Unbiased interrogation of functional lysine residues in human proteome.

CRISPR screens have empowered the high-throughput dissection of gene functions; however, more explicit genetic elements, such as codons of amino acids, require thorough interrogation. Here, we establish a CRISPR strategy for unbiasedly probing functional amino acid residues at the genome scale. By coupling adenine base editors and barcoded sgRNAs, we target 215,689 out of 611,267 (35%) lysine codons, involving 85% of the total protein-coding genes. We identify 1,572 lysine codons whose mutations perturb human cell fitness, with many of them implicated in cancer. These codons are then mirrored to gene knockout screen data to provide functional insights into the role of lysine residues in cellular fitness. Mining these data, we uncover a CUL3-centric regulatory network in which lysine residues of CUL3 CRL complex proteins control cell fitness by specifying protein-protein interactions. Our study offers a general strategy for interrogating genetic elements and provides functional insights into the human proteome.

Type III-A CRISPR-Cas Csm Complexes: Assembly, Periodic RNA Cleavage, DNase Activity Regulation, and Autoimmunity.

Type ΙΙΙ CRISPR-Cas systems provide robust immunity against foreign RNA and DNA by sequence-specific RNase and target RNA-activated sequence-nonspecific DNase and RNase activities. We report on cryo-EM structures of Thermococcus onnurineus CsmcrRNA binary, CsmcrRNA-target RNA and CsmcrRNA-target RNAanti-tag ternary complexes in the 3.1 Å range. The topological features of the crRNA 5'-repeat tag explains the 5'-ruler mechanism for defining target cleavage sites, with accessibility of positions -2 to -5 within the 5'-repeat serving as sensors for avoidance of autoimmunity. The Csm3 thumb elements introduce periodic kinks in the crRNA-target RNA duplex, facilitating cleavage of the target RNA with 6-nt periodicity. Key Glu residues within a Csm1 loop segment of CsmcrRNA adopt a proposed autoinhibitory conformation suggestive of DNase activity regulation. These structural findings, complemented by mutational studies of key intermolecular contacts, provide insights into CsmcrRNA complex assembly, mechanisms underlying RNA targeting and site-specific periodic cleavage, regulation of DNase cleavage activity, and autoimmunity suppression.

Functionality of IAV packaging signals depends on site-specific charges within the viral nucleoprotein.

The coordinated packaging of the segmented genome of the influenza A virus (IAV) into virions is an essential step of the viral life cycle. This process is controlled by the interaction of packaging signals present in all eight viral RNA (vRNA) segments and the viral nucleoprotein (NP), which binds vRNA via a positively charged binding groove. However, mechanistic models of how the packaging signals and NP work together to coordinate genome packaging are missing. Here, we studied genome packaging in influenza A/SC35M virus mutants that carry mutated packaging signals as well as specific amino acid substitutions at the highly conserved lysine (K) residues 184 and 229 in the RNA-binding groove of NP. Because these lysines are acetylated and thus neutrally charged in infected host cells, we replaced them with glutamine to mimic the acetylated, neutrally charged state or arginine to mimic the non-acetylated, positively charged state. Our analysis shows that the coordinated packaging of eight vRNAs is influenced by (i) the charge state of the replacing amino acid and (ii) its location within the RNA-binding groove. Accordingly, we propose that lysine acetylation induces different charge states within the RNA-binding groove of NP, thereby supporting the activity of specific packaging signals during coordinated genome packaging. IMPORTANCE: Influenza A viruses (IAVs) have a segmented viral RNA (vRNA) genome encapsidated by multiple copies of the viral nucleoprotein (NP) and organized into eight distinct viral ribonucleoprotein complexes. Although genome segmentation contributes significantly to viral evolution and adaptation, it requires a highly sophisticated genome-packaging mechanism. How eight distinct genome complexes are incorporated into the virion is poorly understood, but previous research suggests an essential role for both vRNA packaging signals and highly conserved NP amino acids. By demonstrating that the packaging process is controlled by charge-dependent interactions of highly conserved lysine residues in NP and vRNA packaging signals, our study provides new insights into the sophisticated packaging mechanism of IAVs.

Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile.

