TopDomain: Exhaustive Protein Domain Boundary Metaprediction Combining Multisource Information and Deep Learning

Structure and dimerization properties of the plant-specific copper chaperone CCH

Copper chaperones of the ATX1 family are found in a wide range of organisms where these essential soluble carriers strictly control the transport of monovalent copper across the cytoplasm to various targets in diverse cellular compartments thereby preventing detrimental radical formation catalyzed by the free metal ion. Notably, the ATX1 family in plants contains two distinct forms of the cellular copper carrier. In addition to ATX1 having orthologs in other species, they also contain the copper chaperone CCH. The latter features an extra C-terminal extension whose function is still unknown. The secondary structure of this extension was predicted to be disordered in previous studies, although this has not been experimentally confirmed. Solution NMR studies on purified CCH presented in this study disclose that this region is intrinsically disordered regardless of the chaperone's copper loading state. Further biophysical analyses of the purified metallochaperone provide evidence that the C-terminal extension stabilizes chaperone dimerization in the copper-free and copper-bound states. A variant of CCH lacking the C-terminal extension, termed CCHΔ, shows weaker dimerization but similar copper binding. Computational studies further corroborate the stabilizing role of the C-terminal extension in chaperone dimerization and identify key residues that are vital to maintaining dimer stability.

Molecular Mechanisms Underlying Single Nucleotide Polymorphism-Induced Reactivity Decrease in CYP2D6

Cytochrome P450 2D6 (CYP2D6) is one of the most important enzymes involved in drug metabolism. Genetic polymorphism can influence drug metabolism by CYP2D6 such that a therapy is seriously affected by under- or overdosing of drugs. However, a general explanation at the atomistic level for poor activity is missing so far. Here we show for the 20 most common single nucleotide polymorphisms (SNPs) of CYP2D6 that poor metabolism is driven by four mechanisms. We found in extensive all-atom molecular dynamics simulations that the rigidity of the I-helix (central helix), distance between central phenylalanines (stabilizing bound substrate), availability of basic residues on the surface of CYP2D6 (binding of cytochrome P450 reductase), and position of arginine 132 (electron transfer to heme) are essential for an extensive function of the enzyme. These results were applied to SNPs with unknown effects, and potential SNPs that may lead to poor drug metabolism were identified. The revealed molecular mechanisms might be important for other drug-metabolizing cytochrome P450 enzymes.

De novo structure prediction of meteorin and meteorin-like protein for identification of domains, functional receptor binding regions, and their high-risk missense variants

Meteorin (Metrn) and Meteorin-like (Metrnl) are homologous secreted proteins involved in neural development and metabolic regulation. In this study, we have performed de novo structure prediction and analysis of both Metrn and Metrnl using Alphafold2 (AF2) and RoseTTAfold (RF). Based on the domain and structural homology analysis of the predicted structures, we have identified that these proteins are composed of two functional domains, a CUB domain and an NTR domain, connected by a hinge/loop region. We have identified the receptor binding regions of Metrn and Metrnl using the machine-learning tools ScanNet and Masif. These were further validated by docking Metrnl with its reported KIT receptor, thus establishing the role of each domain in the receptor interaction. Also, we have studied the effect of non-synonymous SNPs on the structure and function of these proteins using an array of bioinformatics tools and selected 16 missense variants in Metrn and 10 in Metrnl that can affect the protein stability. This is the first study to comprehensively characterize the functional domains of Metrn and Metrnl at their structural level and identify the functional domains, and protein binding regions. This study also highlights the interaction mechanism of the KIT receptor and Metrnl. The predicted deleterious SNPs will allow further understanding of the role of these variants in modulating the plasma levels of these proteins in disease conditions such as diabetes.Communicated by Ramaswamy H. Sarma.

Expanding the clinical and immunological phenotypes of PAX1-deficient SCID and CID patients

Paired box 1 (PAX1) deficiency has been reported in a small number of patients diagnosed with otofaciocervical syndrome type 2 (OFCS2). We described six new patients who demonstrated variable clinical penetrance. Reduced transcriptional activity of pathogenic variants confirmed partial or complete PAX1 deficiency. Thymic aplasia and hypoplasia were associated with impaired T cell immunity. Corrective treatment was required in 4/6 patients. Hematopoietic stem cell transplantation resulted in poor immune reconstitution with absent naïve T cells, contrasting with the superior recovery of T cell immunity after thymus transplantation. Normal ex vivo differentiation of PAX1-deficient CD34+ cells into mature T cells demonstrated the absence of a hematopoietic cell-intrinsic defect. New overlapping features with DiGeorge syndrome included primary hypoparathyroidism (n = 5) and congenital heart defects (n = 2), in line with PAX1 expression during early embryogenesis. Our results highlight new features of PAX1 deficiency, which are relevant to improving early diagnosis and identifying patients requiring corrective treatment.

Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps

The information about the domain architecture of proteins is useful for studying protein structure and function. However, accurate prediction of protein domain boundaries (i.e., sequence regions separating two domains) from sequence remains a significant challenge. In this work, we develop a deep learning method based on multi-head U-Nets (called DistDom) to predict protein domain boundaries utilizing 1D sequence features and predicted 2D inter-residue distance map as input. The 1D features contain the evolutionary and physicochemical information of protein sequences, whereas the 2D distance map includes the structural information of proteins that was rarely used in domain boundary prediction before. The 1D and 2D features are processed by the 1D and 2D U-Nets respectively to generate hidden features. The hidden features are then used by the multi-head attention to predict the probability of each residue of a protein being in a domain boundary, leveraging both local and global information in the features. The residue-level domain boundary predictions can be used to classify proteins as single-domain or multi-domain proteins. It classifies the CASP14 single-domain and multi-domain targets at the accuracy of 75.9%, 13.28% more accurate than the state-of-the-art method. Tested on the CASP14 multi-domain protein targets with expert annotated domain boundaries, the average per-target F1 measure score of the domain boundary prediction by DistDom is 0.263, 29.56% higher than the state-of-the-art method.

TopProperty: Robust Metaprediction of Transmembrane and Globular Protein Features Using Deep Neural Networks

Transmembrane proteins (TMPs) are critical components of cellular life. However, due to experimental challenges, the number of experimentally resolved TMP structures is severely underrepresented in databases compared to their cellular abundance. Prediction of (per-residue) features such as transmembrane topology, membrane exposure, secondary structure, and solvent accessibility can be a useful starting point for experimental design or protein structure prediction but often requires different computational tools for different features or types of proteins. We present TopProperty, a metapredictor that predicts all of these features for TMPs or globular proteins. TopProperty is trained on datasets without bias toward a high number of sequence homologs, and the predictions are significantly better than the evaluated state-of-the-art primary predictors on all quality metrics. TopProperty eliminates the need for protein type- or feature-tailored tools, specifically for TMPs. TopProperty is freely available as a web server and standalone at https://cpclab.uni-duesseldorf.de/topsuite/.