hierarchical-and-programmable-one-pot-oligosaccharide-synthesis

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

This article presents a general experimental protocol for programmable one-pot oligosaccharide synthesis and demonstrates how to use Auto-CHO software for ...

Academia Sinica

RNA-protein interaction plays an essential role in several biological processes, such as protein synthesis, gene expression, posttranscriptional regulation and viral infectivity. Identification of RNA-binding sites in proteins provides valuable insights for biologists. However, experimental determination of RNA-protein interaction remains time-consuming and labor-intensive. Thus, computational approaches for prediction of RNA-binding sites in proteins have become highly desirable. Extensive studies of RNA-binding site prediction have led to the development of several methods. However, they could yield low sensitivities in trade-off for high specificities.

Predicting RNA-binding sites of proteins using support vector machines and evolutionary information.

TDP-43 is a highly conserved and ubiquitously expressed nuclear protein. It has been implicated in the regulation of transcription, alternative splicing, translation, and neuronal plasticity. TDP-43 has also been shown to be a disease signature protein associated with several neurodegenerative diseases including amyotrophic lateral sclerosis. However, the correlation of the physiological functions of TDP-43 with these diseases remains unknown. We have used the gene targeting approach to disrupt the expression of TDP-43 in mouse. Loss of the TDP-43 expression results in peri-implantation lethality of mice between embryonic days (E) 3.5 and 6.5. Blastocysts of the homozygous Tardbp null mutants are morphologically normal, but exhibit defective outgrowth of the inner cell mass in vitro. Our data demonstrate the essential function of TDP-43 in peri-implantation stage during the embryo development, likely because of its involvement in multiple biological processes in a variety of cell types.

TDP-43, a neuro-pathosignature factor, is essential for early mouse embryogenesis.

α-helical transmembrane (TM) proteins play an important role in many critical and diverse biological processes, and specific associations between TM helices are important determinants for membrane protein folding, dynamics and function. In order to gain insights into the above phenomena, it is necessary to investigate different types of helix-packing modes and interactions. However, such information is difficult to obtain because of the experimental impediment and a lack of a well-annotated source of helix-packing folds in TM proteins. We have developed the TMPad (TransMembrane Protein Helix-Packing Database) which addresses the above issues by integrating experimentally observed helix-helix interactions and related structural information of membrane proteins. Specifically, the TMPad offers pre-calculated geometric descriptors at the helix-packing interface including residue backbone/side-chain contacts, interhelical distances and crossing angles, helical translational shifts and rotational angles. The TMPad also includes the corresponding sequence, topology, lipid accessibility, ligand-binding information and supports structural classification, schematic diagrams and visualization of the above structural features of TM helix-packing. Through detailed annotations and visualizations of helix-packing, this online resource can serve as an information gateway for deciphering the relationship between helix-helix interactions and higher levels of organization in TM protein structure and function. The website of the TMPad is freely accessible to the public at http://bio-cluster.iis.sinica.edu.tw/TMPad.

TMPad: an integrated structural database for helix-packing folds in transmembrane proteins.

Secretome analysis is important in pathogen studies. A fundamental and convenient way to identify secreted proteins is to first predict signal peptides, which are essential for protein secretion. However, signal peptides are highly complex functional sequences that are easily confused with transmembrane domains. Such confusion would obviously affect the discovery of secreted proteins. Transmembrane proteins are important drug targets, but very few transmembrane protein structures have been determined experimentally; hence, prediction of the structures is essential. In the field of structure prediction, researchers do not make assumptions about organisms, so there is a need for a general signal peptide predictor.To improve signal peptide prediction without prior knowledge of the associated organisms, we present a machine-learning method, called SVMSignal, which uses biochemical properties as features, as well as features acquired from a novel encoding, to capture biochemical profile patterns for learning the structures of signal peptides directly.We tested SVMSignal and five popular methods on two benchmark datasets from the SPdb and UniProt/Swiss-Prot databases, respectively. Although SVMSignal was trained on an old dataset, it performed well, and the results demonstrate that learning the structures of signal peptides directly is a promising approach. We also utilized SVMSignal to analyze proteomes in the entire HAMAP microbial database. Finally, we conducted a comparative study of secretome analysis on seven tuberculosis-related strains selected from the HAMAP database. We identified ten potential secreted proteins, two of which are drug resistant and four are potential transmembrane proteins.SVMSignal is publicly available at http://bio-cluster.iis.sinica.edu.tw/SVMSignal. It provides user-friendly interfaces and visualizations, and the prediction results are available for download.

