HADDOCK: a protein-protein docking approach based on biochemical or biophysical information

Binding affinity between coronavirus spike protein and human ACE2 receptor

Coronaviruses (CoVs) pose a major risk to global public health due to their ability to infect diverse animal species and potential for emergence in humans. The CoV spike protein mediates viral entry into the cell and plays a crucial role in determining the binding affinity to host cell receptors. With particular emphasis on α- and β-coronaviruses that infect humans and domestic animals, current research on CoV receptor use suggests that the exploitation of the angiotensin-converting enzyme 2 (ACE2) receptor poses a significant threat for viral emergence with pandemic potential. This review summarizes the approaches used to study binding interactions between CoV spike proteins and the human ACE2 (hACE2) receptor. Solid-phase enzyme immunoassays and cell binding assays allow qualitative assessment of binding but lack quantitative evaluation of affinity. Surface plasmon resonance, Bio-layer interferometry, and Microscale Thermophoresis on the other hand, provide accurate affinity measurement through equilibrium dissociation constants (KD). In silico modeling predicts affinity through binding structure modeling, protein-protein docking simulations, and binding energy calculations but reveals inconsistent results due to the lack of a standardized approach. Machine learning and deep learning models utilize simulated and experimental protein-protein interaction data to elucidate the critical residues associated with CoV binding affinity to hACE2. Further optimization and standardization of existing approaches for studying binding affinity could aid pandemic preparedness. Specifically, prioritizing surveillance of CoVs that can bind to human receptors stands to mitigate the risk of zoonotic spillover.

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.

Utilization of the EpiMed Coronabank Chemical Collection to identify potential SARS-CoV-2 antivirals: in silico studies targeting the nsp14 ExoN domain and PLpro naphthalene binding site

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome encodes 29 proteins including four structural, 16 nonstructural (nsps), and nine accessory proteins (https://epimedlab.org/sars-cov-2-proteome/). Many of these proteins contain potential targetable sites for the development of antivirals. Despite the widespread use of vaccinations, the emergence of variants necessitates the investigation of new therapeutics and antivirals. Here, the EpiMed Coronabank Chemical Collection (https://epimedlab.org/crl/) was utilized to investigate potential antivirals against the nsp14 exoribonuclease (ExoN) domain. Molecular docking was performed to evaluate the binding characteristics of our chemical library against the nsp14 ExoN site. Based on the initial screen, trisjuglone, ararobinol, corilagin, and naphthofluorescein were identified as potential lead compounds. Molecular dynamics (MD) simulations were subsequently performed, with the results highlighting the stability of the lead compounds in the nsp14 ExoN site. Protein-RNA docking revealed the potential for the lead compounds to disrupt the interaction with RNA when bound to the ExoN site. Moreover, hypericin, cyanidin-3-O-glucoside, and rutin were previously identified as lead compounds targeting the papain-like protease (PLpro) naphthalene binding site. Through performing MD simulations, the stability and interactions of lead compounds with PLpro were further examined. Overall, given the critical role of the exonuclease activity of nsp14 in ensuring viral fidelity and the multifunctional role of PLpro in viral pathobiology and replication, these nsps represent important targets for antiviral drug development. Our databases can be utilized for in silico studies, such as the ones performed here, and this approach can be applied to other potentially druggable SARS-CoV-2 protein targets.

De Novo Rational Design of Peptide-Based Protein-Protein Inhibitors (Pep-PPIs) Approach by Mapping the Interaction Motifs of the PP Interface and Physicochemical Filtration: A Case on p25-Cdk5-Mediated Neurodegenerative Diseases

Protein-protein interactions, or PPIs, are a part of every biological activity and have been linked to a number of diseases, including cancer, infectious diseases, and neurological disorders. As such, targeting PPIs is considered a strategic and vital approach in the development of new medications. Nonetheless, the wide and flat contact interface makes it difficult to find small-molecule PP inhibitors. An alternative strategy would be to use the PPI interaction motifs as building blocks for the design of peptide-based inhibitors. Herein, we designed 12-mer peptide inhibitors to target p25-inducing-cyclin-dependent kinase (Cdk5) hyperregulation, a PPI that has been shown to perpetuate neuroinflammation, which is one of the major causal implications of neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, and frontotemporal dementia. We generated a library of 5 062 500 peptide combination sequences (PCS) derived from the interaction motif of Cdk5/p25 PP interface. The 20 amino acids were differentiated into six groups, namely, hydrophobic (aliphatic), aromatic, basic, acidic, unique, and polar uncharged, on the basis of their physiochemical properties. To preserve the interaction motif necessary for ideal binding, de novo modeling of all possible peptide sequence substitutions was considered. A set of filters, backed by the Support Vector Machine (SVM) algorithm, was then used to create a shortlisted custom peptide library that met specific bioavailability, toxicity, and therapeutic relevance, leading to a refined library of 15 PCS. A greedy algorithm and coarse-grained force field were used to predict peptide structure and folding before subsequent modeling studies. Molecular docking was performed to estimate the relative binding affinities, and out of the top hits, Pep15 was subjected to molecular dynamics simulations and binding free-energy calculations in comparison to a known peptide inhibitor with experimental data (template peptide). Interestingly, the identified peptide through our protocol, Pep15, was found to show a significantly higher binding affinity than the reference template peptide (-48.10 ± 0.23 kcal/mol and -17.53 ± 0.27 kcal/mol, respectively). In comparison to the template peptide, Pep15 was found to possess a more compact and buried surface area, tighter binding landscape, and reduced conformational variability, leading to enhanced structural and kinetic stability of the Cdk5/p25 complex. Notably, both peptide inhibitors were found to have a minimal impact on the architectural integrity of the Cdk5/p25 secondary structure. Herein, we propose Pep15 as a novel and potentially disruptive peptide drug for Cdk5/p25-mediated neurodegenerative phenotypes that require further clinical investigation. The systematic protocol and findings of this report would serve as a valuable tool in the identification of critical PPI interface reactive residues, designing of analogs, and identification of more potent peptide-based PPI inhibitors.

Benchmarking Methods for PROTAC Ternary Complex Structure Prediction

Proteolysis targeting chimeras (PROTACs) are bifunctional compounds that recruit an E3 ligase to a target protein to induce ubiquitination and degradation of the target. Rational optimization of PROTAC requires a structural model of the ternary complex. In the absence of an experimental structure, computational tools have emerged that attempt to predict PROTAC ternary complexes. Here, we systematically benchmark three commonly used tools: PRosettaC, MOE, and ICM. We find that these PROTAC-focused methods produce an array of ternary complex structures, including some that are observed experimentally, but also many that significantly deviate from the crystal structure. Molecular dynamics simulations show that PROTAC complexes may exist in a multiplicity of configurational states and question the use of experimentally observed structures as a reference for accurate predictions. The pioneering computational tools benchmarked here highlight the promises and challenges in the field and may be more valuable when guided by clear structural and biophysical data. The benchmarking data set that we provide may also be valuable for evaluating other and future computational tools for ternary complex modeling.

αMI-domain of integrin Mac-1 binds the cytokine pleiotrophin using multiple mechanisms

The integrin Mac-1 (αMβ2, CD11b/CD18, CR3) is an adhesion receptor expressed on macrophages and neutrophils. Mac-1 is also a promiscuous integrin that binds a diverse set of ligands through its αMI-domain. However, the binding mechanism of most ligands remains unclear. We have characterized the interaction of αMI-domain with the cytokine pleiotrophin (PTN), a protein known to bind αMI-domain and induce Mac-1-mediated cell adhesion and migration. Our data show that PTN's N-terminal domain binds a unique site near the N- and C-termini of the αMI-domain using a metal-independent mechanism. However, a stronger interaction is achieved when an acidic amino acid in a zwitterionic motif in PTN's C-terminal domain chelates the divalent cation in the metal ion-dependent adhesion site of active αMI-domain. These results indicate that αMI-domain can bind ligands using multiple mechanisms and that the active αMI-domain has a preference for motifs containing both positively and negatively charged amino acids.

