Protein Datasets Research Articles

SeqDPI: A 1D-CNN approach for predicting binding affinity of kinase inhibitors.

Predicting drug target binding affinity has huge relevance in Modern drug discovery and drug repositioning processes which assist doctors to come up with new drugs or even use the existing drugs for new target proteins. In silico models, using advanced deep learning techniques could further assist these prediction tasks by providing most prominent drug target pairs. Considering these factors, a deep learning based algorithmic framework is developed in this study to support drug target interaction prediction. The proposed SeqDPI model extract the relevant drug and protein features from the one dimensional Sequential representation of the dataset considered using optimized CNN networks that deploy convolutions on varying length of amino acid subsequence's to capture hidden pattern, the convolved drug- protein features obtained are then used as an input to L2 penalized feed forward neural network which matches the local residue patterns in protein classes with molecular fingerprints of drugs to predict the binding strength for all drug target pairs. The proposed model reduces the convolution strain typically encountered in existing in silico models that utilize complex 3D structures of drug protein datasets. The result shows that the SeqDPI model achieves a mean square error MSE of (0.167) across cross validation folds, outperforming baseline models such as KronRLS (0.406), Simboost (0.226), and DeepPS (0.214). Additionally, SeqDPI attains a high CI score of 0.9114 on the benchmark KIBA dataset, demonstrating its statistical significance and computational efficiency compared to existing methods. This gives the relevance and effectiveness of SeqDPI model in accurately predicting binding affinities while working with simpler one-dimensional data, making it a robust and computationally cost-effective solution for drug-target interaction prediction.

Identify Diabetes-related Targets based on ForgeNet_GPC.

Research on potential therapeutic targets and new mechanisms of action can greatly improve the efficiency of new drug development. Polygenic genetic diseases, such as diabetes, are caused by the interaction of multiple gene loci and environmental factors. In this study, a disease target identification algorithm based on protein recognition is proposed. In this method, the related and unrelated targets are collected from literature databases for treating diabetes. The transcribed proteins corresponding to each target are queried in order to construct a protein dataset. Six protein feature extraction algorithms (AAC, CKSAAGP, DDE, DPC, GAAP, and TPC) are utilized to obtain the feature vectors of each protein, which are merged into the full feature vectors. A novel classifier (forgeNet_GPC) based on forgeNet and Gaussian process classifier (GPC) is proposed to classify the proteins. In forgeNet_GPC, forgeNet is utilized to select the important features, and GPC is utilized to solve the classification problem. The experimental results reveal that forgeNet_GPC performs better than 22 classifiers in terms of ROC-AUC, PR-AUC, MCC, Youden Index, and Kappa.

Network Mendelian randomisation analysis deciphers protein pathways linking type 2 diabetes and gastrointestinal disease.

The molecular mechanisms underlying the association between type 2 diabetes (T2D) and gastrointestinal (GI) disease are unclear. To identify protein pathways, we conducted a two-stage network Mendelian randomisation (MR) study. Genetic instruments for T2D were obtained from a large-scale summary-level genome-wide meta-analysis. Genetic associations with blood protein levels were obtained from three genome-wide association studies on plasma proteins (i.e. the deCODE study as the discovery and the UKB-PPP and Fenland studies as the replication). Summary-level data on 10 GI diseases were derived from genome-wide meta-analysis of the UK Biobank and FinnGen. MR and colocalisation analyses were performed. Pathways were constructed according to the directionality of total and indirect effects, and corresponding proportional mediation was estimated. Druggability assessments were conducted across four databases to prioritise protein mediators. Genetic liability to T2D was associated with 69 proteins in the discovery protein dataset after multiple testing corrections. All associations were replicated at the nominal significance level. Among T2D-associated proteins, genetically predicted levels of nine proteins were associated with at least one of the GI diseases. Genetically predicted levels of SULT2A1 (odds ratio = 1.98, 95% CI 1.80-2.18), and ADH1B (odds ratio = 2.05, 95% CI 1.43-2.94) were associated with cholelithiasis and cirrhosis respectively. SULT2A1 and cholelithiasis (PH4 = 0.996) and ADH1B and cirrhosis (PH4 = 0.931) have strong colocalisation support, accounting for the mediation proportion of 72.8% (95% CI 45.7-99.9) and 42.9% (95% CI 15.5-70.4) respectively. The study identified some proteins mediating T2D-GI disease associations, which provided biological insights into the underlying pathways.

