Protein Science最新文献

Human P-glycoprotein (P-gp) utilizes energy from ATP hydrolysis for the efflux of chemically dissimilar amphipathic small molecules and plays an important role in the development of resistance to chemotherapeutic agents in most cancers. Efforts to overcome drug resistance have focused on inhibiting P-gp-mediated drug efflux. Understanding the features distinguishing P-gp inhibitors from substrates is critical. Cryo-electron microscopy has revealed distinct binding patterns, emphasizing the role of the L-site or access tunnel in inhibition. We substituted 5-9 residues of the L-site with alanine to investigate whether the binding of a second inhibitor molecule to the L-site is required for inhibiting drug efflux. We reveal, for the first time, that mutations in the L-site affect the drug efflux activity of P-gp, despite their distance from the substrate-binding pocket (SBP). Surprisingly, after the mutations were introduced, inhibitors such as tariquidar and zosuquidar still inhibited drug efflux by mutant P-gps. Communication between the transmembrane helices (TMHs) and nucleotide-binding domains (NBDs) was evaluated using the ATPase assay, revealing distinct modulation patterns by inhibitors for the mutants, with zosuquidar exhibiting substrate-like stimulation of ATPase. Furthermore, L-site mutations abolished ATP-dependent thermal stabilization. In silico molecular docking studies corroborated the altered inhibitor binding due to mutations in the L-site residues, shedding light on their critical role in substrate transport and inhibitor interactions with P-gp. These findings suggest that inhibitors bind either to the SBP alone, and/or to alternate site(s) when the L-site is disabled by mutagenesis.

Proteins, fundamental to cellular activities, reveal their function and evolution through their structure and sequence. CATH functional families (FunFams) are coherent clusters of protein domain sequences in which the function is conserved across their members. The increasing volume and complexity of protein data enabled by large-scale repositories like MGnify or AlphaFold Database requires more powerful approaches that can scale to the size of these new resources. In this work, we introduce MARC and FRAN, two algorithms developed to build upon and address limitations of GeMMA/FunFHMMER, our original methods developed to classify proteins with related functions using a hierarchical approach. We also present CATH-eMMA, which uses embeddings or Foldseek distances to form relationship trees from distance matrices, reducing computational demands and handling various data types effectively. CATH-eMMA offers a highly robust and much faster tool for clustering protein functions on a large scale, providing a new tool for future studies in protein function and evolution.

Zika virus (ZIKV), a mosquito-borne Flavivirus of international concern, causes congenital microcephaly in newborns and Guillain-Barré syndrome in adults. ZIKV capsid (C) protein, one of three key structural proteins, is essential for viral assembly and encapsidation. In dengue virus, a closely related flavivirus, the homologous C protein interacts with host lipid systems, namely intracellular lipid droplets, for successful viral replication. Here, we investigate ZIKV C interaction with host lipid systems, showing that it binds host lipid droplets but, contrary to expected, in an unspecific manner. Contrasting with other flaviviruses, ZIKV C also does not bind very-low density-lipoproteins. Comparing with other Flavivirus, capsid proteins show that ZIKV C structure is particularly thermostable and seems to be locked into an auto-inhibitory conformation due to a disordered N-terminal, hence blocking specific interactions and supporting the experimental differences observed. Such distinct structural features must be considered when targeting capsid proteins in drug development.

Understanding protein function often necessitates characterizing the flexibility of protein structures. However, simulating protein flexibility poses significant challenges due to the complex dynamics of protein systems, requiring extensive computational resources and accurate modeling techniques. In response to these challenges, the CABS-flex method has been developed as an efficient modeling tool that combines coarse-grained simulations with all-atom detail. Available both as a web server and a standalone package, CABS-flex is dedicated to a wide range of users. The web server version offers an accessible interface for straightforward tasks, while the standalone command-line program is designed for advanced users, providing additional features, analytical tools, and support for handling large systems. This paper examines the application of CABS-flex across various structure-function studies, facilitating investigations into the interplay among protein structure, dynamics, and function in diverse research fields. We present an overview of the current status of the CABS-flex methodology, highlighting its recent advancements, practical applications, and forthcoming challenges.

