Computational classification of core and outer fucosylation of N-glycoproteins in human plasma using collision-induced dissociation in mass spectrometry.

RATIONALE: Glycoprotein fucosylation, one of the major posttranslational modifications, is known to be highly involved in proteins related to various cancers. Fucosylation occurs in the core and/or outer sites of N-glycopeptides. Elucidation of the fucosylation type of N-glycoproteins is therefore important. However, it has remained a challenge to classify the fucosylation types of N-glycopeptides using collision-induced dissociation (CID) tandem mass (MS/MS) spectra. METHODS: The relative intensities of the Y1 F, Y2 F, Y3 F, and Y4 F product ions in the CID-MS/MS spectra of the IgG N-glycopeptides were measured for core fucosylation. The Core Fucose Index (CFI) was then calculated by multiplication of the relative intensities with a weight factor from logistic regression to differentiate between the core and none fucosylation. From the relative intensities of the B2 F and B3 SF ions of the MS/MS spectra of the AGP N-glycopeptides for outer fucosylation, the Outer Fucose Index (OFI) was calculated to differentiate between the outer and none fucosylation. RESULTS: In order to classify core and/or outer fucosylation of N-glycoproteins, we defined the fucosylation score (F-score) by a sigmoidal equation using a combination of the CFI and the OFI. For application, we classified the fucosylation types of N-glycoproteins in human plasma with 99.7% accuracy from the F-score. Human plasma samples showed 54.4%, 33.3%, 10.3%, and 1.6% for none, core, outer, and dual fucosylated N-glycopeptides, respectively. Core fucosylation was abundant at mono- and bi-antennary N-glycopeptides. Outer fucosylation was abundant at tri- and tetra-antennary N-glycopeptides. In total, 113 N-glycopeptides of 29 glycoproteins from 3365 glycopeptide spectral matches (GPSMs) were classified for different types of fucosylation. CONCLUSIONS: We established an F-score to classify three different fucosylation types: core, outer, and dual types of N-glycopeptides. The fucosylation types of 20 new N-glycopeptides from 11 glycoproteins in human plasma were classified using the F-score. Therefore, the F-score can be useful for the automatic classification of different types of fucosylation in N-glycoproteins of biological fluids including plasma, serum, and urine.

Author Correction: Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.

PMFF: Development of a Physics-Based Molecular Force Field for Protein Simulation and Ligand Docking.

The physics-based molecular force field (PMFF) was developed by integrating a set of potential energy functions in which each term in an intermolecular potential energy function is derived based on experimental values, such as the dipole moments, lattice energy, proton transfer energy, and X-ray crystal structures. The term "physics-based" is used to emphasize the idea that the experimental observables that are considered to be the most relevant to each term are used for the parameterization rather than parameterizing all observables together against the target value. PMFF uses MM3 intramolecular potential energy terms to describe intramolecular interactions and includes an implicit solvation model specifically developed for the PMFF. We evaluated the PMFF in three ways. We concluded that the PMFF provides reliable information based on the structure in a biological system and interprets the biological phenomena accurately by providing more accurate evidence of the biological phenomena.

SAAVpedia: Identification, Functional Annotation, and Retrieval of Single Amino Acid Variants for Proteogenomic Interpretation.

Next-generation genome sequencing has enabled the discovery of numerous disease- or drug-response-associated nonsynonymous single nucleotide variants (nsSNVs) that alter the amino acid sequences of a protein. Although several studies have attempted to characterize pathogenic nsSNVs, few have been confirmed as single amino acid variants (SAAVs) at the protein level. Here we developed the SAAVpedia platform to identify, annotate, and retrieve pathogenic SAAV candidates from proteomic and genomic data. The platform consists of four modules: SAAVidentifier, SAAVannotator, SNV/SAAVretriever, and SAAVvisualizer. The SAAVidentifier provides a reference database containing 18 206 090 SAAVs and performs the identification and quality assessment of SAAVs. The SAAVannotator provides functional annotation with biological, clinical, and pharmacological information for the interpretation of condition-specific SAAVs. The SNV/SAAVretriever module enables bidirectional navigation between relevant SAAVs and nsSNVs with diverse genomic and proteomic data. SAAVvisualizer provides various statistical plots based on functional annotations of detected SAAVs. To demonstrate the utility of SAAVpedia, the proteogenomic pipeline with protein-protein interaction network analysis was applied to proteomic data from breast cancer and glioblastoma patients. We identified 1326 and 12 breast-cancer- and glioblastoma-related genes that contained one or more SAAVs, including BRCA2 and FAM49B, respectively. SAAVpedia is a suitable platform for confirming whether a genomic variant is maintained in an amino acid sequence. Furthermore, as a result of the SAAV discovery of these positive controls, the SAAVpedia could play a key role in the protein functional study for the Human Proteome Project (HPP).

