Protein Interface Research Articles

PhoXplex: Combining Phospho-enrichable Cross-Linking with Isobaric Labeling for Quantitative Proteome-Wide Mapping of Protein Interfaces.

Integrating cross-linking mass spectrometry (XL-MS) into structural biology workflows provides valuable information about the spatial arrangement of amino acid stretches, which can guide elucidation of protein assembly architecture. Additionally, the combination of XL-MS with peptide quantitation techniques is a powerful approach to delineate protein interface dynamics across diverse conditions. While XL-MS is increasingly effective with isolated proteins or small complexes, its application to whole-cell samples poses technical challenges related to analysis depth and throughput. The use of enrichable cross-linkers has greatly improved the detectability of protein interfaces in a proteome-wide scale, facilitating global protein-protein interaction mapping. Therefore, bringing together enrichable cross-linking and multiplexed peptide quantification is an appealing approach to enable comparative characterization of structural attributes of proteins and protein interactions. Here, we combined phospho-enrichable cross-linking with TMT labeling to develop a streamline workflow (PhoXplex) for the detection of differential structural features across a panel of cell lines in a global scale. We achieved deep coverage with quantification of over 9000 cross-links and long loop-links in total including potentially novel interactions. Overlaying AlphaFold predictions and disorder protein annotations enables exploration of the quantitative cross-linking data set, to reveal possible associations between mutations and protein structures. Lastly, we discuss current shortcomings and perspectives for deep whole-cell profiling of protein interfaces at large-scale.

MPA-MutPred: a novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes.

Mutations in the interface of membrane protein (MP) complexes are key contributors to a broad spectrum of human diseases, primarily due to changes in their binding affinities. While various methods exist for predicting the mutation-induced changes in binding affinity (ΔΔG) in protein-protein complexes, none are specific to MP complexes. This study proposes a novel strategy for ΔΔG prediction in MP complexes, which combines linear and nonlinear models, to obtain a more robust model with improved prediction accuracy. We used multiple linear regression to extract informative features that influence the binding affinity in MP complexes, which included changes in the stability of the complex, conservation score, electrostatic interaction, relatively accessible surface area, and interface contacts. Further, using gradient boosting regressor on the selected features, we developed MPA-MutPred, a novel method specific for predicting the ΔΔG of membrane protein-protein complexes, and it is freely accessible at https://web.iitm.ac.in/bioinfo2/MPA-MutPred/. Our method achieved a correlation of 0.75 and a mean absolute error (MAE) of 0.73kcal/mol in the jack-knife test conducted on a dataset of 770 mutants. We further validated the method using a blind test set of 86 mutations, obtaining a correlation of 0.85 and an MAE of 0.77kcal/mol. We anticipate that this method can be used for large-scale studies to understand the influence of binding affinity change on disease-causing mutations in MP complexes, thereby aiding in the understanding of disease mechanisms and the identification of potential therapeutic targets.

Discovering Drug Candidates for Charcot Marie Tooth Disease Type-2

Introduction: Charcot Marie Tooth Disease-2 is a debilitating neurogenetic disorder that adversely affects peripheral neurons by disrupting mitochondrial activity. Mutated mitofusin-2 (MFN) is the main culprit behind disruptive mitochondrial function and is considered a therapeutic target in identifying drugs for treating this disease. This disease has no therapeutic medication except for supportive care. Objective: The objective of the current study is to evaluate high-affinity medicinal compounds for mutated MFN-2 and describe their absorption, distribution, metabolism, excretion, and toxic attributes (ADMET). Methods: For ADMET properties, 2,219 medicinal compounds were analyzed with AutoDock Vina using PyRX 0.9 software against MFN-2, SwissADME, and GUSAR. Results: Results from screening studies revealed that three compounds (Liriodenine, Pinocembrin, and Vestitol) show an affinity for amino acids present in the predicted active interface of the MFN-2 protein. Moreover, these compounds render low toxicity and efficient ADME qualities, combined with bloodbrain barrier permeability, drug-likeness, and lead-likeness. Conclusion: Liriodenine, pinocembrin and vestitol are therapeutic compounds for CMT-2 treatment and should be used in further in-vitro studies to confirm the results of this research.

Conformationally Constrained Isoquinolinones as Orally Efficacious Hepatitis B Capsid Assembly Modulators.