This paper describes a method Pprint2, which is an improved version of Pprint developed for predicting RNA-interacting residues in a protein. Training and independent/validation datasets used in this study comprises of 545 and 161 non-redundant RNA-binding proteins, respectively. All models were trained on training dataset and evaluated on the validation dataset. The preliminary analysis reveals that positively charged amino acids such as H, R and K, are more prominent in the RNA-interacting residues. Initially, machine learning based models have been developed using binary profile and obtain maximum area under curve (AUC) 0.68 on validation dataset. The performance of this model improved significantly from AUC 0.68 to 0.76, when evolutionary profile is used instead of binary profile. The performance of our evolutionary profile-based model improved further from AUC 0.76 to 0.82, when convolutional neural network has been used for developing model. Our final model based on convolutional neural network using evolutionary information achieved AUC 0.82 with Matthews correlation coefficient of 0.49 on the validation dataset. Our best model outperforms existing methods when evaluated on the independent/validation dataset. A user-friendly standalone software and web-based server named 'Pprint2' has been developed for predicting RNA-interacting residues (https://webs.iiitd.edu.in/raghava/pprint2 and https://github.com/raghavagps/pprint2).

DP-site: A dual deep learning-based method for protein-peptide interaction site prediction.

BACKGROUND: Protein-peptide interaction prediction is an important topic for several applications including various biological processes, understanding drug discovery, protein function abnormal cellular behaviors, and treating diseases. Over the years, studies have shown that experimental methods have improved the identification of this bio-molecular interaction. However, predicting protein-peptide interactions using these methods is laborious, time-consuming, dependent on third-party tools, and costly. METHOD: To address these previous drawbacks, this study introduces a computational framework called DP-Site. The proposed framework concentrates on using a compound of a dual pipeline along with a combination predictor. A deep convolutional neural network for feature extraction and classification is embedded in pipeline 1. In addition, pipeline 2 includes a deep long-short-term memory-based and a random forest classifier for feature extraction and classification. In this investigation, the evolutionary, structure-based, sequence-based, and physicochemical information of proteins is utilized for identifying protein-peptide interaction at the residue level. RESULTS: The proposed method is evaluated on both the ten-fold cross-validation and independent test sets. The robust and consistent results between cross-validation and independent test sets confirm the ability of the proposed method to predict peptide binding residues in proteins. Moreover, experimental findings demonstrate that DP-Site has significantly outperformed other state-of-the-art sequence-based and structure-based methods. The proposed method achieves a remarkable balance between a specificity of 0.799 and a sensitivity of 0.770, along with the best f-measure of 0.661 and the highest precision of 0.580 using an independent test set. CONCLUSIONS: The outcome of various experiments confirms the proficiency of the proposed method and outperforms state-of-the-art sequence-based and structure-based methods in terms of the mentioned criteria. DP-Site can be accessed at https://github.com/shafiee 95/shima.shafiee.DP-Site.

Dating the emergence of dairying by the first farmers of Central Europe using 14C analysis of fatty acids preserved in pottery vessels.

Direct, accurate, and precise dating of archaeological pottery vessels is now achievable using a recently developed approach based on the radiocarbon dating of purified molecular components of food residues preserved in the walls of pottery vessels. The method targets fatty acids from animal fat residues, making it uniquely suited for directly dating the inception of new food commodities in prehistoric populations. Here, we report a large-scale application of the method by directly dating the introduction of dairying into Central Europe by the Linearbandkeramik (LBK) cultural group based on dairy fat residues. The radiocarbon dates (n = 27) from the 54th century BC from the western and eastern expansion of the LBK suggest dairy exploitation arrived with the first settlers in the respective regions and were not gradually adopted later. This is particularly significant, as contemporaneous LBK sites showed an uneven distribution of dairy exploitation. Significantly, our findings demonstrate the power of directly dating the introduction of new food commodities, hence removing taphonomic uncertainties when assessing this indirectly based on associated cultural materials or other remains.

Structure of PLA2R reveals presentation of the dominant membranous nephropathy epitope and an immunogenic patch.