Blocker-SELEX: a structure-guided strategy for developing inhibitory aptamers disrupting undruggable transcription factor interactions

Despite the well-established significance of transcription factors (TFs) in pathogenesis, their utilization as pharmacological targets has been limited by the inherent challenges in modulating their protein interactions. The lack of defined small-molecule binding pockets and the nuclear localization of TFs do not favor the use of traditional tools. Aptamers possess large molecular weights, expansive blocking surfaces and efficient cellular internalization, making them compelling tools for modulating TF interactions. Here, we report a structure-guided design strategy called Blocker-SELEX to develop inhibitory aptamers (iAptamers) that selectively block TF interactions. Our approach leads to the discovery of iAptamers that cooperatively disrupt SCAF4/SCAF8-RNAP2 interactions, dysregulating RNAP2-dependent gene expression, which impairs cell proliferation. This approach is further applied to develop iAptamers blocking WDR5-MYC interactions. Overall, our study highlights the potential of iAptamers in disrupting pathogenic TF interactions, implicating their potential utility in studying the biological functions of TF interactions and in nucleic acids drug discovery.

Capturing the catalytic intermediates of parkin ubiquitination

Parkin is an E3 ubiquitin ligase implicated in early-onset forms of Parkinson's disease. It catalyzes a transthiolation reaction by accepting ubiquitin (Ub) from an E2 conjugating enzyme, forming a short-lived thioester intermediate, and transfers Ub to mitochondrial membrane substrates to signal mitophagy. A major impediment to the development of Parkinsonism therapeutics is the lack of structural and mechanistic detail for the essential, short-lived transthiolation intermediate. It is not known how Ub is recognized by the catalytic Rcat domain in parkin that enables Ub transfer from an E2~Ub conjugate to the catalytic site and the structure of the transthiolation complex is undetermined. Here, we capture the catalytic intermediate for the Rcat domain of parkin in complex with ubiquitin (Rcat-Ub) and determine its structure using NMR-based chemical shift perturbation experiments. We show that a previously unidentified α-helical region near the Rcat domain is unmasked as a recognition motif for Ub and guides the C-terminus of Ub toward the parkin catalytic site. Further, we apply a combination of guided AlphaFold modeling, chemical cross-linking, and single turnover assays to establish and validate a model of full-length parkin in complex with UbcH7, its donor Ub, and phosphoubiquitin, trapped in the process of transthiolation. Identification of this catalytic intermediate and orientation of Ub with respect to the Rcat domain provides important structural insights into Ub transfer by this E3 ligase and explains how the previously enigmatic Parkinson's pathogenic mutation T415N alters parkin activity.

Clinical and genetic spectrum of Ataxia Telangiectasia Tunisian patients: Bioinformatic analysis unveil mechanisms of ATM variants pathogenicity

Ataxia Telangiectasia (AT) is a rare multisystemic neurodegenerative disease caused by biallelic mutations in the ATM gene. Few clinical studies on AT disease have been conducted in Tunisia, however, the mutational landscape is still undefined. Our aim is to determine the clinical and genetic spectrum of AT Tunisian patients and to explore the potential underlying mechanism of variant pathogenicity. Sanger sequencing was performed for nine AT patients. A comprehensive computational analysis was conducted to evaluate the possible pathogenic effect of ATM identified variants. Genetic screening of ATM gene has identified nine different variants from which six have not been previously reported. In silico analysis have predicted a pathogenic effect of identified mutations. This was corroborated by a structural bioinformatics study based on molecular modeling and docking for novel missense mutations. Our findings suggest a profound impact of identified mutations not only on the ATM protein stability, but also on the ATM-ligand interactions. Our study characterizes the mutational landscape of AT Tunisian patients which will allow to set up genetic counseling and prenatal diagnosis for families at risk and expand the spectrum of ATM variants worldwide. Furthermore, understanding the mechanism that underpin variant pathogenicity could provide further insights into disease pathogenesis.

The association between genetic variations and morphology-based brain networks changes in Alzheimer's disease

Alzheimer's disease (AD) is a highly heritable disease. The morphological changes of cortical cortex (such as, cortical thickness and surface area) in AD always accompany by the change of the functional connectivity to other brain regions and influence the short- and long-range brain network connections, causing functional deficits of AD. In this study, the first hypothesis is that genetic variations might affect morphology-based brain networks, leading to functional deficits; the second hypothesis is that protein-protein interaction (PPI) between the candidate proteins and known interacting proteins to AD might exist and influence AD. 600 470 variants and structural magnetic resonance imaging scans from 175 AD patients and 214 healthy controls were obtained from the Alzheimer's Disease Neuroimaging Initiative-1 database. A co-sparse reduced-rank regression model was fit to study the relationship between non-synonymous mutations and morphology-based brain networks. After that, PPIs between selected genes and BACE1, an enzyme that was known to be related to AD, are explored by using molecular dynamics (MD) simulation and co-immunoprecipitation (Co-IP) experiments. Eight genes affecting morphology-based brain networks were identified. The results of MD simulation showed that the PPI between TGM4 and BACE1 was the strongest among them and its interaction was verified by Co-IP. Hence, gene variations influence morphology-based brain networks in AD, leading to functional deficits. This finding, validated by MD simulation and Co-IP, suggests that the effect is robust.

Mutations in Plasmodium falciparum Kelch13 (PfK13) dysregulates PI3K/AKT signalling pathway in artemisinin resistance

BACKGROUND

PfK13 protein mutations are associated with the emergence of artemisinin resistance in Plasmodium falciparum. PfK13 protein is essential for mediating ubiquitination and controlling the PI3K/AKT pathway. Mutant PfK13 variations can interfere with substrate binding, especially with PfPI3K, which raises PfPI3K levels.

METHODS

DUET, DynaMut2, mCSM, iStable 2.0, I-Mutant 2.0, and MuPro were utilized to study the protein stability and protein-substrate binding was studied using HADDOCK 2.4 docking algorithm between Wild-type and mutant PfK13 with the helical and catalytic domain of PfPI3K.

RESULTS

i-Stable server analysis predicted that seven, out of the nine mutations associated with artemisinin resistance (F446I, Y493H, R539T, I543T, P553L, R561H, C580Y) reduced the protein stability. HADDOCK scores of the catalytic domain underscores the significant impact of the reported mutations on the binding affinity of the PfK13 protein. Further validation through the MM_GBSA technique, the binding free energy (DDG) between the wild-type and the mutant PfK13 protein analysis revealed a loss of interactions resulting from mutations in PfK13.

CONCLUSION

The study finding suggest that mutations in the PfK13 cause destabilization in the protein structure and affects the binding of PfPI3K. Although the findings remain preliminary and require further validation, it provides the basis for further research considering the importance of the interaction of PfK13 and PfPI3K to overcome the impact of artemisinin resistance.