Protein-small molecule binding site prediction based on a pre-trained protein language model with contrastive learning.

Predicting protein-small molecule binding sites, the initial step in structure-guided drug design, remains challenging for proteins lacking experimentally derived ligand-bound structures. Here, we propose CLAPE-SMB, which integrates a pre-trained protein language model with contrastive learning to provide high accuracy predictions of small molecule binding sites that can accommodate proteins without a published crystal structure. We trained and tested CLAPE-SMB on the SJC dataset, a non-redundant dataset based on sc-PDB, JOINED, and COACH420, and achieved an MCC of 0.529. We also compiled the UniProtSMB dataset, which merges sites from similar proteins based on raw data from UniProtKB database, and achieved an MCC of 0.699 on the test set. In addition, CLAPE-SMB achieved an MCC of 0.815 on our intrinsically disordered protein (IDP) dataset that contains 336 non-redundant sequences. Case studies of DAPK1, RebH, and Nep1 support the potential of this binding site prediction tool to aid in drug design. The code and datasets are freely available at https://github.com/JueWangTHU/CLAPE-SMB . SCIENTIFIC CONTRIBUTION: CLAPE-SMB combines a pre-trained protein language model with contrastive learning to accurately predict protein-small molecule binding sites, especially for proteins without experimental structures, such as IDPs. Trained across various datasets, this model shows strong adaptability, making it a valuable tool for advancing drug design and understanding protein-small molecule interactions.

Genomics-driven integrative analysis highlights immune-related plasma proteins for psychiatric disorders

BackgroundGenome-wide association studies (GWAS) have identified numerous variants associated with psychiatric disorders. However, it remains largely unknown on how GWAS risk variants contribute to psychiatric disorders. MethodsThrough integrating two largest, publicly available, independent protein quantitative trait loci datasets of plasma protein and nine large-scale GWAS summary statistics of psychiatric disorders, we first performed proteome-wide association study (PWAS) to identify psychiatric disorders-associated plasma proteins, followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we conducted Mendelian randomization (MR) and Bayesian colocalization (COLOC) analyses, with both discovery and parallel replication datasets, to further identify protein-disorder pairs with putatively causal relationships. We finally prioritized the potential drug targets using Drug Gene Interaction Database. ResultsPWAS totally identified 112 proteins, which were significantly enriched in biological processes relevant to immune regulation and response to stimulus including regulation of immune system process (adjusted P = 1.69 × 10−7) and response to external stimulus (adjusted P = 4.13 × 10−7), and viral infection related pathways, including COVID-19 (adjusted P = 2.94 × 10−2). MR and COLOC analysis further identified 26 potentially causal protein-disorder pairs in both discovery and replication analysis. Notably, eight protein-coding genes were immune-related, such as IRF3, CSK, and ACE, five among 16 druggable genes were reported to interact with drugs, including ACE, CSK, PSMB4, XPNPEP1, and MICB. ConclusionsOur findings highlighted the immunological hypothesis and identified potentially causal plasma proteins for psychiatric disorders, providing biological insights into the pathogenesis and benefit the development of preventive or therapeutic drugs for psychiatric disorders.

Elliptic geometry-based kernel matrix for improved biological sequence classification

Protein sequence classification plays a pivotal role in bioinformatics as it enables the comprehension of protein functions and their involvement in diverse biological processes. While numerous machine learning models have been proposed to tackle this challenge, traditional approaches face limitations in capturing the intricate relationships and hierarchical structures inherent in genomic sequences. These limitations stem from operating within high-dimensional non-Euclidean spaces. To address this issue, we introduce the application of the elliptic geometry-based approach for protein sequence classification. First, we transform the problem in elliptic geometry and integrate it with the Gaussian kernel to map the problem into the Mercer kernel. The Gaussian-Elliptic approach allows for the implicit mapping of data into a higher-dimensional feature space, enabling the capture of complex nonlinear relationships. This feature becomes particularly advantageous when dealing with hierarchical or tree-like structures commonly encountered in biological sequences. Experimental results highlight the effectiveness of the proposed model in protein sequence classification, showcasing the advantages of utilizing elliptic geometry in bioinformatics analyses. It outperforms state-of-the-art methods by achieving 76% and 84% accuracies for DNA and Protein datasets, respectively. Furthermore, we provide theoretical justifications for the proposed model. This study contributes to the burgeoning field of geometric deep learning, offering insights into the potential applications of elliptic representations in the analysis of biological data.

