HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets

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.

Tentonin 3 is a pore-forming subunit of a slow inactivation mechanosensitive channel

Mechanically activating (MA) channels transduce numerous physiological functions. Tentonin 3/TMEM150C (TTN3) confers MA currents with slow inactivation kinetics in somato- and barosensory neurons. However, questions were raised about its role as a Piezo1 regulator and its potential as a channel pore. Here, we demonstrate that purified TTN3 proteins incorporated into the lipid bilayer displayed spontaneous and pressure-sensitive channel currents. These MA currents were conserved across vertebrates and differ from Piezo1 in activation threshold and pharmacological response. Deep neural network structure prediction programs coupled with mutagenetic analysis predicted a rectangular-shaped, tetrameric structure with six transmembrane helices and a pore at the inter-subunit center. The putative pore aligned with two helices of each subunit and had constriction sites whose mutations changed the MA currents. These findings suggest that TTN3 is a pore-forming subunit of a distinct slow inactivation MA channel, potentially possessing a tetrameric structure.

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.

Design and validation of a novel multi-epitopes vaccine against hantavirus

Hantavirus is a member of the order Bunyavirales and an emerging global pathogen. Hantavirus infections have affected millions of people globally based on available epidemiological data and research studies. Hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS) are the two main human diseases associated with hantavirus infections. Hence, efforts are required to develop a potent vaccine against the pathogen. The only vaccine that is in use for hantavirus is an inactivated virus vaccine, "Hantavax", but it failed to produce neutralizing antibodies. Vaccine development is of much importance in dealing with the surge of hantavirus globally. In this study, hantavirus five proteins (N protein, G1 and G2, L protein, and non-structural proteins) were used in NetCTL 1.2 program to predict T-cell epitopes. To predict major histocompatibility complex (MHC) binding alleles, an immune epitope database (IEDB) was used. All predicted epitopes were then investigated for different immunoinformatics analyses such as antigenicity and toxicity analyses. The good water-soluble, non-toxic, probable antigenic, and DRB*0101 binder was selected. A multi-epitopes-based vaccine designing was then done where linkers were used to connect the shortlisted epitopes. In addition, an adjuvant molecule was supplementary to the multi-epitopes peptide to improve the vaccine's immunogenic potential. The final vaccine construct's three-dimensional structure was modeled by ab initio method. The vaccine molecule was then evaluated for its binding potential with TLR-3 immune receptor, which is key for its recognition and processing by the host immune system. Docking studies were performed using HADDOCK software. The best-docked complex was selected and visualized for intermolecular binding and interactions using UCSF Chimera 1.16 software. The findings revealed that the designed vaccine might be a potential vaccine against hantavirus and can be used in experimental animal model testings.Communicated by Ramaswamy H. Sarma.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

A computational quest for identifying potential vaccine candidates against Moraxella lacunata: a multi-pronged approach

Moraxella lacunata is an emerging gram-negative bacterium that is responsible for multiple nosocomial infections. The bacterium is evolving resistance to several antibiotics, and currently, no effective licensed vaccines are available, which warrants the search for new therapeutics. A multi-epitope-based vaccine has been designed for M. lacunata. The complete proteome of M. lacunata contains 10,110 core proteins. Subcellular localization analysis revealed the presence of five proteins in the extracellular matrix, while 19 proteins were predicted to be located in the outer membrane, and 21 proteins were predicted to be located in the periplasmic region. Only two proteins, the type VI secretion system tube protein (Hcp) and the transporter substrate-binding domain-containing protein, were selected for epitope prediction as they fulfilled all the criteria for being potential vaccine candidates. Shortlisted epitopes from the selected proteins were fused together using "GPGPG" linkers to overcome the limitations of single-epitope vaccines. Next, the cholera toxin-B adjuvant was attached to the peptide epitope using an EAAAK linker. Docking analysis was performed to examine the interaction between the vaccine and immune cell receptors, revealing robust intermolecular interactions and a stable binding conformation. Molecular dynamics simulation findings revealed no drastic changes in the binding conformation of complexes during the simulation period. The net binding free energy of vaccine-receptor complexes was estimated using the molecular mechanics energies combined with the Poisson-Boltzmann and surface area continuum solvation (MM-PBSA) method. The reported values were -586.38 kcal/mol, -283.74 kcal/mol, and -296.88 kcal/mol for the TLR-4-vaccine complex, MHC-I-vaccine complex, and MHC-II-vaccine complex, respectively. Furthermore, the molecular mechanics energies combined with the generalized Born and surface area continuum solvation (MM-GBSA) analysis predicted binding free energies of -596.69 kcal/mol, -287.39 kcal/mol, and -298.28 kcal/mol for the TLR-4-vaccine complex, MHC-I-vaccine complex, and MHC-II-vaccine complex, respectively. The theoretical vaccine design proposed in the study could potentially serve as a powerful therapeutic against targeted pathogens, subject to validation through experimental studies.Communicated by Ramaswamy H. Sarma.

