MOTIVATION: Post-translational modifications (PTMs) alter functional states and interaction specificity largely through the conformational changes they impose on protein structure. However, most existing resources remain sequence-centric and cannot reveal how chemical modifications reshape 3D structures. To address this gap, we propose a structural database that systematically extracts and contextualizes modification sites within experimentally determined protein structures, providing a foundation for future studies of protein structure, function, and regulatory mechanisms. RESULTS: We present StrucPTM, a database that extracts modified residues directly from the Protein Data Bank (PDB) structures using atom-level composition rules, substantially expanding coverage beyond annotation-dependent methods. Each validated PTM modification is mapped onto a UniProt entry. The database further characterizes residues using key structural descriptors-including secondary structure, relative solvent accessibility (RSA), and whether the PTM site lies at an interchain interface. All chains associated with the same UniProt ID are compared and grouped into homolog sets based on sequence identity. This emphasizes structural conservation among homologs, allowing PTM-induced conformational deviations to be distinguished from unrelated sequence divergence. AVAILABILITY AND IMPLEMENTATION: StrucPTM offers searchable access, interactive 3D visualization, and homolog-based structural comparison through its web interface: https://prix.hanyang.ac.kr/strucptm. The source code and datasets are permanently archived on Zenodo (DOI: 10.5281/zenodo.18939125) and are accessible via GitHub (https://github.com/HanyangBISLab/StrucPTM.git).

MOTIVATION: Predicting protein structures with high accuracy is a critical challenge for the broad community of life sciences and industry. Despite progress made by deep neural networks like AlphaFold2, there is a need for further improvements in the quality of detailed structures, such as side-chains, along with protein backbone structures. RESULTS: Building upon the successes of AlphaFold2, the modifications we made include changing the losses of side-chain torsion angles and frame aligned point error, adding loss functions for side chain confidence and secondary structure prediction, and replacing template feature generation with a new alignment method based on conditional random fields. We also performed re-optimization by conformational space annealing using a molecular mechanics energy function which integrates the potential energies obtained from distogram and side-chain prediction. In the CASP15 blind test for single protein and domain modeling (109 domains), DeepFold ranked fourth among 132 groups with improvements in the details of the structure in terms of backbone, side-chain, and Molprobity. In terms of protein backbone accuracy, DeepFold achieved a median GDT-TS score of 88.64 compared with 85.88 of AlphaFold2. For TBM-easy/hard targets, DeepFold ranked at the top based on Z-scores for GDT-TS. This shows its practical value to the structural biology community, which demands highly accurate structures. In addition, a thorough analysis of 55 domains from 39 targets with publicly available structures indicates that DeepFold shows superior side-chain accuracy and Molprobity scores among the top-performing groups. AVAILABILITY AND IMPLEMENTATION: DeepFold tools are open-source software available at https://github.com/newtonjoo/deepfold.

MOTIVATION: Tandem mass tag (TMT)-based tandem mass spectrometry (MS/MS) has become the method of choice for the quantification of post-translational modifications in complex mixtures. Many cancer proteogenomic studies have highlighted the importance of large-scale phosphopeptide quantification coupled with TMT labeling. Herein, we propose a predicted Spectral DataBase (pSDB) search strategy called Deephos that can improve both sensitivity and specificity in identifying MS/MS spectra of TMT-labeled phosphopeptides. RESULTS: With deep learning-based fragment ion prediction, we compiled a pSDB of TMT-labeled phosphopeptides generated from ∼8000 human phosphoproteins annotated in UniProt. Deep learning could successfully recognize the fragmentation patterns altered by both TMT labeling and phosphorylation. In addition, we discuss the decoy spectra for false discovery rate (FDR) estimation in the pSDB search. We show that FDR could be inaccurately estimated by the existing decoy spectra generation methods and propose an innovative method to generate decoy spectra for more accurate FDR estimation. The utilities of Deephos were demonstrated in multi-stage analyses (coupled with database searches) of glioblastoma, acute myeloid leukemia and breast cancer phosphoproteomes. AVAILABILITY AND IMPLEMENTATION: Deephos pSDB and the search software are available at https://github.com/seungjinna/deephos.

BACKGROUND: In shotgun proteomics, database search engines have been developed to assign peptides to tandem mass (MS/MS) spectra and at the same time post-processing (or rescoring) approaches over the search results have been proposed to increase the number of confident peptide identifications. The most popular post-processing approaches such as Percolator and PeptideProphet have improved rates of peptide identifications by combining multiple scores from database search engines while applying machine learning techniques. Existing post-processing approaches, however, are limited when dealing with results from new search engines because their features for machine learning must be optimized specifically for each search engine. RESULTS: We propose a universal post-processing tool, called TIDD, which supports confident peptide identifications regardless of the search engine adopted. TIDD can work for any (including newly developed) search engines because it calculates universal features that assess peptide-spectrum match quality while it allows additional features provided by search engines (or users) as well. Even though it relies on universal features independent of search tools, TIDD showed similar or better performance than Percolator in terms of peptide identification. TIDD identified 10.23-38.95% more PSMs than target-decoy estimation for MSFragger, which is not supported by Percolator. TIDD offers an easy-to-use simple graphical user interface for user convenience. CONCLUSIONS: TIDD successfully eliminated the requirement for an optimal feature engineering per database search tool, and thus, can be applied directly to any database search results including newly developed ones.