Membranous nephropathy is an autoimmune kidney disease caused by autoantibodies targeting antigens present on glomerular podocytes, instigating a cascade leading to glomerular injury. The most prevalent circulating autoantibodies in membranous nephropathy are against phospholipase A2 receptor (PLA2R), a cell surface receptor. The dominant epitope in PLA2R is located within the cysteine-rich domain, yet high-resolution structure-based mapping is lacking. In this study, we define the key nonredundant amino acids in the dominant epitope of PLA2R involved in autoantibody binding. We further describe two essential regions within the dominant epitope and spacer requirements for a synthetic peptide of the epitope for drug discovery. In addition, using cryo-electron microscopy, we have determined the high-resolution structure of PLA2R to 3.4 Å resolution, which shows that the dominant epitope and key residues within the cysteine-rich domain are accessible at the cell surface. In addition, the structure of PLA2R not only suggests a different orientation of domains but also implicates a unique immunogenic signature in PLA2R responsible for inducing autoantibody formation and recognition.

Relationship between thermal stability of collagens and the fraction of hydrophobic residues in their molecules.

In this study, a database of the thermal stability of collagens and their synthetic analogues has been compiled taking into account literature sources. In total, our database includes 1200 records. As a result of a comparative theoretical analysis of the collected experimental data, the relationship between the melting temperature (Tm) or denaturation temperature (Td) of collagens and the fraction of hydrophobic residues (f) in their molecules has been established. It is shown that this relationship is linear: the larger the f value, the higher the denaturation or melting temperature of a given collagen.

Interfacial residues in protein-protein complexes are in the eyes of the beholder.

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.

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds.

The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.

Substrate selectivity and unique sequence signatures in SWEET/semiSWEET homologs of four taxonomic groups: Sequence analysis and phylogenetic studies.

The recently discovered SWEET (Sugar Will Eventually be Exported Transporter) proteins are involved in the selective transport of monosaccharides and disaccharides. The prokaryotic counterparts, semiSWEETs, form dimers with each monomer forming a triple-helix transmembrane bundle (THB). The longer eukaryotic SWEETs have seven transmembrane helices with two THBs and a linker helix. Structures of semiSWEETs/SWEETs have been determined experimentally. Experimental studies revealed the role of plant SWEETs in vital physiological processes and identified residues responsible for substrate selectivity. However, SWEETs/semiSWEETs from metazoans and bacteria are not characterized. In this study, we used structure-based sequence alignment and compared more than 2000 SWEET/semiSWEETs from four different taxonomic groups. Conservation of residue/chemical property was examined at all positions. Properties of clades/subclades of phylogenetic trees from each taxonomic group were analyzed. Conservation pattern of known residues in the selectivity-filter was used to predict the substrate preference of plant SWEETs and some clusters of metazoans and bacteria. Some residues at the gating and substrate-binding regions, pore-facing positions and at the helix-helix interface are conserved across all taxonomic groups. Conservation of polar/charged residues at specific pore-facing positions, helix-helix interface and in loops seems to be unique for plant SWEETs. Overall, the number of conserved residues is less in metazoan SWEETs. Plant and metazoan SWEETs exhibit high conservation of four and three proline residues respectively in "proline tetrad." Further experimental studies can validate the predicted substrate selectivity and significance of conserved polar/charged/aromatic residues at structurally and functionally important positions of SWEETs/semiSWEETs in plants, metazoans and bacteria.

Identification of heparin-binding amino acid residues in antibody HS4C3 with the potential to design antibodies against heparan sulfate domains.

Heparan sulfate (HS) is a linear polysaccharide with high structural and functional diversity. Detection and localization of HS in tissues can be performed using single chain variable fragment (scFv) antibodies. Although several anti-HS antibodies recognizing different sulfation motifs have been identified, little is known about their interaction with HS. In this study the interaction between the scFv antibody HS4C3 and heparin was investigated. Heparin-binding lysine and arginine residues were identified using a protect and label methodology. Site-directed mutagenesis was applied to further identify critical heparin-binding lysine/arginine residues using immunohistochemical and biochemical assays. In addition, computational docking of a heparin tetrasaccharide towards a 3-D homology model of HS4C3 was applied to identify potential heparin-binding sites. Of the 12 lysine and 15 arginine residues within the HS4C3 antibody, 6 and 9, respectively, were identified as heparin-binding. Most of these residues are located within one of the complementarity determining regions (CDR) or in their proximity. All basic amino acid residues in the CDR3 region of the heavy chain were involved in binding. Computational docking showed a heparin tetrasaccharide close to these regions. Mutagenesis of heparin-binding residues reduced or altered reactivity towards HS and heparin. Identification of heparin-binding arginine and lysine residues in HS4C3 allows for better understanding of the interaction with HS and creates a framework to rationally design antibodies targeting specific HS motifs.

