The SH2 domains of SHP2 play a crucial role in determining the function of the SHP2 protein. While the folding and binding properties of the isolated NSH2 and CSH2 domains have been extensively studied, there is limited information about the tandem SH2 domains. This study aims to elucidate the folding and binding kinetics of the NSH2-CSH2 tandem domains of SHP2 through rapid kinetic experiments, complementing existing data on the isolated domains. The results indicate that while the domains generally fold and unfold independently, acidic pH conditions induce complex scenarios involving the formation of a misfolded intermediate. Furthermore, a comparison of the binding kinetics of isolated NSH2 and CSH2 domains with the NSH2-CSH2 tandem domains, using peptides that mimic specific portions of Gab2, suggests a dynamic interplay between NSH2 and CSH2 in binding Gab2 that modulate the microscopic association rate constant of the binding reaction. These findings, discussed in the context of previous research on the NSH2 and CSH2 domains, enhance our understanding of the function of the SH2 domain tandem of SHP2.

Continuous gain and loss of genes are the primary driving forces of bacterial evolution and environmental adaptation. Studying bacterial evolution in terms of protein domain, which is the fundamental function and evolutionary unit of proteins, can provide a more comprehensive understanding of bacterial differentiation and phenotypic adaptation processes. Therefore, we proposed a phylogenetic tree-based method for detecting genetic gain and loss events in terms of protein domains. Specifically, the method focuses on a single domain to trace its evolution process or on multiple domains to investigate their co-evolution principles. This novel method was validated using 122 Shigella isolates. We found that the loss of a significant number of domains was likely the main driving force behind the evolution of Shigella, which could reduce energy expenditure and preserve only the most essential functions. Additionally, we observed that simultaneously gained and lost domains were often functionally related, which can facilitate and accelerate phenotypic evolutionary adaptation to the environment. All results obtained using our method agree with those of previous studies, which validates our proposed method.

Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient\'s response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.

Glycosyltransferases (GT) catalyze the glycosylation of bioactive natural products, including peptides and proteins, flavonoids, and sterols, and have been extensively used as biocatalysts to generate glycosides. However, the often narrow substrate specificity of wild-type GTs requires engineering strategies to expand it. The GT-B structural family is constituted by GTs that share a highly conserved tertiary structure in which the sugar donor and acceptor substrates bind in dedicated domains. Here, we have used this selective binding feature to design an engineering process to generate chimeric glycosyltransferases that combine auto-assembled domains from two different GT-B enzymes. Our approach enabled the generation of a stable dimer with broader substrate promiscuity than the parent enzymes that were related to relaxed interactions between domains in the dimeric GT-B. Our findings provide a basis for the development of a novel class of heterodimeric GTs with improved substrate promiscuity for applications in biotechnology and natural product synthesis.

A single-domain protein catenane refers to two mechanically interlocked polypeptide rings that fold synergistically into a compact and integrated structure, which is extremely rare in nature. Here, we report a single-domain protein catenane of dihydrofolate reductase (cat-DHFR). This design was achieved by rewiring the connectivity between secondary motifs to introduce artificial entanglement and synthesis was readily accomplished through a series of programmed and streamlined post-translational processing events in cells without any additional in vitro reactions. The target molecule contained few exogenous motifs and was thoroughly characterized using a combination of ultra-performance liquid chromatography-mass spectrometry, sodium dodecyl sulfate-polyacrylamide gel electrophoresis, protease cleavage experiments and ion mobility spectrometry-mass spectrometry. Compared with the linear control, cat-DHFR retained its catalytic capability and exhibited enhanced stability against thermal or chemical denaturation due to conformational restriction. These results suggest that linear proteins may be converted into their concatenated single-domain counterparts with almost identical chemical compositions, well-preserved functions and elevated stabilities, representing an entirely new horizon in protein science.

Classification of rare missense variants remains an ongoing challenge in genomic medicine. Evidence of pathogenicity is often sparse, and decisions about how to weigh different evidence classes may be subjective. We used a Bayesian variant classification framework to investigate the performance of variant co-localisation, missense constraint, and aggregating data across paralogous protein domains (\"meta-domains\").
We constructed a database of all possible coding single nucleotide variants in the human genome and used PFam predictions to annotate structurally-equivalent positions across protein domains. We counted the number of pathogenic and benign missense variants at these equivalent positions in the ClinVar database, calculated a regional constraint score for each meta-domain, and assessed this approach versus existing missense constraint metrics for classifying variant pathogenicity and benignity.
Alternative pathogenic missense variants at the same amino acid position in the same protein provide strong evidence of pathogenicity (positive likelihood ratio, LR+  = 85). Additionally, clinically annotated pathogenic or benign missense variants at equivalent positions in different proteins can provide moderate evidence of pathogenicity (LR+  = 7) or benignity (LR+  = 5), respectively. Applying these approaches sequentially (through PM5) increases sensitivity for classifying pathogenic missense variants from 27 to 41%. Missense constraint can also provide strong evidence of pathogenicity for some variants, but its absence provides no evidence of benignity.
We propose using structurally equivalent positions across related protein domains from different genes to augment evidence for variant co-localisation when classifying novel missense variants. Additionally, we advocate adopting a numerical evidence-based approach to integrating diverse data in variant interpretation.