MOTIVATION: Proteogenomics has proven its utility by integrating genomics and proteomics. Typical approaches use data from next-generation sequencing to infer proteins expressed. A sample-specific protein sequence database is often adopted to identify novel peptides from matched mass spectrometry-based proteomics; nevertheless, there is no software that can practically identify all possible forms of mutated peptides suggested by various genomic information sources. RESULTS: We propose MutCombinator, which enables us to practically identify mutated peptides from tandem mass spectra allowing combinatorial mutations during the database search. It uses an upgraded version of a variant graph, keeping track of frame information. The variant graph is indexed by nine nucleotides for fast access. Using MutCombinator, we could identify more mutated peptides than previous methods, because combinations of point mutations are considered and also because it can be practically applied together with a large mutation database such as COSMIC. Furthermore, MutCombinator supports in-frame search for coding regions and three-frame search for non-coding regions. AVAILABILITY AND IMPLEMENTATION: https://prix.hanyang.ac.kr/download/mutcombinator.jsp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: In many proteogenomic applications, mapping peptide sequences onto genome sequences can be very useful, because it allows us to understand origins of the gene products. Existing software tools either take the genomic position of a peptide start site as an input or assume that the peptide sequence exactly matches the coding sequence of a given gene model. In case of novel peptides resulting from genomic variations, especially structural variations such as alternative splicing, these existing tools cannot be directly applied unless users supply information about the variant, either its genomic position or its transcription model. Mapping potentially novel peptides to genome sequences, while allowing certain genomic variations, requires introducing novel gene models when aligning peptide sequences to gene structures. We have developed a new tool called ACTG (Amino aCids To Genome), which maps peptides to genome, assuming all possible single exon skipping, junction variation allowing three edit distances from the original splice sites, exon extension and frame shift. In addition, it can also consider SNVs (single nucleotide variations) during mapping phase if a user provides the VCF (variant call format) file as an input. Availability and Implementation: Available at http://prix.hanyang.ac.kr/ACTG/search.jsp . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

UNLABELLED: Peptide identification is an important problem in proteomics. One of the most popular scoring schemes for peptide identification is XCorr (cross-correlation). Since calculating XCorr is computationally intensive, a lot of efforts have been made to develop fast XCorr engines. However, the existing XCorr engines are not suitable for high-resolution MS/MS spectrometry because they are either slow or require a specific type of CPU. We present a portable high-speed XCorr engine for high-resolution tandem mass spectrometry by developing a novel algorithm for calculating XCorr. The algorithm enables XCorr calculation 1.25-49 times faster than previous algorithms for 0.01 Da fragment tolerance. Furthermore, our engine is easily portable to any machine with different types of CPU because it is developed in C language. Hence, our XCorr engine will expedite peptide identification by high-resolution tandem mass spectrometry. AVAILABILITY AND IMPLEMENTATION: Available at http://isa.hanyang.ac.kr/HiXCorr/HiXCorr.html.

BACKGROUND: Protein quantification is an essential step in many proteomics experiments. A number of labeling approaches have been proposed and adopted in mass spectrometry (MS) based relative quantification. The mTRAQ, one of the stable isotope labeling methods, is amine-specific and available in triplex format, so that the sample throughput could be doubled when compared with duplex reagents. METHODS AND RESULTS: Here we propose a novel data analysis algorithm for peptide quantification in triplex mTRAQ experiments. It improved the accuracy of quantification in two features. First, it identified and separated triplex isotopic clusters of a peptide in each full MS scan. We designed a schematic model of triplex overlapping isotopic clusters, and separated triplex isotopic clusters by solving cubic equations, which are deduced from the schematic model. Second, it automatically determined the elution areas of peptides. Some peptides have similar atomic masses and elution times, so their elution areas can have overlaps. Our algorithm successfully identified the overlaps and found accurate elution areas. We validated our algorithm using standard protein mixture experiments. CONCLUSIONS: We showed that our algorithm was able to accurately quantify peptides in triplex mTRAQ experiments. Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.

Article Automating a classification task based on an augmented thesaurus Share on Authors: Eunok Paek Information Technology Lab., LG Electronics Research Center, 16 Woomyeon-dong, Seocho-gu, Seoul, 137-140, Republic of Korea Information Technology Lab., LG Electronics Research Center, 16 Woomyeon-dong, Seocho-gu, Seoul, 137-140, Republic of KoreaView Profile , Hye-Jeong Jeon Information Technology Lab., LG Electronics Research Center, 16 Woomyeon-dong, Seocho-gu, Seoul, 137-140, Republic of Korea Information Technology Lab., LG Electronics Research Center, 16 Woomyeon-dong, Seocho-gu, Seoul, 137-140, Republic of KoreaView Profile Authors Info & Claims IUI '97: Proceedings of the 2nd international conference on Intelligent user interfacesJanuary 1997 Pages 221–224https://doi.org/10.1145/238218.238328Online:06 January 1997Publication History 0citation240DownloadsMetricsTotal Citations0Total Downloads240Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

This article provides a formal framework for dealing with two common concepts in causal reasoning, namely causal/evidential support and causal independence. We focus our attention on the phenomenon of explaining away that plays an important role in analyzing and understanding both concepts. In order to provide a qualitative account of the explaining away phenomenon, we present a justification-based approach that uses circumscription to obtain the minimality of causes. We give a logical formulation of causal and evidential support in terms of a justification change with respect to a circumscriptive theory and show how the definition provides desirable interactions between causal and evidential support. Also, we give a qualitative analysis of causal independence in terms of justification, by restricting it to be expressed only in terms that are considered relevant to the independence query. In both attempts of logical formulation, the notion of justification and the circumscriptive encoding of causal relationship among events provide a flexible and yet rigorous framework. © 1994 John Wiley & Sons, Inc.