GraphPI: Efficient Protein Inference with Graph Neural Networks.

The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled data sets, a challenge compounded by the high costs and complexities of accurate protein annotation. In this study, we introduce GraphPI, a novel framework that treats protein inference as a node classification problem. We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a graph neural network-based architecture to elucidate their interrelations. To address label scarcity, we train the model on a set of unlabeled public protein data sets with pseudolabels derived from an existing protein inference algorithm, enhanced by self-training to iteratively refine labels based on confidence scores. Contrary to prevalent methodologies necessitating data set-specific training, our research illustrates that GraphPI, due to the well-normalized nature of Percolator features, exhibits universal applicability without data set-specific fine-tuning, a feature that not only mitigates the risk of overfitting but also enhances computational efficiency. Our empirical experiments reveal notable performance on various test data sets and deliver significantly reduced computation times compared to common protein inference algorithms.

Proteogenomic characterization of highly enriched viable leukemic blasts in acute myeloid leukemia: A SWOG report

AbstractIntroductionAcute myeloid leukemia (AML) remains one of the deadliest hematopoietic malignancies. A better understanding of the molecular biology governing AML may lead to improved risk stratification and facilitate the development of novel therapies. Proteins are responsible for much of the biology of cells. Several studies have examined the global proteome in bulk mononuclear cells (MNCs) from AML specimens, which are comprised a heterogenous population of cells at various stages of differentiation.MethodsGiven the potential impact of the nonleukemic cells on protein expression profiles, we applied an integrative proteogenomic approach utilizing next‐generation sequencing and mass spectrometry‐based proteomics to identify novel protein biomarkers in unsorted MNCs and viable leukemic blasts (VLBs) isolated from blood and bone marrow specimens obtained at the time of AML diagnosis.ResultsWe identified significant differences in protein expression between VLBs and MNCs. Subsequent studies (N = 27) focused on proteomic profiling of VLBs that identified novel candidate biomarkers associated with mutational genotypes and clinical outcome, some of which were recapitulated in an independent cohort of patients. Using mass spectrometry, we also detected mutated protein products, some of which were predicted via in silico analyses to be potential neoantigens amenable to adoptive immunotherapy. As previously described, analyses comparing transcript and protein expression showed an overall modest correlation between mRNA and protein dataset, but enriching for genes associated with mutations significantly improved the protein–RNA correlation.ConclusionTogether, the results provide insight into the biology of VLBs and demonstrate the gains derived from examining the proteome in addition to genome and transcriptome.

Proteomic Profiling of Endothelial Cell Secretomes After Exposure to Calciprotein Particles Reveals Downregulation of Basement Membrane Assembly and Increased Release of Soluble CD59.

Calciprotein particles (CPPs) are essential circulating scavengers of excessive Ca2+ and PO43- ions, representing a vehicle that removes them from the human body and precludes extraskeletal calcification. Having been internalised by endothelial cells (ECs), CPPs induce their dysfunction, which is accompanied by a remarkable molecular reconfiguration, although little is known about this process's extracellular signatures. Here, we applied ultra-high performance liquid chromatography-tandem mass spectrometry to perform a secretome-wide profiling of the cell culture supernatant from primary human coronary artery ECs (HCAECs) and internal thoracic artery ECs (HITAECs) treated with primary CPPs (CPP-P), secondary CPPs (CPP-S), magnesiprotein particles (MPPs), or Ca2+/Mg2+-free Dulbecco's phosphate-buffered saline (DPBS) for 24 h. Incubation with CPP-P/CPP-S significantly altered the profiles of secreted proteins, delineating physiological and pathological endothelial secretomes. Neither pathway enrichment analysis nor the interrogation of protein-protein interactions detected extracellular matrix- and basement membrane-related molecular terms in the protein datasets from CPP-P/CPP-S-treated ECs. Both proteomic profiling and enzyme-linked immunosorbent assay identified an increased level of protectin (CD59) and reduced levels of osteonectin (SPARC), perlecan (HSPG2), and fibronectin (FN1) in the cell culture supernatant upon CPP-P/CPP-S treatment. Elevated soluble CD59 and decreased release of basement membrane components might be considered as potential signs of dysfunctional endothelium.

