Natural and Pathogenic Protein Sequence Variation Affecting Prion-Like Domains Within and Across Human Proteomes

Mapping Intimacies ◽

10.1101/626648 ◽

2019 ◽

Author(s):

Sean M. Cascarina ◽

Eric D. Ross

Keyword(s):

Protein Aggregation ◽

Molecular Mechanism ◽

Neurodegenerative Disorders ◽

Protein Sequence ◽

Human Disease ◽

Large Scale ◽

Sequence Variation ◽

Sequence Variants ◽

Aggregation Propensity ◽

Propensity Analysis

ABSTRACTProtein aggregation is involved in a variety of muscular and neurodegenerative disorders. For many of these disorders, current models suggest a prion-like molecular mechanism of disease, whereby proteins aggregate and spread to neighboring cells in an infectious manner. A variety of proteins with prion-like domains (PrLDs) have recently been linked to these disorders. The development of prion prediction algorithms has facilitated the large-scale identification of PrLDs among “reference” proteomes for various organisms. However, the degree to which intraspecies protein sequence diversity influences predicted aggregation propensity for PrLDs has not been systematically examined. Here, we explore protein sequence variation introduced at genetic, post-transcriptional, and post-translational levels, and its influence on predicted aggregation propensity for human PrLDs. We find that sequence variation is relatively common among PrLDs and in some cases can result in relatively large differences in predicted aggregation propensity. Analysis of a database of sequence variants associated with human disease reveals a number of mutations within PrLDs that are predicted to increase aggregation propensity. Our analyses expand the list of candidate human PrLDs, estimate the effects of sequence variation on the aggregation propensity of PrLDs, and suggest the involvement of prion-like mechanisms in additional human diseases.

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Natural and pathogenic protein sequence variation affecting prion-like domains within and across human proteomes

BMC Genomics ◽

10.1186/s12864-019-6425-3 ◽

2020 ◽

Vol 21 (1) ◽

Cited By ~ 2

Author(s):

Sean M. Cascarina ◽

Eric D. Ross

Keyword(s):

Alternative Splicing ◽

Neurodegenerative Disorders ◽

Protein Sequence ◽

Human Disease ◽

Large Scale ◽

Sequence Variation ◽

Human Diseases ◽

Transcriptional Level ◽

Sequence Variants ◽

Aggregation Propensity

Abstract Background Impaired proteostatic regulation of proteins with prion-like domains (PrLDs) is associated with a variety of human diseases including neurodegenerative disorders, myopathies, and certain forms of cancer. For many of these disorders, current models suggest a prion-like molecular mechanism of disease, whereby proteins aggregate and spread to neighboring cells in an infectious manner. The development of prion prediction algorithms has facilitated the large-scale identification of PrLDs among “reference” proteomes for various organisms. However, the degree to which intraspecies protein sequence diversity influences predicted prion propensity has not been systematically examined. Results Here, we explore protein sequence variation introduced at genetic, post-transcriptional, and post-translational levels, and its influence on predicted aggregation propensity for human PrLDs. We find that sequence variation is relatively common among PrLDs and in some cases can result in relatively large differences in predicted prion propensity. Sequence variation introduced at the post-transcriptional level (via alternative splicing) also commonly affects predicted aggregation propensity, often by direct inclusion or exclusion of a PrLD. Finally, analysis of a database of sequence variants associated with human disease reveals a number of mutations within PrLDs that are predicted to increase prion propensity. Conclusions Our analyses expand the list of candidate human PrLDs, quantitatively estimate the effects of sequence variation on the aggregation propensity of PrLDs, and suggest the involvement of prion-like mechanisms in additional human diseases.

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Inducing protein aggregation by extensional flow

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1702724114 ◽

2017 ◽

Vol 114 (18) ◽

pp. 4673-4678 ◽

Cited By ~ 26

Author(s):

John Dobson ◽

Amit Kumar ◽

Leon F. Willis ◽

Roman Tuma ◽

Daniel R. Higazi ◽

...

Keyword(s):

Flow Field ◽

Protein Aggregation ◽

Large Scale ◽

Extensional Flow ◽

Cysteine Residue ◽

Hydrodynamic Flow ◽

Extrinsic Factors ◽

Aggregation Propensity ◽

Industrial Manufacture

Relative to other extrinsic factors, the effects of hydrodynamic flow fields on protein stability and conformation remain poorly understood. Flow-induced protein remodeling and/or aggregation is observed both in Nature and during the large-scale industrial manufacture of proteins. Despite its ubiquity, the relationships between the type and magnitude of hydrodynamic flow, a protein’s structure and stability, and the resultant aggregation propensity are unclear. Here, we assess the effects of a defined and quantified flow field dominated by extensional flow on the aggregation of BSA, β2-microglobulin (β2m), granulocyte colony stimulating factor (G-CSF), and three monoclonal antibodies (mAbs). We show that the device induces protein aggregation after exposure to an extensional flow field for 0.36–1.8 ms, at concentrations as low as 0.5 mg mL−1. In addition, we reveal that the extent of aggregation depends on the applied strain rate and the concentration, structural scaffold, and sequence of the protein. Finally we demonstrate the in situ labeling of a buried cysteine residue in BSA during extensional stress. Together, these data indicate that an extensional flow readily unfolds thermodynamically and kinetically stable proteins, exposing previously sequestered sequences whose aggregation propensity determines the probability and extent of aggregation.

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Brain Metabolite, N-Acetylaspartate Is a Potent Protein Aggregation Inhibitor

Frontiers in Cellular Neuroscience ◽

10.3389/fncel.2021.617308 ◽

2021 ◽

Vol 15 ◽

Author(s):

Marina Warepam ◽

Awdhesh Kumar Mishra ◽

Gurumayum Suraj Sharma ◽

Kritika Kumari ◽

Snigdha Krishna ◽

...