Design of multi-epitope vaccine candidate based on OmpA, CarO and ZnuD proteins against multi-drug resistant Acinetobacter baumannii

Acinetobacter baumannii has been identified as a major cause of nosocomial infections. Acinetobacter infections are often difficult to treat with multidrug resistant phenotypes. One of the most effective ways to combat infectious diseases is through vaccination. In this study, an attempt was made to select the most protective and potent immunostimulatory epitopes based on the epitope-rich domains of the ZnuD, OmpA and CarO proteins of Acinetobacter baumannii to design a vaccine that can protect against this infection. After predicting the epitope of B- and T-cells, seven antigenic regions of three proteins CarO, ZnuD and OmpA, were selected. These regions were bound by a GGGS linker. The binding affinity and molecular interactions of the vaccine with the immune receptors TLR2 and TLR4 were studied using molecular docking analysis. This vaccine design was subjected to in silico immune simulations using C-ImmSim. The designed vaccine was highly antigenic, non-allergenic and stable. TLR2 and TLR4 were selected to analyze the ability of the modeled chimeric protein to interact with immune system receptors. The results showed strong interaction between the designed protein vaccine with TLR2 (-18.8 kcal mol-1) and TLR4 (-15.1 kcal mol-1). To verify the stability of the interactions and the structure of the designed protein, molecular dynamics (MD) simulations were performed for 200 ns. Various analyses using MD showed that the protein structure is stable alone and in interaction with TLR2 and TLR4. The ability of the vaccine candidate protein to stimulate the immune system to produce the necessary cytokines and antibodies against Acinetobacter baumannii was also demonstrated by the ability of the protein designed using the C-ImmSim web server to induce an immune response. Therefore, the designed protein vaccine may be a suitable candidate for in vivo as well as in vitro studies against Acinetobacter baumannii infections.

Precision immunotherapy for cholangiocarcinoma: Pioneering the use of human-derived anti-cMET single chain variable fragment in anti-cMET chimeric antigen receptor (CAR) NK cells

Cholangiocarcinoma (CCA) presents a significant clinical challenge which is often identified in advanced stages, therby restricting the effectiveness of surgical interventions for most patients. The high incidence of cancer recurrence and resistance to chemotherapy further contribute to a bleak prognosis and low survival rates. To address this pressing need for effective therapeutic strategies, our study focuses on the development of an innovative cellular immunotherapy, specifically utilizing chimeric antigen receptor (CAR)-engineered natural killer (NK) cells designed to target the cMET receptor tyrosine kinase. In this investigation, we initiated the screening of a phage library displaying human single-chain variable fragment (ScFv) to identify novel ScFv molecules with specificity for cMET. Remarkably, ScFv11, ScFv72, and ScFv114 demonstrated exceptional binding affinity, confirmed by molecular docking analysis. These selected ScFvs, in addition to the well-established anti-cMET ScFvA, were integrated into a CAR cassette harboring CD28 transmembrane region-41BB-CD3ζ domains. The resulting anti-cMET CAR constructs were transduced into NK-92 cells, generating potent anti-cMET CAR-NK-92 cells. To assess the specificity and efficacy of these engineered cells, we employed KKU213A cells with high cMET expression and KKU055 cells with low cMET levels. Notably, co-culture of anti-cMET CAR-NK-92 cells with KKU213A cells resulted in significantly increased cell death, whereas no such effect was observed with KKU055 cells. In summary, our study identified cMET as a promising therapeutic target for CCA. The NK-92 cells, armed with the anti-cMET CAR molecule, have shown strong ability to kill cancer cells specifically, indicating their potential as a promising treatment for CCA in the future.

Chemical genetics reveals cross-activation of plant developmental signaling by the immune peptide-receptor pathway

Cells sense and integrate multiple signals to coordinate development and defence. A receptor-kinase signaling pathway for plant stomatal development shares components with the immunity pathway. The mechanism ensuring their signal specificities remains unclear. Using chemical genetics, here we report the identification of a small molecule, kC9, that triggers excessive stomatal differentiation by inhibiting the canonical ERECTA receptor-kinase pathway. kC9 binds to and inhibits the downstream MAP kinase MPK6, perturbing its substrate interaction. Strikingly, activation of immune signaling by a bacterial flagellin peptide nullified kC9's effects on stomatal development. This cross-activation of stomatal development by immune signaling depends on the immune receptor FLS2 and occurs even in the absence of kC9 if the ERECTA-family receptor population becomes suboptimal. Furthermore, proliferating stomatal-lineage cells are vulnerable to the immune signal penetration. Our findings suggest that the signal specificity between development and immunity can be ensured by MAP Kinase homeostasis reflecting the availability of upstream receptors, thereby providing a novel view on signal specificity.

An Amphiphilic Cell-Penetrating Macrocycle for Efficient Cytosolic Delivery of Proteins, DNA, and CRISPR Cas9

The discovery of safe platforms that can circumvent the endocytic pathway is of great significance for biological therapeutics that are usually degraded during endocytosis. Here we show that a self-assembled and dynamic macrocycle can passively diffuse through the cell membrane and deliver a broad range of biologics, including proteins, CRISPR Cas9, and ssDNA, directly to the cytosol while retaining their bioactivity. Cell-penetrating macrocycle CPM can be easily prepared from the room temperature condensation of diketopyrrolopyrrole lactams with diamines. We attribute the high cellular permeability of CPM to its amphiphilic nature and chameleonic properties. It adopts conformations that partially bury polar groups and expose hydrophobic side chains, thus self-assembling into micellar-like structures. Its superior fluorescence makes CPM trackable inside cells where it follows the endomembrane system. CPM outperformed commercial reagents for biologics delivery and showed high RNA knockdown efficiency of CRISPR Cas9. We envisage that this macrocycle will be an ideal starting point to design and synthesize biomimetic macrocyclic tags that can readily facilitate the interaction and uptake of biomolecules and overcome endosomal digestion.

UNC-45 assisted myosin folding depends on a conserved FX3HY motif implicated in Freeman Sheldon Syndrome

Myosin motors are critical for diverse motility functions, ranging from cytokinesis and endocytosis to muscle contraction. The UNC-45 chaperone controls myosin function mediating the folding, assembly, and degradation of the muscle protein. Here, we analyze the molecular mechanism of UNC-45 as a hub in myosin quality control. We show that UNC-45 forms discrete complexes with folded and unfolded myosin, forwarding them to downstream chaperones and E3 ligases. Structural analysis of a minimal chaperone:substrate complex reveals that UNC-45 binds to a conserved FX3HY motif in the myosin motor domain. Disrupting the observed interface by mutagenesis prevents myosin maturation leading to protein aggregation in vivo. We also show that a mutation in the FX3HY motif linked to the Freeman Sheldon Syndrome impairs UNC-45 assisted folding, reducing the level of functional myosin. These findings demonstrate that a faulty myosin quality control is a critical yet unexplored cause of human myopathies.

Ribosome External Electric Field Regulates Metabolic Enzyme Activity: The RAMBO Effect

Ribosomes bind to many metabolic enzymes and change their activity. A general mechanism for ribosome-mediated amplification of metabolic enzyme activity, RAMBO, was formulated and elucidated for the glycolytic enzyme triosephosphate isomerase, TPI. The RAMBO effect results from a ribosome-dependent electric field-substrate dipole interaction energy that can increase or decrease the ground state of the reactant and product to regulate catalytic rates. NMR spectroscopy was used to determine the interaction surface of TPI binding to ribosomes and to measure the corresponding kinetic rates in the absence and presence of intact ribosome particles. Chemical cross-linking and mass spectrometry revealed potential ribosomal protein binding partners of TPI. Structural results and related changes in TPI energetics and activity show that the interaction between TPI and ribosomal protein L11 mediate the RAMBO effect.

Identification of small-molecule ligand-binding sites on and in the ARNT PAS-B domain

Transcription factors are challenging to target with small-molecule inhibitors due to their structural plasticity and lack of catalytic sites. Notable exceptions include naturally ligand-regulated transcription factors, including our prior work with the hypoxia-inducible factor (HIF)-2 transcription factor, showing that small-molecule binding within an internal pocket of the HIF-2α Per-Aryl hydrocarbon Receptor Nuclear Translocator (ARNT)-Sim (PAS)-B domain can disrupt its interactions with its dimerization partner, ARNT. Here, we explore the feasibility of targeting small molecules to the analogous ARNT PAS-B domain itself, potentially opening a promising route to modulate several ARNT-mediated signaling pathways. Using solution NMR fragment screening, we previously identified several compounds that bind ARNT PAS-B and, in certain cases, antagonize ARNT association with the transforming acidic coiled-coil containing protein 3 transcriptional coactivator. However, these ligands have only modest binding affinities, complicating characterization of their binding sites. We address this challenge by combining NMR, molecular dynamics simulations, and ensemble docking to identify ligand-binding "hotspots" on and within the ARNT PAS-B domain. Our data indicate that the two ARNT/transforming acidic coiled-coil containing protein 3 inhibitors, KG-548 and KG-655, bind to a β-sheet surface implicated in both HIF-2 dimerization and coactivator recruitment. Furthermore, while KG-548 binds exclusively to the β-sheet surface, KG-655 can additionally bind within a water-accessible internal cavity in ARNT PAS-B. Finally, KG-279, while not a coactivator inhibitor, exemplifies ligands that preferentially bind only to the internal cavity. All three ligands promoted ARNT PAS-B homodimerization, albeit to varying degrees. Taken together, our findings provide a comprehensive overview of ARNT PAS-B ligand-binding sites and may guide the development of more potent coactivator inhibitors for cellular and functional studies.