Cyanobacteriochromes (CBCRs) are unique cyanobacteria-specific photoreceptors that share a distant relation with phytochromes. Most CBCRs contain conserved cysteine residues known as canonical Cys, while some CBCRs have additional cysteine residues called second Cys within the DXCF motif, leading to their classification as DXCF CBCRs. They typically undergo a process where they incorporate phycocyanobilin (PCB) and subsequently isomerize it to phycoviolobilin (PVB). Conversely, CBCRs with conserved Trp residues and without the second Cys are called extended red/green (XRG) CBCRs. Typical XRG CBCRs bind PCB without undergoing PCB-to-PVB isomerization, displaying red/green reversible photoconversion, and there are also atypical CBCRs that exhibit diverse photoconversions. We discovered novel XRG CBCRs with Cys residue instead of the conserved Trp residue. These novel XRG CBCRs exhibited the ability to isomerize PCB to PVB, displaying green/teal reversible photoconversion. Through sequence- and structure-based comparisons coupled with mutagenesis experiments, we identified three amino acid residues, including the Cys residue, crucial for facilitating PCB-to-PVB isomerization. This research expands our understanding of the diversity of XRG CBCRs, highlighting the remarkable molecular plasticity of CBCRs.

Affinity precipitation is an attractive method for protein purification due to its many advantages, including the rapid capture of target proteins, simple processing, high specificity, and ease of scale-up. We previously reported a robust antibody purification method using Ca²⁺-dependent precipitation of ZZ-hCSQ2, a fusion protein of human calsequestrin 2, and the antibody-binding protein ZZ. However, the stability of this fusion protein was not sufficiently high for industrial use because the antibody recovery yield decreased to 60% after being reused 10 times. To identify a more stable calsequestrin (CSQ), we calculated Rosetta energy values for the folding stabilities of various CSQ homologs and selected human CSQ1 (hCSQ1) with lowest energy value (-992.6) as the new CSQ platform. We also identified that the linker sequence between ZZ and CSQ was vulnerable to proteases and alkaline pH by N-terminal protein sequencing. Therefore, we changed the linker to four asparagine (4N) sequences, which were shorter and less flexible than the previous glycine-rich linker. The new version of ZZ-CSQ, ZZ-4N-hCSQ1, was stable in a protease-containing conditioned medium obtained from the cultured Chinese hamster ovary cell or high pH condition (0.1M sodium hydroxide) for more than 5 days and could be reused at least 25 times for antibody purification without loss of recovery yield. The antibodies purified by ZZ-4N-hCSQ1 precipitation also showed greater purity (~33.6-fold lower host cell DNA and ~6.4-fold lower host cell protein) than those purified by protein A chromatography. These data suggest that ZZ-4N-hCSQ1 precipitation is more efficient and can achieve cost-effectiveness of up to 12.5-fold cheaper than previous antibody purification methods and can lower the production costs of therapeutic antibodies.

The Duffy-binding protein (DBP) is a promising antigen for a malaria vaccine that would protect against clinical symptoms caused by Plasmodium vivax infection. Region II of DBP (DBP-II) contains the receptor-binding domain that engages host red blood cells, but DBP-II vaccines elicit many non-neutralizing antibodies that bind distal to the receptor-binding surface. Here, we engineered a truncated DBP-II immunogen that focuses the immune response to the receptor-binding surface. This immunogen contains the receptor-binding subdomain S1S2 and lacks the immunodominant subdomain S3. Structure-based computational design of S1S2 identified combinatorial amino acid changes that stabilized the isolated S1S2 without perturbing neutralizing epitopes. This immunogen elicited DBP-II-specific antibodies in immunized mice that were significantly enriched for blocking activity compared to the native DBP-II antigen. This generalizable design process successfully stabilized an integral core fragment of a protein and focused the immune response to desired epitopes to create a promising new antigen for malaria vaccine development.

Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing distinct NMR signals in different chemical environments. Apprehending chemical shifts from NMR signals can be challenging since having an NMR structure does not necessarily provide all the required chemical shift information, making predictive models essential for accurately deducing chemical shifts, either from protein structures or, more ideally, directly from amino acid sequences. Here, we present EFG-CS, a web server that specializes in chemical shift prediction. EFG-CS employs a machine learning-based transfer prediction model for backbone atom chemical shift prediction, using ESMFold-predicted protein structures. Additionally, ESG-CS incorporates a graph neural network-based model to provide comprehensive side-chain atom chemical shift predictions. Our method demonstrated reliable performance in backbone atom prediction, achieving comparable accuracy levels with root mean square errors (RMSE) of 0.30 ppm for H, 0.22 ppm for Hα, 0.89 ppm for C, 0.89 ppm for Cα, 0.84 ppm for Cβ, and 1.69 ppm for N. Moreover, our approach also showed predictive capabilities in side-chain atom chemical shift prediction achieving RMSE values of 0.71 ppm for Hβ, 0.74-1.15 ppm for Hδ, and 0.58-0.94 ppm for Hγ, solely utilizing amino acid sequences without homology or feature curation. This work shows for the first time that generative AI protein models can predict NMR shifts nearly comparable to experimental models. This web server is freely available at https://biosig.lab.uq.edu.au/efg_cs, and the chemical shift prediction results can be downloaded in tabular format and visualized in 3D format.

Mitochondrial magnesium (Mg²⁺) is a crucial modulator of protein stability, enzymatic activity, ATP synthesis, and cell death. Mitochondrial RNA splicing protein 2 (MRS2) is the main Mg²⁺ channel in the inner mitochondrial membrane that mediates influx into the matrix. Recent cryo-electron microscopy (cryo-EM) human MRS2 structures exhibit minimal conformational changes at high and low Mg²⁺, yet the regulation of human MRS2 and orthologues by Mg²⁺ binding to analogous matrix domains has been well established. Further, a missense variation at D216 has been identified associated with malignant melanoma and MRS2 expression and activity is implicated in gastric cancer. Thus, to gain more mechanistic and functional insight into Mg²⁺ sensing by the human MRS2 matrix domain and the association with proliferative disease, we assessed the structural, biophysical, and functional effects of a D216Q mutant. We show that the D216Q mutation is sufficient to abrogate Mg²⁺-binding and associated conformational changes including increased α-helicity, stability, and monomerization. Further, we reveal that the MRS2 matrix domains interact with ~μM affinity, which is weakened by up to two orders of magnitude in the presence of Mg²⁺ for wild-type but unaffected for D216Q. Finally, we demonstrate the importance of Mg²⁺ sensing by MRS2 to prevent matrix Mg²⁺ overload as HeLa cells overexpressing MRS2 show enhanced Mg²⁺ uptake, cell migration, and resistance to apoptosis while MRS2 D216Q robustly potentiates these cancer phenotypes. Collectively, our findings further define the MRS2 matrix domain as a critical Mg²⁺ sensor that undergoes conformational and assembly changes upon Mg²⁺ interactions dependent on D216 to temper matrix Mg²⁺ overload.

The missense tolerance ratio (MTR) was developed as a novel approach to assess the deleteriousness of variants. Its three-dimensional successor, MTR3D, was demonstrated powerful at discriminating pathogenic from benign variants. However, its reliance on experimental structures and homologs limited its coverage of the proteome. We have now utilized AlphaFold2 models to develop MTR3D-AF2, which covers 89.31% of proteins and 85.39% of residues across the human proteome. This work has improved MTR3D's ability to distinguish clinically established pathogenic from benign variants. MTR3D-AF2 is freely available as an interactive web server at https://biosig.lab.uq.edu.au/mtr3daf2/.