Myelin Basic Protein Attenuates Furin-Mediated Bri2 Cleavage and Postpones Its Membrane Trafficking

Myelin basic protein (MBP) is the second most abundant protein in the central nervous system and is responsible for structural maintenance of the myelin sheath covering axons. Previously, we showed that MBP has a more proactive role in the oligodendrocyte homeostasis, interacting with membrane-associated proteins, including integral membrane protein 2B (ITM2B or Bri2) that is associated with familial dementias. Here, we report that the molecular dynamics of the in silico-generated MBP-Bri2 complex revealed that MBP covers a significant portion of the Bri2 ectodomain, assumingly trapping the furin cleavage site, while the surface of the BRICHOS domain, which is responsible for the multimerization and activation of the Bri2 high-molecular-weight oligomer chaperone function, remains unmasked. These observations were supported by the co-expression of MBP with Bri2, its mature form, and disease-associated mutants, which showed that in mammalian cells, MBP indeed modulates the post-translational processing of Bri2 by restriction of the furin-catalyzed release of its C-terminal peptide. Moreover, we showed that the co-expression of MBP and Bri2 also leads to an altered cellular localization of Bri2, restricting its membrane trafficking independently of the MBP-mediated suppression of the Bri2 C-terminal peptide release. Further investigations should elucidate if these observations have physiological meaning in terms of Bri2 as a MBP chaperone activated by the MBP-dependent postponement of Bri2 membrane trafficking.

Flexible protein-protein docking with a multitrack iterative transformer

Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and reranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, for example, structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multitrack iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments, GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. On the Database of Interacting Protein Structures (DIPS) test set, GeoDock achieves a 43% top-1 success rate, outperforming all other tested methods. However, in the standard DIPS train/test splits, we discovered contamination of close homologs in the training set. After decontaminating the training set, the success rate is 31%. On the DB5.5 test set and a benchmark dataset of antibody-antigen complexes, GeoDock outperforms the deep learning models trained using the same dataset but falls behind most of the conventional methods and AlphaFold-Multimer. GeoDock attains an average inference speed of under 1 s on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.

Interface-Based Design of High-Affinity Affibody Ligands for the Purification of RBD from Spike Proteins

The outbreak of coronavirus disease 2019 (COVID-19) has sparked an urgent demand for advanced diagnosis and vaccination worldwide. The discovery of high-affinity ligands is of great significance for vaccine and diagnostic reagent manufacturing. Targeting the receptor binding domain (RBD) from the spike protein of severe acute respiratory syndrome-coronavirus 2, an interface at the outer surface of helices on the Z domain from protein A was introduced to construct a virtual library for the screening of ZRBD affibody ligands. Molecular docking was performed using HADDOCK software, and three potential ZRBD affibodies, ZRBD-02, ZRBD-04, and ZRBD-07, were obtained. Molecular dynamics (MD) simulation verified that the binding of ZRBD affibodies to RBD was driven by electrostatic interactions. Per-residue free energy decomposition analysis further substantiated that four residues with negative-charge characteristics on helix α1 of the Z domain participated in this process. Binding affinity analysis by microscale thermophoresis showed that ZRBD affibodies had high affinity for RBD binding, and the lowest dissociation constant was 36.3 nmol/L for ZRBD-07 among the three potential ZRBD affibodies. Herein, ZRBD-02 and ZRBD-07 affibodies were selected for chromatographic verifications after being coupled to thiol-activated Sepharose 6 Fast Flow (SepFF) gel. Chromatographic experiments showed that RBD could bind on both ZRBD SepFF gels and was eluted by 0.1 mol/L NaOH. Moreover, the ZRBD-07 SepFF gel had a higher affinity for RBD. This research provided a new idea for the design of affibody ligands and validated the potential of affibody ligands in the application of RBD purification from complex feedstock.

Flexible Protein-Protein Docking with a Multi-Track Iterative Transformer

Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and re-ranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, e.g., structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multi-track iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments (MSAs), GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. For a benchmark set of rigid targets, GeoDock obtains a 41% success rate, outperforming all the other tested methods. For a more challenging benchmark set of flexible targets, GeoDock achieves a similar number of top-model successes as the traditional method ClusPro [1], but fewer than ReplicaDock2 [2]. GeoDock attains an average inference speed of under one second on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.

