancer cell line, using both shotgun and N-terminal proteomics, next to ribosome profiling, which was used to delineate (alternative) translational reading frames. By including protein-level evidence of sample-specific genetic variation and alternative translation, this strategy improved the identification score of 69 proteins and identified 22 new proteins in the shotgun experiment. Furthermore, we discovered 18 new alternative translation start sites in the N-terminal proteomics data and observed a correlation between the quantitative measures of ribo-seq and shotgun proteomics with a Pearson correlation coefficient ranging from 0.483 to 0.664. Overall, this study demonstrated the benefits of ribosome profiling for MS-based protein and peptide identification and we believe this approach could develop into a common practice for next-generation proteomics.

Bone Marrow Sinusoidal Integrity Regulates Hematopoietic Stem and Progenitor Cell Trafficking

Bone marrow sinusoidal endothelial cells (ECs) are an integral component of the hematopoietic stromal microenvironment and have been implicated in hematopoietic stem and progenitor cell (HSPC) homeostasis, though their role in regulation of hematopoietic trafficking remains uncertain. In this study, we demonstrate that bone marrow (BM) sinusoidal ECs actively regulate HSPC retention and enforced egress out of BM quite surprisingly by serving as “gatekeepers” of HSPC within the marrow niche, a non-paradigmatic mechanism independent of traditional adhesive/chemotactic mechanisms.Treatment with G-CSF (100 µg/kg, 4 days) resulted in down-regulated expression of VE-cadherin (50±3.3% reduction) and CD31 (35±2.1%) along the gap junctions connecting ECs (CD45neg Ter119- VEGFR3+) forming BM sinuosoids, reducing cell-cell contact and allowing for increased HSPC trafficking to the peripheral blood. To validate the functional contribution of ECs in HSPC trafficking, mouse (C166 cells) or human (HUVEC) endothelial monolayers were treated with G-CSF for 24 hours in vitro. Treatment of EC monolayers with G-CSF increased dextran-FITC permeability across EC monolayers and increased migration of HSPCs across endothelium, suggesting that a component of G-CSF mobilization activity is the “opening” of EC gap junctions to allow successful HSPC migration through the EC layer. To confirm this increase in endothelial monolayer permeability in response to G-CSF treatment, we measured EC cell-cell contacts both in vitro and in vivo. Immunofluorescence analysis of HUVECs demonstrated significant VE-cadherin+ cell-cell contact in our cultured HUVEC monolayers with a significant loss of cell-cell contact after G-CSF exposure. Histological examination of femur sections from G-CSF treated mice showed a similar disruption of cell-cell contact of VEGFR+and Dil-Ac-LDL sinusoids.Previous reports have demonstrated that pharmacologic antagonism of the dipeptidyl peptidase CD26, or its gene deletion, results in reduced HSPC egress in response to G-CSF. As CD26 is highly expressed on BM ECs, and expression dynamically changes in response to G-CSF treatment, we hypothesized that sinusoidal ECs expressing CD26 may regulate sinusoidal integrity and HSPC egress. Blockade of CD26 activity, or gene deletion of CD26 in ECs, completely reversed the G-CSF-mediated reduction in EC cell-cell junctions accompanied by reduced trans-endothelial migration of HSPC. Given that CD26 is an enzyme that exerts physiologic effects by N-terminal cleavage of effector proteins, we performed a systematic protein sequence search for proteins containing putative CD26 recognition sites. Intriguingly, the neurotransmitter neuropeptide Y (NPY) can be cleaved by CD26, and the full length and cleaved versions have differing functions on NPY receptors expressed on EC. While HSPC mobilization in response to G-CSF was reduced in the absence of CD26 activity, mobilization was restored by treating mice with the cleaved NPY (NPY3-36) and was inhibited by antagonizing NPY2 and Y5 receptors on ECs, suggesting that reduced mobilization in CD26 knockout mice is caused by a lack of cleaved NPY, preventing an opening of EC cell-cell junctions and allowing for HSPC egress.If ECs are acting as active “gatekeepers” of HSPC within the marrow space, regulated by CD26 and NPY, we hypothesized HSPC egress in response to agents other than G-CSF would also depend on EC cell-cell junction opening. Interestingly, LPS administration, a mimic of bacterial infection, and AMD3100, a CXCR4 antagonist, also reduced VE-cadherin and CD31 expression along BM sinusoidal EC junctions coincident with enhanced HSPC mobilization. Our results identify an unappreciated active role for bone marrow niche ECs in maintaining HSPC within the marrow space, and describe a heretofore unknown CD26-mediated EC-neuropeptide Y axis regulating optimal HSPC trafficking. These results further refine our knowledge of hematopoietic trafficking mechanisms and identify potential new pharmaceutic targets to regulate trafficking in homeostatic and stress conditions. Disclosures:Hoggatt:Fate Therapeutics: Consultancy. Pelus:Fate Therapeutics: Consultancy.

Deep Proteome Coverage Based on Ribosome Profiling Aids Mass Spectrometry-based Protein and Peptide Discovery and Provides Evidence of Alternative Translation Products and Near-cognate Translation Initiation Events*

An increasing number of studies involve integrative analysis of gene and protein expression data, taking advantage of new technologies such as next-generation transcriptome sequencing and highly sensitive mass spectrometry (MS) instrumentation. Recently, a strategy, termed ribosome profiling (or RIBO-seq), based on deep sequencing of ribosome-protected mRNA fragments, indirectly monitoring protein synthesis, has been described. We devised a proteogenomic approach constructing a custom protein sequence search space, built from both Swiss-Prot- and RIBO-seq-derived translation products, applicable for MS/MS spectrum identification. To record the impact of using the constructed deep proteome database, we performed two alternative MS-based proteomic strategies as follows: (i) a regular shotgun proteomic and (ii) an N-terminal combined fractional diagonal chromatography (COFRADIC) approach. Although the former technique gives an overall assessment on the protein and peptide level, the latter technique, specifically enabling the isolation of N-terminal peptides, is very appropriate in validating the RIBO-seq-derived (alternative) translation initiation site profile. We demonstrate that this proteogenomic approach increases the overall protein identification rate 2.5% (e.g. new protein products, new protein splice variants, single nucleotide polymorphism variant proteins, and N-terminally extended forms of known proteins) as compared with only searching UniProtKB-SwissProt. Furthermore, using this custom database, identification of N-terminal COFRADIC data resulted in detection of 16 alternative start sites giving rise to N-terminally extended protein variants besides the identification of four translated upstream ORFs. Notably, the characterization of these new translation products revealed the use of multiple near-cognate (non-AUG) start codons. As deep sequencing techniques are becoming more standard, less expensive, and widespread, we anticipate that mRNA sequencing and especially custom-tailored RIBO-seq will become indispensable in the MS-based protein or peptide identification process. The underlying mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000124.