Proteins-Structure Function and Bioinformatics最新文献

{"title":"Insight into the autoproteolysis mechanism of the RsgI9 anti‐σ factor from Clostridium thermocellum","authors":"Allen Takayesu, Brendan J. Mahoney, Andrew K. Goring, Tobie Jessup, Rachel R. Ogorzalek Loo, Joseph A. Loo, Robert T. Clubb","doi":"10.1002/prot.26690","DOIUrl":"https://doi.org/10.1002/prot.26690","url":null,"abstract":"<jats:italic>Clostridium thermocellum</jats:italic> is a potential microbial platform to convert abundant plant biomass to biofuels and other renewable chemicals. It efficiently degrades lignocellulosic biomass using a surface displayed cellulosome, a megadalton sized multienzyme containing complex. The enzymatic composition and architecture of the cellulosome is controlled by several transmembrane biomass‐sensing RsgI‐type anti‐σ factors. Recent studies suggest that these factors transduce signals from the cell surface via a conserved RsgI extracellular (CRE) domain (also called a periplasmic domain) that undergoes autoproteolysis through an incompletely understood mechanism. Here we report the structure of the autoproteolyzed CRE domain from the <jats:italic>C. thermocellum</jats:italic> RsgI9 anti‐σ factor, revealing that the cleaved fragments forming this domain associate to form a stable α/β/α sandwich fold. Based on AlphaFold2 modeling, molecular dynamics simulations, and tandem mass spectrometry, we propose that a conserved Asn‐Pro bond in RsgI9 autoproteolyzes via a succinimide intermediate whose formation is promoted by a conserved hydrogen bond network holding the scissile peptide bond in a strained conformation. As other RsgI anti‐σ factors share sequence homology to RsgI9, they likely autoproteolyze through a similar mechanism.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adhesin domains responsible for binding bacteria to surfaces they colonize project outwards from companion split domains","authors":"Laurie A. Graham, Thomas Hansen, Yanzhi Yang, Mustafa Sherik, Qilu Ye, Blake P. Soares, Brett Kinrade, Shuaiqi Guo, Peter L. Davies","doi":"10.1002/prot.26689","DOIUrl":"https://doi.org/10.1002/prot.26689","url":null,"abstract":"Bacterial adhesins attach their hosts to surfaces that the bacteria will colonize. This surface adhesion occurs through specific ligand‐binding domains located towards the distal end of the long adhesin molecules. However, recognizing which of the many adhesin domains are structural and which are ligand binding has been difficult up to now. Here we have used the protein structure modeling program AlphaFold2 to predict structures for these giant 0.2‐ to 1.5‐megadalton proteins. Crystal structures previously solved for several adhesin regions are in good agreement with the models. Whereas most adhesin domains are linked in a linear fashion through their N‐ and C‐terminal ends, ligand‐binding domains can be recognized by budding out from a companion core domain so that their ligand‐binding sites are projected away from the axis of the adhesin for maximal exposure to their targets. These companion domains are “split” in their continuity by projecting the ligand‐binding domain outwards. The “split domains” are mostly β‐sandwich extender modules, but other domains like a β‐solenoid can serve the same function. Bioinformatic analyses of Gram‐negative bacterial sequences revealed wide variety ligand‐binding domains are used in their Repeats‐in‐Toxin adhesins. The ligands for many of these domains have yet to be identified but known ligands include various cell‐surface glycans, proteins, and even ice. Recognizing the ligands to which the adhesins bind could lead to ways of blocking colonization by bacterial pathogens. Engineering different ligand‐binding domains into an adhesin has the potential to change the surfaces to which bacteria bind.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Allosteric modulation of fluorescence revealed by hydrogen bond dynamics in a genetically encoded maltose biosensor","authors":"Melike Berksoz, Canan Atilgan","doi":"10.1002/prot.26688","DOIUrl":"https://doi.org/10.1002/prot.26688","url":null,"abstract":"Genetically encoded fluorescent biosensors (GEFBs) proved to be reliable tracers for many metabolites and cellular processes. In the simplest case, a fluorescent protein (FP) is genetically fused to a sensing protein which undergoes a conformational change upon ligand binding. This drives a rearrangement in the chromophore environment and changes the spectral properties of the FP. Structural determinants of successful biosensors are revealed only in hindsight when the crystal structures of both ligand‐bound and ligand‐free forms are available. This makes the development of new biosensors for desired analytes a long trial‐and‐error process. In the current study, we conducted μs‐long all atom molecular dynamics (MD) simulations of a maltose biosensor in both the <jats:italic>apo</jats:italic> (dark) and <jats:italic>holo</jats:italic> (bright) forms. We performed detailed hydrogen bond occupancy analyses to shed light on the mechanism of ligand induced conformational change in the sensor protein and its allosteric effect on the chromophore environment. We find that two strong indicators for distinguishing bright and dark states of biosensors are due to substantial changes in hydrogen bond dynamics in the system and solvent accessibility of the chromophore.","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Location of S-nitrosylated cysteines in protein three-dimensional structures.","authors":"Oliviero Carugo","doi":"10.1002/prot.26629","DOIUrl":"10.1002/prot.26629","url":null,"abstract":"<p><p>Although S-nitrosylation of cysteines is a common protein posttranslational modification, little is known about its three-dimensional structural features. This paper describes a systematic survey of the data available in the Protein Data Bank. Several interesting observations could be made. (1) As a result of radiation damage, S-nitrosylated cysteines (Snc) are frequently reduced, at least partially. (2) S-nitrosylation may be a protection against irreversible thiol oxidation; because the NO group of Snc is relatively accessible to the solvent, it may act as a cork to protect the sulfur atoms of cysteines from oxidation by molecular oxygen to sulfenic, sulfinic, and sulfonic acid; moreover, Snc are frequently found at the start or end of helices and strands and this might shield secondary structural elements from unfolding.