Molecular dynamics simulation of the interaction of a raspberry polygalacturonase (RiPG) with a PG inhibiting protein (RiPGIP) isolated from ripening raspberry (Rubus idaeus cv. Heritage) fruit as a model to understand proteins interaction during fruit softening

Polygalacturonase (PG) is an important hydrolytic enzyme involved in pectin disassembly and the subsequent textural changes during fruit ripening. Although the interaction of fungal PGs with other proteins has been documented, the interaction of plant PGs with other plant proteins has not yet been studied. In this study, the molecular mechanisms involved in raspberry fruit ripening, particularly the polygalacturonase (RiPG) interaction with polygalacturonase inhibiting protein (RiPGIP) and substrate, were investigated with a structural approach. The 3D model of RiPG2 and RiPGIP3 was built using a comparative modeling strategy and validated using molecular dynamics (MD) simulations. The RiPG2 model structure comprises 11 complete coils of right-handed parallel β-helix architecture, with an average of 27 amino acid residues per turn. The structural model of the RiPGIP3 displays a typical structure of LRR protein, with the right-handed superhelical fold with an extended parallel β-sheet. The conformational interaction between the RiPG2 protein and RiPGIP3 showed that RiPGIP3 could bind to the enzyme and thereby leave the active site cleft accessible to the substrate. All this evidence indicates that RiPG2 enzyme could interact with RiPGIP3 protein. It can be a helpful model for evaluating protein-protein interaction as a potential regulator mechanism of hydrolase activity during pectin disassembly in fruit ripening.

Improving classification of correct and incorrect protein-protein docking models by augmenting the training set

Motivation

Protein-protein interactions drive many relevant biological events, such as infection, replication and recognition. To control or engineer such events, we need to access the molecular details of the interaction provided by experimental 3D structures. However, such experiments take time and are expensive; moreover, the current technology cannot keep up with the high discovery rate of new interactions. Computational modeling, like protein-protein docking, can help to fill this gap by generating docking poses. Protein-protein docking generally consists of two parts, sampling and scoring. The sampling is an exhaustive search of the tridimensional space. The caveat of the sampling is that it generates a large number of incorrect poses, producing a highly unbalanced dataset. This limits the utility of the data to train machine learning classifiers.

Results

Using weak supervision, we developed a data augmentation method that we named hAIkal. Using hAIkal, we increased the labeled training data to train several algorithms. We trained and obtained different classifiers; the best classifier has 81% accuracy and 0.51 Matthews' correlation coefficient on the test set, surpassing the state-of-the-art scoring functions.

Availability and implementation

Docking models from Benchmark 5 are available at https://doi.org/10.5281/zenodo.4012018. Processed tabular data are available at https://repository.kaust.edu.sa/handle/10754/666961. Google colab is available at https://colab.research.google.com/drive/1vbVrJcQSf6\_C3jOAmZzgQbTpuJ5zC1RP?usp=sharing.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Elucidating the role of N440K mutation in SARS-CoV-2 spike - ACE-2 binding affinity and COVID-19 severity by virtual screening, molecular docking and dynamics approach

COVID-19 has become a public health concern around the world. The frequency of N440K variant was higher during the second wave in South India. The mutation was observed in the Receptor Binding Domain region (RBD) of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) spike (S) protein. The binding affinity of SARS-CoV-2-Angiotensin-Converting Enzyme-2 (ACE-2) plays a major role in the transmission and severity of the disease. To understand the binding affinity of the wild and mutant SARS-CoV-2 S with ACE2, molecular modeling studies were carried out. We discovered that the wild SARS-CoV-2 S RBD-ACE-2 complex has a high binding affinity and stability than that of the mutant. The N440K strain escapes from antibody neutralization, which might increase reinfection and decrease vaccine efficiency. To find a potential inhibitor against mutant N440K SARS-CoV-2, a virtual screening process was carried out and found ZINC169293961, ZINC409421825 and ZINC22060839 as the best binding energy compounds. Communicated by Ramaswamy H. Sarma.

MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations

Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and time-consuming experimental approaches for determining the 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking-the so-called scoring problem-still has considerable room for improvement. We present MetaScore, a new machine-learning-based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using their protein-protein interfacial features. The features include physicochemical properties, energy terms, interaction-propensity-based features, geometric properties, interface topology features, evolutionary conservation, and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of the nine traditional SFs included in this work in terms of success rate and hit rate evaluated over conformations ranked among the top 10; (ii) an ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by using machine learning to judiciously leverage protein-protein interfacial features and by using ensemble methods to combine multiple scoring functions.

A scoring function for the prediction of protein complex interfaces based on the neighborhood preferences of amino acids

Proteins often assemble into functional complexes, the structures of which are more difficult to obtain than those of the individual protein molecules. Given the structures of the subunits, it is possible to predict plausible complex models via computational methods such as molecular docking. Assessing the quality of the predicted models is crucial to obtain correct complex structures. Here, an energy-scoring function was developed based on the interfacial residues of structures in the Protein Data Bank. The statistically derived energy function (Nepre) imitates the neighborhood preferences of amino acids, including the types and relative positions of neighboring residues. Based on the preference statistics, a program iNepre was implemented and its performance was evaluated with several benchmarking decoy data sets. The results show that iNepre scores are powerful in model ranking to select the best protein complex structures.