Adaptation of turnip mosaic virus to Arabidopsis thaliana involves rewiring of VPg-host proteome interactions

The outcome of a viral infection depends on a complex interplay between the host physiology and the virus, mediated through numerous protein-protein interactions. In a previous study, we used high-throughput yeast two-hybrid (HT-Y2H) to identify proteins in Arabidopsis thaliana that bind to the proteins encoded by the turnip mosaic virus (TuMV) genome. Furthermore, after experimental evolution of TuMV lineages in plants with mutations in defense-related or proviral genes, most mutations observed in the evolved viruses affected the VPg cistron. Among these mutations, D113G was a convergent mutation selected in many lineages across different plant genotypes, including cpr5-2 with constitutive expression of systemic acquired resistance. In contrast, mutation R118H specifically emerged in the jin1 mutant with affected jasmonate signaling. Using the HT-Y2H system, we analyzed the impact of these two mutations on VPg's interaction with plant proteins. Interestingly, both mutations severely compromised the interaction of VPg with the translation initiation factor eIF(iso)4E, a crucial interactor for potyvirus infection. Moreover, mutation D113G, but not R118H, adversely affected the interaction with RHD1, a zinc-finger homeodomain transcription factor involved in regulating DNA demethylation. Our results suggest that RHD1 enhances plant tolerance to TuMV infection. We also discuss our findings in a broad virus evolution context.

Cumulative phylogenetic, sequence and structural analysis of Insulin superfamily proteins provide unique structure-function insights

The insulin superfamily proteins (ISPs), in particular, insulin, IGFs and relaxin proteins are key modulators of animal physiology. They are known to have evolved from the same ancestral gene and have diverged into proteins with varied sequences and distinct functions, but maintain a similar structural architecture stabilized by highly conserved disulphide bridges. The recent surge of sequence data and the structures of these proteins prompted a need for a comprehensive analysis, which connects the evolution of these sequences (427 sequences) in the light of available functional and structural information including representative complex structures of ISPs with their cognate receptors. This study reveals (a) unusually high sequence conservation of IGFs (>90 % conservation in 184 sequences) and provides a possible structure-based rationale for such high sequence conservation; (b) provides an updated definition of the receptor-binding signature motif of the functionally diverse relaxin family members (c) provides a probable non-canonical C-peptide cleavage site in a few insulin sequences. The high conservation of IGFs appears to represent a classic case of resistance to sequence diversity exerted by physiologically important interactions with multiple partners. We also propose a probable mechanism for C-peptide cleavage in a few distinct insulin sequences and redefine the receptor-binding signature motif of the relaxin family. Lastly, we provide a basis for minimally modified insulin mutants with potential therapeutic application, inspired by concomitant changes observed in other insulin superfamily protein members supported by molecular dynamics simulation.

Toxic Small Alarmone Synthetase FaRel2 inhibits translation by pyrophosphorylating tRNAGly and tRNAThr

Translation-targeting toxic Small Alarmone Synthetases (toxSAS) are effectors of bacterial Toxin-Antitoxin systems that pyrophosphorylate the 3'-CCA end of tRNA to prevent aminoacylation. toxSAS are implicated in antiphage immunity: phage detection triggers the toxSAS activity to shut down viral production. We show that the toxSAS FaRel2 inspects the tRNA acceptor stem to specifically select tRNAGly and tRNAThr. The 1st, 2nd, 4th and 5th base pairs the stem act as the specificity determinants. We show that the toxSASs PhRel2 and CapRelSJ46 differ in tRNA specificity from FaRel2, and rationalise this through structural modelling: while the universal 3'-CCA end slots into a highly conserved CCA recognition groove, the acceptor stem recognition region is variable across toxSAS diversity. As phages use tRNA isoacceptors to overcome tRNA-targeting defences, we hypothesise that highly evolvable modular tRNA recognition allows for the escape of viral countermeasures through tRNA substrate specificity switching.

Unraveling the Molecular Dance: Insights into TREM2/DAP12 Complex Formation in Alzheimer's Disease through Molecular Dynamics Simulations

Alzheimer's disease (AD) is a widespread neurodegenerative condition affecting millions globally. Recent research has implicated variants of the triggering receptor expressed in myeloid cells 2 (TREM2) as risk factors for AD. TREM2, an immunomodulatory receptor on microglial surfaces, plays a pivotal role in regulating microglial activation by association with DNAX-activation protein 12 (DAP12). Despite its significance, the mechanism underlying the formation of the complex between the transmembrane domains (TMDs) of TREM2 and DAP12 remains unclear. This study employs multiscale molecular dynamics (MD) simulations to investigate three TMD complex models, including two derived from experiments and one generated by AlphaFold2. Conducted within a lipid membrane consisting of an 80:20 mixture of phosphatidylcholine (POPC) and cholesterol, our analysis reveals hydrogen-bonding interactions between K26 of TREM2 and D16 of DAP12 in all three models, consistent with previous experimental findings. Our results elucidate the different spatial conformations observed in the models and offer insights into the structure of the TREM2/DAP12 TMD complex. Furthermore, we elucidate the role of charged residues in the assembly structure of the complex within the lipid membrane. These findings enhance our understanding of the molecular mechanism governing TREM2/DAP12 complex formation, providing a foundation for designing novel therapeutic strategies to address AD and other neurodegenerative diseases.

Current treatment strategies for targeting virulence factors and biofilm formation in Acinetobacter baumannii

A higher prevalence of Acinetobacter baumannii infections and mortality rate has been reported recently in hospital-acquired infections (HAI). The biofilm-forming capability of A. baumannii makes it an extremely dangerous pathogen, especially in device-associated hospital-acquired infections (DA-HAI), thereby it resists the penetration of antibiotics. Further, the transmission of the SARS-CoV-2 virus was exacerbated in DA-HAI during the epidemic. This review specifically examines the complex interconnections between several components and genes that play a role in the biofilm formation and the development of infections. The current review provides insights into innovative treatments and therapeutic approaches to combat A. baumannii biofilm-related infections, thereby ultimately improving patient outcomes and reducing the burden of HAI.

Computational evidences of a misfolding event in an aggregation-prone light chain preceding the formation of the non-native pathogenic dimer

Antibody light chain amyloidosis is a disorder in which protein aggregates, mainly composed of immunoglobulin light chains, deposit in diverse tissues impairing the correct functioning of organs. Interestingly, due to the high susceptibility of antibodies to mutations, AL amyloidosis appears to be strongly patient-specific. Indeed, every patient will display their own mutations that will make the proteins involved prone to aggregation thus hindering the study of this disease on a wide scale. In this framework, determining the molecular mechanisms that drive the aggregation could pave the way to the development of patient-specific therapeutics. Here, we focus on a particular patient-derived light chain, which has been experimentally characterized. We investigated the early phases of the aggregation pathway through extensive full-atom molecular dynamics simulations, highlighting a structural rearrangement and the exposure of two hydrophobic regions in the aggregation-prone species. Next, we moved to consider the pathological dimerization process through docking and molecular dynamics simulations, proposing a dimeric structure as a candidate pathological first assembly. Overall, our results shed light on the first phases of the aggregation pathway for a light chain at an atomic level detail, offering new structural insights into the corresponding aggregation process.

