A large-scale assessment of sequence database search tools for homology-based protein function prediction.

Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. These searches are also a critical component in most state-of-the-art machine learning and deep learning-based protein function predictors. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND-one of the most popular tools for function prediction-under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. Additionally, we developed a new scoring function to derive GO prediction from homologous hits that consistently outperform previously proposed scoring functions. These findings enable the improvement of almost all protein function prediction algorithms with a few easily implementable changes in their sequence homolog-based component. This study emphasizes the critical role of search parameter settings in homology-based function transfer and should have an important contribution to the development of future protein function prediction algorithms.

PIPI2: Sensitive Tag-Based Database Search to Identify Peptides with Multiple Post-translational Modifications.

Peptide identification is important in bottom-up proteomics. Post-translational modifications (PTMs) are crucial in regulating cellular activities. Many database search methods have been developed to identify peptides with PTMs and characterize the PTM patterns. However, the PTMs on peptides hinder the peptide identification rate and the PTM characterization precision, especially for peptides with multiple PTMs. To address this issue, we present a sensitive open search engine, PIPI2, with much better performance on peptides with multiple PTMs than other methods. With a greedy approach, we simplify the PTM characterization problem into a linear one, which enables characterizing multiple PTMs on one peptide. On the simulation data sets with up to four PTMs per peptide, PIPI2 identified over 90% of the spectra, at least 56% more than five other competitors. PIPI2 also characterized these PTM patterns with the highest precision of 77%, demonstrating a significant advantage in handling peptides with multiple PTMs. In the real applications, PIPI2 identified 30% to 88% more peptides with PTMs than its competitors.

Increasing the Throughput and Reproducibility of Activity-Based Proteome Profiling Studies with Hyperplexing and Intelligent Data Acquisition.

Intelligent data acquisition (IDA) strategies, such as a real-time database search (RTS), have improved the depth of proteome coverage for experiments that utilize isobaric labels and gas phase purification techniques (i.e., SPS-MS3). In this work, we introduce inSeqAPI, an instrument application programing interface (iAPI) program that enables construction of novel data acquisition algorithms. First, we analyze biotinylated cysteine peptides from ABPP experiments to demonstrate that a real-time search method within inSeqAPI performs similarly to an equivalent vendor method. Then, we describe PairQuant, a method within inSeqAPI designed for the hyperplexing approach that utilizes protein-level isotopic labeling and peptide-level TMT labeling. PairQuant allows for TMT analysis of 36 conditions in a single sample and achieves ∼98% coverage of both peptide pair partners in a hyperplexed experiment as well as a 40% improvement in the number of quantified cysteine sites compared with non-RTS acquisition. We applied this method in the ABPP study of ligandable cysteine sites in the nucleus leading to an identification of additional druggable sites on protein- and DNA-interaction domains of transcription regulators and on nuclear ubiquitin ligases.

Sulfoproteomics Workflow with Precursor Ion Accurate Mass Shift Analysis Reveals Novel Tyrosine Sulfoproteins in the Golgi.

Tyrosine sulfation in the Golgi of secreted and membrane proteins is an important post-translational modification (PTM). However, its labile nature has limited analysis by mass spectrometry (MS), a major reason why no sulfoproteome studies have been previously reported. Here, we show that a phosphoproteomics experimental workflow, which includes serial enrichment followed by high resolution, high mass accuracy MS, and tandem MS (MS/MS) analysis, enables sulfopeptide coenrichment and identification via accurate precursor ion mass shift open MSFragger database search. This approach, supported by manual validation, allows the confident identification of sulfotyrosine-containing peptides in the presence of high levels of phosphorylated peptides, thus enabling these two sterically and ionically similar isobaric PTMs to be distinguished and annotated in a single proteomic analysis. We applied this approach to isolated interphase and mitotic rat liver Golgi membranes and identified 67 tyrosine sulfopeptides, corresponding to 26 different proteins. This work discovered 23 new sulfoproteins with functions related to, for example, Ca2+-binding, glycan biosynthesis, and exocytosis. In addition, we report the first preliminary evidence for crosstalk between sulfation and phosphorylation in the Golgi, with implications for functional control.