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71523523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biotin protein ligase as you like it: Either extraordinarily specific or promiscuous protein biotinylation.","authors":"John E Cronan","doi":"10.1002/prot.26642","DOIUrl":"10.1002/prot.26642","url":null,"abstract":"<p><p>Biotin (vitamin H or B7) is a coenzyme essential for all forms of life. Biotin has biological activity only when covalently attached to a few key metabolic enzyme proteins. Most organisms have only one attachment enzyme, biotin protein ligase (BPL), which attaches biotin to all target proteins. The sequences of these proteins and their substrate proteins are strongly conserved throughout biology. Structures of both the biotin ligase- and biotin-acceptor domains of mammals, plants, several bacterial species, and archaea have been determined. These, together with mutational analyses of ligases and their protein substrates, illustrate the exceptional specificity of this protein modification. For example, the Escherichia coli BPL biotinylates only one of the >4000 cellular proteins. Several bifunctional bacterial biotin ligases transcriptionally regulate biotin synthesis and/or transport in concert with biotinylation. The human BPL has been demonstrated to play an important role in that mutations in the BPL encoding gene cause one form of the disease, biotin-responsive multiple carboxylase deficiency. Promiscuous mutant versions of several BPL enzymes release biotinoyl-AMP, the active intermediate of the ligase reaction, to solvent. The released biotinoyl-AMP acts as a chemical biotinylation reagent that modifies lysine residues of neighboring proteins in vivo. This proximity-dependent biotinylation (called BioID) approach has been heavily utilized in cell biology.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10932917/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138300797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interfacial residues in protein-protein complexes are in the eyes of the beholder.","authors":"Jayadevan Parvathy, Arangasamy Yazhini, Narayanaswamy Srinivasan, Ramanathan Sowdhamini","doi":"10.1002/prot.26628","DOIUrl":"10.1002/prot.26628","url":null,"abstract":"<p><p>Interactions between proteins are vital in almost all biological processes. The characterization of protein-protein interactions helps us understand the mechanistic basis of biological processes, thereby enabling the manipulation of proteins for biotechnological and clinical purposes. The interface residues of a protein-protein complex are assumed to have the following two properties: (a) they always interact with a residue of a partner protein, which forms the basis for distance-based interface residue identification methods, and (b) they are solvent-exposed in the isolated form of the protein and become buried in the complex form, which forms the basis for Accessible Surface Area (ASA)-based methods. The study interrogates this popular assumption by recognizing interface residues in protein-protein complexes through these two methods. The results show that a few residues are identified uniquely by each method, and the extent of conservation, propensities, and their contribution to the stability of protein-protein interaction varies substantially between these residues. The case study analyses showed that interface residues, unique to distance, participate in crucial interactions that hold the proteins together, whereas the interface residues unique to the ASA method have a potential role in the recognition, dynamics, and specificity of the complex and can also be a hotspot. Overall, the study recommends applying both distance and ASA methods so that some interface residues missed by either method but crucial to the stability, recognition, dynamics, and function of protein-protein complexes are identified in a complementary manner.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set.","authors":"Burcu Ozden, Eda Şamiloğlu, Atakan Özsan, Mehmet Erguven, Can Yükrük, Mehdi Koşaca, Melis Oktayoğlu, Muratcan Menteş, Nazmiye Arslan, Gökhan Karakülah, Ayşe Berçin Barlas, Büşra Savaş, Ezgi Karaca","doi":"10.1002/prot.26645","DOIUrl":"10.1002/prot.26645","url":null,"abstract":"<p><p>Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). When we simplified the prediction problem to a binary classification, that is, whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with a 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138292541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of interactions between cell surface proteins by machine learning.","authors":"Zhaoqian Su, Brian Griffin, Scott Emmons, Yinghao Wu","doi":"10.1002/prot.26648","DOIUrl":"10.1002/prot.26648","url":null,"abstract":"<p><p>Cells detect changes in their external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and, thus, challenging to detect using traditional experimental techniques. Here, we tackle this challenge using a computational framework. The primary focus of the framework is to develop new tools to identify interactions between domains in the immunoglobulin (Ig) fold, which is the most abundant domain family in cell surface proteins. These interactions could be formed between ligands and receptors from different cells or between proteins on the same cell surface. In practice, we collected all structural data on Ig domain