Modulating mycobacterial envelope integrity for antibiotic synergy with benzothiazoles

Developing effective tuberculosis drugs is hindered by mycobacteria's intrinsic antibiotic resistance because of their impermeable cell envelope. Using benzothiazole compounds, we aimed to increase mycobacterial cell envelope permeability and weaken the defenses of Mycobacterium marinum, serving as a model for Mycobacterium tuberculosis Initial hit, BT-08, significantly boosted ethidium bromide uptake, indicating enhanced membrane permeability. It also demonstrated efficacy in the M. marinum-zebrafish embryo infection model and M. tuberculosis-infected macrophages. Notably, BT-08 synergized with established antibiotics, including vancomycin and rifampicin. Subsequent medicinal chemistry optimization led to BT-37, a non-toxic and more potent derivative, also enhancing ethidium bromide uptake and maintaining synergy with rifampicin in infected zebrafish embryos. Mutants of M. marinum resistant to BT-37 revealed that MMAR_0407 (Rv0164) is the molecular target and that this target plays a role in the observed synergy and permeability. This study introduces novel compounds targeting a new mycobacterial vulnerability and highlights their cooperative and synergistic interactions with existing antibiotics.

Potent and specific antibiotic combination therapy against Clostridioides difficile

Keratinicyclins and keratinimicins are recently discovered glycopeptide antibiotics. Keratinimicins show broad-spectrum activity against Gram-positive bacteria, while keratinicyclins form a new chemotype by virtue of an unusual oxazolidinone moiety and exhibit specific antibiosis against Clostridioides difficile. Here we report the mechanism of action of keratinicyclin B (KCB). We find that steric constraints preclude KCB from binding peptidoglycan termini. Instead, KCB inhibits C. difficile growth by binding wall teichoic acids (WTAs) and interfering with cell wall remodeling. A computational model, guided by biochemical studies, provides an image of the interaction of KCB with C. difficile WTAs and shows that the same H-bonding framework used by glycopeptide antibiotics to bind peptidoglycan termini is used by KCB for interacting with WTAs. Analysis of KCB in combination with vancomycin (VAN) shows highly synergistic and specific antimicrobial activity, and that nanomolar combinations of the two drugs are sufficient for complete growth inhibition of C. difficile, while leaving common commensal strains unaffected.

Mechanism of the blood-brain barrier modulation by cadherin peptides

Aim

This study was aimed at finding the binding site on the human E-cadherin for Ala-Asp-Thr Cyclic 5 (ADTC5), ADTC7, and ADTC9 peptides as blood-brain barrier modulator (BBBM) for determining their mechanism of action in modulating the blood-brain barrier (BBB).

Methods

ADTC7 and ADTC9 were derivatives of ADTC5 where the Val6 residue in ADTC5 was replaced by Glu6 and Tyr6 residues, respectively. The binding properties of ADTC5, ADTC7, and ADTC9 to the extracellular-1 (EC1) domain of E-cadherin were evaluated using chemical shift perturbation (CSP) method in the two dimensional (2D) 1H-15N-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectroscopy. Molecular docking experiments were used to determine the binding sites of these peptides to the EC1 domain of E-cadherin.

Results

This study indicates that ADTC5 has the highest binding affinity to the EC1 domain of E-cadherin compared to ADTC7 and ADTC9, suggesting the importance of the Val6 residue as shown in our previous in vitro study. All three peptides have a similar binding site at the hydrophobic binding pocket where the domain swapping occurs. ADTC5 has a higher overlapping binding site with ADTC7 than that of ADTC9. Binding of ADTC5 on the EC1 domain influences the conformation of the EC1 C-terminal tail.

Conclusions

These peptides bind the domain swapping region of the EC1 domain to inhibit the trans-cadherin interaction that creates intercellular junction modulation to increase the BBB paracellular porosity.

Impact of subtype C-specific amino acid variants on HIV-1 Tat-TAR interaction: insights from molecular modelling and dynamics

BACKGROUND

HIV-1 produces Tat, a crucial protein for transcription, viral replication, and CNS neurotoxicity. Tat interacts with TAR, enhancing HIV reverse transcription. Subtype C Tat variants (C31S, R57S, Q63E) are associated with reduced transactivation and neurovirulence compared to subtype B. However, their precise impact on Tat-TAR binding is unclear. This study investigates how these substitutions affect Tat-TAR interaction.

METHODS

We utilized molecular modelling techniques, including MODELLER, to produce precise three-dimensional structures of HIV-1 Tat protein variants. We utilized Tat subtype B as the reference or wild type, and generated Tat variants to mirror those amino acid variants found in Tat subtype C. Subtype C-specific amino acid substitutions were selected based on their role in the neuropathogenesis of HIV-1. Subsequently, we conducted molecular docking of each Tat protein variant to TAR using HDOCK, followed by molecular dynamic simulations.

RESULTS

Molecular docking results indicated that Tat subtype B (TatWt) showed the highest affinity for the TAR element (-262.07), followed by TatC31S (-261.61), TatQ63E (-256.43), TatC31S/R57S/Q63E (-238.92), and TatR57S (-222.24). However, binding free energy analysis showed higher affinities for single variants TatQ63E (-349.2 ± 10.4 kcal/mol) and TatR57S (-290.0 ± 9.6 kcal/mol) compared to TatWt (-247.9 ± 27.7 kcal/mol), while TatC31S and TatC31S/R57SQ/63E showed lower values. Interactions over the protein trajectory were also higher for TatQ63E and TatR57S compared to TatWt, TatC31S, and TatC31S/R57SQ/63E, suggesting that modifying amino acids within the Arginine/Glutamine-rich region notably affects TAR interaction. Single amino acid mutations TatR57S and TatQ63E had a significant impact, while TatC31S had minimal effect. Introducing single amino acid variants from TatWt to a more representative Tat subtype C (TatC31S/R57SQ/63E) resulted in lower predicted binding affinity, consistent with previous findings.

CONCLUSIONS

These identified amino acid positions likely contribute significantly to Tat-TAR interaction and the differential pathogenesis and neuropathogenesis observed between subtype B and subtype C. Additional experimental investigations should prioritize exploring the influence of these amino acid signatures on TAR binding to gain a comprehensive understanding of their impact on viral transactivation, potentially identifying them as therapeutic targets.

The specificity of intermodular recognition in a prototypical nonribosomal peptide synthetase depends on an adaptor domain

In the quest for new bioactive substances, nonribosomal peptide synthetases (NRPS) provide biodiversity by synthesizing nonproteinaceous peptides with high cellular activity. NRPS machinery consists of multiple modules, each catalyzing a unique series of chemical reactions. Incomplete understanding of the biophysical principles orchestrating these reaction arrays limits the exploitation of NRPSs in synthetic biology. Here, we use nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry to solve the conundrum of how intermodular recognition is coupled with loaded carrier protein specificity in the tomaymycin NRPS. We discover an adaptor domain that directly recruits the loaded carrier protein from the initiation module to the elongation module and reveal its mechanism of action. The adaptor domain of the type found here has specificity rules that could potentially be exploited in the design of engineered NRPS machinery.

