Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation.

Protein aggregation is behind the genesis of incurable diseases and imposes constraints on drug discovery and the industrial production and formulation of proteins. Over the years, we have been advancing the Aggresscan3D (A3D) method, aiming to deepen our comprehension of protein aggregation and assist the engineering of protein solubility. Since its inception, A3D has become one of the most popular structure-based aggregation predictors because of its performance, modular functionalities, RESTful service for extensive screenings, and intuitive user interface. Building on this foundation, we introduce Aggrescan4D (A4D), significantly extending A3D's functionality. A4D is aimed at predicting the pH-dependent aggregation of protein structures, and features an evolutionary-informed automatic mutation protocol to engineer protein solubility without compromising structure and stability. It also integrates precalculated results for the nearly 500,000 jobs in the A3D Model Organisms Database and structure retrieval from the AlphaFold database. Globally, A4D constitutes a comprehensive tool for understanding, predicting, and designing solutions for specific protein aggregation challenges. The A4D web server and extensive documentation are available at https://biocomp.chem.uw.edu.pl/a4d/. This website is free and open to all users without a login requirement.

A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms.

Protein aggregation has been associated with aging and different pathologies and represents a bottleneck in the industrial production of biotherapeutics. Numerous past studies performed in Escherichia coli and other model organisms have allowed to dissect the biophysical principles underlying this process. This knowledge fuelled the development of computational tools, such as Aggrescan 3D (A3D) to forecast and re-design protein aggregation. Here, we present the A3D Model Organism Database (A3D-MODB) http://biocomp.chem.uw.edu.pl/A3D2/MODB, a comprehensive resource for the study of structural protein aggregation in the proteomes of 12 key model species spanning distant biological clades. In addition to A3D predictions, this resource incorporates information useful for contextualizing protein aggregation, including membrane protein topology and structural model confidence, as an indirect reporter of protein disorder. The database is openly accessible without any need for registration. We foresee A3D-MOBD evolving into a central hub for conducting comprehensive, multi-species analyses of protein aggregation, fostering the development of protein-based solutions for medical, biotechnological, agricultural and industrial applications.

A3DyDB: exploring structural aggregation propensities in the yeast proteome.

BACKGROUND: The budding yeast Saccharomyces cerevisiae (S. cerevisiae) is a well-established model system for studying protein aggregation due to the conservation of essential cellular structures and pathways found across eukaryotes. However, limited structural knowledge of its proteome has prevented a deeper understanding of yeast functionalities, interactions, and aggregation. RESULTS: In this study, we introduce the A3D yeast database (A3DyDB), which offers an extensive catalog of aggregation propensity predictions for the S. cerevisiae proteome. We used Aggrescan 3D (A3D) and the newly released protein models from AlphaFold2 (AF2) to compute the structure-based aggregation predictions for 6039 yeast proteins. The A3D algorithm exploits the information from 3D protein structures to calculate their intrinsic aggregation propensities. To facilitate simple and intuitive data analysis, A3DyDB provides a user-friendly interface for querying, browsing, and visualizing information on aggregation predictions from yeast protein structures. The A3DyDB also allows for the evaluation of the influence of natural or engineered mutations on protein stability and solubility. The A3DyDB is freely available at http://biocomp.chem.uw.edu.pl/A3D2/yeast . CONCLUSION: The A3DyDB addresses a gap in yeast resources by facilitating the exploration of correlations between structural aggregation propensity and diverse protein properties at the proteome level. We anticipate that this comprehensive database will become a standard tool in the modeling of protein aggregation and its implications in budding yeast.

A3D database: structure-based predictions of protein aggregation for the human proteome.

SUMMARY: Protein aggregation is associated with many human disorders and constitutes a major bottleneck for producing therapeutic proteins. Our knowledge of the human protein structures repertoire has dramatically increased with the recent development of the AlphaFold (AF) deep-learning method. This structural information can be used to understand better protein aggregation properties and the rational design of protein solubility. This article uses the Aggrescan3D (A3D) tool to compute the structure-based aggregation predictions for the human proteome and make the predictions available in a database form. In the A3D database, we analyze the AF-predicted human protein structures (for over 20.5 thousand unique Uniprot IDs) in terms of their aggregation properties using the A3D tool. Each entry of the A3D database provides a detailed analysis of the structure-based aggregation propensity computed with A3D. The A3D database implements simple but useful graphical tools for visualizing and interpreting protein structure datasets. It also enables testing the influence of user-selected mutations on protein solubility and stability, all integrated into a user-friendly interface. AVAILABILITY AND IMPLEMENTATION: A3D database is freely available at: http://biocomp.chem.uw.edu.pl/A3D2/hproteome. The data underlying this article are available in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protocols for Rational Design of Protein Solubility and Aggregation Properties Using Aggrescan3D Standalone.