Ensemble learning model for Protein-Protein interaction prediction with multiple Machine learning techniques

Protein-Protein Interactions (PPIs) encompass the physical interactions or chemical combinations between proteins, which cells employ to carry out diverse biological functions. PPIs Prediction is of great significance for understanding and studying molecular mechanisms and disease mechanisms in organisms. Traditional machine learning algorithms for predicting PPIs face challenges related to imbalanced sample data, loss of long sequence features, and variations in datasets across different species. This paper takes advantage of the attention mechanism in processing sequence tasks and proposes the fusion of Bi-directional Long Short- Term Memory, an integrated learning model of convolutional neural network and multi-head attention mechanism, referred to as CNN BiLSTM Multi-head Att. The model first performs one-hot encoding on the protein sequence and obtains a low-dimensional word vector representation, and then calculates the encoding matrix through the embedding matrix; it uses the convolutional neural network (CNN) and the bidirectional long short-term memory network (Bi-LSTM) to extract the amino acids in the protein sequence. The feature matrix is obtained by collecting information on the time, site, and physical and chemical characteristics; the multi-head attention mechanism is used in weighted calculation and merger of the subspace features of each sequence, and classification is performed after global average pooling and the PPI prediction is output. Cross-validation experiments on different proportions of positive and negative protein data sets and proteins of different species show that CNN BiLSTM Multi-head Att can better predict imbalanced data sets and protein interactions of different species. Compared to the state-of-the-art model, our model improves accuracy by 0.025, precision by 0.046, F1-score by 0.052, and recall by 0.014. Exploring the scalability and adaptability of the proposed model for large-scale biological datasets, along with investigating its potential in real-time applications, could lead to significant advancements in the field of protein–protein interaction prediction.

Novel amino acid distance matrices based on conductance measure

Ancestral relationships among biological species are often represented and analyzed by means of phylogenetic trees. Substitution and distance matrices are two main types of matrices that are used in phylogeny analyses. Substitution matrices describe a frequency change of amino acids in nucleotide or protein sequence over time, while distance matrices estimate phylogeny using a matrix of pairwise distances based on a particular code or analytical concept. Recent investigation by Elena Fimmel and coworkers (Life 11:1338, 2021) showed that: 1. the robustness of a genetic code against point mutations can be described using the conductance measure, and 2. all possible point mutations of the genetic code can be represented as a weighted graph with weights that correspond to the probabilities of these mutations. In this article, we constructed and tested three novel distance matrices based on conductance measure, that take into account the point mutation robustness of the Standard Genetic Code (SGC). These distance matrices are based on maximum (CMAX), average (CAVG), and minimum (CMIN) conductance-optimized distances between codons coding for individual amino acids. The performance of those distance matrices was tested on a dataset of RecA proteins in Bacteria, Archaea (RadA homolog) and Eukarya (Rad51 homolog). RecA protein and its functional homologs were selected for this investigation since they are essential for the repair and maintenance of DNA, and consequently well conserved and present in all domains of life. PAM250 and BLOSUM62 matrices were usually used as a standard for distance matrix testing. PAM250 and BLOSUM62 substitution matrices specified accurately three biological domains of life according to Carl Woese and George Fox (Proc Natl Acad Sci U S A 74:5088, 1977). An identical result was obtained using three novel distance matrices (CMIN, CMAX, CAVG). This result supports the applicability of novel distance matrices based on the conductance method and suggests that further investigations based on this approach are justified.

Topology-Driven Discovery of Transmembrane Protein S-Palmitoylation.

Protein S-palmitoylation is a reversible lipophilic posttranslational modification regulating a diverse number of signaling pathways. Within transmembrane proteins (TMPs), S-palmitoylation is implicated in conditions from inflammatory disorders to respiratory viral infections. Many small-scale experiments have observed S-palmitoylation at juxtamembrane Cys residues. However, most large-scale S-palmitoyl discovery efforts rely on trypsin-based proteomics within which hydrophobic juxtamembrane regions are likely underrepresented. Machine learning- by virtue of its freedom from experimental constraints - is particularly well suited to address this discovery gap surrounding TMP S-palmitoylation. Utilizing a UniProt-derived feature set, a gradient boosted machine learning tool (TopoPalmTree) was constructed and applied to a holdout dataset of viral S-palmitoylated proteins. Upon application to the mouse TMP proteome, 1591 putative S-palmitoyl sites (i.e. not listed in SwissPalm or UniProt) were identified. Two lung-expressed S-palmitoyl candidates (synaptobrevin Vamp5 and water channel Aquaporin-5) were experimentally assessed. Finally, TopoPalmTree was used for rational design of an S-palmitoyl site on KDEL-Receptor 2. This readily interpretable model aligns the innumerable small-scale experiments observing juxtamembrane S-palmitoylation into a proteomic tool for TMP S-palmitoyl discovery and design, thus facilitating future investigations of this important modification.