The HADDOCK2.4 web server for integrative modeling of biomolecular complexes

Interactions between macromolecules, such as proteins and nucleic acids, are essential for cellular functions. Experimental methods can fail to provide all the information required to fully model biomolecular complexes at atomic resolution, particularly for large and heterogeneous assemblies. Integrative computational approaches have, therefore, gained popularity, complementing traditional experimental methods in structural biology. Here, we introduce HADDOCK2.4, an integrative modeling platform, and its updated web interface ( https://wenmr.science.uu.nl/haddock2.4 ). The platform seamlessly integrates diverse experimental and theoretical data to generate high-quality models of macromolecular complexes. The user-friendly web server offers automated parameter settings, access to distributed computing resources, and pre- and post-processing steps that enhance the user experience. To present the web server's various interfaces and features, we demonstrate two different applications: (i) we predict the structure of an antibody-antigen complex by using NMR data for the antigen and knowledge of the hypervariable loops for the antibody, and (ii) we perform coarse-grained modeling of PRC1 with a nucleosome particle guided by mutagenesis and functional data. The described protocols require some basic familiarity with molecular modeling and the Linux command shell. This new version of our widely used HADDOCK web server allows structural biologists and non-experts to explore intricate macromolecular assemblies encompassing various molecule types.

Epitope Identification of an mGlu5 Receptor Nanobody Using Physics-Based Molecular Modeling and Deep Learning Techniques

The world has witnessed a revolution in therapeutics with the development of biological medicines such as antibodies and antibody fragments, notably nanobodies. These nanobodies possess unique characteristics including high specificity and modulatory activity, making them promising candidates for therapeutic applications. Identifying their binding mode is essential for their development. Experimental structural techniques are effective to get such information, but they are expensive and time-consuming. Here, we propose a computational approach, aiming to identify the epitope of a nanobody that acts as an agonist and a positive allosteric modulator at the rat metabotropic glutamate receptor 5. We employed multiple structure modeling tools, including various artificial intelligence algorithms for epitope mapping. The computationally identified epitope was experimentally validated, confirming the success of our approach. Additional dynamics studies provided further insights on the modulatory activity of the nanobody. The employed methodologies and approaches initiate a discussion on the efficacy of diverse techniques for epitope mapping and later nanobody engineering.

The Legionella collagen-like protein employs a distinct binding mechanism for the recognition of host glycosaminoglycans

Bacterial adhesion is a fundamental process which enables colonisation of niche environments and is key for infection. However, in Legionella pneumophila, the causative agent of Legionnaires' disease, these processes are not well understood. The Legionella collagen-like protein (Lcl) is an extracellular peripheral membrane protein that recognises sulphated glycosaminoglycans on the surface of eukaryotic cells, but also stimulates bacterial aggregation in response to divalent cations. Here we report the crystal structure of the Lcl C-terminal domain (Lcl-CTD) and present a model for intact Lcl. Our data reveal that Lcl-CTD forms an unusual trimer arrangement with a positively charged external surface and negatively charged solvent exposed internal cavity. Through molecular dynamics simulations, we show how the glycosaminoglycan chondroitin-4-sulphate associates with the Lcl-CTD surface via distinct binding modes. Our findings show that Lcl homologs are present across both the Pseudomonadota and Fibrobacterota-Chlorobiota-Bacteroidota phyla and suggest that Lcl may represent a versatile carbohydrate-binding mechanism.

Identification of Small Molecule Ligand Binding Sites On and In the ARNT PAS-B Domain

Transcription factors are generally challenging to target with small molecule inhibitors due to their structural plasticity and lack of catalytic sites. Notable exceptions include several naturally ligand-regulated transcription factors, including our prior work with the heterodimeric HIF-2 transcription factor which showed that small molecule binding within an internal pocket of the HIF-2α PAS-B domain can disrupt its interactions with its dimerization partner, ARNT. Here, we explore the feasibility of similarly targeting small molecules to the analogous ARNT PAS-B domain itself, potentially opening a promising route to simultaneously modulate several ARNT-mediated signaling pathways. Using solution NMR screening of an in-house fragment library, we previously identified several compounds that bind ARNT PAS-B and, in certain cases, antagonize ARNT association with the TACC3 transcriptional coactivator. However, these ligands have only modest binding affinities, complicating characterization of their binding sites. We address this challenge by combining NMR, MD simulations, and ensemble docking to identify ligand-binding 'hotspots' on and within the ARNT PAS-B domain. Our data indicate that the two ARNT/TACC3 inhibitors, KG-548 and KG-655, bind to a β-sheet surface implicated in both HIF-2 dimerization and coactivator recruitment. Furthermore, while KG-548 binds exclusively to the β-sheet surface, KG-655 can additionally bind within a water-accessible internal cavity in ARNT PAS-B. Finally, KG-279, while not a coactivator inhibitor, exemplifies ligands that preferentially bind only to the internal cavity. All three ligands promoted ARNT PAS-B homodimerization, albeit to varying degrees. Taken together, our findings provide a comprehensive overview of ARNT PAS-B ligand-binding sites and may guide the development of more potent coactivator inhibitors for cellular and functional studies.

Predicting binding events in very flexible, allosteric, multi-domain proteins

Knowledge of the structures formed by proteins and small ligands is of fundamental importance for understanding molecular principles of chemotherapy and for designing new and more effective drugs. Due to the still high costs and to the several limitations of experimental techniques, it is most often desirable to predict these ligand-protein complexes in silico, particularly when screening for new putative drugs from databases of millions of compounds. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology aimed at generating bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites. Validation was performed on the enzyme adenylate kinase (ADK), a paradigmatic example of proteins that undergo very large conformational changes upon ligand binding. By only exploiting the unbound structure and the putative binding sites of the protein, we generated a significant fraction of bound-like structures, which employed in ensemble-docking calculations allowed to find native-like poses of substrates, inhibitors, and catalytically incompetent binders. Our protocol provides a general framework for the generation of bound-like conformations of flexible proteins that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening for difficult targets, prediction of the impact of amino acid mutations on structure and dynamics, and protein engineering.

A multifunctional antibody fusion protein 57103 targeting CD24, IL-4R, and αvβ3 for treating cancer and regulating the tumor microenvironment

Cancer is one of the top 10 fatal diseases worldwide, among which advanced metastatic carcinoma has the highest mortality rate. Sunitinib and immune checkpoint blockers are commonly used to treat metastatic renal carcinoma with limited efficacy. Therefore, there is an urgent need to develop novel targeted therapies for metastatic renal cancer. In this study, we designed an antibody fusion protein, 57103, that simultaneously targeted the cluster of differentiation 24 (CD24), interleukin 4 receptor (IL-4R), and integrin receptors αvβ3 and α5β1. In vitro assays showed that 57103 significantly suppressed the proliferation, migration, invasion, colony formation, and adhesion abilities of renal cancer cells, resulting in a comprehensive and significant antitumor effect. Furthermore, 57103 inhibited angiogenesis, promoted THP1-derived M0-type macrophage phagocytosis, and enhanced the antibody-dependent cellular cytotoxicity of peripheral blood mononuclear and NK92MI-CD16a cells. In vivo experiments revealed significant inhibition of tumor growth in ACHN cell xenograft nude mice and an MC38-hCD24 tumor-bearing mouse model. Immunohistochemical analysis showed that 57103 decreased the proliferation and induced the apoptosis of renal cancer cells, while inhibiting angiogenesis. The MC38-hPDL1 and MC38-hCD24-hPDL1 tumor-bearing mouse models further offer the possibility of combining 57103 with the PDL1 antagonist atezolizumab. In conclusion, 57103 is a potential candidate drug for the treatment of metastatic renal carcinoma or PDL1-overexpressing cancer.

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.