The Anti-Cancer Activity of Pentamidine and Its Derivatives (WLC-4059) Is through Blocking the Interaction between S100A1 and RAGE V Domain

The S100A1 protein in humans is a calcium-binding protein. Upon Ca²⁺ binding to S100A1 EF-hand motifs, the conformation of S100A1 changes and promotes interactions with target proteins. RAGE consists of three domains: the cytoplasmic, transmembrane, and extracellular domains. The extracellular domain consists of C1, C2, and V domains. V domains are the primary receptors for the S100 protein. It was reported several years ago that S100A1 and RAGE V domains interact in a pathway involving S100A1-RAGE signaling, whereby S100A1 binds to the V domain, resulting in RAGE dimerization. The autophosphorylation of the cytoplasmic domain initiates a signaling cascade that regulates cell proliferation, cell growth, and tumor formation. In this study, we used pentamidine and a newly synthesized pentamidine analog (WLC-4059) to inhibit the S100A1-RAGE V interaction. ¹H-¹⁵N HSQC NMR titration was carried out to characterize the interaction between mS100A1 (mutant S100A1, C86S) and pentamidine analogs. We found that pentamidine analogs interact with S100A1 via ¹H-¹⁵N HSQC NMR spectroscopy. Based on the results, we utilized the HADDOCK program to generate structures of the mS100A1-WLC-4059 binary complex. Interestingly, the binary complex overlapped with the complex crystal structure of the mS100A1-RAGE-V domain, proving that WLC-4059 blocks interaction sites between S100A1 and RAGE-V. A WST-1 cell proliferation assay also supported these results. We conclude that pentamidine analogs could potentially enhance therapeutic approaches against cancers.

Mapping of Methyl Epitopes of a Peptide-Drug with Its Receptor by 2D STDD-Methyl TROSY NMR Spectroscopy

The current trend in the biopharmaceutical market has boosted the development and production of biological drugs with high efficacy and fidelity for receptor binding. While high-resolution structural insights into binding epitopes of the receptor are indispensable for better therapeutic design, it is tedious and costly. In this work, we develop a protocol by integrating two well-known NMR-based solution-state methods. Saturation transfer double-difference with methyl-TROSY (STDD-Methyl TROSY NMR) was used to probe methyl binding epitopes of the ligand in a label-free environment. This study was carried out with Human insulin as a model peptide drug, with the insulin growth factor receptor (IGFR), which is an off-target receptor for insulin. Methyl epitopes identified from STDD-Methyl TROSY NMR spectroscopy were validated through the HADDOCK platform to generate a drug-receptor model. Since this method can be applied at natural abundance, it has the potential to screen a large set of peptide-drug interactions for optimum receptor binding. Thus, we propose STDD-Methyl TROSY NMR spectroscopy as a technique for rapid screening of biologics for the development of optimized biopharmaceutics.

On Quality Thresholds for the Clustering of Molecular Structures

It has been recently suggested that diametral (so-called quality) similarity thresholds are superior to radial ones for the clustering of molecular three-dimensional structures (González-Alemán et al., 2020). The argument has been made for two clustering algorithms available in various software packages for the analysis of molecular structures from ensembles generated by computer simulations, attributed to Daura et al. (1999) (radial threshold) and Heyer et al. (1999) (diametral threshold). Here, we compare these two algorithms using the root-mean-squared difference (rmsd) between the Cartesian coordinates of selected atoms as pairwise similarity metric. We discuss formally the relation between these two methods and illustrate their behavior with two examples, a set of points in two dimensions and the coordinates of the tau polypeptide along a trajectory extracted from a replica-exchange molecular-dynamics simulation (Shea and Levine, 2016). We show that the two methods produce equally sized clusters as long as adequate choices are made for the respective thresholds. The real issue is not whether the threshold is radial or diametral but how to choose in either case a threshold value that is physically meaningful. We will argue that, when clustering molecular structures with the rmsd as a metric, the simplest best guess for a threshold is actually radial in nature.

