Themed collection Computational protein design and structure prediction: Celebrating the 2024 Nobel Prize in Chemistry

Proposed de novo peptide design strategy against amyloidogenic targets. After initial computational preparation of the binder and target, the computational and experimental validation are incorporated in iterative machine learning powered cycles to generate better and improved peptide-based targets.

Efficiently harnessing big data by combining molecular modelling and machine learning accelerates rational enzyme design for its applications in fine chemical synthesis and waste valorization, to address global environmental issues and sustainable development.

Enzymes can be optimized to accelerate chemical transformations via a range of methods. In this review, we showcase how protein engineering and computational design techniques can be interfaced to develop highly efficient and selective biocatalysts.

Rosetta design software was employed to remodel the substrate specificity of Drosophila melanogaster 2′-deoxyribonucleoside kinase for efficient phosphorylation of the nucleoside analog prodrug 3′-deoxythymidine.

De novo designed peptides bind specific conformers of α-synuclein fibrils.

Hydrogen atom transfer (HAT) reactions, as they occur in many biological systems, are here predicted by machine learning.

Pairing experiments with simulations, we predict spectroscopic fingerprints, enhancing understanding of disordered peptides' conformational ensembles. This helps rationalize elusive structure-spectra relationships for these peptides and proteins.

Structure-based three-parameter model that integrates local binding site information, coevolutionary information, and information on dynamic allostery to identify potentially hidden allosteric sites in ensembles of protein structures.

We develop a computational method integrating a genetic algorithm with a residue-level coarse-grained model of intrinsically disordered proteins in order to uncover the molecular origins of multiphase condensates and enable their controlled design.

We apply the Rosetta algorithm to repack the hydrophobic core of a β-peptide bundle while retaining both structure and stability.

A substrate binding induced conformational change was found to be essential for the occurrence of RrUGT3 catalyzed transglycosylation reactions.

ProtAgents is a de novo protein design platform based on multimodal LLMs, where distinct AI agents with expertise in knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis tackle tasks in a dynamic setting.

PIGNet2, a versatile protein–ligand interaction prediction model that performs well in both molecule identification and optimization, demonstrates its potential in early-stage drug discovery.

A computational strategy using synergetic energy and correlated configuration for redesigning enzymes (SECURE) is proposed for the thermostability engineering of multimeric proteins.

Utilizing a large protein language model, we have formulated a deep learning framework designed for predicting type II polyketide natural products.

Machine learning models provide an informed and efficient strategy to create novel peptide and protein sequences with the desired profiles.

Yeast surface display using multi target selections enables monitoring of specificity profiles for thousands of proteins in parallel.

Tethering ferredoxin (PetF) to photosystem I increased light-induced PetF-mediated electron transfer to soluble acceptors. Tethering was equivalent to using a ten-to-one molar ratio of soluble PetF to PSI.