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Badaczewska-Dawid, A.

Publications and source records attributed to Badaczewska-Dawid, A..

5 recordsLinked to original sources
bioRxiv2021

BIAPSS - BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences

Liquid-liquid phase separation (LLPS) has recently emerged as a cornerstone mechanism underlying the biogenesis of membraneless organelles (MLOs). However, a quantitative molecular grammar of protein sequences that controls the LLPS remains poorly understood. The progress in this field is hampered by the insufficiency of comprehensive databases and associated computational infrastructure for targeting biophysical and statistical analysis of phase separating biopolymers. Therefore, we have created a novel open-source web platform named BIAPSS (BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences) which contains interactive data analytic tools in combination with a comprehensive repository of bioinformatic data for on-the-fly exploration of sequence-dependent properties of proteins with known LLPS behavior. BIAPSS includes a residue-resolution biophysical analyzer for interrogating individual protein sequences (SingleSEQ tab). The latter allows users to correlate regions prone to phase separation with a large array of physicochemical attributes and various short linear motifs. BIAPSS also includes global statistics derived over the universe of most of the known LLPS-driver protein sequences (MultiSEQ tab) for revealing the regularities and sequence-specific signals driving phase separation. Finally, BIAPSS incorporates an extensive cross-reference section that links all entries to primary LLPS databases and other external resources thereby serving as a central navigation hub for the phase separation community. All of the data used by BIAPSS is freely available for download as well-formatted pre-processed data with detailed descriptions, facilitating rapid implementation in user-defined computational protocols. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/430806v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1d90ddorg.highwire.dtl.DTLVardef@111d084org.highwire.dtl.DTLVardef@90b3b0org.highwire.dtl.DTLVardef@51d71_HPS_FORMAT_FIGEXP M_FIG TOC - graphical abstract C_FIG Author summaryProteins, especially those with low complexity and intrinsically disordered regions, have recently come into the limelight because of mounting evidence showing that these regions can drive the formation of membraneless organelles (MLOs) in cells. The underlying physical mechanism for forming MLOs is liquid-liquid phase separation (LLPS); a thermodynamically driven process whereby a cellular milieu with a relatively well-mixed distribution of biomolecules gets decomposed into liquid droplets where the concentration of selected biomolecules is higher. Deciphering molecular sequence grammar of phase separation has turned out to be challenging because of the complexity of this process in cells and the vastness of sequence space of LLPS-driver proteins. While the field is still in its infancy the growth of experimental data has already spurred the creation of several major databases which collect and annotate bimolecular systems with confirmed LLPS behavior. What is currently missing is a framework that would leverage the existing databases by integrating them with deep biophysical and bioinformatic analysis for identifying statistically significant features of protein sequences implicated in LLPS. In this work, we have addressed this challenge by creating an open-source web platform named BIAPSS (BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences) which integrates a comprehensive repository of pre-processed bioinformatic data for LLPS-driver protein sequences with interactive analytic applications for on-the-fly analysis of biophysical features relevant for LLPS behavior. BIAPSS empowers users with novel and effective tools for exploring LLPS-related sequence signals for individual proteins (SingleSEQ tab) and globally by integrating common regularities across subgroups or the entire LLPS sequence superset (MultiSEQ). The long-term plan for BIAPSS is to serve as a unifying hub for the experimental and computational community with a comprehensive set of analytic tools, biophysically featured data, and standardized protocols facilitating the identification of sequence hot spots driving the LLPS, which all can support applications for designing new sequences of biomedical interest.

bioinformatics↗
bioRxiv2020

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.

bioinformatics↗
bioRxiv2020

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement

The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein-peptide complex structures and model scoring. We evaluated the proposed protocol on the set of 7 different GPCR-peptide complexes (including one containing a cyclic peptide) for which crystallographic structures are available. We show that CABS-dock produces high resolution models in the sets of top-scored models. These sets of models, after reconstruction to all-atom representation, can be further improved by Rosetta high-resolution refinement and/or minimization, leading in most of the cases to sub-Angstrom accuracy in terms of interface RMSD measure.

bioinformatics↗
bioRxiv2019

Protocols for all-atom reconstruction and high-resolution refinement of protein-peptide complex structures

Structural characterizations of protein-peptide complexes may require further improvements. These may include reconstruction of missing atoms and/or structure optimization leading to higher accuracy models. In this work, we describe a workflow that generates accurate structural models of peptide-protein complexes starting from protein-peptide models in C-alpha representation generated using CABS-dock molecular docking. First, protein-peptide models are reconstructed from their C-alpha traces to all-atom representation using MODELLER. Next, they are refined using RosettaFlexPepDock. The described workflow allows for reliable all-atom reconstruction of CABS-dock models and their further improvement to high-resolution models.

bioinformatics↗
bioRxiv2019

Protocols for fast simulations of protein structure flexibility using CABS-flex and SURPASS

Conformational flexibility of protein structures can play an important role in protein function. The flexibility is often studied using computational methods, since experimental characterization can be difficult. Depending on protein system size; computational tools may require large computational resources or significant simplifications in the modeled systems to speed-up calculations. In this work, we present the protocols for efficient simulations of flexibility of folded protein structures that use coarse-grained simulation tools of different resolutions: medium, represented by CABS-flex, and low, represented by SUPRASS. We test the protocols using a set of 140 globular proteins and compare the results with structure fluctuations observed in MD simulations, ENM modeling and NMR ensembles. As demonstrated, CABS-flex predictions show high correlation to experimental and MD simulation data, while SURPASS is less accurate but promising in terms of future developments.

bioinformatics↗