Title, abstract and keywords: a practical guide to maximize the visibility and impact of academic papers.

In a growing digital landscape, enhancing the discoverability and resonance of scientific articles is essential. Here, we offer 10 recommendations to amplify the discoverability of studies in search engines and databases. Particularly, we argue that the strategic use and placement of key terms in the title, abstract and keyword sections can boost indexing and appeal. By surveying 230 journals in ecology and evolutionary biology, we found that current author guidelines may unintentionally limit article findability. Our survey of 5323 studies revealed that authors frequently exhaust abstract word limits-particularly those capped under 250 words. This suggests that current guidelines may be overly restrictive and not optimized to increase the dissemination and discoverability of digital publications. Additionally, 92% of studies used redundant keywords in the title or abstract, undermining optimal indexing in databases. We encourage adopting structured abstracts to maximize the incorporation of key terms in titles, abstracts and keywords. In addition, we encourage the relaxation of abstract and keyword limitations in journals with strict guidelines, and the inclusion of multilingual abstracts to broaden global accessibility. These recommendations to editors are designed to improve article engagement and facilitate evidence synthesis, thereby aligning scientific publishing with the modern needs of academic research.

Enhancing systematic reviews in orthodontics: a comparative examination of GPT-3.5 and GPT-4 for generating PICO-based queries with tailored prompts and configurations.

OBJECTIVES: The rapid advancement of Large Language Models (LLMs) has prompted an exploration of their efficacy in generating PICO-based (Patient, Intervention, Comparison, Outcome) queries, especially in the field of orthodontics. This study aimed to assess the usability of Large Language Models (LLMs), in aiding systematic review processes, with a specific focus on comparing the performance of ChatGPT 3.5 and ChatGPT 4 using a specialized prompt tailored for orthodontics. MATERIALS/METHODS: Five databases were perused to curate a sample of 77 systematic reviews and meta-analyses published between 2016 and 2021. Utilizing prompt engineering techniques, the LLMs were directed to formulate PICO questions, Boolean queries, and relevant keywords. The outputs were subsequently evaluated for accuracy and consistency by independent researchers using three-point and six-point Likert scales. Furthermore, the PICO records of 41 studies, which were compatible with the PROSPERO records, were compared with the responses provided by the models. RESULTS: ChatGPT 3.5 and 4 showcased a consistent ability to craft PICO-based queries. Statistically significant differences in accuracy were observed in specific categories, with GPT-4 often outperforming GPT-3.5. LIMITATIONS: The study's test set might not encapsulate the full range of LLM application scenarios. Emphasis on specific question types may also not reflect the complete capabilities of the models. CONCLUSIONS/IMPLICATIONS: Both ChatGPT 3.5 and 4 can be pivotal tools for generating PICO-driven queries in orthodontics when optimally configured. However, the precision required in medical research necessitates a judicious and critical evaluation of LLM-generated outputs, advocating for a circumspect integration into scientific investigations.

Efficient Indexing of Peptides for Database Search Using Tide.

The first step in the analysis of protein tandem mass spectrometry data typically involves searching the observed spectra against a protein database. During database search, the search engine must digest the proteins in the database into peptides, subject to digestion rules that are under user control. The choice of these digestion parameters, as well as selection of post-translational modifications (PTMs), can dramatically affect the size of the search space and hence the statistical power of the search. The Tide search engine separates the creation of the peptide index from the database search step, thereby saving time by allowing a peptide index to be reused in multiple searches. Here we describe an improved implementation of the indexing component of Tide that consumes around four times less resources (CPU and RAM) than the previous version and can generate arbitrarily large peptide databases, limited by only the amount of available disk space. We use this improved implementation to explore the relationship between database size and the parameters controlling digestion and PTMs, as well as database size and statistical power. Our results can help guide practitioners in proper selection of these important parameters.

The Crux Toolkit for Analysis of Bottom-Up Tandem Mass Spectrometry Proteomics Data.