Protein aggregation is a major hurdle in the development and manufacturing of protein-based therapeutics. Development of aggregation-resistant and stable protein variants can be guided by rational redesign using computational tools. Here, we describe the architecture and functionalities of the Aggrescan3D (A3D) standalone package for the rational design of protein solubility and aggregation properties based on three-dimensional protein structures. We present the case studies of the three therapeutic proteins, including antibodies, exploring the practical use of the A3D standalone tool. The case studies demonstrate that protein solubility can be easily improved by the A3D prediction of non-destabilizing amino acid mutations at the protein surfaces.

A3D 2.0 Update for the Prediction and Optimization of Protein Solubility.

Protein aggregation propensity is a property imprinted in protein sequences and structures, being associated with the onset of human diseases and limiting the implementation of protein-based biotherapies. Computational approaches stand as cost-effective alternatives for reducing protein aggregation and increasing protein solubility. AGGRESCAN 3D (A3D) is a structure-based predictor of aggregation that takes into account the conformational context of a protein, aiming to identify aggregation-prone regions exposed in protein surfaces. Here we inspect the updated 2.0 version of the algorithm, which extends the application of A3D to previously inaccessible proteins and incorporates new modules to assist protein redesign. Among these features, the new server includes stability calculations and the possibility to optimize protein solubility using an experimentally validated computational pipeline. Finally, we employ defined examples to navigate the A3D RESTful service, a routine to handle extensive protein collections. Altogether, this chapter is conceived to train and assist A3D non-experts in the study of aggregation-prone regions and protein solubility redesign.

Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility.

Protein aggregation is a hallmark of a growing number of human disorders and constitutes a major bottleneck in the manufacturing of therapeutic proteins. Therefore, there is a strong need of in-silico methods that can anticipate the aggregative properties of protein variants linked to disease and assist the engineering of soluble protein-based drugs. A few years ago, we developed a method for structure-based prediction of aggregation properties that takes into account the dynamic fluctuations of proteins. The method has been made available as the Aggrescan3D (A3D) web server and applied in numerous studies of protein structure-aggregation relationship. Here, we present a major update of the A3D web server to version 2.0. The new features include: extension of dynamic calculations to significantly larger and multimeric proteins, simultaneous prediction of changes in protein solubility and stability upon mutation, rapid screening for functional protein variants with improved solubility, a REST-ful service to incorporate A3D calculations in automatic pipelines, and a new, enhanced web server interface. A3D 2.0 is freely available at: http://biocomp.chem.uw.edu.pl/A3D2/.

CABS-dock standalone: a toolbox for flexible protein-peptide docking.

SUMMARY: CABS-dock standalone is a multiplatform Python package for protein-peptide docking with backbone flexibility. The main feature of the CABS-dock method is its ability to simulate significant backbone flexibility of the entire protein-peptide system in a reasonable computational time. In the default mode, the package runs a simulation of fully flexible peptide searching for a binding site on the surface of a flexible protein receptor. The flexibility level of the molecules may be defined by the user. Furthermore, the CABS-dock standalone application provides users with full control over the docking simulation from the initial setup to the analysis of results. The standalone version is an upgrade of the original web server implementation-it introduces a number of customizable options, provides support for large-sized systems and offers a framework for deeper analysis of docking results. AVAILABILITY AND IMPLEMENTATION: CABS-dock standalone is distributed under the MIT licence, which is free for academic and non-profit users. It is implemented in Python and Fortran. The CABS-dock standalone source code, wiki with documentation and examples of use and installation instructions for Linux, macOS and Windows are available in the CABS-dock standalone repository at https://bitbucket.org/lcbio/cabsdock.

Aggrescan3D standalone package for structure-based prediction of protein aggregation properties.