In silico analysis of the different variable domain oriented single-chain variable fragment antibody-antigen complexes

Single-chain variable fragment (scFv) antibodies hold great potential as diagnostic tools and therapeutic agents, especially for tumor cells. Since these applications require their production with improved properties, the design strategy of scFvs is crucial for their active, soluble, and high yield expression with high affinity towards their antigens. The order of VL and VH domains is one of the important parameters that affect the expression and binding affinity properties of scFvs. In addition, the optimum order of VL and VH domains could change for each scFv. In the present study, we used computer simulation tools to evaluate the effect of variable domain orientation on structure, stability, interacting residues of scFvs, and binding free energies of scFv-antigen complexes. We selected anti-HER2 scFv, which is specific for human epidermal growth receptor 2 (HER2) overexpressed in breast cancer, and anti-IL-1β scFv against IL-1β which is an important inflammatory biomarker, as model scFvs. Molecular dynamics simulations of the scFv-antigen complexes for 100 ns resulted in stability and compactness for both scFv constructs. Interaction and binding free energies calculated by the Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) approach suggested that the relative binding energies of anti-HER2 scFv-VLVH and anti-HER2 scFv-VHVL constructs had similar binding affinity towards HER2, while a relatively more negative binding free energy obtained between anti-IL-1β scFv-VHVL and IL-1β pointed to a higher binding affinity. The in silico approach and the results obtained here could be applied as a guide for future experimental interaction studies for highly specific scFvs used as biotechnological tools.Communicated by Ramaswamy H. Sarma.

SurfPro-NN: A 3D point cloud neural network for the scoring of protein-protein docking models based on surfaces features and protein language models

Protein-protein interactions (PPI) play a crucial role in numerous key biological processes, and the structure of protein complexes provides valuable clues for in-depth exploration of molecular-level biological processes. Protein-protein docking technology is widely used to simulate the spatial structure of proteins. However, there are still challenges in selecting candidate decoys that closely resemble the native structure from protein-protein docking simulations. In this study, we introduce a docking evaluation method based on three-dimensional point cloud neural networks named SurfPro-NN, which represents protein structures as point clouds and learns interaction information from protein interfaces by applying a point cloud neural network. With the continuous advancement of deep learning in the field of biology, a series of knowledge-rich pre-trained models have emerged. We incorporate protein surface representation models and language models into our approach, greatly enhancing feature representation capabilities and achieving superior performance in protein docking model scoring tasks. Through comprehensive testing on public datasets, we find that our method outperforms state-of-the-art deep learning approaches in protein-protein docking model scoring. Not only does it significantly improve performance, but it also greatly accelerates training speed. This study demonstrates the potential of our approach in addressing protein interaction assessment problems, providing strong support for future research and applications in the field of biology.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Identification of a broad-spectrum high-affinity peptide ligand for the purification of spike proteins

Since the outbreak of coronavirus disease 2019, the global demand for vaccines has increased rapidly to prevent infection and protect high-risk populations. However, identifying viral mutations poses an additional challenge for chromatographic purification of vaccines and subunit vaccines. In this study, a new affinity peptide model, X1VX2GLNX3WX4RYSK, was established, and a library of 612 peptides was generated for ligand screening. Based on a multistep strategy of ligand screening, 18 candidate peptides were obtained. The top ranking peptide, LP14 (YVYGLNIWLRYSK), and two other representative peptides, LP02 and LP06, with lower rankings were compared via molecular dynamics simulation. The results revealed that peptide binding to the receptor binding domain (RBD) was driven by hydrophobic interactions and the key residues involved in the binding were identified. Surface plasmon resonance analysis further confirmed that LP14 had the highest affinity for the wild RBD (Kd=0.520 μmol/L), and viral mutation had little influence on the affinity of LP14, demonstrating its great potential as a broad-spectrum ligand for RBD purification. Finally, chromatographic performance of LP14-coupled gel-packed column verified that both wild and omicron RBDs could be purified and were eluted by 0.1 mol/L Gly-HCl buffer (pH 3.0). This research identified a broad-spectrum peptide for RBD purification based on rational design and demonstrated its potential application in the purification of RBDs from complex feedstock.

ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

In silico analysis of the wild-type and mutant-type of BRCA2 gene

BACKGROUND

The aim of this study was to conduct an in silico analysis of a novel compound heterozygous variant in breast cancer susceptibility gene 2 (BRCA2) to clarify its structure-function relationship and elucidate the molecular mechanisms underlying triple-negative breast cancer (TNBC).

METHODS

A tumor biopsy sample was obtained from a 42-year-old Chinese woman during surgery, and a maxBRCA™ test was conducted using the patient's whole blood. We obtained an experimentally determined 3D structure (1mje.pdb) of the BRCA2 protein from the Protein Data Bank (PDB) as a relatively reliable reference. Subsequently, the wild-type and mutant structures were predicted using SWISS-MODEL and AlphaFold, and the accuracy of these predictions was assessed through the SAVES online server. Furthermore, we utilized a high ambiguity-driven protein-protein docking (HADDOCK) algorithm and protein-ligand interaction profiler (PLIP) to predict the pathogenicity of the mutations and elucidate pathogenic mechanisms that potentially underlies TNBC.

RESULTS

Histological examination revealed that the tumor biopsy sample exhibited classical pathological characteristics of TNBC. Furthermore, the maxBRCA™ test revealed two compound heterozygous BRCA2 gene mutations (c.7670 C > T.pA2557V and c.8356G > A.pA2786T). Through performing in silico structural analyses and constructing of 3D models of the mutants, we established that the mutant amino acids valine and threonine were located in the helical domain and oligonucleotide binding 1 (OB1), regions that interact with DSS1.

CONCLUSION

Our analysis revealed that substituting valine and threonine in the helical domain region alters the structure and function of BRCA2 proteins. This mutation potentially affects the binding of proteins and DNA fragments and disrupts interactions between the helical domain region and OB1 with DSS1, potentially leading to the development of TNBC. Our findings suggest that the identified compound heterozygous mutation contributes to the clinical presentation of TNBC, providing new insights into the pathogenesis of TNBC and the influence of compound heterozygous mutations in BRCA2.

Molecular mechanism of trehalose 6-phosphate inhibition of the plant metabolic sensor kinase SnRK1

SUCROSE-NON-FERMENTING1-RELATED PROTEIN KINASE1 (SnRK1), a central plant metabolic sensor kinase, phosphorylates its target proteins, triggering a global shift from anabolism to catabolism. Molecular modeling revealed that upon binding of KIN10 to GEMINIVIRUS REP-INTERACTING KINASE1 (GRIK1), KIN10's activation T-loop reorients into GRIK1's active site, enabling its phosphorylation and activation. Trehalose 6-phosphate (T6P) is a proxy for cellular sugar status and a potent inhibitor of SnRK1. T6P binds to KIN10, a SnRK1 catalytic subunit, weakening its affinity for GRIK1. Here, we investigate the molecular details of T6P inhibition of KIN10. Molecular dynamics simulations and in vitro phosphorylation assays identified and validated the T6P binding site on KIN10. Under high-sugar conditions, T6P binds to KIN10, blocking the reorientation of its activation loop and preventing its phosphorylation and activation by GRIK1. Under these conditions, SnRK1 maintains only basal activity levels, minimizing phosphorylation of its target proteins, thereby facilitating a general shift from catabolism to anabolism.

The molecular interaction of six single-stranded DNA aptamers to cardiac troponin I revealed by docking and molecular dynamics simulation

Cardiac troponin I (cTnI) is a cardiac biomarker for diagnosing ischemic heart disease and acute myocardial infarction. Current biochemical assays use antibodies (Abs) due to their high specificity and sensitivity. However, there are some limitations, such as the high-cost production of Abs due to complex instruments, reagents, and steps; the variability of Abs quality from batch to batch; the low stability at high temperatures; and the difficulty of chemical modification. Aptamer overcomes the limitations of antibodies, such as relatively lower cost, high reproducibility, high stability, and ease of being chemically modified. Aptamers are three-dimensional architectures of single-stranded RNA or DNA that bind to targets such as proteins. Six aptamers (Tro1-Tro6) with higher binding affinity than an antibody have been identified, but the molecular interaction has not been studied. In this study, six DNA aptamers were modeled and docked to cTnI protein. Molecular docking revealed that the interaction between all aptamer and cTnI happened in the similar cTnI region. The interaction between aptamer and cTnI involved hydrophobic interaction, hydrogen bonds, π-cation interactions, π-stack interactions, and salt-bridge formation. The calculated binding energy of all complexes was negative, which means that the complex formation was thermodynamically favorable. The electrostatic energy term was the main driving force of the interaction between all aptamer and cTnI. This study could be used to predict the behavior of further modified aptamer to improve aptamer performance.