Accurate and efficient protein embedding using multi-teacher distillation learning.

Protein embedding, which represents proteins as numerical vectors, is a crucial step in various learning-based protein annotation/classification problems, including gene ontology prediction, protein-protein interaction prediction, and protein structure prediction. However, existing protein embedding methods are often computationally expensive due to their large number of parameters, which can reach millions or even billions. The growing availability of large-scale protein datasets and the need for efficient analysis tools have created a pressing demand for efficient protein embedding methods. We propose a novel protein embedding approach based on multi-teacher distillation learning, which leverages the knowledge of multiple pre-trained protein embedding models to learn a compact and informative representation of proteins. Our method achieves comparable performance to state-of-the-art methods while significantly reducing computational costs and resource requirements. Specifically, our approach reduces computational time by ∼70% and maintains ±1.5% accuracy as the original large models. This makes our method well-suited for large-scale protein analysis and enables the bioinformatics community to perform protein embedding tasks more efficiently. The source code of MTDP is available via https://github.com/KennthShang/MTDP.

PB-GPT: An innovative GPT-based model for protein backbone generation

With advanced computational methods, it is now feasible to modify or design proteins for specific functions, a process with significant implications for disease treatment and other medical applications. Protein structures and functions are intrinsically linked to their backbones, making the design of these backbones a pivotal aspect of protein engineering. In this study, we focus on the task of unconditionally generating protein backbones. By means of codebook quantization and compression dictionaries, we convert protein backbone structures into a distinctive coded language and propose a GPT-based protein backbone generation model, PB-GPT. To validate the generalization performance of the model, we trained and evaluated the model on both public datasets and small protein datasets. The results demonstrate that our model has the capability to unconditionally generate elaborate, highly realistic protein backbones with structural patterns resembling those of natural proteins, thus showcasing the significant potential of large language models in protein structure design.

AlphaFold2-based prediction of the co-condensation propensity of proteins

The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational predictions of the conformational properties of proteins from their amino acid sequences. We thus developed the CoDropleT (co-condensation into droplet transformer) method of predicting the propensity of co-condensation of protein pairs. The method was trained by combining experimental datasets of co-condensing proteins from the CD-CODE database with curated negative datasets of non-co-condensing proteins. To illustrate the performance of the method, we applied it to estimate the propensity of proteins to co-condense into MLOs. Our results suggest that CoDropleT could facilitate functional and therapeutic studies on protein condensation by predicting the composition of protein condensates.

Incorporating Tissue-Specific Gene Expression Data to Improve Chemical-Disease Inference of in Silico Toxicogenomics Methods.

In silico toxicogenomics methods are resource- and time-efficient approaches for inferring chemical-protein-disease associations with potential mechanism information for exploring toxicological effects. However, current in silico toxicogenomics systems make inferences based on only chemical-protein interactions without considering tissue-specific gene/protein expressions. As a result, inferred diseases could be overpredicted with false positives. In this work, six tissue-specific expression datasets of genes and proteins were collected from the Expression Atlas. Genes were then categorized into high, medium, and low expression levels in a tissue- and dataset-specific manner. Subsequently, the tissue-specific expression datasets were incorporated into the chemical-protein-disease inference process of our ChemDIS system by filtering out relatively low-expressed genes. By incorporating tissue-specific gene/protein expression data, the enrichment rate for chemical-disease inference was largely improved with up to 62.26% improvement. A case study of melamine showed the ability of the proposed method to identify more specific disease terms that are consistent with the literature. A user-friendly user interface was implemented in the ChemDIS system. The methodology is expected to be useful for chemical-disease inference and can be implemented for other in silico toxicogenomics tools.

Correction of preferred orientation-induced distortion in cryo-electron microscopy maps.