Isoquinolinone-based HBV capsid assembly modulators that bind at the dimer:dimer interface of HBV core protein have been shown to suppress viral replication in chronic hepatitis B patients. Analysis of their binding mode by protein X-ray crystallography has identified a region of the small molecule where the application of a constraint can lock the preferred binding conformation and has allowed for further optimization of this class of compounds. Key analogues demonstrated single digit nM EC50 values in reducing HBV DNA in a HepDE19 cellular assay in addition to favorable ADME and pharmacokinetic properties, leading to a high degree of oral efficacy in a relevant in vivo hydrodynamic injection mouse model of HBV infection, with 12e effecting a 3 log10 decline in serum HBV DNA levels at a once daily dose of 1 mg/kg. Additionally, maintenance of activity was observed in clinically relevant HBV core protein variants T33N and I105T.

N terminus of SARS-CoV-2 nonstructural protein 3 interrupts RNA-driven phase separation of N protein by displacing RNA

The connection between SARS-CoV-2 replication-transcription complexes (RTCs) and nucleocapsid (N) protein is critical for regulating genomic RNA replication and virion packaging over the viral life cycle. However, the mechanism that dynamically regulates genomic RNA packaging and replication remains elusive. Here, we demonstrate that the N-terminal domain (NTD) of SARS-CoV-2 nonstructural protein 3 (Nsp3), a core component of viral RTCs, binds N protein and displaces RNA in a concentration-dependent manner. This interaction disrupts liquid-liquid phase separation of N protein driven by N protein-RNA interactions which is crucial for virion packaging and viral replication. We also report a high-resolution crystal structure of the Nsp3 ubiquitin-like domain 1 (Ubl1) at 1.49 Å, which reveals abundant negative charges on the protein surface. Sequence and structural analyses identify several conserved motifs at the Ubl1-N protein interface and a previously unexplored highly negative groove, providing insights into the molecular mechanism of Ubl1-mediated modulation of N protein-RNA binding. Our findings elucidate the mechanism of dynamic regulation of SARS-CoV-2 genomic RNA replication and packaging over the viral life cycle. Targeting the conserved Ubl1-N protein interaction hotspots also promises to aid in the development of broad-spectrum antivirals against pathogenic coronaviruses.

Construction of An Artificial Photosynthesis System with A Single CdS QDs-Ferritin Hybrid Molecule.

Establishing artificial photosynthesis systems in a simple but effective manner to mitigate the greenhouse effect and address the energy crisis remains challenging. The combination of photocatalysis technology with bioengineering is an emerging field with great potential to construct such artificial photosynthesis systems, but so far, it has barely been explored in this area. Herein, an artificial photocatalysis platform is constructed with high CO2 conversion and H2O splitting capability by integration of CdS QDs into the intra-subunit interface of H-type ferritin (Marsupenaeus japonicus), a natural ferroxidase through protein interface redesign. The crystal structure of the synthesized CdS QDs with engineered ferritin molecules as bio-templates confirmed the design at an atomic level. Notably, upon absorbing desirable visible light (≈420nm), such a single CdS-ferritin hybrid molecule is able to selectively catalyze the reduction of CO2 into HCOOH (≈90%), meanwhile catalyzing the oxidation of H2O into O2 in an aqueous environment. The O2 production rate reached to 180µmolg-1h-1, and the HCOOH output hit almost 800µmolg-1h-1. This work advances the utilization of the four-helix bundle structure for crafting artificial photosynthesis systems, facilitating the seamless integration of bioengineering and photocatalysis technology.

In silico drug repurposing using molecular docking and dynamics to target the protein interaction between the SARS-CoV-2 S-glycoprotein and the ACE2 receptor.

