Biology subjects

De Maria, L.

Publications and source records attributed to De Maria, L..

3 recordsLinked to original sources

bioRxiv2024

Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/590325v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1c5e607org.highwire.dtl.DTLVardef@1810ee5org.highwire.dtl.DTLVardef@1f8f1acorg.highwire.dtl.DTLVardef@c28053_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIReliable calculation of relative binding free energy changes for most protein mutations to within [~]1 kcal/mol. C_LIO_LIAutomated Protein FEP+ Groups treatment of alternate protonation states for titratable residues. C_LIO_LIApplication of FEP+ methodology to "real-world" protein design projects. C_LI

biophysics↗

bioRxiv2024

Ancestral versus Modern Substrate Scope in Family-1 Glycosidases

Experimental studies support that protein engineering based on ancestral sequence reconstruction often leads to variants with biotechnologically useful biomolecular properties. These may include high stability, enhanced conformational flexibility and a modified catalysis range. Carbohydrate-active enzymes have numerous applications related with the degradation and synthesis of carbohydrates and glycoconjugates. Here, we explore how ancestral reconstruction may impact substrate scope in glycosidases, highly diverse enzymes that catalyze the hydrolysis of glycosidic bonds in all living cells and find applications as catalysts of the reverse reaction. To this end, we screen a library of [~]500 potential glycosidase substrates for degradation by both, a modern family-1 glycosidase from Halothermothrix orenii and a putative ancestral family-1 glycosidase derived from sequence reconstruction at a bacterial-eukaryotic common ancestor. The modern enzyme is the better catalyst for most substrates. But the ancestral glycosidase is more efficient with flavonoid glycosides bearing large aglycon moieties. Analysis of the catalytic parameters for a selected set of substrates, alongside analysis of the library data using a supervised learning algorithm, support the hypothesis that the modern enzyme tends to become less catalytically efficient with increasing substrate size, while this trend is not observed for the ancestral glycosidase. Molecular simulations support that the ancestral catalysis pattern is linked to the existence of a highly flexible region of the ancestral structure and a cavity capable of accommodating large aglycons. Our results provide guidelines for the engineering of enzymes for the synthesis and hydrolysis of large glycoconjugates.

biochemistry↗

bioRxiv2023

Sequence-based prediction of the solubility of peptides containing non-natural amino acids

Non-natural amino acids are increasingly used as building blocks in the development of peptide-based drugs, as they expand the available chemical space to tailor function, half-life and other key properties. However, while the chemical space of modified amino acids (mAAs) is potentially vast, experimental methods for measuring the developability properties of mAA-containing peptides are expensive and time consuming. To facilitate developability programs through computational methods, we present CamSol-PTM, a method that enables the fast and reliable sequence-based prediction of the solubility of mAA-containing peptides. From a computational screening of 50,000 mAA-containing variants of three peptides, we selected five different mAAs for a total number of 30 peptide variants for experimental validation. We demonstrate the accuracy of the predictions by comparing the calculated and experimental solubility values. Our results indicate that the computational screening of mAA-containing peptides can extend by over four orders of magnitude the ability to explore the solubility chemical space of peptides. This method is available as a web server at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolptm.

biophysics↗