Reconstruction maps of cryo-electron microscopy (cryo-EM) exhibit distortion when the cryo-EM dataset is incomplete, usually caused by unevenly distributed orientations. Prior efforts had been attempted to address this preferred orientation problem using tilt-collection strategy and modifications to grids or to air-water interfaces. However, these approaches often require time-consuming experiments, and the effect was always protein dependent. Here, we developed a procedure containing removing misaligned particles and an iterative reconstruction method based on signal-to-noise ratio of Fourier component to correct this distortion by recovering missing data using a purely computational algorithm. This procedure called signal-to-noise ratio iterative reconstruction method (SIRM) was applied on incomplete datasets of various proteins to fix distortion in cryo-EM maps and to a more isotropic resolution. In addition, SIRM provides a better reference map for further reconstruction refinements, resulting in an improved alignment, which ultimately improves map quality and benefits model building.

Secretome analysis of oligodendrocytes and precursors reveals their roles as contributors to the extracellular matrix and potential regulators of inflammation.

Oligodendrocytes form myelin that ensheaths axons and accelerates the speed of action potential propagation. Oligodendrocyte progenitor cells (OPCs) proliferate and replenish oligodendrocytes. While the myelin-forming role of oligodendrocytes and OPCs is well-established, potential additional roles of these cells are yet to be fully explored. Here, we analyzed the secreted proteome of oligodendrocytes and OPCs in vitro to determine whether these cell types are major sources of secreted proteins in the central nervous system (CNS). Interestingly, we found that both oligodendrocytes and OPCs secret various extracellular matrix proteins. Considering the critical role of neuroinflammation in neurological disorders, we evaluated the responses and potential contributions of oligodendrocytes and OPCs to this process. By characterizing the secreted proteomes of these cells after pro-inflammatory cytokine treatment, we discovered the secretion of immunoregulators such as C2 and B2m. This finding sheds new light on the hitherto underappreciated role of oligodendrocytes and OPCs in actively modulating neuroinflammation. Our study provides a comprehensive and unbiased proteomic dataset of proteins secreted by oligodendrocyte and OPC under both physiological and inflammatory conditions. It revealed the potential of these cells to secrete matrix and signaling molecules, highlighting their multifaceted function beyond their conventional myelin-forming roles.

ProteinFlow: An advanced framework for feature engineering in protein data analysis.

In the burgeoning field of proteins, the effective analysis of intricate protein data remains a formidable challenge, necessitating advanced computational tools for data processing, feature extraction, and interpretation. This study introduces ProteinFlow, an innovative framework designed to revolutionize feature engineering in protein data analysis. ProteinFlow stands out by offering enhanced efficiency in data collection and preprocessing, along with advanced capabilities in feature extraction, directly addressing the complexities inherent in multidimensional protein data sets. Through a comparative analysis, ProteinFlow demonstrated a significant improvement over traditional methods, notably reducing data preprocessing time and expanding the scope of biologically significant features identified. The framework's parallel data processing strategy and advanced algorithms ensure not only rapid data handling but also the extraction of comprehensive, meaningful insights from protein sequences, structures, and interactions. Furthermore, ProteinFlow exhibits remarkable scalability, adeptly managing large-scale data sets without compromising performance, a crucial attribute in the era of big data.

Large-scale identification of calcium oxalate stone inhibitory proteins in normal human urine

Recent evidence has shown that proteins in normal human urine can inhibit calcium oxalate (CaOx) kidney stone formation. Herein, we performed fast protein liquid chromatography (FPLC) to fractionate normal human urinary proteins using anion-exchange (DEAE) and size-exclusion (Superdex 200) materials. FPLC fractions (F1-F15) were examined by CaOx crystallization, growth, aggregation and crystal-cell adhesion assays. The fractions with potent inhibitory activities against CaOx crystals were then subjected to mass spectrometric protein identification. The data revealed that 13 of 15 fractions showed inhibitory activities in at least one crystal assay. Integrating CaOx inhibitory scores demonstrated that F6, F7 and F8 had the most potent inhibitory activities. NanoLC-ESI-Qq-TOF MS/MS identified 105, 93 and 53 proteins in F6, F7 and F8, respectively. Among them, 60 were found in at least two fractions and/or listed among known inhibitors with solid experimental evidence in the StoneMod database (https://www.stonemod.org). Interestingly, 10 of these 60 potential inhibitors have been reported with lower urinary levels in CaOx stone formers compared with healthy (non-stone) individuals, strengthening their roles as potent CaOx stone inhibitors. Our study provides the largest dataset of potential CaOx stone inhibitory proteins that will be useful for further elucidations of stone-forming mechanisms and ultimately for therapeutic/preventive applications.