Background: The protein interaction between the viral surface S-glycoprotein and the host angiotensin converting enzyme-2 receptor (ACE2) is key to the virulent nature of SARS-CoV-2. The potential role that effective drug repurposing strategies may have to help stem the impact of future outbreaks has been brought to light in the recent COVID-19 pandemic. This study outlines a comprehensive approach towards in-silico drug discovery which aims to identify hit agents that can be suitably translated into a clinical setting. Methods: We use two different computational platforms to analyze the viral S-glycoprotein in its bound conformational state to the ACE2 receptor. We employed a comprehensive screening approach to shortlist compounds capable of binding to the viral target interface and corroborated these findings using both Schrödinger's Glide and AutoDock Vina. Molecular dynamic simulation studies further verified the stability of the interaction at the viral-host protein interface. Results: Lymecycline, pentagalloylglucose, polydatin, and hexoprenaline were identified as prime candidates for further studies given the robust and stable nature of their interaction at the viral-host interface and relevance for clinical testing. These agents were shown in a 100-nanosecond simulation trajectory to favorably disrupt key binding interactions at the viral-host interface and may potentially inhibit viral entry into host cells. In all hit molecules it was observed that inhibiting the interaction with the following key viral binding residues: Lys17, Gly496, Tyr 505, and key host residues: His34, Asp38, Lys353, played a critical role toward the inhibition of the viral-host protein interaction. Conclusions: Our study is unique in its comprehensive approach to identify agents that can bind to the S-glycoprotein-ACE2 interface using multiple computational platforms. Among the hit compounds shortlisted in this study, both lymecycline and hexoprenaline may be considered as candidates for preliminarily clinical studies to assess their therapeutic potential in the management of COVID-19 infections.

Structural Basis for Evasion of New SARS-CoV-2 Variants from the Potent Virus-Neutralizing Nanobody Targeting the S-Protein Receptor-Binding Domain

COVID-19 has caused millions of deaths and many times more infections worldwide, emphasizing the unpreparedness of the global health system in the face of new infections and the key role for vaccines and therapeutics, including virus-neutralizing antibodies, in prevention and containment of the disease. Continuous evolution of the SARS-CoV-2 coronavirus has been causing its new variants to evade the action of the immune system, which highlighted the importance of detailed knowledge of the epitopes of already selected potent virus-neutralizing antibodies. A single-chain antibody (“nanobody”) targeting the SARS-CoV-2 receptor-binding domain (RBD), clone P2C5, had exhibited robust virus-neutralizing activity against all SARS-CoV-2 variants and, being a major component of the anti-COVID-19 formulation “GamCoviMab”, had successfully passed Phase I of clinical trials. However, after the emergence of the Delta and XBB variants, a decrease in the neutralizing activity of this nanobody was observed. Here we report on the successful crystal structure determination of the RBD:P2C5 complex at 3.1 Å, which revealed the intricate protein–protein interface, sterically occluding full ACE2 receptor binding by the P2C5-neutralized RBD. Moreover, the structure revealed the developed RBD:P2C5 interface centered around residues Leu452 and Phe490, thereby explaining the evasion of the Delta or Omicron XBB, but not Omicron B.1.1.529 variant, as a result of the single L452R or F490S mutations, respectively, from the action of P2C5. The structure obtained is expected to foster nanobody engineering in order to rescue neutralization activity and will facilitate epitope mapping for other neutralizing nanobodies by competition assays.

Elucidation of the Reversible Self-Association Interface of a Diabody-Interleukin Fusion Protein Using Hydrogen-Exchange Mass Spectrometry and In Silico Modeling.

Reversible self-association (RSA) of therapeutic proteins presents major challenges in the development of high-concentration formulations, especially those intended for subcutaneous administration. Understanding self-association mechanisms is therefore critical to the design and selection of candidates with acceptable developability to advance to clinical trials. The combination of experiments and in silico modeling presents a powerful tool to elucidate the interface of self-association. RSA of monoclonal antibodies has been studied extensively under different solution conditions and have been shown to involve interactions for both the antigen-binding fragment and the crystallizable fragment. Novel modalities such as bispecific antibodies, antigen-binding fragments, single-chain-variable fragments, and diabodies constitute a fast-growing class of antibody-based therapeutics that have unique physiochemical properties compared to monoclonal antibodies. In this study, the RSA interface of a diabody-interleukin 22 fusion protein (FP-1) was studied using hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) in combination with in silico modeling. Taken together, the results show that a complex solution behavior underlies the self-association of FP-1 and that the interface thereof can be attributed to a specific segment in the variable light chain of the diabody. These findings also demonstrate that the combination of HDX-MS with in silico modeling is a powerful tool to guide the design and candidate selection of novel biotherapeutic modalities.