Computational comparative study of tuberculosis proteomes using a model learned from signal peptide structures.

ALS, or amyotrophic lateral sclerosis, is a progressive and fatal motor neuron disease with no effective medicine. Importantly, the majority of the ALS cases are with TDP-43 proteinopathies characterized with TDP-43-positive, ubiquitin-positive inclusions (UBIs) in the cytosol. However, the role of the mismetabolism of TDP-43 in the pathogenesis of ALS with TDP-43 proteinopathies is unclear. Using the conditional mouse gene targeting approach, we show that mice with inactivation of the Tardbp gene in the spinal cord motor neurons (HB9:Cre-Tardbp(lx/-)) exhibit progressive and male-dominant development of ALS-related phenotypes including kyphosis, motor dysfunctions, muscle weakness/atrophy, motor neuron loss, and astrocytosis in the spinal cord. Significantly, ubiquitinated proteins accumulate in the TDP-43-depleted motor neurons of the spinal cords of HB9:Cre-Tardbp(lx/-) mice with the ALS phenotypes. This study not only establishes an important role of TDP-43 in the long term survival and functioning of the mammalian spinal cord motor neurons, but also establishes that loss of TDP-43 function could be one major cause for neurodegeneration in ALS with TDP-43 proteinopathies.

Targeted depletion of TDP-43 expression in the spinal cord motor neurons leads to the development of amyotrophic lateral sclerosis-like phenotypes in mice.

Impairment of learning and memory is a significant pathological feature of many neurodegenerative diseases including FTLD-TDP. Appropriate regulation and fine tuning of spinogenesis of the dendrites, which is an integral part of the learning/memory program of the mammalian brain, are essential for the normal function of the hippocampal neurons. TDP-43 is a nucleic acid-binding protein implicated in multi-cellular functions and in the pathogenesis of a range of neurodegenerative diseases including FTLD-TDP and ALS. We have combined the use of single-cell dye injection, shRNA knockdown, plasmid rescue, immunofluorescence staining, Western blot analysis and patch clamp electrophysiological measurement of primary mouse hippocampal neurons in culture to study the functional role of TDP-43 in mammalian spinogenesis. We found that depletion of TDP-43 leads to an increase in the number of protrusions/spines as well as the percentage of matured spines among the protrusions. Significantly, the knockdown of TDP-43 also increases the level of Rac1 and its activated form GTP-Rac1, a known positive regulator of spinogenesis. Clustering of the AMPA receptors on the dendritic surface and neuronal firing are also induced by depletion of TDP-43. Furthermore, use of an inhibitor of Rac1 activation negatively regulated spinogenesis of control hippocampal neurons as well as TDP-43-depleted hippocampal neurons. Mechanistically, RT-PCR assay and cycloheximide chase experiments have indicated that increases in Rac1 protein upon TDP-43 depletion is regulated at the translational level. These data together establish that TDP-43 is an upstream regulator of spinogenesis in part through its action on the Rac1 → GTP-Rac1 → AMPAR pathway. This study provides the first evidence connecting TDP-43 with the GTP-Rac1 → AMPAR regulatory pathway of spinogenesis. It establishes that mis-metabolism of TDP-43, as occurs in neurodegenerative diseases with TDP-43 proteinopathies, e.g., FTLD-TDP, would alter its homeostatic cellular concentration, thus leading to impairment of hippocampal plasticity.

TDP-43 regulates the mammalian spinogenesis through translational repression of Rac1.

Identification of subcellular localization in proteins is crucial to elucidate cellular processes and molecular functions in a cell. However, given a tremendous amount of sequence data generated in the post-genomic era, determining protein localization based on biological experiments can be expensive and time-consuming. Therefore, developing prediction systems to analyze uncharacterised proteins efficiently has played an important role in high-throughput protein analyses. In a eukaryotic cell, many essential biological processes take place in the nucleus. Nuclear proteins shuttle between nucleus and cytoplasm based on recognition of nuclear translocation signals, including nuclear localization signals (NLSs) and nuclear export signals (NESs). Currently, only a few approaches have been developed specifically to predict nuclear localization using sequence features, such as putative NLSs. However, it has been shown that prediction coverage based on the NLSs is very low. In addition, most existing approaches only attained prediction accuracy and Matthew's correlation coefficient (MCC) around 54%~70% and 0.250~0.380 on independent test set, respectively. Moreover, no predictor can generate sequence motifs to characterize features of potential NESs, in which biological properties are not well understood from existing experimental studies.

Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing.

Development of computational tools that can accurately predict presence and location of B-cell epitopes on pathogenic proteins has a valuable application to the field of vaccinology. Because of the highly variable yet enigmatic nature of B-cell epitopes, their prediction presents a great challenge to computational immunologists.

Prediction of B-cell epitopes using evolutionary information and propensity scales.

TDP-43, a multi-functional DNA/ RNA-binding protein encoded by the TARDBP gene, has emerged as a major patho-signature factor of the ubiquitinated intracellular inclusions (UBIs) in the diseased cells of a range of neurodegenerative diseases. Mutations in at least 9 different genes including TARDBP have been identified in ALS with TDP-43 (+)-UBIs. Thus far, the pathogenic role(s) of the more than 30 ALS-associated mutations in the TARDBP gene has not been well defined.

Similar dose-dependence of motor neuron cell death caused by wild type human TDP-43 and mutants with ALS-associated amino acid substitutions.

Since membrane protein structures are challenging to crystallize, computational approaches are essential for elucidating the sequence-to-structure relationships. Structural modeling of membrane proteins requires a multidimensional approach, and one critical geometric parameter is the rotational angle of transmembrane helices. Rotational angles of transmembrane helices are characterized by their folded structures and could be inferred by the hydrophobic moment; however, the folding mechanism of membrane proteins is not yet fully understood. The rotational angle of a transmembrane helix is related to the exposed surface of a transmembrane helix, since lipid exposure gives the degree of accessibility of each residue in lipid environment. To the best of our knowledge, there have been few advances in investigating whether an environment descriptor of lipid exposure could infer a geometric parameter of rotational angle.

Lipid exposure prediction enhances the inference of rotational angles of transmembrane helices.

Glycosylation is a highly complex modification influencing the functions and activities of proteins. Interpretation of intact glycopeptide spectra is crucial but challenging. In this paper, we present a mass spectrometry-based automated glycopeptide identification platform (MAGIC) to identify peptide sequences and glycan compositions directly from intact N-linked glycopeptide collision-induced-dissociation spectra. The identification of the Y1 (peptideY0 + GlcNAc) ion is critical for the correct analysis of unknown glycoproteins, especially without prior knowledge of the proteins and glycans present in the sample. To ensure accurate Y1-ion assignment, we propose a novel algorithm called Trident that detects a triplet pattern corresponding to [Y0, Y1, Y2] or [Y0-NH3, Y0, Y1] from the fragmentation of the common trimannosyl core of N-linked glycopeptides. To facilitate the subsequent peptide sequence identification by common database search engines, MAGIC generates in silico spectra by overwriting the original precursor with the naked peptide m/z and removing all of the glycan-related ions. Finally, MAGIC computes the glycan compositions and ranks them. For the model glycoprotein horseradish peroxidase (HRP) and a 5-glycoprotein mixture, a 2- to 31-fold increase in the relative intensities of the peptide fragments was achieved, which led to the identification of 7 tryptic glycopeptides from HRP and 16 glycopeptides from the mixture via Mascot. In the HeLa cell proteome data set, MAGIC processed over a thousand MS(2) spectra in 3 min on a PC and reported 36 glycopeptides from 26 glycoproteins. Finally, a remarkable false discovery rate of 0 was achieved on the N-glycosylation-free Escherichia coli data set. MAGIC is available at http://ms.iis.sinica.edu.tw/COmics/Software_MAGIC.html .

MAGIC: an automated N-linked glycoprotein identification tool using a Y1-ion pattern matching algorithm and in silico MS² approach.

MAGIC-web is the first web server, to the best of our knowledge, that performs both untargeted and targeted analyses of mass spectrometry-based glycoproteomics data for site-specific N-linked glycoprotein identification. The first two modules, MAGIC and MAGIC+, are designed for untargeted and targeted analysis, respectively. MAGIC is implemented with our previously proposed novel Y1-ion pattern matching method, which adequately detects Y1- and Y0-ion without prior information of proteins and glycans, and then generates in silico MS(2) spectra that serve as input to a database search engine (e.g. Mascot) to search against a large-scale protein sequence database. On top of that, the newly implemented MAGIC+ allows users to determine glycopeptide sequences using their own protein sequence file. The third module, Reports Integrator, provides the service of combining protein identification results from Mascot and glycan-related information from MAGIC-web to generate a complete site-specific protein-glycan summary report. The last module, Glycan Search, is designed for the users who are interested in finding possible glycan structures with specific numbers and types of monosaccharides. The results from MAGIC, MAGIC+ and Reports Integrator can be downloaded via provided links whereas the annotated spectra and glycan structures can be visualized in the browser. MAGIC-web is accessible from http://ms.iis.sinica.edu.tw/MAGIC-web/index.html. 