In vivo high-throughput profiling of CRISPR-Cpf1 activity.

CRISPR from Prevotella and Francisella 1 (Cpf1) is an effector endonuclease of the class 2 CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins) gene editing system. We developed a method for evaluating Cpf1 activity, based on target sequence composition in mammalian cells, in a high-throughput manner. A library of >11,000 target sequence and guide RNA pairs was delivered into human cells using lentiviral vectors. Subsequent delivery of Cpf1 into this cell library induced insertions and deletions (indels) at the integrated synthetic target sequences, which allowed en masse evaluation of Cpf1 activity by using deep sequencing. With this approach, we determined protospacer-adjacent motif sequences of two Cpf1 nucleases, one from Acidaminococcus sp. BV3L6 (hereafter referred to as AsCpf1) and the other from Lachnospiraceae bacterium ND2006 (hereafter referred to as LbCpf1). We also defined target-sequence-dependent activity profiles of AsCpf1, which enabled the development of a web tool that predicts the indel frequencies for given target sequences (http://big.hanyang.ac.kr/cindel). Both the Cpf1 characterization profile and the in vivo high-throughput evaluation method will greatly facilitate Cpf1-based genome editing.

Human nephrotoxicity prediction models for three types of kidney injury based on data sets of pharmacological compounds and their metabolites.

The kidney is the most important organ for the excretion of pharmaceuticals and their metabolites. Among the complex structures of the kidney, the proximal tubule and renal interstitium are major targets of nephrotoxins. Despite its importance, there are only a few in silico models for predicting human nephrotoxicity for drug candidates. Here, we present quantitative structure-activity relationship (QSAR) models for three common patterns of drug-induced kidney injury, i.e., tubular necrosis, interstitial nephritis, and tubulo-interstitial nephritis. A support vector machine (SVM) was used to build the binary classification models of nephrotoxin versus non-nephrotoxin with eight fingerprint descriptors. To build the models, we constructed two types of data sets, i.e., parent compounds of pharmaceuticals (251 nephrotoxins and 387 non-nephrotoxins) and their major urinary metabolites (307 nephrotoxins and 233 non-nephrotoxins). Information on the nephrotoxicity of the pharmaceuticals was taken from clinical trial and postmarketing safety data. Though the mechanisms of nephrotoxicity are very complex, by using the metabolite information, the predictive accuracies of the best models for each type of kidney injury were better than 83% for external validation sets. Software to predict nephrotoxicity is freely available from our Web site at http://bmdrc.org/DemoDownload .

A generalized G-SFED continuum solvation free energy calculation model.

An empirical continuum solvation model, solvation free energy density (SFED), has been developed to calculate solvation free energies of a molecule in the most frequently used solvents. A generalized version of the SFED model, generalized-SFED (G-SFED), is proposed here to calculate molecular solvation free energies in virtually any solvent. G-SFED provides an accurate and fast generalized framework without a complicated description of a solution. In the model, the solvation free energy of a solute is represented as a linear combination of empirical functions of the solute properties representing the effects of solute on various solute-solvent interactions, and the complementary solvent effects on these interactions were reflected in the linear expansion coefficients with a few solvent properties. G-SFED works well for a wide range of sizes and polarities of solute molecules in various solvents as shown by a set of 5,753 solvation free energies of diverse combinations of 103 solvents and 890 solutes. Octanol-water partition coefficients of small organic compounds and peptides were calculated with G-SFED with accuracy within 0.4 log unit for each group. The G-SFED computation time depends linearly on the number of nonhydrogen atoms (n) in a molecule, O(n).

A robust method for searching the smallest set of smallest rings with a path-included distance matrix.

The perception of rings in graphs is widely used in many fields of science and engineering. Algorithms developed in the chemistry community, called smallest set of smallest rings (SSSR), applicable only for simple graphs or chemical structures. In contrast, algorithms developed by the computer science community, called minimum cycle basis (MCB) are identical to SSSR yet exhibit greater robustness. MCB-based algorithms can correctly reveal all rings in any complex graph. However, they are slow when applied to large complex graphs due to the inherent limitations of the algorithms used. Here, we suggest a heuristic method called RP-Path. This method is a robust, simple, and fast search method with O(n(3)) runtime algorithm that correctly identifies the SSSR of all of the test case of complex graphs by using approach different from the MCB-based method. Both the robustness and improvement in speed are achieved by using a path-included distance matrix and describing the characteristic features of rings in the matrix. This method is accurate and faster than any other methods and may find many application in various fields of science and engineering that use complicated graphs with thousands of nodes.