The Crux tandem mass spectrometry data analysis toolkit provides a collection of algorithms for analyzing bottom-up proteomics tandem mass spectrometry data. Many publications have described various individual components of Crux, but a comprehensive summary has not been published since 2014. The goal of this work is to summarize the functionality of Crux, focusing on developments since 2014. We begin with empirical results demonstrating our recently implemented speedups to the Tide search engine. Other new features include a new score function in Tide, two new confidence estimation procedures, as well as three new tools: Param-medic for estimating search parameters directly from mass spectrometry data, Kojak for searching cross-linked mass spectra, and DIAmeter for searching data independent acquisition data against a sequence database.

Exhaustive Cross-Linking Search with Protein Feedback.

Improving the sensitivity of protein-protein interaction detection and protein structure probing is a principal challenge in cross-linking mass spectrometry (XL-MS) data analysis. In this paper, we propose an exhaustive cross-linking search method with protein feedback (ECL-PF) for cleavable XL-MS data analysis. ECL-PF adopts an optimized α/ß mass detection scheme and establishes protein-peptide association during the identification of cross-linked peptides. Existing major scoring functions can all benefit from the ECL-PF workflow to a great extent. In comparisons using synthetic data sets and hybrid simulated data sets, ECL-PF achieved 3-fold higher sensitivity over standard techniques. In experiments using real data sets, it also identified 65.6% more cross-link spectrum matches and 48.7% more unique cross-links.

The Choice of Search Engine Affects Sequencing Depth and HLA Class I Allele-Specific Peptide Repertoires.

Standardization of immunopeptidomics experiments across laboratories is a pressing issue within the field, and currently a variety of different methods for sample preparation and data analysis tools are applied. Here, we compared different software packages to interrogate immunopeptidomics datasets and found that Peaks reproducibly reports substantially more peptide sequences (~30-70%) compared with Maxquant, Comet, and MS-GF+ at a global false discovery rate (FDR) of <1%. We noted that these differences are driven by search space and spectral ranking. Furthermore, we observed differences in the proportion of peptides binding the human leukocyte antigen (HLA) alleles present in the samples, indicating that sequence-related differences affected the performance of each tested engine. Utilizing data from single HLA allele expressing cell lines, we observed significant differences in amino acid frequency among the peptides reported, with a broadly higher representation of hydrophobic amino acids L, I, P, and V reported by Peaks. We validated these results using data generated with a synthetic library of 2000 HLA-associated peptides from four common HLA alleles with distinct anchor residues. Our investigation highlights that search engines create a bias in peptide sequence depth and peptide amino acid composition, and resulting data should be interpreted with caution.

Comparison of Databases for Complex Evidence Syntheses on Education for the Health Professions.

Increased requests for assistance with literature searches on educational topics within the health professions motivated two health science librarians to analyze search function and results in eleven bibliographic databases on questions representing three allied health instructional target populations (patient, caregiver, and future health professionals). Results overlap and relevance were estimated and useful functions and subject headings were examined, as evidence for future search and database recommendations. This research confirmed the authors' hypothesis that PubMed and CINAHL overlapped significantly yet yielded sufficient unique citations to recommend searching both, plus at least one education-specific database. For the six questions researched, psychology and sports medicine databases were less productive.

Librarian and Researcher Assessments of Search Result Relevance: How Well Do They Align?

Conducting comprehensive but efficient literature searches for complex evidence syntheses involves selecting databases that will retrieve the greatest number of relevant results on the question. Lack of a comprehensive single database on allied health educational topics challenges those seeking such literature. In this study, six participants contributed research questions on instructional methods and materials for allied health patients, caregivers, and future health professionals. Two health sciences librarians created search strategies for these questions and searched eleven databases. Both the librarians and the six participants evaluated the search results using a rubric based on PICO to assess extent of alignment between the librarians' and requestors' relevance judgments. Intervention, Outcome, and Assessment Method constituted the most frequent bases for assessments of relevance by both librarians and participants. The librarians were more restrictive in all of their assessments except in a preliminary search yielding twelve citations without abstracts. The study's results could be used to identify effective techniques for reference interviewing, selecting databases, and weeding search results.

Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data.

Advances in library-based methods for peptide detection from data-independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high-quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico, but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate-controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide-window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.