PPInterface: A Comprehensive Dataset of 3D Protein-Protein Interface Structures

The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein–protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein–protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset’s utility and accessibility.The dataset and web server can be found at https://3dpath.ku.edu.tr/PPInt/.

Scattering approaches to unravel protein solution behaviors in ionic liquids and deep eutectic solvents: From basic principles to recent developments

Proteins in ionic liquids (ILs) and deep eutectic solvents (DESs) have gained significant attention due to their potential applications in various fields, including biocatalysis, bioseparation, biomolecular delivery, and structural biology. Scattering approaches including dynamic light scattering (DLS) and small-angle X-ray and neutron scattering (SAXS and SANS) have been used to understand the solution behavior of proteins at the nanoscale and microscale. This review provides a thorough exploration of the application of these scattering techniques to elucidate protein properties in ILs and DESs. Specifically, the review begins with the theoretical foundations of the relevant scattering approaches and describes the essential solvent properties of ILs and DESs linked to scattering such as refractive index, scattering length density, ion-pairs, liquid nanostructure, solvent aggregation, and specific ion effects. Next, a detailed introduction is provided on protein properties such as type, concentration, size, flexibility and structure as observed through scattering methodologies. This is followed by a review of the literature on the use of scattering for proteins in ILs and DESs. It is highlighted that enhanced data analysis and modeling tools are necessary for assessing protein flexibility and structure, and for understanding protein hydration, aggregation and specific ion effects. It is also noted that complementary approaches are recommended for comprehensively understanding the behavior of proteins in solution due to the complex interplay of factors, including ion-binding, dynamic hydration, intermolecular interactions, and specific ion effects. Finally, the challenges and potential research directions for this field are proposed, including experimental design, data analysis approaches, and supporting methods to obtain fundamental understandings of complex protein behavior and protein systems in solution. We envisage that this review will support further studies of protein interface science, and in particular studies on solvent and ion effects on proteins.

Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics

The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein–protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.

Rapid, DNA-induced interface swapping by DNA gyrase

DNA gyrase, a ubiquitous bacterial enzyme, is a type IIA topoisomerase formed by heterotetramerisation of 2 GyrA subunits and 2 GyrB subunits, to form the active complex. DNA gyrase can loop DNA around the C-terminal domains (CTDs) of GyrA and pass one DNA duplex through a transient double-strand break (DSB) established in another duplex. This results in the conversion from a positive (+1) to a negative (–1) supercoil, thereby introducing negative supercoiling into the bacterial genome by steps of 2, an activity essential for DNA replication and transcription. The strong protein interface in the GyrA dimer must be broken to allow passage of the transported DNA segment and it is generally assumed that the interface is usually stable and only opens when DNA is transported, to prevent the introduction of deleterious DSBs in the genome. In this paper, we show that DNA gyrase can exchange its DNA-cleaving interfaces between two active heterotetramers. This so-called interface ‘swapping’ (IS) can occur within a few minutes in solution. We also show that bending of DNA by gyrase is essential for cleavage but not for DNA binding per se and favors IS. Interface swapping is also favored by DNA wrapping and an excess of GyrB. We suggest that proximity, promoted by GyrB oligomerization and binding and wrapping along a length of DNA, between two heterotetramers favors rapid interface swapping. This swapping does not require ATP, occurs in the presence of fluoroquinolones, and raises the possibility of non-homologous recombination solely through gyrase activity. The ability of gyrase to undergo interface swapping explains how gyrase heterodimers, containing a single active-site tyrosine, can carry out double-strand passage reactions and therefore suggests an alternative explanation to the recently proposed ‘swivelling’ mechanism for DNA gyrase (Gubaev et al., 2016).

Rapid, DNA-induced interface swapping by DNA gyrase.

