Biology subjects

Kwasigroch, J. M.

Publications and source records attributed to Kwasigroch, J. M..

5 recordsLinked to original sources

bioRxiv2023

pyScoMotif: Discovery of similar 3D structural motifs across proteins

MotivationThe fast and accurate detection of similar geometrical arrangements of protein residues, known as 3-dimensional (3D) structural motifs, is highly relevant for many applications such as binding region and catalytic site detection, drug discovery and structure conservation analyses. With the recent publication of new protein structure prediction methods, the number of available protein structures is exploding, which makes efficient and easy-to-use tools for identifying 3D structural motifs essential. ResultsWe present an open-source Python package that enables the search for both exact and mutated motifs with position-specific residue substitutions. The tool is fast, accurate and suitable to run both on computer clusters and personal laptops. Successful application of pyScoMotif to catalytic site identification is showcased. AvailabilityThe pyScoMotif package can be installed from the PyPI repository and is also available at https://github.com/3BioCompBio/pyScoMotif. It is free to use for non-commercial purposes; for commercial purposes, please contact us.

bioinformatics↗

bioRxiv2020

SWOTein: A structure-based approach to predict stability Strengths and Weaknesses of prOTEINs

MotivationAlthough structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes. ResultsWe developed the SWOTein predictor which identifies strong and weak residues in proteins on the basis of three types of statistical energy functions describing local interactions along the chain, hydrophobic forces and tertiary interactions. The large-scale comparison of the different types of strengths and weaknesses showed their complementarity and the enhancement of the information they provide. We applied SWOTein to apocytochrome b562 and found good agreement between predicted strengths and weaknesses and native hydrogen exchange data. Its application to an amino acid-binding protein identified the hinge at the basis of the conformational change. SWOTein is both fast and accurate and can be applied at small and large scale to analyze and modulate folding and molecular recognition processes. AvailabilityThe SWOTein webserver provides the list of predicted strengths and weaknesses and a protein structure visualization tool that facilitates the interpretation of the predictions. It is freely available for academic use at http://babylone.ulb.ac.be/SWOTein.

bioinformatics↗

bioRxiv2019

SOLart: a structure-based method to predict protein solubility and aggregation

MotivationThe solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools.\n\nResultsWe have recently introduced solubility-dependent distance potentials that are able to unravel the role of residue-residue interactions in promoting or decreasing protein solubility. Here, we extended their construction by defining solubility-dependent potentials based on backbone torsion angles and solvent accessibility, and integrated them, together with other structure- and sequence-based features, into a random forest model trained on a set of E. coli proteins with experimental structures and solubility values. We thus obtained the SOLart protein solubility predictor, whose most informative features turned out to be folding free energy differences computed from our solubility-dependent statistical potentials. SOLart performances are very good, with a Pearson correlation coefficient between experimental and predicted solubility values of 0.7 both in the training dataset and on an independent set of S. Cerevisiae proteins. On test sets of modeled structures, only a limited drop in performance is observed. SOLart can thus be used with both high-resolution and low-resolution structures, and clearly outperforms state-of-art solubility predictors. It is available through a user-friendly webserver, which is easy to use by non-expert scientists.\n\nAvailabilityThe SOLart webserver is freely available at babylone.ulb.ac.be/SOLART/

bioinformatics↗

bioRxiv2019

Protein thermal stability engineering using HoTMuSiC

The rational design of enzymes is a challenging research field, which plays an important role in the optimization of a wide series of biotechnological processes. Computational approaches allow to screen all possible amino acid substitutions in a target protein and to identify a subset likely to have the desired properties. They can thus be used to guide and restrict the huge, time-consuming, search in sequence space to reach protein optimality. Here we present HoTMuSiC, a tool that predicts the impact of point mutations on the protein melting temperature, which uses the experimental or modelled protein structure as sole input, and is available at dezyme.com. Its main advantages include accuracy and speed, which makes it a perfect instrument for thermal stability engineering projects aiming to design new proteins that feature increased heat resistance or remain active and stable in non-physiological conditions. We set up a HoTMuSiC-based pipeline, which uses additional information to avoid mutations of functionally important residues, identified as being too well conserved among homologous proteins or too close to annotated functional sites. The efficiency of this pipeline is successfully demonstrated on Rhizomucor miehei lipase.

bioinformatics↗

bioRxiv2018

Quantification of biases in predictions of protein stability changes upon mutations

Bioinformatics tools that predict protein stability changes upon point mutations have made a lot of progress in the last decades and have become accurate and fast enough to make computational mutagenesis experiments feasible, even on a proteome scale. Despite these achievements, they still suffer from important issues that must be solved to allow further improving their performances and utilizing them to deepen our insights into protein folding and stability mechanisms. One of these problems is their bias towards the learning datasets which, being dominated by destabilizing mutations, causes predictions to be better for destabilizing than for stabilizing mutations.\n\nWe thoroughly analyzed the biases in the prediction of folding free energy changes upon point mutations ({Delta}{Delta}G0) and proposed some unbiased solutions. We started by constructing a dataset Ssym of experimentally measured {Delta}{Delta}G0s with an equal number of stabilizing and destabilizing mutations, by collecting mutations for which the structure of both the wild type and mutant protein is available. On this balanced dataset, we assessed the performances of fifteen widely used{Delta}{Delta} G0 predictors. After the astonishing observation that almost all these methods are strongly biased towards destabilizing mutations, especially those that use black-box machine learning, we proposed an elegant way to solve the bias issue by imposing physical symmetries under inverse mutations on the model structure, which we implemented in PoPMuSiCsym. This new predictor constitutes an efficient trade-off between accuracy and absence of biases. Some final considerations and suggestions for further improvement of the predictors are discussed.

bioinformatics↗