Improving Peptide-Level Mass Spectrometry Analysis via Double Competition.

The analysis of shotgun proteomics data often involves generating lists of inferred peptide-spectrum matches (PSMs) and/or of peptides. The canonical approach for generating these discovery lists is by controlling the false discovery rate (FDR), most commonly through target-decoy competition (TDC). At the PSM level, TDC is implemented by competing each spectrum's best-scoring target (real) peptide match with its best match against a decoy database. This PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here, we first highlight and empirically augment a little known previous work by He et al., which showed that TDC-based PSM-level FDR estimates can be liberally biased. We thus propose that researchers instead focus on peptide-level analysis. We then investigate three ways to carry out peptide-level TDC and show that the most common method ("PSM-only") offers the lowest statistical power in practice. An alternative approach that carries out a double competition, first at the PSM and then at the peptide level ("PSM-and-peptide"), is the most powerful method, yielding an average increase of 17% more discovered peptides at 1% FDR threshold relative to the PSM-only method.

Databases for Researching Athletic Training Literature.

Searching the athletic training literature can be confusing and overwhelming with many possible databases for locating relevant peer-reviewed scholarship. Finding evidence-based literature from respected publications is helpful in clinical decision-making for athletic training practitioners. This column details recommended databases and search tips to help students, staff, clinicians, and faculty in the field of athletic training find the literature they need to help make evidence-based decisions and to stay current with the published literature. Databases discussed include Cochrane, PubMed, SPORTDiscus, CINAHL, PEDro, Sports Medicine, and Education Index (formerly Physical Education Index), and Google Scholar.

Benefits of Iterative Searches of Large Databases to Interpret Large Human Gut Metaproteomic Data Sets.

The gut microbiota are increasingly considered as a main partner of human health. Metaproteomics enables us to move from the functional potential revealed by metagenomics to the functions actually operating in the microbiome. However, metaproteome deciphering remains challenging. In particular, confident interpretation of a myriad of MS/MS spectra can only be pursued with smart database searches. Here, we compare the interpretation of MS/MS data sets from 48 individual human gut microbiomes using three interrogation strategies of the dedicated Integrated nonredundant Gene Catalog (IGC 9.9 million genes from 1267 individual fecal samples) together with the Homo sapiens database: the classical single-step interrogation strategy and two iterative strategies (in either two or three steps) aimed at preselecting a reduced-sized, more targeted search space for the final peptide spectrum matching. Both iterative searches outperformed the single-step classical search in terms of the number of peptides and protein clusters identified and the depth of taxonomic and functional knowledge, and this was the most convincing with the three-step approach. However, iterative searches do not help in reducing variability of repeated analyses, which is inherent to the traditional data-dependent acquisition mode, but this variability did not affect the hierarchical relationship between replicates and all other samples.

Accurately Assigning Peptides to Spectra When Only a Subset of Peptides Are Relevant.

The standard proteomics database search strategy involves searching spectra against a peptide database and estimating the false discovery rate (FDR) of the resulting set of peptide-spectrum matches. One assumption of this protocol is that all the peptides in the database are relevant to the hypothesis being investigated. However, in settings where researchers are interested in a subset of peptides, alternative search and FDR control strategies are needed. Recently, two methods were proposed to address this problem: subset-search and all-sub. We show that both methods fail to control the FDR. For subset-search, this failure is due to the presence of "neighbor" peptides, which are defined as irrelevant peptides with a similar precursor mass and fragmentation spectrum as a relevant peptide. Not considering neighbors compromises the FDR estimate because a spectrum generated by an irrelevant peptide can incorrectly match well to a relevant peptide. Therefore, we have developed a new method, "subset-neighbor search" (SNS), that accounts for neighbor peptides. We show evidence that SNS controls the FDR when neighbors are present and that SNS outperforms group-FDR, the only other method that appears to control the FDR relative to a subset of relevant peptides.

PSpecteR: A User-Friendly and Interactive Application for Visualizing Top-Down and Bottom-Up Proteomics Data in R.