DNA gyrase, a ubiquitous bacterial enzyme, is a type IIA topoisomerase formed by heterotetramerisation of 2 GyrA subunits and 2 GyrB subunits, to form the active complex. DNA gyrase can loop DNA around the C-terminal domains (CTDs) of GyrA and pass one DNA duplex through a transient double-strand break (DSB) established in another duplex. This results in the conversion from a positive (+1) to a negative (-1) supercoil, thereby introducing negative supercoiling into the bacterial genome by steps of 2, an activity essential for DNA replication and transcription. The strong protein interface in the GyrA dimer must be broken to allow passage of the transported DNA segment and it is generally assumed that the interface is usually stable and only opens when DNA is transported, to prevent the introduction of deleterious DSBs in the genome. In this paper, we show that DNA gyrase can exchange its DNA-cleaving interfaces between two active heterotetramers. This so-called interface 'swapping' (IS) can occur within a few minutes in solution. We also show that bending of DNA by gyrase is essential for cleavage but not for DNA binding per se and favors IS. Interface swapping is also favored by DNA wrapping and an excess of GyrB. We suggest that proximity, promoted by GyrB oligomerization and binding and wrapping along a length of DNA, between two heterotetramers favors rapid interface swapping. This swapping does not require ATP, occurs in the presence of fluoroquinolones, and raises the possibility of non-homologous recombination solely through gyrase activity. The ability of gyrase to undergo interface swapping explains how gyrase heterodimers, containing a single active-site tyrosine, can carry out double-strand passage reactions and therefore suggests an alternative explanation to the recently proposed 'swivelling' mechanism for DNA gyrase (Gubaev et al., 2016).

Water position sampling on protein structures based on a 3D distribution function using a weighted Monte Carlo method

Abstract To gain a detailed understanding of protein structure, function, and interaction, water molecules around proteins are important. Therefore, computational methods for predicting water positions are required. When a hydration water distribution such as a 3D distribution function is available, methods to predict water positions explicitly from the water distribution are useful. In this paper, we introduce DroPred, a method for predicting water positions based on a 3D distribution function of water oxygen atoms using a weighted Monte Carlo method. The probability density derived from the 3D distribution function is used as weight in the weighted Monte Carlo method. DroPred generates multiple samples from a single 3D distribution function. We evaluated the performance of DroPred by predicting water positions at protein–protein interface structures. By adjusting the weight using an exponential parameter, prediction performance of DroPred in water position sampling was improved. This method will be helpful for understanding protein structure, function, and interaction.

Structural insights into the role of deleterious mutations at the dimeric interface of Toll-like receptor interferon-β related adaptor protein.

Toll-like receptors (TLRs) are major players in the innate immune system-recognizing pathogens and differentiating self/non-self components of immunity. These proteins are present either on the plasma membrane or endosome and recognize pathogens at their extracellular domains. They are characterized by a single transmembrane helix and an intracellular toll-interleukin-1 receptor (TIR) domain. Few TIRs directly invoke downstream signaling, while others require other TIR domains of adaptors like TIR domain-containing adaptor-inducing interferon-β (TRIF) and TRIF-related adaptor molecule (TRAM). On recognizing pathogenic lipopolysaccharides, TLR4 dimerises and interacts with the intracellular TRAM dimer through the TIR domain to recruit a downstream signaling adaptor (TRIF). We have performed an in-depth study of the structural effect of two mutations (P116H and C117H) at the dimeric interface of the adaptor TRAM, which are known to abrogate downstream signaling. We modeled the structure and performed molecular dynamics studies in order to decipher the structural basis of this effect. We observed that these mutations led to an increased radius of gyration of the complex and resulted in several changes to the interaction energy values when compared against the wild type (WT) and positive control mutants. We identified highly interacting residues as hubs in the WT dimer, and a few such hubs that were lost in the mutant dimers. Changes in the protein residue path, hampering the information flow between the crucial A86/E87/D88/D89 and T155/S156 sites, were observed for the mutants. Overall, we show that such residue changes can have subtle but long-distance effects, impacting the signaling path allosterically.

Protein-Protein Interfaces: A Graph Neural Network Approach.

Protein-protein interactions (PPIs) are fundamental processes governing cellular functions, crucial for understanding biological systems at the molecular level. Compared to experimental methods for PPI prediction and site identification, computational deep learning approaches represent an affordable and efficient solution to tackle these problems. Since protein structure can be summarized as a graph, graph neural networks (GNNs) represent the ideal deep learning architecture for the task. In this work, PPI prediction is modeled as a node-focused binary classification task using a GNN to determine whether a generic residue is part of the interface. Biological data were obtained from the Protein Data Bank in Europe (PDBe), leveraging the Protein Interfaces, Surfaces, and Assemblies (PISA) service. To gain a deeper understanding of how proteins interact, the data obtained from PISA were assembled into three datasets: Whole, Interface, and Chain, consisting of data on the whole protein, couples of interacting chains, and single chains, respectively. These three datasets correspond to three different nuances of the problem: identifying interfaces between protein complexes, between chains of the same protein, and interface regions in general. The results indicate that GNNs are capable of solving each of the three tasks with very good performance levels.