MAGIC-web: a platform for untargeted and targeted N-linked glycoprotein identification.

The programmable one-pot oligosaccharide synthesis method was designed to enable the rapid synthesis of a large number of oligosaccharides, using the software Optimer to search Building BLocks (BBLs) with defined relative reactivity values (RRVs) to be used sequentially in the one-pot reaction. However, there were only about 50 BBLs with measured RRVs in the original library and the method could only synthesize small oligosaccharides due to the RRV ordering requirement. Here, we increase the library to include 154 validated BBLs and more than 50,000 virtual BBLs with predicted RRVs by machine learning. We also develop the software Auto-CHO to accommodate more data handling and support hierarchical one-pot synthesis using fragments as BBLs generated by the one-pot synthesis. This advanced programmable one-pot method provides potential synthetic solutions for complex glycans with four successful examples demonstrated in this work.

Hierarchical and programmable one-pot synthesis of oligosaccharides.

TAR DNA-binding protein (TDP-43) is a ubiquitously expressed nuclear protein, which participates in a number of cellular processes and has been identified as the major pathological factor in amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). Here we constructed a conditional TDP-43 mouse with depletion of TDP-43 in the mouse forebrain and find that the mice exhibit a whole spectrum of age-dependent frontotemporal dementia-like behaviour abnormalities including perturbation of social behaviour, development of dementia-like behaviour, changes of activities of daily living, and memory loss at a later stage of life. These variations are accompanied with inflammation, neurodegeneration, and abnormal synaptic plasticity of the mouse CA1 neurons. Importantly, analysis of the cortical RNA transcripts of the conditional knockout mice at the pre-/post-symptomatic stages and the corresponding wild type mice reveals age-dependent alterations in the expression levels and RNA processing patterns of a set of genes closely associated with inflammation, social behaviour, synaptic plasticity, and neuron survival. This study not only supports the scenario that loss-of-function of TDP-43 in mice may recapitulate key behaviour features of the FTLD diseases, but also provides a list of TDP-43 target genes/transcript isoforms useful for future therapeutic research.

Article	Year
Predicting RNA-binding sites of proteins using support vector machines and evolutionary information. BMC bioinformatics\| PubMed ID: 19091029	2008
TDP-43, a neuro-pathosignature factor, is essential for early mouse embryogenesis. Genesis (New York, N.Y. : 2000)\| PubMed ID: 20014337	2010
TMPad: an integrated structural database for helix-packing folds in transmembrane proteins. Nucleic acids research\| PubMed ID: 21177659	2011
Computational comparative study of tuberculosis proteomes using a model learned from signal peptide structures. PloS one\| PubMed ID: 22496884	2012
Targeted depletion of TDP-43 expression in the spinal cord motor neurons leads to the development of amyotrophic lateral sclerosis-like phenotypes in mice. The Journal of biological chemistry\| PubMed ID: 22718760	2012
TDP-43 regulates the mammalian spinogenesis through translational repression of Rac1. Acta neuropathologica\| PubMed ID: 22760527	2012
Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing. BMC bioinformatics\| PubMed ID: 23282098	2012
Prediction of B-cell epitopes using evolutionary information and propensity scales. BMC bioinformatics\| PubMed ID: 23484214	2013
Similar dose-dependence of motor neuron cell death caused by wild type human TDP-43 and mutants with ALS-associated amino acid substitutions. Journal of biomedical science\| PubMed ID: 23721326	2013
Lipid exposure prediction enhances the inference of rotational angles of transmembrane helices. BMC bioinformatics\| PubMed ID: 24112406	2013
MAGIC: an automated N-linked glycoprotein identification tool using a Y1-ion pattern matching algorithm and in silico MS² approach. Analytical chemistry\| PubMed ID: 25629585	2015
MAGIC-web: a platform for untargeted and targeted N-linked glycoprotein identification. Nucleic acids research\| PubMed ID: 27084943	2016
Hierarchical and programmable one-pot synthesis of oligosaccharides. Nature communications\| PubMed ID: 30523255	2018
Transcriptomopathies of pre- and post-symptomatic frontotemporal dementia-like mice with TDP-43 depletion in forebrain neurons. Acta neuropathologica communications\| PubMed ID: 30922385	2019

Cheng-Wei Cheng

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