NMR Structures of Calmodulin Bound to Two Separate Regulatory Sites in the Retinal Cyclic Nucleotide-Gated Channel

Retinal cyclic nucleotide-gated (CNG) channels (composed of three CNGA1 and one CNGB1 subunits) exhibit a Ca²⁺-induced reduction in channel open probability mediated by calmodulin (CaM). Defects in the Ca²⁺-dependent regulation of CNG channels may be linked to autosomal recessive retinitis pigmentosa and other inherited forms of blindness. Here, we report the NMR structure and binding analysis of CaM bound to two separate sites within CNGB1 (CaM1: residues 565-589 and CaM2: residues 1120-1147). Our binding studies reveal that CaM1 binds to the Ca²⁺-bound CaM N-lobe with at least fivefold higher affinity than it binds to the CaM C-lobe. By contrast, the CaM2 site binds to the Ca²⁺-bound CaM C-lobe with higher affinity than it binds to the N-lobe. CaM1 and CaM2 both exhibited very weak binding to Ca²⁺-free CaM. We present separate NMR structures of Ca²⁺-saturated CaM bound to CaM1 and CaM2 that define key intermolecular contacts: CaM1 residue F575 interacts with the CaM N-lobe while CaM2 residues L1129, L1132, and L1136 each make close contact with the CaM C-lobe. The CNGB1 mutation F575E abolishes CaM1 binding to the CaM N-lobe while L1132E and L1136E each abolish CaM2 binding to the CaM C-lobe. Thus, a single CaM can bind to both sites in CNGB1 in which the CaM N-lobe binds to CaM1 and the CaM C-lobe binds to CaM2. We propose a Ca²⁺-dependent conformational switch in the CNG channel caused by CaM binding, which may serve to attenuate cGMP binding to CNG channels at high cytosolic Ca²⁺ levels in dark-adapted photoreceptors.

High-resolution structures of a siderophore-producing cyclization domain from Yersinia pestis offer a refined proposal of substrate binding

Nonribosomal peptide synthetase heterocyclization (Cy) domains generate biologically important oxazoline/thiazoline groups found in natural products, including pharmaceuticals and virulence factors such as some siderophores. Cy domains catalyze consecutive condensation and cyclodehydration reactions, although the mechanism is unknown. To better understand Cy domain catalysis, here we report the crystal structure of the second Cy domain (Cy2) of yersiniabactin synthetase from the causative agent of the plague, Yersinia pestis. Our high-resolution structure of Cy2 adopts a conformation that enables exploration of interactions with the extended thiazoline-containing cyclodehydration intermediate and the acceptor carrier protein (CP) to which it is tethered. We also report complementary electrostatic interfaces between Cy2 and its donor CP that mediate donor binding. Finally, we explored domain flexibility through normal mode analysis and identified small-molecule fragment-binding sites that may inform future antibiotic design targeting Cy function. Our results suggest how CP binding may influence global Cy conformations, with consequences for active-site remodeling to facilitate the separate condensation and cyclodehydration steps as well as potential inhibitor development.

Epitope length variants balance protective immune responses and viral escape in HIV-1 infection

Cytotoxic T lymphocyte (CTL) and natural killer (NK) cell responses to a single optimal 10-mer epitope (KK10) in the human immunodeficiency virus type-1 (HIV-1) protein p24Gag are associated with enhanced immune control in patients expressing human leukocyte antigen (HLA)-B∗27:05. We find that proteasomal activity generates multiple length variants of KK10 (4-14 amino acids), which bind TAP and HLA-B∗27:05. However, only epitope forms ≥8 amino acids evoke peptide length-specific and cross-reactive CTL responses. Structural analyses reveal that all epitope forms bind HLA-B∗27:05 via a conserved N-terminal motif, and competition experiments show that the truncated epitope forms outcompete immunogenic epitope forms for binding to HLA-B∗27:05. Common viral escape mutations abolish (L136M) or impair (R132K) production of KK10 and longer epitope forms. Peptide length influences how well the inhibitory NK cell receptor KIR3DL1 binds HLA-B∗27:05 peptide complexes and how intraepitope mutations affect this interaction. These results identify a viral escape mechanism from CTL and NK responses based on differential antigen processing and peptide competition.

Intragenic suppressors unravel the role of the SCREAM ACT-like domain for bHLH partner selectivity in stomatal development

Multicellular organisms develop specialized cell types to achieve complex functions of tissues and organs. The basic helix-loop-helix (bHLH) proteins act as master regulatory transcription factors of such specialized cell types. Plant stomata are cellular valves in the aerial epidermis for efficient gas exchange and water control. Stomatal differentiation is governed by sequential actions of three lineage-specific bHLH proteins, SPEECHLESS (SPCH), MUTE, and FAMA, specifying initiation and proliferation, commitment, and terminal differentiation, respectively. A broadly expressed bHLH, SCREAM (SCRM), heterodimerizes with SPCH/MUTE/FAMA and drives stomatal differentiation via switching its partners. Yet nothing is known about its heterodimerization properties or partner preference. Here, we report the role of the SCRM C-terminal ACT-like (ACTL) domain for heterodimerization selectivity. Our intragenic suppressor screen of a dominant scrm-D mutant identified the ACTL domain as a mutation hotspot. Removal of this domain or loss of its structural integrity abolishes heterodimerization with MUTE, but not with SPCH or FAMA, and selectively abrogates the MUTE direct target gene expression. Consequently, the scrm-D ACTL mutants confer massive clusters of arrested stomatal precursor cells that cannot commit to differentiation when redundancy is removed. Structural and biophysical studies further show that SPCH, MUTE, and FAMA also possess the C-terminal ACTL domain, and that ACTL•ACTL heterodimerization is sufficient for partner selectivity. Our work elucidates a role for the SCRM ACTL domain in the MUTE-governed proliferation-differentiation switch and suggests mechanistic insight into the biological function of the ACTL domain, a module uniquely associated with plant bHLH proteins, as a heterodimeric partner selectivity interface.

