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Estimating the accuracy of protein structural models is a critical task in protein bioinformatics. The need for robust methods in the estimation of protein model accuracy (EMA) is prevalent in the field of protein structure prediction, where computationally‐predicted structures need to be screened rapidly for the reliability of the positions predicted for each of their amino acid residues and their overall quality. Current methods proposed for EMA are either coupled tightly to existing protein structure prediction methods or evaluate protein structures without sufficiently leveraging the rich, geometric information available in such structures to guide accuracy estimation. In this work, we propose a geometric message passing neural network referred to as the geometry‐complete perceptron network for protein structure EMA (GCPNet‐EMA), where we demonstrate through rigorous computational benchmarks that GCPNet‐EMA's accuracy estimations are 47% faster and more than 10% (6%) more correlated with ground‐truth measures of per‐residue (per‐target) structural accuracy compared to baseline state‐of‐the‐art methods for tertiary (multimer) structure EMA including AlphaFold 2. The source code and data for GCPNet‐EMA are available on GitHub, and a public web server implementation is freely available.

 

Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure predictions to account for tertiary structural changes induced by protein-protein interaction. The system participated in the tertiary structure prediction in 2022 CASP15 experiment. Our server predictor MULTICOM_refine ranked 3rd among 47 CASP15 server predictors and our human predictor MULTICOM ranked 7th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are ~0.80 and ~0.92, 9.6% and 8.2% higher than ~0.73 and 0.85 of the standard AlphaFold2 predictor respectively.

 

Unexpected, yet useful functionalities emerge when two or more materials merge coherently. Artificial oxide superlattices realize atomic and crystal structures that are not available in nature, thus providing controllable correlated quantum phenomena. This review focuses on 4d and 5d perovskite oxide superlattices, in which the spin–orbit coupling plays a significant role compared with conventional 3d oxide superlattices. Modulations in crystal structures with octahedral distortion, phonon engineering, electronic structures, spin orderings, and dimensionality control are discussed for 4d oxide superlattices. Atomic and magnetic structures,J_eff= 1/2 pseudospin and charge fluctuations, and the integration of topology and correlation are discussed for 5d oxide superlattices. This review provides insights into how correlated quantum phenomena arise from the deliberate design of superlattice structures that give birth to novel functionalities.

 

Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery.

In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures.

The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.