Molecular Analysis of HLA Genes in Romanian Patients with Chronic Hepatitis B Virus Infection.

Hepatitis B, a persistent inflammatory liver condition, stands as a significant global health issue. In Romania, the prevalence of chronic hepatitis B virus (CHB) infection ranks among the highest in the European Union. The HLA genotype significantly impacts hepatitis B virus infection progression, indicating that certain HLA variants can affect the infection's outcome. The primary goal of the present work is to identify HLA alleles and specific amino acid residues linked to hepatitis B within the Romanian population. The study enrolled 247 patients with chronic hepatitis B; HLA typing was performed using next-generation sequencing. This study's main findings include the identification of certain HLA alleles, such as DQB1*06:03:01, DRB1*13:01:01, DQB1*06:02:01, DQA1*01:03:01, DRB5*01:01:01, and DRB1*15:01:01, which exhibit a significant protective effect against HBV. Additionally, the amino acid residue alanine at DQB1_38 is associated with a protective role, while valine presence may signal an increased risk of hepatitis B. The present findings are important in addressing the urgent need for improved methods of diagnosing and managing CHB, particularly when considering the disease's presence in diverse population groups and geographical regions.

Controlling 1D Nanostructures and Handedness by Polar Residue Chirality of Amphiphilic Peptides.

Peptide assemblies are promising nanomaterials, with their properties and technological applications being highly hinged on their supramolecular architectures. Here, how changing the chirality of the terminal charged residues of an amphiphilic hexapeptide sequence Ac-I4 K2 -NH2 gives rise to distinct nanostructures and supramolecular handedness is reported. Microscopic imaging and neutron scattering measurements show thin nanofibrils, thick nanofibrils, and wide nanotubes self-assembled from four stereoisomers. Spectroscopic and solid-state nuclear magnetic resonance (NMR) analyses reveal that these isomeric peptides adopt similar anti-parallel ß-sheet secondary structures. Further theoretical calculations demonstrate that the chiral alterations of the two C-terminal lysine residues cause the formation of diverse single ß-strand conformations, and the final self-assembled nanostructures and handedness are determined by the twisting direction and degree of single ß-strands. This work not only lays a useful foundation for the fabrication of diverse peptide nanostructures by manipulating the chirality of specific residues but also provides a framework for predicting the supramolecular structures and handedness of peptide assemblies from single molecule conformations.

A Cysteine Residue of Human Cytomegalovirus vMIA Protein Plays a Crucial Role in Viperin Trafficking to Control Viral Infectivity.

Viperin is a multifunctional interferon-inducible protein that is directly induced in cells by human cytomegalovirus (HCMV) infection. The viral mitochondrion-localized inhibitor of apoptosis (vMIA) interacts with viperin at the early stages of infection and translocates it from the endoplasmic reticulum to the mitochondria, where viperin modulates the cellular metabolism to increase viral infectivity. Viperin finally relocalizes to the viral assembly compartment (AC) at late stages of infection. Despite the importance of vMIA interactions with viperin during viral infection, their interacting residues are unknown. In the present study, we showed that cysteine residue 44 (Cys44) of vMIA and the N-terminal domain (amino acids [aa] 1 to 42) of viperin are necessary for their interaction and for the mitochondrial localization of viperin. In addition, the N-terminal domain of mouse viperin, which is structurally similar to that of human viperin, interacted with vMIA. This indicates that the structure, rather than the sequence composition, of the N-terminal domain of viperin, is required for the interaction with vMIA. Recombinant HCMV, in which Cys44 of vMIA was replaced by an alanine residue, failed to translocate viperin to the mitochondria at the early stages of infection and inefficiently relocalized it to the AC at late stages of infection, resulting in the impairment of viperin-mediated lipid synthesis and a reduction in viral replication. These data indicate that Cys44 of vMIA is therefore essential for the intracellular trafficking and function of viperin to increase viral replication. Our findings also suggest that the interacting residues of these two proteins are potential therapeutic targets for HCMV-associated diseases. IMPORTANCE Viperin traffics to the endoplasmic reticulum (ER), mitochondria, and viral assembly compartment (AC) during human cytomegalovirus (HCMV) infection. Viperin has antiviral activity at the ER and regulates cellular metabolism at the mitochondria. Here, we show that Cys44 of HCMV vMIA protein and the N-terminal domain (aa 1 to 42) of viperin are necessary for their interaction. Cys44 of vMIA also has a critical role for viperin trafficking from the ER to the AC via the mitochondria during viral infection. Recombinant HCMV expressing a mutant vMIA Cys44 has impaired lipid synthesis and viral infectivity, which are attributed to mislocalization of viperin. Cys44 of vMIA is essential for the trafficking and function of viperin and may be a therapeutic target for HCMV-associated diseases.