Deconvolution of the MBP-Bri2 Interaction by a Yeast Two Hybrid System and Synergy of the AlphaFold2 and High Ambiguity Driven Protein-Protein Docking

Myelin basic protein (MBP) is one of the key proteins in the development of multiple sclerosis (MS). However, very few intracellular MBP partners have been identified up to now. In order to find proteins interacting with MBP in the brain, an expression library from the human brain was screened using a yeast two-hybrid system. Here we showed that MBP interacts with the C-terminal 24-residue peptide of Integral transmembrane protein II associated with familial British and Danish dementia (ITM2B/Bri2 or Bri2). This peptide (Bri23R) was one residue longer than the known Bri23 peptide, which is cleaved from the C-terminus of Bri2 during its maturation in the Golgi and has physiological activity as a modulator of amyloid precursor protein processing. Since the spatial structures for both MBP and Bri2 were not known, we used computational methods of structural biology including an artificial intelligence system AlphaFold2 and high ambiguity driven protein-protein docking (HADDOCK 2.1) to gain a mechanistic explanation of the found protein-protein interaction and elucidate a possible structure of the complex of MBP with Bri23R peptide. As expected, MBP was mostly unstructured, although it has well-defined α-helical regions, while Bri23R forms a stable β-hairpin. Simulation of the interaction between MBP and Bri23R in two different environments, as parts of the two-hybrid system fusion proteins and in the form of single polypeptides, showed that MBP twists around Bri23R. The observed interaction results in the adjustment of the size of the internal space between MBP α-helices to the size of the β-hairpin of Bri23R. Since Bri23 is known to inhibit aggregation of amyloid oligomers, and the association of MBP to the inner leaflet of the membrane bilayer shares features with amyloid fibril formation, Bri23 may serve as a peptide chaperon for MBP, thus participating in myelin membrane assembly.

Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules

Knowledge-based approaches use the statistics collected from protein data-bank structures to estimate effective interaction potentials between amino acid pairs. Empirical relations are typically employed that are based on the crucial choice of a reference state associated to the null interaction case. Despite their significant effectiveness, the physical interpretation of knowledge-based potentials has been repeatedly questioned, with no consensus on the choice of the reference state. Here we use the fact that the Flory theorem, originally derived for chains in a dense polymer melt, holds also for chain fragments within the core of globular proteins, if the average over buried fragments collected from different non-redundant native structures is considered. After verifying that the ensuing Gaussian statistics, a hallmark of effectively non-interacting polymer chains, holds for a wide range of fragment lengths, although with significant deviations at short spatial scales, we use it to define a ‘bona fide’ reference state. Notably, despite the latter does depend on fragment length, deviations from it do not. This allows to estimate an effective interaction potential which is not biased by the presence of correlations due to the connectivity of the protein chain. We show how different sequence-independent effective statistical potentials can be derived using this approach by coarse-graining the protein representation at varying levels. The possibility of defining sequence-dependent potentials is explored.

Synthesis of Novel Suramin Analogs With Anti-Proliferative Activity via FGF1 and FGFRD2 Blockade

A promising approach in cancer therapy is the inhibition of cell proliferation using small molecules. In this study, we report the synthesis of suramin derivatives and their applications. We used NMR spectroscopy and docking simulations to confirm binding sites and three-dimensional models of the ligand-protein complex. The WST-1 assay was used to assess cell viability and cell proliferation in vitro to evaluate the inhibition of protein-protein interactions and to investigate the anti-proliferative activities in a breast cancer cell line. All the suramin derivatives showed anti-proliferative activity by blocking FGF1 binding to its receptor FGFRD2. The dissociation constant was measured by fluorescence spectroscopy. The suramin compound derivatives synthesized herein show potential as novel therapeutic agents for their anti-proliferative activity via the inhibition of protein-protein interactions. The cytotoxicity of these suramin derivatives was lower than that of the parent suramin compound, which may be considered a significant advancement in this field. Thus, these novel suramin derivatives may be considered superior anti-metastasis molecules than those of suramin.

