Structure prediction articles from across Nature Portfolio

Normal mode analysis is a crucial step in structural biology, but is based on an expensive diagonalisation of the system’s Hessian. Here the authors present INCHING, a GPU-based approach to accelerate this task up to >250 times over current methods for macromolecular assemblies.

The reconstruction of rutile TiO2 (110) impacts its surface chemistry and catalytic properties. Here, the authors offer a detailed understanding of the asymmetric surface reconstruction of TiO2 (110)-(1×2) through a combination of STEM and DFT calculations.

A computational model generates conformational ensembles of 28,058 intrinsically disordered proteins and regions (IDRs) in the human proteome and sheds light on the relationship between sequence, conformational properties and functions of IDRs.

Descriptor design in catalyst informatics necessitates adequate prior knowledge for delving into unknown territories, particularly when confronted with limited data, thus presenting a logical contradiction. Here, the authors report a technique for automatic feature engineering that works on small catalyst datasets without reliance on pre-existing knowledge about the target catalysis.

Previous theoretical interpretations of the Rydberg spectra of dimethylpiperazine (DMP) debated the existence of a localized minimum on the surface of the DMP+ cation. Here, the authors show a substantial influence of the Rydberg electron on the molecular structure, restoring the localized minimum.

The nucleation of calcium silicate hydrate is a crucial step in cement hydration, but is still a poorly understood process. Here the authors use atomistic simulations to study primary particles and their aggregation, revealing a potential C-S-H “basic building block”.

A fully automated, high-throughput computational framework accurately predicts stable species in liquid solutions by computing the nuclear magnetic resonance chemical shifts. Data collected from the framework can provide fingerprints to guide the rational design of liquid solutions with optimal properties.

Machine learning algorithms are fast surpassing human abilities in multiple tasks, from image recognition to medical diagnostics. Now, machine learning algorithms have been shown to be capable of accurately predicting the folded structures of proteins.

The Hückel rule defines that monocyclic and planar conjugated systems containing [4n + 2] π electrons are aromatic. Here, the authors highlight boron species that feature a globally 4n π system, defying the Hückel rule, but nonetheless exhibit aromaticity.

Models for polyhedral clusters with different symmetries enable us to understand their electronic structures and predict the formation of new clusters.

A comprehensive chemical space of potential inorganic ternary metal nitrides has been explored by computational methods as a guideline for their experimental synthesis and discovery.