BSA Binding and Aggregate Formation of a Synthetic Amino Acid with Potential for Promoting Fibroblast Proliferation: An In Silico, CD Spectroscopic, DLS, and Cellular Study

This study presents the chemical synthesis, purification, and characterization of a novel non-natural synthetic amino acid. The compound was synthesized in solution, purified, and characterized using NMR spectroscopy, polarimetry, and melting point determination. Dynamic Light Scattering (DLS) analysis demonstrated its ability to form aggregates with an average size of 391 nm, extending to the low micrometric size range. Furthermore, cellular biological assays revealed its ability to enhance fibroblast cell growth, highlighting its potential for tissue regenerative applications. Circular dichroism (CD) spectroscopy showed the ability of the synthetic amino acid to bind serum albumins (using bovine serum albumin (BSA) as a model), and CD deconvolution provided insights into the changes in the secondary structures of BSA upon interaction with the amino acid ligand. Additionally, molecular docking using HDOCK software elucidated the most likely binding mode of the ligand inside the BSA structure. We also performed in silico oligomerization of the synthetic compound in order to obtain a model of aggregate to investigate computationally. In more detail, the dimer formation achieved by molecular self-docking showed two distinct poses, corresponding to the lowest and comparable energies, with one pose exhibiting a quasi-coplanar arrangement characterized by a close alignment of two aromatic rings from the synthetic amino acids within the dimer, suggesting the presence of π-π stacking interactions. In contrast, the second pose displayed a non-coplanar configuration, with the aromatic rings oriented in a staggered arrangement, indicating distinct modes of interaction. Both poses were further utilized in the self-docking procedure. Notably, iterative molecular docking of amino acid structures resulted in the formation of higher-order aggregates, with a model of a 512-mer aggregate obtained through self-docking procedures. This model of aggregate presented a cavity capable of hosting therapeutic cargoes and biomolecules, rendering it a potential scaffold for cell adhesion and growth in tissue regenerative applications. Overall, our findings highlight the potential of this synthetic amino acid for tissue regenerative therapeutics and provide valuable insights into its molecular interactions and aggregation behavior.

Probing Protein Structural Changes in Alzheimer's Disease via Quantitative Cross-linking Mass Spectrometry

Alzheimer's disease (AD) is a progressive neurological disorder featuring abnormal protein aggregation in the brain, including the pathological hallmarks of amyloid plaques and hyperphosphorylated tau. Despite extensive research efforts, understanding the molecular intricacies driving AD development remains a formidable challenge. This study focuses on identifying key protein conformational changes associated with the progression of AD. To achieve this, we employed quantitative cross-linking mass spectrometry (XL-MS) to elucidate conformational changes in the protein networks in cerebrospinal fluid (CSF). By using isotopically labeled cross-linkers BS3d0 and BS3d4, we reveal a dynamic shift in protein interaction networks during AD progression. Our comprehensive analysis highlights distinct alterations in protein-protein interactions within mild cognitive impairment (MCI) states. This study accentuates the potential of cross-linked peptides as indicators of AD-related conformational changes, including previously unreported site-specific binding between α-1-antitrypsin (A1AT) and complement component 3 (CO3). Furthermore, this work enables detailed structural characterization of apolipoprotein E (ApoE) and reveals modifications within its helical domains, suggesting their involvement in MCI pathogenesis. The quantitative approach provides insights into site-specific interactions and changes in the abundance of cross-linked peptides, offering an improved understanding of the intricate protein-protein interactions underlying AD progression. These findings lay a foundation for the development of potential diagnostic or therapeutic strategies aimed at mitigating the negative impact of AD.

A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models

The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies, data and feature construction. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.

Dental biomaterials redefined: molecular docking and dynamics-driven dental resin composite optimization

BACKGROUND

Dental resin-based composites are widely recognized for their aesthetic appeal and adhesive properties, which make them integral to modern restorative dentistry. Despite their advantages, adhesion and biomechanical performance challenges persist, necessitating innovative strategies for improvement. This study addressed the challenges associated with adhesion and biomechanical properties in dental resin-based composites by employing molecular docking and dynamics simulation.

METHODS

Molecular docking assesses the binding energies and provides valuable insights into the interactions between monomers, fillers, and coupling agents. This investigation prioritizes SiO2 and TRIS, considering their consistent influence. Molecular dynamics simulations, executed with the Forcite module and COMPASS II force field, extend the analysis to the mechanical properties of dental composite complexes. The simulations encompassed energy minimization, controlled NVT and NPT ensemble simulations, and equilibration stages. Notably, the molecular dynamics simulations spanned a duration of 50 ns.

RESULTS

SiO2 and TRIS consistently emerged as influential components, showcasing their versatility in promoting solid interactions. A correlation matrix underscores the significant roles of van der Waals and desolvation energies in determining the overall binding energy. Molecular dynamics simulations provide in-depth insights into the mechanical properties of dental composite complexes. HEMA-SiO2-TRIS excelled in stiffness, BisGMA-SiO2-TRIS prevailed in terms of flexural strength, and EBPADMA-SiO2-TRIS offered a balanced combination of mechanical properties.

CONCLUSION

These findings provide valuable insights into optimizing dental composites tailored to diverse clinical requirements. While EBPADMA-SiO2-TRIS demonstrates distinct strengths, this study emphasizes the need for further research. Future investigations should validate the computational findings experimentally and assess the material's response to dynamic environmental factors.

Delineating organizational principles of the endogenous L-A virus by cryo-EM and computational analysis of native cell extracts

The high abundance of most viruses in infected host cells benefits their structural characterization. However, endogenous viruses are present in low copy numbers and are therefore challenging to investigate. Here, we retrieve cell extracts enriched with an endogenous virus, the yeast L-A virus. The determined cryo-EM structure discloses capsid-stabilizing cation-π stacking, widespread across viruses and within the Totiviridae, and an interplay of non-covalent interactions from ten distinct capsomere interfaces. The capsid-embedded mRNA decapping active site trench is supported by a constricting movement of two flexible opposite-facing loops. tRNA-loaded polysomes and other biomacromolecules, presumably mRNA, are found in virus proximity within the cell extract. Mature viruses participate in larger viral communities resembling their rare in-cell equivalents in terms of size, composition, and inter-virus distances. Our results collectively describe a 3D-architecture of a viral milieu, opening the door to cell-extract-based high-resolution structural virology.

De novo design of potential peptide analogs against the main protease of Omicron variant using in silico studies

SARS-CoV-2 and its variants are crossing the immunity barrier induced through vaccination. Recent Omicron sub-variants are highly transmissible and have a low mortality rate. Despite the low severity of Omicron variants, these new variants are known to cause acute post-infectious syndromes. Nowadays, novel strategies to develop new potential inhibitors for SARS-CoV-2 and other Omicron variants have gained prominence. For viral replication and survival the main protease of SARS-CoV-2 plays a vital role. Peptide-like inhibitors that mimic the substrate peptide have already proved to be effective in inhibiting the Mpro of SARS-CoV-2 variants. Our systematic canonical amino acid point mutation analysis on the native peptide has revealed various ways to improve the native peptide of the main protease. Multi mutation analysis has led us to identify and design potent peptide-analog inhibitors that act against the Mpro of the Omicron sub-variants. Our in-depth analysis of all-atom molecular dynamics studies has paved the way to characterize the atomistic behavior of Mpro in Omicron variants. Our goal is to develop potent peptide-analogs that could be therapeutically effective against Omicron and its sub-variants.