Tercyclic scaffolds, designed to have improved synthetic accessibility and aqueous solubility, were evaluated as structural α-helix mimetics by using an iterative in silico approach. The synthesis of these tercyclic scaffolds was accomplished using a modular synthetic approach by employing functionalised methoxyphenyl units which were readily manipulated to allow the introduction of various nitrogen-based heterocycles. The ability of these scaffolds to mimic the key i, i + 3 and i + 7 residues of a polyalanine α-helix was ratified by in silico studies, X-ray crystallographic and NOESY analysis, and their aqueous solubility was measured by a kinetic turbidimetric method.
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http://dx.doi.org/10.1039/c4ob00647j | DOI Listing |
Nat Commun
January 2025
School of Natural Sciences, and ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, Australia.
The Sc2.0 global consortium to design and construct a synthetic genome based on the Saccharomyces cerevisiae genome commenced in 2006, comprising 16 synthetic chromosomes and a new-to-nature tRNA neochromosome. In this paper we describe assembly and debugging of the 902,994-bp synthetic Saccharomyces cerevisiae chromosome synXVI of the Sc2.
View Article and Find Full Text PDFExpert Rev Proteomics
January 2025
Biological and Environmental Science & Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
Introduction: The DeepMind's AlphaFold (AF) has revolutionized biomedical research by providing both experts and non-experts with an invaluable tool for predicting protein structures. However, while AF is highly effective for predicting structures of rigid and globular proteins, it is not able to fully capture the dynamics, conformational variability, and interactions of proteins with ligands and other biomacromolecules.
Areas Covered: In this review, we present a comprehensive overview of the latest advancements in 3D model predictions for biomacromolecules using AF.
JMIR Res Protoc
January 2025
Health Services Research Centre, Singapore Health Services Pte Ltd, Singapore, Singapore.
Background: Integrating algorithm-based clinical decision support (CDS) systems poses significant challenges in evaluating their actual clinical value. Such CDS systems are traditionally assessed via controlled but resource-intensive clinical trials.
Objective: This paper presents a review protocol for preimplementation in silico evaluation methods to enable broadened impact analysis under simulated environments before clinical trials.
J Chromatogr A
February 2025
Analytical Chemistry Group, Van 't Hoff Institute for Molecular Sciences, Science Park 904, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), Amsterdam, the Netherlands; AI4Science Lab, Informatics Institute, University of Amsterdam, Science Park 904, the Netherlands. Electronic address:
Optimization algorithms play an important role in method development workflows for gradient elution liquid chromatography. Their effectiveness has not been evaluated for chromatographic method development using standardized comparisons across factors such as sample complexity, chromatographic response functions (CRFs), gradient complexity, and application type. This study compares six optimization algorithms - Bayesian optimization (BO), differential evolution (DE), a genetic algorithm (GA), covariance-matrix adaptation evolution strategy (CMA-ES), random search, and grid search - for the development of gradient elution LC methods.
View Article and Find Full Text PDFFront Bioeng Biotechnol
December 2024
AO Vector-Best, Novosibirsk, Russia.
Introduction: Modification of natural enzymes to introduce new properties and enhance existing ones is a central challenge in bioengineering. This study is focused on the development of Taq polymerase mutants that show enhanced reverse transcriptase (RTase) activity while retaining other desirable properties such as fidelity, 5'- 3' exonuclease activity, effective deoxyuracyl incorporation, and tolerance to locked nucleic acid (LNA)-containing substrates. Our objective was to use AI-driven rational design combined with multiparametric wet-lab analysis to identify and validate Taq polymerase mutants with an optimal combination of these properties.
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