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24. Seq-SetNet: Exploring Sequence Sets for Inferring Structures

Author

Ju, Fusong, Zhu, Jianwei, Wei, Guozheng, Zhang, Qi, Sun, Shiwei, and Bu, Dongbo

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Sequence set is a widely-used type of data source in a large variety of fields. A typical example is protein structure prediction, which takes an multiple sequence alignment (MSA) as input and aims to infer structural information from it. Almost all of the existing approaches exploit MSAs in an indirect fashion, i.e., they transform MSAs into position-specific scoring matrices (PSSM) that represent the distribution of amino acid types at each column. PSSM could capture column-wise characteristics of MSA, however, the column-wise characteristics embedded in each individual component sequence were nearly totally neglected. The drawback of PSSM is rooted in the fact that an MSA is essentially an unordered sequence set rather than a matrix. Specifically, the interchange of any two sequences will not affect the whole MSA. In contrast, the pixels in an image essentially form a matrix since any two rows of pixels cannot be interchanged. Therefore, the traditional deep neural networks designed for image processing cannot be directly applied on sequence sets. Here, we proposed a novel deep neural network framework (called Seq-SetNet) for sequence set processing. By employing a {\it symmetric function} module to integrate features calculated from preceding layers, Seq-SetNet are immune to the order of sequences in the input MSA. This advantage enables us to directly and fully exploit MSAs by considering each component protein individually. We evaluated Seq-SetNet by using it to extract structural information from MSA for protein secondary structure prediction. Experimental results on popular benchmark sets suggests that Seq-SetNet outperforms the state-of-the-art approaches by 3.6% in precision. These results clearly suggest the advantages of Seq-SetNet in sequence set processing and it can be readily used in a wide range of fields, say natural language processing.

Published
2019

25. NIST Interlaboratory Study on Glycosylation Analysis of Monoclonal Antibodies: Comparison of Results from Diverse Analytical Methods*

Author

De Leoz, Maria Lorna A, Duewer, David L, Fung, Adam, Liu, Lily, Yau, Hoi Kei, Potter, Oscar, Staples, Gregory O, Furuki, Kenichiro, Frenkel, Ruth, Hu, Yunli, Sosic, Zoran, Zhang, Peiqing, Altmann, Friedrich, Grunwald-Grube, Clemens, Shao, Chun, Zaia, Joseph, Evers, Waltraud, Pengelley, Stuart, Suckau, Detlev, Wiechmann, Anja, Resemann, Anja, Jabs, Wolfgang, Beck, Alain, Froehlich, John W, Huang, Chuncui, Li, Yan, Liu, Yaming, Sun, Shiwei, Wang, Yaojun, Seo, Youngsuk, An, Hyun Joo, Reichardt, Niels-Christian, Ruiz, Juan Echevarria, Archer-Hartmann, Stephanie, Azadi, Parastoo, Bell, Len, Lakos, Zsuzsanna, An, Yanming, Cipollo, John F, Pucic-Bakovic, Maja, Štambuk, Jerko, Lauc, Gordan, Li, Xu, Wang, Peng George, Bock, Andreas, Hennig, René, Rapp, Erdmann, Creskey, Marybeth, Cyr, Terry D, Nakano, Miyako, Sugiyama, Taiki, Leung, Pui-King Amy, Link-Lenczowski, Paweł, Jaworek, Jolanta, Yang, Shuang, Zhang, Hui, Kelly, Tim, Klapoetke, Song, Cao, Rui, Kim, Jin Young, Lee, Hyun Kyoung, Lee, Ju Yeon, Yoo, Jong Shin, Kim, Sa-Rang, Suh, Soo-Kyung, de Haan, Noortje, Falck, David, Lageveen-Kammeijer, Guinevere SM, Wuhrer, Manfred, Emery, Robert J, Kozak, Radoslaw P, Liew, Li Phing, Royle, Louise, Urbanowicz, Paulina A, Packer, Nicolle H, Song, Xiaomin, Everest-Dass, Arun, Lattová, Erika, Cajic, Samanta, Alagesan, Kathirvel, Kolarich, Daniel, Kasali, Toyin, Lindo, Viv, Chen, Yuetian, Goswami, Kudrat, Gau, Brian, Amunugama, Ravi, Jones, Richard, Stroop, Corné JM, Kato, Koichi, Yagi, Hirokazu, Kondo, Sachiko, Yuen, CT, Harazono, Akira, Shi, Xiaofeng, Magnelli, Paula E, Kasper, Brian T, Mahal, Lara, Harvey, David J, and O'Flaherty, Roisin