interactions and transformed them into an interface fragment pair library. A high-dimensional profile can then be constructed from the library for a given pair of query protein sequences. Multiple machine learning models were used to read this profile so that the probability of interaction between the query proteins could be predicted. We tested our models on an experimentally derived dataset that contains 564 cell surface proteins in humans. The cross-validation results show that we can achieve higher than 70% accuracy in identifying the PPIs within this dataset. We then applied this method to a group of 46 cell surface proteins in Caenorhabditis elegans. We screened every possible interaction between these proteins. Many interactions recognized by our machine learning classifiers have been experimentally confirmed in the literature. In conclusion, our computational platform serves as a useful tool to help identify potential new interactions between cell surface proteins in addition to current state-of-the-art experimental techniques. The tool is freely accessible for use by the scientific community. Moreover, the general framework of the machine learning classification can also be extended to study the interactions of proteins in other domain superfamilies.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138483510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural implications of amyloidogenic rare variants Ser282Leu and Gln356Arg identified in h-BRCA1.","authors":"Neha Mishra, Suchita Dubey, Anchala Kumari, Mudassar Ali Khan, Ekaterina S Kuligina, Elena V Preobrazhenskaya, Alexandr A Romanko, Lumbini R Yadav, Rajiv Sarin, Evgeny N Imyanitov, Ashok K Varma","doi":"10.1002/prot.26638","DOIUrl":"10.1002/prot.26638","url":null,"abstract":"<p><p>Preliminary studies have shown BRCA1 (170-1600) residues to be intrinsically disordered with unknown structural details. However, thousands of clinically reported variants have been identified in this central region of BRCA1. Therefore, we aimed to characterize h-BRCA1(260-553) to assess the structural basis for pathogenicity of two rare missense variants Ser282Leu, Gln356Arg identified from the Indian and Russian populations respectively. Small-angle X-ray scattering analysis revealed WT scores R_g -32 Å, D_max -93 Å, and Rflex-51% which are partially disordered, whereas Ser282Leu variant displayed a higher degree of disorderedness and Gln356Arg was observed to be aggregated. WT protein also possesses an inherent propensity to undergo a disorder-to-order transition in the presence of cruciform DNA and 2,2,2-Trifluoroethanol (TFE). An increased alpha-helical pattern was observed with increasing concentration of TFE for the Gln356Arg mutant whereas Ser282Leu mutant showed significant differences only at the highest TFE concentration. Furthermore, higher thermal shift was observed for WT-DNA complex compared to the Gln356Arg and Ser282Leu protein-DNA complex. Moreover, mature amyloid-like fibrils were observed with 30 μM thioflavin T (ThT) at 37°C for Ser282Leu and Gln356Arg proteins while the WT protein exists in a protofibril state as observed by TEM. Gln356Arg formed higher-order aggregates with amyloidogenesis over time as monitored by ThT fluorescence. In addition, computational analyses confirmed larger conformational fluctuations for Ser282Leu and Gln356Arg mutants than for the WT. The global structural alterations caused by these variants provide a mechanistic approach for further classification of the variants of uncertain clinical significance in BRCA1 into amyloidogenic variants which may have a significant role in disease pathogenesis.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138464678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Community analysis of large-scale molecular dynamics simulations elucidated dynamics-driven allostery in tyrosine kinase 2.","authors":"Nastazia Lesgidou, Metaxia Vlassi","doi":"10.1002/prot.26631","DOIUrl":"10.1002/prot.26631","url":null,"abstract":"<p><p>TYK2 is a nonreceptor tyrosine kinase, member of the Janus kinases (JAK), with a central role in several diseases, including cancer. The JAKs' catalytic domains (KD) are highly conserved, yet the isolated TYK2-KD exhibits unique specificities. In a previous work, using molecular dynamics (MD) simulations of a catalytically impaired TYK2-KD variant (P1104A) we found that this amino acid change of its JAK-characteristic insert (αFG), acts at the dynamics level. Given that structural dynamics is key to the allosteric activation of protein kinases, in this study we applied a long-scale MD simulation and investigated an active TYK2-KD form in the presence of adenosine 5'-triphosphate and one magnesium ion that represents a dynamic and crucial step of the catalytic cycle, in other protein kinases. Community analysis of the MD trajectory shed light, for the first time, on the dynamic profile and dynamics-driven allosteric communications within the TYK2-KD during activation and revealed that αFG and amino acids P1104, P1105, and I1112 in particular, hold a pivotal role and act synergistically with a dynamically coupled communication network of amino acids serving intra-KD signaling for allosteric regulation of TYK2 activity. Corroborating our findings, most of the identified amino acids are associated with cancer-related missense/splice-site mutations of the Tyk2 gene. We propose that the conformational dynamics at this step of the catalytic cycle, coordinated by αFG, underlie TYK2-unique substrate recognition and account for its distinct specificity. In total, this work adds to knowledge towards an in-depth understanding of TYK2 activation and may be valuable towards a rational design of allosteric TYK2-specific inhibitors.</p>","PeriodicalId":56271,"journal":{"name":"Proteins-Structure Function and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72212081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}