f33e605b7a0f59b2417f5e9318b9ad4b biophysica-03-00001-v2.pdf 295d38602c51bc59a7cb2d333ca643ccbd995fa6 biophysica-03-00001-v2.pdf afbc81467fc0bb5e6a369d9ea2d72e15a3c2b365317d66ad13a4ba8cbb88d080 biophysica-03-00001-v2.pdf Title: A Review of Fifteen Years Developing Computational Tools to Study Protein Aggregation Subject: The presence of insoluble protein deposits in tissues and organs is a hallmark of many human pathologies. In addition, the formation of protein aggregates is considered one of the main bottlenecks to producing protein-based therapeutics. Thus, there is a high interest in rationalizing and predicting protein aggregation. For almost two decades, our laboratory has been working to provide solutions for these needs. We have traditionally combined the core tenets of both bioinformatics and wet lab biophysics to develop algorithms and databases to study protein aggregation and its functional implications. Here, we review the computational toolbox developed by our lab, including programs for identifying sequential or structural aggregation-prone regions at the individual protein and proteome levels, engineering protein solubility, finding and evaluating prion-like domains, studying disorder-to-order protein transitions, or categorizing non-conventional amyloid regions of polar nature, among others. In perspective, the succession of the tools we describe illustrates how our understanding of the protein aggregation phenomenon has evolved over the last fifteen years. Keywords: protein aggregation; bioinformatics; biophysics; computational tools; amyloid; protein structure; protein folding Author: Carlos Pintado-Grima, Oriol Bárcenas, Andrea Bartolomé-Nafría, Marc Fornt-Suñé, Valentín Iglesias, Javier Garcia-Pardo and Salvador Ventura Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Feb 1 10:20:35 2023 CET ModDate: Wed Feb 1 10:24:29 2023 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 20 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 3862167 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- IOJCNK+VnURWPalladioL-Bold Type 1 Custom yes yes no 10 0 NNKDOL+VnURWPalladioL Type 1 Custom yes yes no 15 0 NOPWPS+URWPalladioL-Bold Type 1 Custom yes yes no 19 0 LQGEOT+URWPalladioL-Roma Type 1 Custom yes yes no 24 0 ZYBLHJ+URWPalladioL-Ital Type 1 Custom yes 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1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4d3167f4 Item: Title: Computational Tools to Study Protein Aggregation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@ed9d034 Children: Item: Title: Aggrescan: Prediction of “Hot Spots” of Aggregation in Polypeptides Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@6121c9d6 Item: Title: Aggrescan 3D: A Server for Prediction of Aggregation Propensity in Protein Structures and Rational Design of Protein Solubility Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@87f383f Item: Title: A3D Database: Structure-Based Predictions of Protein Aggregation for the Human Proteome Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4eb7f003 Item: Title: Computational Tools to Study Prion-like Proteins Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@eafc191 Children: Item: Title: PrionScan: An Online Database of Predicted Prion Domains in Complete Proteomes Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@612fc6eb Item: Title: pWaltz and PrionW: Identification of Prion-like Protein Domains Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1060b431 Item: Title: AMYCO: A Server for Prediction of the Impact of Mutations on the Aggregation Propensity of Prion-like Proteins Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@612679d6 Item: Title: SGnn: A Server for the Prediction of Prion-like Domains Recruitment to Stress Granules upon Heat Stress Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@11758f2a Item: Title: Computational Tools to Study Intrinsically Disordered Proteins (IDPs) Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@e720b71 Children: Item: Title: DispHred and DispHScan: Predicting Protein Disorder as a Function of pH Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1b26f7b2 Item: Title: SolupHred: A Server to Predict the pH-Dependent Aggregation of Intrinsically Disordered Proteins Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@491cc5c9 Item: Title: CARs-DB: A Database of Cryptic Amyloidogenic Regions in Intrinsically Disordered Proteins Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@74ad1f1f Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@6a1aab78 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@462d5aee Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@69b0fd6f Info: Title: A Review of Fifteen Years Developing Computational Tools to Study Protein Aggregation Author: Carlos Pintado-Grima, Oriol Bárcenas, Andrea Bartolomé-Nafría, Marc Fornt-Suñé, Valentín Iglesias, Javier Garcia-Pardo and Salvador Ventura Subject: The presence of insoluble protein deposits in tissues and organs is a hallmark of many human pathologies. In addition, the formation of protein aggregates is considered one of the main bottlenecks to producing protein-based therapeutics. Thus, there is a high interest in rationalizing and predicting protein aggregation. For almost two decades, our laboratory has been working to provide solutions for these needs. We have traditionally combined the core tenets of both bioinformatics and wet lab biophysics to develop algorithms and databases to study protein aggregation and its functional implications. Here, we review the computational toolbox developed by our lab, including programs for identifying sequential or structural aggregation-prone regions at the individual protein and proteome levels, engineering protein solubility, finding and evaluating prion-like domains, studying disorder-to-order protein transitions, or categorizing non-conventional amyloid regions of polar nature, among others. In perspective, the succession of the tools we describe illustrates how our understanding of the protein aggregation phenomenon has evolved over the last fifteen years. 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