Structure, dynamics, and function of SrnR, a transcription factor for nickel-dependent gene expression

Streptomyces griseus, a bacterium producing antibacterial drugs and featuring possible application in phytoremediation, expresses two metal-dependent superoxide dismutase (SOD) enzymes, containing either Fe(II) or Ni(II) in their active site. In particular, the alternative expression of the two proteins occurs in a metal-dependent mode, with the Fe(II)-enzyme gene (sodF) repressed at high intracellular Ni(II) concentrations by a two-component system (TCS). This complex involves two proteins, namely SgSrnR and SgSrnQ, which represent the transcriptional regulator and the Ni(II) sensor of the system, respectively. SgSrnR belongs to the ArsR/SmtB family of metal-dependent transcription factors; in the apo-form and in the absence of SgSrnQ, it can bind the DNA operator of sodF, upregulating gene transcription. According to a recently proposed hypothesis, Ni(II) binding to SgSrnQ would promote its interaction with SgSrnR, causing the release of the complex from DNA and the consequent downregulation of the sodF expression. SgSrnQ is predicted to be highly disordered, thus the understanding, at the molecular level, of how the SgSrnR/SgSrnQ TCS specifically responds to Ni(II) requires the knowledge of the structural, dynamic, and functional features of SgSrnR. These were investigated synergistically in this work using X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, atomistic molecular dynamics calculations, isothermal titration calorimetry, and in silico molecular docking. The results reveal that the homodimeric apo-SgSrnR binds to its operator in a two-step process that involves the more rigid globular portion of the protein and leaves its largely disordered regions available to possibly interact with the disordered SgSrnQ in a Ni-dependent process.

A random forest classifier for protein-protein docking models

Herein, we present the results of a machine learning approach we developed to single out correct 3D docking models of protein-protein complexes obtained by popular docking software. To this aim, we generated 3 × 10 4 docking models for each of the 230 complexes in the protein-protein benchmark, version 5, using three different docking programs (HADDOCK, FTDock and ZDOCK), for a cumulative set of ≈ 7 × 10 6 docking models. Three different machine learning approaches (Random Forest, Supported Vector Machine and Perceptron) were used to train classifiers with 158 different scoring functions (features). The Random Forest algorithm outperformed the other two algorithms and was selected for further optimization. Using a features selection algorithm, and optimizing the random forest hyperparameters, allowed us to train and validate a random forest classifier, named COnservation Driven Expert System (CoDES). Testing of CoDES on independent datasets, as well as results of its comparative performance with machine learning methods recently developed in the field for the scoring of docking decoys, confirm its state-of-the-art ability to discriminate correct from incorrect decoys both in terms of global parameters and in terms of decoys ranked at the top positions.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Software and data availability statement

The docking models are available at https://doi.org/10.5281/zenodo.4012018. The programs underlying this article will be shared on request to the corresponding authors.

Evidence of bovine leukemia virus circulating in sheep and buffaloes in Colombia: insights into multispecies infection

Bovine leukemia virus (BLV) is the causative agent of leukemia/lymphoma in cattle. However, previous evidence has shown its presence in other species of livestock as well as in humans, suggesting that other species can be accidental hosts of the virus. In viral infections, receptors that are common to different animal species are proposed to be involved in cross-species infections. For BLV, AP3D1 has been proposed to be its receptor, and this protein is conserved in most mammalian species. In Colombia, BLV has been reported in cattle with high prevalence rates, but there has been no evidence of BLV infections in other animal species. In this study, we tested for the virus in sheep (n = 44) and buffaloes (n = 61) from different regions of Colombia by nested PCR, using peripheral blood samples collected from the animals. BLV was found in 25.7% of the animals tested (12 buffaloes and 15 sheep), and the results were confirmed by Sanger sequencing. In addition, to gain more information about the capacity of the virus to infect these species, the predicted interactions of AP3D1 of sheep and buffaloes with the BLV-gp51 protein were analyzed in silico. Conserved amino acids in the binding domains of the proteins were identified. The detection of BLV in sheep and buffaloes suggests circulation of the virus in multiple species, which could be involved in dissemination of the virus in mixed livestock production settings. Due to the presence of the virus in multiple species and the high prevalence rates observed, integrated prevention and control strategies in the livestock industry should be considered to decrease the spread of BLV.

Predicting stable binding modes from simulated dimers of the D76N mutant of β 2-microglobulin

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β2m D76N mutant populates an aggregation-prone monomer (I2) with unstructured termini.

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MD and MM-PBSA indicate that I2 dimers are stabilized by hydrophobic interactions.

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The termini regions and BC- and DE-loops are prevalent in the most stable interfaces.

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The most stable dimer has a limited growth potential without structural rearrangement.