Antigenic Characterization of Human Monoclonal Antibodies for Therapeutic Use against H7N9 Avian Influenza Virus.

Since 2013, H7N9 avian influenza viruses (AIVs) have caused more than 1,500 human infections and the culling of millions of poultry. Despite large-scale poultry vaccination, H7N9 AIVs continue to circulate among poultry in China and pose a threat to human health. Previously, we isolated and generated four monoclonal antibodies (mAbs) derived from humans naturally infected with H7N9 AIV. Here, we investigated the hemagglutinin (HA) epitopes of H7N9 AIV targeted by these mAbs (L3A-44, K9B-122, L4A-14, and L4B-18) using immune escape studies. Our results revealed four key antigenic epitopes at HA amino acid positions 125, 133, 149, and 217. The mutant H7N9 viruses representing escape mutations containing an alanine-to-threonine substitution at residue 125 (A125T), a glycine-to-glutamic acid substitution at residue 133 (G133E), an asparagine-to-aspartic acid substitution at residue 149 (N149D), or a leucine-to-glutamine substitution at residue 217 (L217Q) showed reduced or completely abolished cross-reactivity with the mAbs, as measured by a hemagglutination inhibition (HI) assay. We further assessed the potential risk of these mutants to humans should they emerge following mAb treatment by measuring the impact of these HA mutations on virus fitness and evasion of host adaptive immunity. Here, we showed that the L4A-14 mAb had broad neutralizing capabilities, and its escape mutant N149D had reduced viral stability and human receptor binding and could be neutralized by both postinfection and antigen-induced sera. Therefore, the L4A-14 mAb could be a therapeutic candidate for H7N9 AIV infection in humans and warrants further investigation for therapeutic applications. IMPORTANCE Avian influenza virus (AIV) H7N9 continues to circulate and evolve in birds, posing a credible threat to humans. Antiviral drugs have proven useful for the treatment of severe influenza infections in humans; however, concerns have been raised as antiviral-resistant mutants have emerged. Monoclonal antibodies (mAbs) have been studied for both prophylactic and therapeutic applications in infectious disease control and have demonstrated great potential. For example, mAb treatment has significantly reduced the risk of people developing severe disease with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In addition to the protection efficiency, we should also consider the potential risk of the escape mutants generated by mAb treatment to public health by assessing their viral fitness and potential to compromise host adaptive immunity. Considering these parameters, we assessed four human mAbs derived from humans naturally infected with H7N9 AIV and showed that the mAb L4A-14 displayed potential as a therapeutic candidate.

TSNAPred: predicting type-specific nucleic acid binding residues via an ensemble approach.

MOTIVATION: The interplay between protein and nucleic acid participates in diverse biological activities. Accurately identifying the interaction between protein and nucleic acid can strengthen the understanding of protein function. However, conventional methods are too time-consuming, and computational methods are type-agnostic predictions. We proposed an ensemble predictor termed TSNAPred and first used it to identify residues that bind to A-DNA, B-DNA, ssDNA, mRNA, tRNA and rRNA. TSNAPred combines LightGBM and capsule network, both learned on the feature derived from protein sequence. TSNAPred utilizes the sliding window technique to extract long-distance dependencies between residues and a weighted ensemble strategy to enhance the prediction performance. The results show that TSNAPred can effectively identify type-specific nucleic acid binding residues in our test set. What is more, it also can discriminate DNA-binding and RNA-binding residues, which has improved 5% to 10% on the AUC value compared with other state-of-the-art methods. The dataset and code of TSNAPred are available at: https://github.com/niewenjuan-csu/TSNAPred.