Visual examination of mass spectrometry data is necessary to assess data quality and to facilitate data exploration. Graphics provide the means to evaluate spectral properties, test alternative peptide/protein sequence matches, prepare annotated spectra for publication, and fine-tune parameters during wet lab procedures. Visual inspection of LC-MS data is constrained by proteomics visualization software designed for particular workflows or vendor-specific tools without open-source code. We built PSpecteR, an open-source and interactive R Shiny web application for visualization of LC-MS data, with support for several steps of proteomics data processing, including reading various mass spectrometry files, running open-source database search engines, labeling spectra with fragmentation patterns, testing post-translational modifications, plotting where identified fragments map to reference sequences, and visualizing algorithmic output and metadata. All figures, tables, and spectra are exportable within one easy-to-use graphical user interface. Our current software provides a flexible and modern R framework to support fast implementation of additional features. The open-source code is readily available (https://github.com/EMSL-Computing/PSpecteR), and a PSpecteR Docker container (https://hub.docker.com/r/emslcomputing/pspecter) is available for easy local installation.

Tailoring to Search Engines: Bottom-Up Proteomics with Collision Energies Optimized for Identification Confidence.

Bottom-up proteomics relies on identification of peptides from tandem mass spectra, usually via matching against sequence databases. Confidence in a peptide-spectrum match can be characterized by a score value given by the database search engines, and it depends on the information content and the quality of the spectrum. The latter are influenced by experimental parameters, of which the collision energy is the most important one in the case of collision-induced dissociation. We examined how the identification score of the Byonic and Andromeda (MaxQuant) engines varies with collision energy for more than a thousand individual peptides from a HeLa tryptic digest on a QTof instrument. We thereby extended our earlier study on Mascot scores and corroborated its findings on the potential bimodal nature of this energy dependence. Optimal energies as a function of m/z show comparable linear trends for the three engines. On the basis of peptide-level results, we designed methods with one or two liquid chromatography-tandem mass spectrometry (LC-MS/MS) runs and various collision energy settings and assessed their practical performance in peptide and protein identification from the HeLa standard sample. A 10-40% gain in various measures, such as the number of identified proteins or sequence coverage, was obtained over the factory default settings. Best performing methods differ for the three engines, suggesting that the experimental parameters should be fine-tuned to the choice of the engine. We also recommend a simple approach and provide reference data to ease the transfer of the optimized methods to other mass spectrometers relevant for proteomics. We demonstrate the utility of this approach on an Orbitrap instrument. Data sets can be accessed via the MassIVE repository (MSV000086379).

Post-marketing analysis for biosimilar CT-P13 in inflammatory bowel disease compared with external data of originator infliximab in Japan.

BACKGROUND AND AIM: CT-P13, an infliximab (IFX) biosimilar, was approved for treatment of inflammatory bowel disease. However, no comparison with the originator IFX in this indication has been conducted in Japan where endemic levels of tuberculosis and hepatitis virus infection are not low. We evaluated the safety and efficacy in real-world data of CT-P13 and compared with originator IFX data in Japan. METHODS: In a prospective post-marketing surveillance (PMS) study, patients who received CT-P13 in a 28-month period from January 2015 were followed up for 2 years. By conducting Japanese administrative database search (DBS) for the same period of PMS, data of the originator IFX including treatment persistence, tuberculosis incidence, and liver injury were analyzed retrospectively and compared with the corresponding PMS data of CT-P13. RESULTS: CT-P13 persistence in PMS (n = 640) and IFX persistence in DBS (n = 4113) were almost similar between patients who switched from the originator and patients who continued on the originator, and also between the biologics-naïve patient groups. There were no differences in the incidences of tuberculosis and hepatic injury (Tuberculosis: 2 patients [0.31%] with CT-P13, 10 patients [0.24%] with the originator, P = 0.75; Hepatic injury: 18.5% with CT-P13, 15.4% with the originator, P = 0.22). Most of the patients with hepatic injury continued treatment in PMS and DBS at similar rates (80.8% vs 83.6%, P = 0.65). CONCLUSION: The results of long-term PMS of CT-P13 compared with external reference data from an administrative database suggested that the biosimilar and its originator were comparably useful in real-world clinical practice.