TAPASS: Tool for annotation of protein amyloidogenicity in the context of other structural states

Falgarone, Théo; Villain, Etienne; Guettaf, Abdelkader; Leclercq, Jeremy Y; Kajava, Andrey V.

doi:10.1016/j.jsb.2022.107840

Cited by 11 publications

(24 citation statements)

References 30 publications

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“…Rawat et al developed an AbsoluRATE algorithm to predict the aggregation kinetics of native proteins, which used an SVM based regression model to calculate aggregation rates using parameters of environmental conditions, disorders, and aggregation propensities . Moreover, TAPASS developed by Falgarone et al carries out amyloidogenicity prediction in the context of other known or predicted structural states, which was used in the proteome-wide analysis to discover new amyloid-forming proteins . Ventura and co-worker developed a web-based interface called SolupHred that implements the aforementioned theoretical framework to compute aggregation propensities of intrinsically disordered proteins as a function of pH .…”

Section: Computational Algorithms and Application Of Machine Learning...mentioning

confidence: 99%

“…97 Moreover, TAPASS developed by Falgarone et al carries out amyloidogenicity prediction in the context of other known or predicted structural states, which was used in the proteome-wide analysis to discover new amyloidforming proteins. 779 Ventura and co-worker developed a webbased interface called SolupHred that implements the aforementioned theoretical framework to compute aggregation propensities of intrinsically disordered proteins as a function of pH. 780 This algorithm calculates the sequence lipophilicity and net charge at each pH through an empirical equation.…”

Section: Algorithms To Predict Protein Solubility and Aggregationmentioning

confidence: 99%

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Protein Design: From the Aspect of Water Solubility and Stability

et al. 2022

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Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

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Section: Computational Algorithms and Application Of Machine Learning...mentioning

confidence: 99%

Section: Algorithms To Predict Protein Solubility and Aggregationmentioning

confidence: 99%

Protein Design: From the Aspect of Water Solubility and Stability

et al. 2022

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“…This conclusion suggested a lower level of involvement of isoforms in functional activity than of canonical ones. We examined this conclusion by using our datasets and the TAPASS pipeline [ 20 ] (see Section 2.1.3 ). Our analysis showed that the proportion of proteins containing unstructured regions is slightly higher in the isoform set ( Figure 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…In the early days of structural bioinformatics, the foremost efforts of researchers were devoted to proteins with globular 3D structures. However, today, it is becoming clear that non-globular protein regions, having either intrinsically disordered conformations, membrane domains, elongated structures with tandem repeats or being aggregation-prone also have important functional roles [ 19 , 20 , 21 ]. Thus, an accurate structural and functional prediction of protein molecule can only be achieved when accounting for all these structural states.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, an accurate structural and functional prediction of protein molecule can only be achieved when accounting for all these structural states. Recently, in line with this need, we developed a computational pipeline called TAPASS, which was designed to do just that [ 20 ]. The TAPASS pipeline is using known cutting-edge predictors able to detect intrinsically disordered regions (IDRs), transmembrane regions, signal peptides, conserved structured domains, short linear motifs (SLiMs) and aggregation-prone regions in protein sequences.…”

Section: Introductionmentioning

confidence: 99%

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The Difference in Structural States between Canonical Proteins and Their Isoforms Established by Proteome-Wide Bioinformatics Analysis

et al. 2022

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Alternative splicing is an important means of generating the protein diversity necessary for cellular functions. Hence, there is a growing interest in assessing the structural and functional impact of alternative protein isoforms. Typically, experimental studies are used to determine the structures of the canonical proteins ignoring the other isoforms. Therefore, there is still a large gap between abundant sequence information and meager structural data on these isoforms. During the last decade, significant progress has been achieved in the development of bioinformatics tools for structural and functional annotations of proteins. Moreover, the appearance of the AlphaFold program opened up the possibility to model a large number of high-confidence structures of the isoforms. In this study, using state-of-the-art tools, we performed in silico analysis of 58 eukaryotic proteomes. The evaluated structural states included structured domains, intrinsically disordered regions, aggregation-prone regions, and tandem repeats. Among other things, we found that the isoforms have fewer signal peptides, transmembrane regions, or tandem repeat regions in comparison with their canonical counterparts. This could change protein function and/or cellular localization. The AlphaFold modeling demonstrated that frequently isoforms, having differences with the canonical sequences, still can fold in similar structures though with significant structural rearrangements which can lead to changes of their functions. Based on the modeling, we suggested classification of the structural differences between canonical proteins and isoforms. Altogether, we can conclude that a majority of isoforms, similarly to the canonical proteins are under selective pressure for the functional roles.

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A sequence‐based model for identifying proteins undergoing liquid–liquid phase separation/forming fibril aggregates via machine learning

Liao,

Zhang,

Han

et al. 2024

Protein Science

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Liquid–liquid phase separation (LLPS) and the solid aggregate (also referred to as amyloid aggregates) formation of proteins, have gained significant attention in recent years due to their associations with various physiological and pathological processes in living organisms. The systematic investigation of the differences and connections between proteins undergoing LLPS and those forming amyloid fibrils at the sequence level has not yet been explored. In this research, we aim to address this gap by comparing the two types of proteins across 36 features using collected data available currently. The statistical comparison results indicate that, 24 of the selected 36 features exhibit significant difference between the two protein groups. A LLPS‐Fibrils binary classification model built on these 24 features using random forest reveals that the fraction of intrinsically disordered residues (FIDR) is identified as the most crucial feature. While, in the further three‐class LLPS‐Fibrils‐Background classification model built on the same screened features, the composition of cysteine and that of leucine show more significant contributions than others. Through feature ablation analysis, we finally constructed a model FLFB (Feature‐based LLPS‐Fibrils‐Background protein predictor) using six refined features, with an average area under the receiver operating characteristics of 0.83. This work indicates using sequence features and a machine learning model, proteins undergoing LLPS or forming amyloid fibrils can be identified.

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TAPASS: Tool for annotation of protein amyloidogenicity in the context of other structural states

Cited by 11 publications

References 30 publications

Protein Design: From the Aspect of Water Solubility and Stability

Protein Design: From the Aspect of Water Solubility and Stability

The Difference in Structural States between Canonical Proteins and Their Isoforms Established by Proteome-Wide Bioinformatics Analysis

A sequence‐based model for identifying proteins undergoing liquid–liquid phase separation/forming fibril aggregates via machine learning

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