MUTATIONEXPLORER- A WEBSERVER FOR MUTATION OF PROTEINS AND 3D VISUALIZATION OF ENERGETIC IMPACTS

Dataset on double mutation in PGIP of Glycine max improves defense to PG of Sclerotinia sclerotiorum

The cell wall of the Glycine max altered by the polygalacturonases (PGs) secreted by the fungus Sclerotinia sclerotiorum, causes disease and quality losses. In soybeans, a resistance protein called polygalacturonases-inhibiting proteins (PGIPs) binds to the PG to block fungal infection. The active site residues of PGIP3, VAL170 and GLN242 are mutated naturally by various amino acids in different types of PGIPs. Therefore, the mutation of VAL170 to GLY is ineffective but the GLN242 amino acid mutation by LYS significantly alters the structure and is crucial for interacting with the PG protein. Docking and Molecular Dynamics simulation provide a comprehensive evaluation of the interactions between gmPGIP and ssPG. By elucidating the structural basis of the interaction between gmPGIP and ssPG, this investigation lays a foundation for the development of targeted strategies in-order to enhance soybean resistance against Sclerotinia sclerotiorum. By leveraging this knowledge, researchers can potentially engineer soybean varieties with improved resistance to the fungus, thereby reducing disease incidence and improving crop yields.

Exome-wide analysis implicates rare protein-altering variants in human handedness

Handedness is a manifestation of brain hemispheric specialization. Left-handedness occurs at increased rates in neurodevelopmental disorders. Genome-wide association studies have identified common genetic effects on handedness or brain asymmetry, which mostly involve variants outside protein-coding regions and may affect gene expression. Implicated genes include several that encode tubulins (microtubule components) or microtubule-associated proteins. Here we examine whether left-handedness is also influenced by rare coding variants (frequencies ≤ 1%), using exome data from 38,043 left-handed and 313,271 right-handed individuals from the UK Biobank. The beta-tubulin gene TUBB4B shows exome-wide significant association, with a rate of rare coding variants 2.7 times higher in left-handers than right-handers. The TUBB4B variants are mostly heterozygous missense changes, but include two frameshifts found only in left-handers. Other TUBB4B variants have been linked to sensorineural and/or ciliopathic disorders, but not the variants found here. Among genes previously implicated in autism or schizophrenia by exome screening, DSCAM and FOXP1 show evidence for rare coding variant association with left-handedness. The exome-wide heritability of left-handedness due to rare coding variants was 0.91%. This study reveals a role for rare, protein-altering variants in left-handedness, providing further evidence for the involvement of microtubules and disorder-relevant genes.

A Perspective on the Prospective Use of AI in Protein Structure Prediction

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.