The development and improvement of methods for comparing and searching for three-dimensional protein structures remain urgent tasks in modern structural biology. To solve this problem, we developed a new tool, SAFoldNet, which allows for searching, aligning, superimposing, and determining the exact coordinates of fragments of protein structures. The proposed search and alignment tool was built using neural networking. Specifically, we implemented the integrative synergy of neural network predictions and the well-known BLAST algorithm for searching and aligning sequences. The proposed method involves multistage processing, comprising a stage for converting the geometry of protein structures into sequences of a structural alphabet using a neural network, a search stage for forming a set of candidate structures, and a refinement stage for calculating the structural alignment and overlap and evaluating the similarity with the starting structure of the search. The effectiveness and practical applicability of the proposed tool were compared with those of several widely used services for searching and aligning protein structures. The results of the comparisons confirmed that the proposed method is effective and competitive relative to the available modern services. Furthermore, using the proposed approach, a service with a user-friendly web interface was developed, which allows for searching, aligning, and superimposing protein structures; determining the location of protein fragments; mapping onto a protein molecule chain; and providing structural similarity metrices (expected value and root mean square deviation).

Microbial extracellular reduction of insoluble compounds requires soluble electron shuttles that diffuse in the environment, freely diffusing cytochromes, or direct contact with cellular conductive appendages that release or harvest electrons to assure a continuous balance between cellular requirements and environmental conditions. In this work, we produced and characterized the three cytochrome domains of PgcA, an extracellular triheme cytochrome that contributes to Fe(III) and Mn(IV) oxides reduction in Geobacter sulfurreducens. The three monoheme domains are structurally homologous, but their heme groups show variable axial coordination and reduction potential values. Electron transfer experiments monitored by NMR and visible spectroscopy show the variable extent to which the domains promiscuously exchange electrons while reducing different electron acceptors. The results suggest that PgcA is part of a new class of cytochromes - microbial heme-tethered redox strings - that use low-complexity protein stretches to bind metals and promote intra- and intermolecular electron transfer events through its cytochrome domains.

Many transcripts are targeted by nonsense-mediated decay (NMD), leading to their degradation and the inhibition of their translation. We found that the protein SUZ domain-containing protein 1 (SZRD1) interacts with the key NMD factor up-frameshift 1. When recruited to NMD-sensitive reporter gene transcripts, SZRD1 increased protein production, at least in part, by relieving translational inhibition. The conserved SUZ domain in SZRD1 was required for this effect. The SUZ domain is present in only three other human proteins besides SZRD1: R3H domain-containing protein 1 and 2 (R3HDM1, R3HDM2) and cAMP-regulated phosphoprotein 21 (ARPP21). We found that ARPP21, similarly to SZRD1, can increase protein production from NMD-sensitive reporter transcripts in an SUZ domain-dependent manner. This indicated that the SUZ domain-containing proteins could prevent translational inhibition of transcripts targeted by NMD. Consistent with the idea that SZRD1 mainly prevents translational inhibition, we did not observe a systematic decrease in the abundance of NMD targets when we knocked down SZRD1. Surprisingly, knockdown of SZRD1 in two different cell lines led to reduced levels of the NMD component UPF3B, which was accompanied by increased levels in a subset of NMD targets. This suggests that SZRD1 is required to maintain normal UPF3B levels and indicates that the effect of SZRD1 on NMD targets is not limited to a relief from translational inhibition. Overall, our study reveals that human SUZ domain-containing proteins play a complex role in regulating protein output from transcripts targeted by NMD.

PARP14/BAL2 is a large multidomain enzyme involved in signaling pathways with relevance to cancer, inflammation, and infection. Inhibition of its mono-ADP-ribosylating PARP homology domain and its three ADP-ribosyl binding macro domains has been regarded as a potential means of therapeutic intervention. Macrodomains-2 and -3 are known to stably bind to ADP-ribosylated target proteins, but the function of macrodomain-1 has remained somewhat elusive. Here, we used biochemical assays of ADP-ribosylation levels to characterize PARP14 macrodomain-1 and the homologous macrodomain-1 of PARP9. Our results show that both macrodomains display an ADP-ribosyl glycohydrolase activity that is not directed toward specific protein side chains. PARP14 macrodomain-1 is unable to degrade poly(ADP-ribose), the enzymatic product of PARP1. The F926A mutation of PARP14 and the F244A mutation of PARP9 strongly reduced ADP-ribosyl glycohydrolase activity of the respective macrodomains, suggesting mechanistic homology to the Mac1 domain of the SARS-CoV-2 Nsp3 protein. This study adds two new enzymes to the previously known six human ADP-ribosyl glycohydrolases. Our results have key implications for how PARP14 and PARP9 will be studied and how their functions will be understood.