Keyword(s):

Protein Aggregation ◽

Neurodegenerative Disorders ◽

Neurological Disorders ◽

Biological Marker ◽

Present Communication ◽

Toxic Protein ◽

Aggregation Propensity ◽

Brain Metabolite ◽

Aggregation Inhibitor

Deposition of toxic protein inclusions is a common hallmark of many neurodegenerative disorders including Alzheimer's disease, Parkinson disease etc. N-acetylaspartate (NAA) is an important brain metabolite whose levels got altered under various neurodegenerative conditions. Indeed, NAA has been a widely accepted biological marker for various neurological disorders. We have also reported that NAA is a protein stabilizer. In the present communication, we investigated the role of NAA in modulating the aggregation propensity on two model proteins (carbonic anhydrase and catalase). We discovered that NAA suppresses protein aggregation and could solubilize preformed aggregates.

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Large Scale Protein Sequence Alignment Using FPGA Reprogrammable Logic Devices

Field Programmable Logic and Application - Lecture Notes in Computer Science ◽

10.1007/978-3-540-30117-2_5 ◽

2004 ◽

pp. 23-32 ◽

Cited By ~ 19

Author(s):

Stefan Dydel ◽

Piotr Bała

Keyword(s):

Sequence Alignment ◽

Protein Sequence ◽

Large Scale ◽

Protein Sequence Alignment ◽

Logic Devices

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Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information

Scientific Reports ◽

10.1038/s41598-021-96265-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yang Li ◽

Zheng Wang ◽

Li-Ping Li ◽

Zhu-Hong You ◽

Wen-Zhun Huang ◽

...

Keyword(s):

Protein Interactions ◽

Protein Sequence ◽

Large Scale ◽

False Positive Rate ◽

Computational Method ◽

Evolutionary Information ◽

Local Alignment ◽

Protein Interaction Data ◽

Sequence Information ◽

Protein Protein Interactions

AbstractVarious biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

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Accurate Representation of Protein-Ligand Structural Diversity in the Protein Data Bank (PDB)

International Journal of Molecular Sciences ◽

10.3390/ijms21062243 ◽

2020 ◽

Vol 21 (6) ◽

pp. 2243

Author(s):

Nicolas K. Shinada ◽

Peter Schmidtke ◽

Alexandre G. de Brevern

Keyword(s):

Protein Data Bank ◽

Protein Sequence ◽

Large Scale ◽

Protein Structures ◽

Structural Diversity ◽

Data Bank ◽

Protein Distribution ◽

Research Areas ◽

Identity Threshold ◽

Protein Sequence Identity

The number of available protein structures in the Protein Data Bank (PDB) has considerably increased in recent years. Thanks to the growth of structures and complexes, numerous large-scale studies have been done in various research areas, e.g., protein–protein, protein–DNA, or in drug discovery. While protein redundancy was only simply managed using simple protein sequence identity threshold, the similarity of protein-ligand complexes should also be considered from a structural perspective. Hence, the protein-ligand duplicates in the PDB are widely known, but were never quantitatively assessed, as they are quite complex to analyze and compare. Here, we present a specific clustering of protein-ligand structures to avoid bias found in different studies. The methodology is based on binding site superposition, and a combination of weighted Root Mean Square Deviation (RMSD) assessment and hierarchical clustering. Repeated structures of proteins of interest are highlighted and only representative conformations were conserved for a non-biased view of protein distribution. Three types of cases are described based on the number of distinct conformations identified for each complex. Defining these categories decreases by 3.84-fold the number of complexes, and offers more refined results compared to a protein sequence-based method. Widely distinct conformations were analyzed using normalized B-factors. Furthermore, a non-redundant dataset was generated for future molecular interactions analysis or virtual screening studies.

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Virion protein sequence variation among Australian isolatesof turnip yellow mosaic tymovirus

Archives of Virology ◽

10.1007/s007050050279 ◽

1998 ◽

Vol 143 (1) ◽

pp. 191-201 ◽

Cited By ~ 6

Author(s):

C. M. Hayden ◽

A. M. Mackenzie ◽

A. J. Gibbs

Keyword(s):

Protein Sequence ◽

Sequence Variation ◽

Virion Protein

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Inter- and Intraspecific Variation in Drosophila Genes with Sex-Biased Expression

International Journal of Evolutionary Biology ◽

10.1155/2012/963976 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10 ◽

Cited By ~ 11

Author(s):

Lena Müller ◽

Sonja Grath ◽

Korbinian von Heckel ◽

John Parsch

Keyword(s):

Gene Expression ◽

Drosophila Melanogaster ◽

Molecular Evolution ◽

Protein Sequence ◽

Sequence Variation ◽

Purifying Selection ◽

Sexually Dimorphic ◽

Expression Divergence ◽

Sexually Dimorphic Expression ◽

Dimorphic Expression

Genes with sexually dimorphic expression (sex-biased genes) often evolve rapidly and are thought to make an important contribution to reproductive isolation between species. We examined the molecular evolution of sex-biased genes in Drosophila melanogaster and D. ananassae, which represent two independent lineages within the melanogaster group. We find that strong purifying selection limits protein sequence variation within species, but that a considerable fraction of divergence between species can be attributed to positive selection. In D. melanogaster, the proportion of adaptive substitutions between species is greatest for male-biased genes and is especially high for those on the X chromosome. In contrast, male-biased genes do not show unusually high variation within or between populations. A similar pattern is seen at the level of gene expression, where sex-biased genes show high expression divergence between species, but low divergence between populations. In D. ananassae, there is no increased rate of adaptation of male-biased genes, suggesting that the type or strength of selection acting on sex-biased genes differs between lineages.

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