Biology subjects

Schliep, K.

Publications and source records attributed to Schliep, K..

3 recordsLinked to original sources

bioRxiv2026

Ancestors of Arylmalonate Decarboxylase show increased Activity, Stability and Stereoselectivity

Bacterial aryl malonate decarboxylase (AMDase) generates a wide spectrum of alpha-chiral carboxylic acids in outstanding optical purity, including several non-steroidal anti-inflammatory drugs and chiral building blocks. However, extant AMDase from Bordetella bronchiseptica (BbAMDase) and related enzymes of the same family have three main limitations: (i) low operational and thermal stability, (ii) limited substrate spectrum regarding the size of the smaller substituent on the alpha-C-atom, and (iii) low stereoselectivity towards alpha-alkenyl-alpha-alkyl malonic acids. To address these limitations, we expanded the structural diversity of the AMDase family by ancestral sequence reconstruction (ASR). The phylogenetic analysis of the decarboxylase revealed conserved structural motifs and key amino acids in the hydrophobic active-site cavity, a catalytic motif crucial for activity and selectivity of the enzyme. The analysis highlighted the natural distribution of amino acid exchanges previously identified via enzyme engineering. AMDase ancestors showed higher stability, activity, and, in one case, also stereoselectivity than BbAMDase. While a 10 degree higher unfolding temperature of AMDase ancestors is a frequent ASR result, ancestor N131 exhibited an exceptional 294-fold improvement in half-life time and a 1.8-fold increased specific activity (25.1 U / mg) toward 2-methyl-2-phenyl malonate in a preparative scale application. Computational analysis attributes this improved stability to a substantial increase in surface Glu side chains, which form a protective hydration shell that enhances solubility and mitigates aggregation. Ancestor N31 formed 2-methyl-but-3-enoic acid from its corresponding malonic acid in excellent optical purity (99.7% eeR). Finally, the stereoselectivity of the ancestors was completely inverted by switching the catalytic cysteine residues (G74C/C188G).

biochemistry↗

bioRxiv2022

A new framework for detecting copy number variants from single nucleotide polymorphism data: 'rCNV', a versatile R package for paralogs and CNVs detection

Studies show that copy number variants (CNVs), due to their ubiquitous presence in eukaryotes, contribute to phenotypic variation, environmental adaptation, and fuel species divergence at a previously unknown rate. However, the detection of CNVs in genomes, especially in non-model organisms is challenging due to the need for costly genomic resources and complex computational infrastructure. Therefore, to provide researchers with a low-cost and easily accessible resource, we developed a robust statistical framework and an R software package to detect CNVs using allelic-read depth from SNPs data. The core of the framework exploits i) the allelic-read depth ratio distribution in heterozygotes for individual SNPs and testing it against an expected distribution under a binomial sampling, and ii) SNPs showing an apparent excess of heterozygotes under Hardy-Weinberg equilibrium, to detect alleles in putatively multi-copy regions. The use of multiple statistical tests to find the deviation in allelic-read depth ratio distribution makes our method sensitive to sampling and aware of reference biases thereby minimizing false detection of CNVs. Our framework is well-catered for high throughput short-reads data, hence, most GBS technologies (e.g., RADseq, Exome-capture, WGS). As such, it allows calling CNVs from genomes of varying complexity. The framework is implemented in the R package "rCNV" which effortlessly automates the analysis. We trained our models on simulated data and tested on four datasets obtained from different sequencing technologies (i.e., RADseq: Chinook salmon - Oncorhynchus tshawytscha, American lobster - Homarus americanus, Exome-capture: Norway Spruce - Picea abies, and WGS: Malaria mosquito -Anopheles gambiae).

evolutionary biology↗

bioRxiv2020

phyloregion: R package for biogeographic regionalization and spatial conservation

O_LIBiogeographical regionalization is the classification of regions in terms of their biotas and is key to understanding biodiversity patterns across the world. Previously, it was only possible to perform analysis of biogeographic regionalization on small datasets, often using tools that are difficult to replicate. C_LIO_LIHere, we present phyloregion, a package for the analysis of biogeographic regionalization and spatial conservation in the R computing environment, tailored for mega phylogenies and macroecological datasets of ever-increasing size and complexity. C_LIO_LICompared to available packages, phyloregion is three to four orders of magnitude faster and memory efficient for cluster analysis, determining optimal number of clusters, evolutionary distinctiveness of regions, as well as analysis of more standard conservation measures of phylogenetic diversity, phylogenetic endemism, and evolutionary distinctiveness and global endangerment. C_LIO_LIA case study of zoogeographic regionalization for 9574 species of squamate reptiles (amphisbaenians, lizards, and snakes) across the globe, reveals their evolutionary affinities, using visualization tools that allow rapid identification of patterns and underlying processes with user-friendly colours-for example- indicating the levels of differentiation of the taxa in different regions. C_LIO_LIUltimately, phyloregion would facilitate rapid biogeographic analyses that accommodates the ongoing mass-production of species occurrence records and phylogenetic datasets at any scale and for any taxonomic group into completely reproducible R workflows. C_LI

bioinformatics↗