A deep learning-based method for the prediction of DNA interacting residues in a protein.

DNA-protein interaction is one of the most crucial interactions in the biological system, which decides the fate of many processes such as transcription, regulation and splicing of genes. In this study, we trained our models on a training dataset of 646 DNA-binding proteins having 15 636 DNA interacting and 298 503 non-interacting residues. Our trained models were evaluated on an independent dataset of 46 DNA-binding proteins having 965 DNA interacting and 9911 non-interacting residues. All proteins in the independent dataset have less than 30% of sequence similarity with proteins in the training dataset. A wide range of traditional machine learning and deep learning (1D-CNN) techniques-based models have been developed using binary, physicochemical properties and Position-Specific Scoring Matrix (PSSM)/evolutionary profiles. In the case of machine learning technique, eXtreme Gradient Boosting-based model achieved a maximum area under the receiver operating characteristics (AUROC) curve of 0.77 on the independent dataset using PSSM profile. Deep learning-based model achieved the highest AUROC of 0.79 on the independent dataset using a combination of all three profiles. We evaluated the performance of existing methods on the independent dataset and observed that our proposed method outperformed all the existing methods. In order to facilitate scientific community, we developed standalone software and web server, which are accessible from https://webs.iiitd.edu.in/raghava/dbpred.

Cross-reaction mediated by distinct key amino acid combinations in the complementary-determining region (CDR) of a monoclonal antibody.

In immunology, cross-reaction between antigens and antibodies are commonly observed. Prior research has shown that various monoclonal antibodies (mAbs) can recognize a broad spectrum of epitopes related to influenza viruses. However, existing theories on cross-reactions fall short in explaining the phenomena observed. This study explored the interaction characteristics of H1-74 mAb with three peptides: two natural peptides, LVLWGIHHP and LPFQNI, derived from the hemagglutinin (HA) antigen of the H1N1 influenza virus, and one synthetic peptide, WPFQNY. Our findings indicate that the complementarity-determining region (CDR) of H1-74 mAb comprised five antigen-binding sites, containing eight key amino acid residues from the light chain variable region and 16 from the heavy chain variable region. These critical residues formed distinct hydrophobic or hydrophilic clusters and functional groups within the binding sites, facilitating interaction with antigen epitopes through hydrogen bonding, salt bridge formation, and π-π stacking. The study revealed that the formation of the antibody molecule led to the creation of binding groups and small units in the CDR, allowing the antibody to attach to a variety of antigen epitopes through diverse combinations of these small units and functional groups. This unique ability of the antibody to bind with antigen epitopes provides a new molecular basis for explaining the phenomenon of antibody cross-reaction.

Canopy and understory nitrogen additions differently affect soil microbial residual carbon in a temperate forest.

Atmospheric nitrogen (N) deposition in forests can affect soil microbial growth and turnover directly through increasing N availability and indirectly through altering plant-derived carbon (C) availability for microbes. This impacts microbial residues (i.e., amino sugars), a major component of soil organic carbon (SOC). Previous studies in forests have so far focused on the impact of understory N addition on microbes and microbial residues, but the effect of N deposition through plant canopy, the major pathway of N deposition in nature, has not been explicitly explored. In this study, we investigated whether and how the quantities (25 and 50 kg N ha-1 year-1) and modes (canopy and understory) of N addition affect soil microbial residues in a temperate broadleaf forest under 10-year N additions. Our results showed that N addition enhanced the concentrations of soil amino sugars and microbial residual C (MRC) but not their relative contributions to SOC, and this effect on amino sugars and MRC was closely related to the quantities and modes of N addition. In the topsoil, high-N addition significantly increased the concentrations of amino sugars and MRC, regardless of the N addition mode. In the subsoil, only canopy N addition positively affected amino sugars and MRC, implying that the indirect pathway via plants plays a more important role. Neither canopy nor understory N addition significantly affected soil microbial biomass (as represented by phospholipid fatty acids), community composition and activity, suggesting that enhanced microbial residues under N deposition likely stem from increased microbial turnover. These findings indicate that understory N addition may underestimate the impact of N deposition on microbial residues and SOC, highlighting that the processes of canopy N uptake and plant-derived C availability to microbes should be taken into consideration when predicting the impact of N deposition on the C sequestration in temperate forests.