Subjects
Immunization, Antibodies, Monoclonal, Biological Products, Biopharmaceutics, Glycomics, Glycopeptides, Glycosylation, Humans, Laboratories, Polysaccharides, Protein Processing, Post-Translational, Proteomics, mass spectrometry, fluorescence, glycosylation, glycoproteins, glycan, glycopeptide, interlaboratory study, NISTmAb, reference antibody, Biochemistry & Molecular Biology
Abstract

Glycosylation is a topic of intense current interest in the development of biopharmaceuticals because it is related to drug safety and efficacy. This work describes results of an interlaboratory study on the glycosylation of the Primary Sample (PS) of NISTmAb, a monoclonal antibody reference material. Seventy-six laboratories from industry, university, research, government, and hospital sectors in Europe, North America, Asia, and Australia submitted a total of 103 reports on glycan distributions. The principal objective of this study was to report and compare results for the full range of analytical methods presently used in the glycosylation analysis of mAbs. Therefore, participation was unrestricted, with laboratories choosing their own measurement techniques. Protein glycosylation was determined in various ways, including at the level of intact mAb, protein fragments, glycopeptides, or released glycans, using a wide variety of methods for derivatization, separation, identification, and quantification. Consequently, the diversity of results was enormous, with the number of glycan compositions identified by each laboratory ranging from 4 to 48. In total, one hundred sixteen glycan compositions were reported, of which 57 compositions could be assigned consensus abundance values. These consensus medians provide community-derived values for NISTmAb PS. Agreement with the consensus medians did not depend on the specific method or laboratory type. The study provides a view of the current state-of-the-art for biologic glycosylation measurement and suggests a clear need for harmonization of glycosylation analysis methods.

Published
2020

32. Protocol for automated N-glycan sequencing using mass spectrometry and computer-assisted intelligent fragmentation

Author

Huang, Chuncui, primary, Wang, Hui, additional, Zhou, Jinyu, additional, Huang, Yikang, additional, Ren, Yihui, additional, Zhao, Keli, additional, Wang, Yaojun, additional, Hou, Meijie, additional, Zhang, Jingwei, additional, Liu, Yaming, additional, Ma, Xinyue, additional, Yan, Jingyu, additional, Bu, Dongbo, additional, Chai, Wengang, additional, Sun, Shiwei, additional, and Li, Yan, additional

Published
2024
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34. Predicting protein inter-residue contacts using composite likelihood maximization and deep learning

Author

Zhang, Haicang, Zhang, Qi, Ju, Fusong, Zhu, Jianwei, Sun, Shiwei, Gao, Yujuan, Xie, Ziwei, Deng, Minghua, Zheng, Wei-Mou, and Bu, Dongbo

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning, Statistics - Methodology
Abstract

Accurate prediction of inter-residue contacts of a protein is important to calcu- lating its tertiary structure. Analysis of co-evolutionary events among residues has been proved effective to inferring inter-residue contacts. The Markov ran- dom field (MRF) technique, although being widely used for contact prediction, suffers from the following dilemma: the actual likelihood function of MRF is accurate but time-consuming to calculate, in contrast, approximations to the actual likelihood, say pseudo-likelihood, are efficient to calculate but inaccu- rate. Thus, how to achieve both accuracy and efficiency simultaneously remains a challenge. In this study, we present such an approach (called clmDCA) for contact prediction. Unlike plmDCA using pseudo-likelihood, i.e., the product of conditional probability of individual residues, our approach uses composite- likelihood, i.e., the product of conditional probability of all residue pairs. Com- posite likelihood has been theoretically proved as a better approximation to the actual likelihood function than pseudo-likelihood. Meanwhile, composite likelihood is still efficient to maximize, thus ensuring the efficiency of clmDCA. We present comprehensive experiments on popular benchmark datasets, includ- ing PSICOV dataset and CASP-11 dataset, to show that: i) clmDCA alone outperforms the existing MRF-based approaches in prediction accuracy. ii) When equipped with deep learning technique for refinement, the prediction ac- curacy of clmDCA was further significantly improved, suggesting the suitability of clmDCA for subsequent refinement procedure. We further present successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset. Accessibility: The software clmDCA and a server are publicly accessible through http://protein.ict.ac.cn/clmDCA/.

Published
2018

47. SVLR: Genome Structure Variant Detection Using Long Read Sequencing Data

Author

Gu, Wenyan, Zhou, Aizhong, Wang, Lusheng, Sun, Shiwei, Cui, Xuefeng, Zhu, Daming, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cai, Zhipeng, editor, Mandoiu, Ion, editor, Narasimhan, Giri, editor, Skums, Pavel, editor, and Guo, Xuan, editor

Published
2020
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