SUMMARY: Aggrescan3D (A3D) standalone is a multiplatform Python package for structure-based prediction of protein aggregation properties and rational design of protein solubility. A3D allows the re-design of protein solubility by combining structural aggregation propensity and stability predictions, as demonstrated by a recent experimental study. It also enables predicting the impact of protein conformational fluctuations on the aggregation properties. The standalone A3D version is an upgrade of the original web server implementation-it introduces a number of customizable options, automated analysis of multiple mutations and offers a flexible computational framework for merging it with other computational tools. AVAILABILITY AND IMPLEMENTATION: A3D standalone is distributed under the MIT license, which is free for academic and non-profit users. It is implemented in Python. The A3D standalone source code, wiki with documentation and examples of use, and installation instructions for Linux, macOS and Windows are available in the A3D standalone repository at https://bitbucket.org/lcbio/aggrescan3d.

CABS-flex standalone: a simulation environment for fast modeling of protein flexibility.

SUMMARY: CABS-flex standalone is a Python package for fast simulations of protein structure flexibility. The package combines simulations of protein dynamics using CABS coarse-grained protein model with the reconstruction of selected models to all-atom representation and analysis of modeling results. CABS-flex standalone is designed to allow for command-line access to the CABS computations and complete control over simulation process. CABS-flex standalone is equipped with features such as: modeling of multimeric and large-size protein systems, contact map visualizations, analysis of similarities to the reference structure and configurable modeling protocol. For instance, the user may modify the simulation parameters, distance restraints, structural clustering scheme or all-atom reconstruction parameters. With these features CABS-flex standalone can be easily incorporated into other methodologies of structural biology. AVAILABILITY AND IMPLEMENTATION: CABS-flex standalone is distributed under the MIT license, which is free for academic and non-profit users. It is implemented in Python. CABS-flex source code, wiki with examples of use and installation instructions for Linux, macOS and Windows are available from the CABS-flex standalone repository at https://bitbucket.org/lcbio/cabsflex.

Combining Structural Aggregation Propensity and Stability Predictions To Redesign Protein Solubility.

The aggregation propensity of each particular protein seems to be shaped by evolution according to its natural abundance in the cell. The production and downstream processing of recombinant polypeptides implies attaining concentrations that are orders of magnitude above their natural levels, often resulting in their aggregation; a phenomenon that precludes the marketing of many globular proteins for biomedical or biotechnological applications. Therefore, there is a huge interest in methods aimed to increase the proteins solubility above their natural limits. Here, we demonstrate that an updated version of our AGGRESCAN 3D structural aggregation predictor, that now takes into account protein stability, allows for designing mutations at specific positions in the structure that improve the solubility of proteins without compromising their conformation. Using this approach, we have designed a highly soluble variant of the green fluorescent protein and a human single-domain VH antibody displaying significantly reduced aggregation propensity. Overall, our data indicate that the solubility of unrelated proteins can be easily tuned by in silico-designed nondestabilizing amino acid changes at their surfaces.

CABS-flex 2.0: a web server for fast simulations of flexibility of protein structures.

Classical simulations of protein flexibility remain computationally expensive, especially for large proteins. A few years ago, we developed a fast method for predicting protein structure fluctuations that uses a single protein model as the input. The method has been made available as the CABS-flex web server and applied in numerous studies of protein structure-function relationships. Here, we present a major update of the CABS-flex web server to version 2.0. The new features include: extension of the method to significantly larger and multimeric proteins, customizable distance restraints and simulation parameters, contact maps and a new, enhanced web server interface. CABS-flex 2.0 is freely available at http://biocomp.chem.uw.edu.pl/CABSflex2.

The structure of adsorbed cyclic chains.

In order to determine the structure of polymer films formed of cyclic chains (rings) we developed and studied a simple coarse-grained model. Our main goal was to check how the percolation and jamming thresholds in such a system were related to the thresholds obtained for linear flexible chains system, i.e., how the geometry of objects influenced both thresholds. All atomic details were suppressed and polymers were represented as a sequence of identical beads and the chains were embedded to a square lattice (a strictly 2D model). The system was athermal and the excluded volume was the only potential introduced. A random sequential adsorption algorithm was chosen to determine the properties of a polymer monolayer. It was shown that the percolation threshold of cyclic chains was considerably higher than those of linear flexible chains while the jamming thresholds for both chain architectures are very similar. The shape of adsorbed cyclic chains was found to be more prolate when compared to average single chain.