A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions

Protein functions are characterized by interactions with proteins, drugs, and other biomolecules. Understanding these interactions is essential for deciphering the molecular mechanisms underlying biological processes and developing new therapeutic strategies. Current computational methods mostly predict interactions based on either molecular network or structural information, without integrating them within a unified multi-scale framework. While a few multi-view learning methods are devoted to fusing the multi-scale information, these methods tend to rely intensively on a single scale and under-fitting the others, likely attributed to the imbalanced nature and inherent greediness of multi-scale learning. To alleviate the optimization imbalance, we present MUSE, a multi-scale representation learning framework based on a variant expectation maximization to optimize different scales in an alternating procedure over multiple iterations. This strategy efficiently fuses multi-scale information between atomic structure and molecular network scale through mutual supervision and iterative optimization. MUSE outperforms the current state-of-the-art models not only in molecular interaction (protein-protein, drug-protein, and drug-drug) tasks but also in protein interface prediction at the atomic structure scale. More importantly, the multi-scale learning framework shows potential for extension to other scales of computational drug discovery.

In silico and in vivo analysis reveal impact of c-Myc tag in FMC63 scFv-CD19 protein interface and CAR-T cell efficacy

Anti-CD19 CAR-T cell therapy represents a breakthrough in the treatment of B-cell malignancies, and it is expected that this therapy modality will soon cover a range of solid tumors as well. Therefore, a universal cheap and sensitive method to detect CAR expression is of foremost importance. One possibility is the use of epitope tags such as c-Myc, HA or FLAG tags attached to the CAR extracellular domain, however, it is important to determine whether these tags can influence binding of the CAR with its target molecule. Here, we conducted in-silico structural modelling of an FMC63-based anti-CD19 single-chain variable fragment (scFv) with and without a c-Myc peptide tag added to the N-terminus portion and performed molecular dynamics simulation of the scFv with the CD19 target. We show that the c-Myc tag presence in the N-terminus portion does not affect the scFv’s structural equilibrium and grants more stability to the scFv. However, intermolecular interaction potential (IIP) analysis reveals that the tag can approximate the complementarity-determining regions (CDRs) present in the scFv and cause steric impediment, potentially disturbing interaction with the CD19 protein. We then tested this possibility with CAR-T cells generated from human donors in a Nalm-6 leukemia model, showing that CAR-T cells with the c-Myc tag have overall worse antitumor activity, which was also observed when the tag was added to the C-terminus position. Ultimately, our results suggest that tag addition is an important aspect of CAR design and can influence CAR-T cell function, therefore its use should be carefully considered.

Gossypium hirsutum calmodulin-like protein (CML 11) interaction with geminivirus encoded protein using bioinformatics and molecular techniques

Plant viruses are the most abundant destructive agents that exist in every ecosystem, causing severe diseases in multiple crops worldwide. Currently, a major gap is present in computational biology determining plant viruses interaction with its host. We lay out a strategy to extract virus-host protein interactions using various protein binding and interface methods for Geminiviridae, a second largest virus family. Using this approach, transcriptional activator protein (TrAP/C2) encoded by Cotton leaf curl Kokhran virus (CLCuKoV) and Cotton leaf curl Multan virus (CLCuMV) showed strong binding affinity with calmodulin-like (CML) protein of Gossypium hirsutum (Gh-CML11). Higher negative value for the change in Gibbs free energy between TrAP and Gh-CML11 indicated strong binding affinity. Consensus from gene ontology database and in-silico nuclear localization signal (NLS) tools identified subcellular localization of TrAP in the nucleus associated with Gh-CML11 for virus infection. Data based on interaction prediction and docking methods present evidences that full length and truncated C2 strongly binds with Gh-CML11. This computational data was further validated with molecular results collected from yeast two-hybrid, bimolecular fluorescence complementation system and pull down assay. In this work, we also show the outcomes of full length and truncated TrAP on plant machinery. This is a first extensive report to delineate a role of CML protein from cotton with begomoviruses encoded transcription activator protein.