The D76N mutant of the β2m protein is a biologically motivated model system to study protein aggregation. There is strong experimental evidence, supported by molecular simulations, that D76N populates a highly dynamic conformation (which we originally named I2) that exposes aggregation-prone patches as a result of the detachment of the two terminal regions. Here, we use Molecular Dynamics simulations to study the stability of an ensemble of dimers of I2 generated via protein–protein docking. MM-PBSA calculations indicate that within the ensemble of investigated dimers the major contribution to interface stabilization at physiological pH comes from hydrophobic interactions between apolar residues. Our structural analysis also reveals that the interfacial region associated with the most stable binding modes are particularly rich in residues pertaining to both the N- and C-terminus, as well residues from the BC- and DE-loops. On the other hand, the less stable interfaces are stabilized by intermolecular interactions involving residues from the CD- and EF-loops. By focusing on the most stable binding modes, we used a simple geometric rule to propagate the corresponding dimer interfaces. We found that, in the absence of any kind of structural rearrangement occurring at an early stage of the oligomerization pathway, some interfaces drive a self-limited growth process, while others can be propagated indefinitely allowing the formation of long, polymerized chains. In particular, the interfacial region of the most stable binding mode reported here falls in the class of self-limited growth.

A distinct ssDNA/RNA binding interface in the Nsp9 protein from SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a novel, highly infectious RNA virus that belongs to the coronavirus family. Replication of the viral genome is a fundamental step in the virus life cycle and SARS‐CoV‐2 non‐structural protein 9 (Nsp9) is shown to be essential for virus replication through its ability to bind RNA in the closely related SARS‐CoV‐1 strain. Two recent studies revealing the three‐dimensional structure of Nsp9 from SARS‐CoV‐2 have demonstrated a high degree of similarity between Nsp9 proteins within the coronavirus family. However, the binding affinity to RNA is very low which, until now, has prevented the determination of the structural details of this interaction. In this study, we have utilized nuclear magnetic resonance spectroscopy (NMR) in combination with surface biolayer interferometry (BLI) to reveal a distinct binding interface for both ssDNA and RNA that is different to the one proposed in the recently solved SARS‐CoV‐2 replication and transcription complex (RTC) structure. Based on these data, we have proposed a structural model of a Nsp9‐RNA complex, shedding light on the molecular details of these important interactions.

Definition of the Binding Architecture to a Target Promoter of HP1043, the Essential Master Regulator of Helicobacter pylori

HP1043 is an essential orphan response regulator of Helicobacter pylori orchestrating multiple crucial cellular processes. Classified as a member of the OmpR/PhoB family of two-component systems, HP1043 exhibits a highly degenerate receiver domain and evolved to function independently of phosphorylation. Here, we investigated the HP1043 binding mode to a target sequence in the hp1227 promoter (P_hp1227). Scanning mutagenesis of HP1043 DNA-binding domain and consensus sequence led to the identification of residues relevant for the interaction of the protein with a target DNA. These determinants were used as restraints to guide a data-driven protein-DNA docking. Results suggested that, differently from most other response regulators of the same family, HP1043 binds in a head-to-head conformation to the P_hp1227 target promoter. HP1043 interacts with DNA largely through charged residues and contacts with both major and minor grooves of the DNA are required for a stable binding. Computational alanine scanning on molecular dynamics trajectory was performed to corroborate our findings. Additionally, in vitro transcription assays confirmed that HP1043 positively stimulates the activity of RNA polymerase.

Repurposed floxacins targeting RSK4 prevent chemoresistance and metastasis in lung and bladder cancer

Lung and bladder cancers are mostly incurable because of the early development of drug resistance and metastatic dissemination. Hence, improved therapies that tackle these two processes are urgently needed to improve clinical outcome. We have identified RSK4 as a promoter of drug resistance and metastasis in lung and bladder cancer cells. Silencing this kinase, through either RNA interference or CRISPR, sensitized tumor cells to chemotherapy and hindered metastasis in vitro and in vivo in a tail vein injection model. Drug screening revealed several floxacin antibiotics as potent RSK4 activation inhibitors, and trovafloxacin reproduced all effects of RSK4 silencing in vitro and in/ex vivo using lung cancer xenograft and genetically engineered mouse models and bladder tumor explants. Through x-ray structure determination and Markov transient and Deuterium exchange analyses, we identified the allosteric binding site and revealed how this compound blocks RSK4 kinase activation through binding to an allosteric site and mimicking a kinase autoinhibitory mechanism involving the RSK4's hydrophobic motif. Last, we show that patients undergoing chemotherapy and adhering to prophylactic levofloxacin in the large placebo-controlled randomized phase 3 SIGNIFICANT trial had significantly increased (P = 0.048) long-term overall survival times. Hence, we suggest that RSK4 inhibition may represent an effective therapeutic strategy for treating lung and bladder cancer.

Functional and in silico Characterization of Neutralizing Interactions Between Antibodies and the Foot-and-Mouth Disease Virus Immunodominant Antigenic Site

Molecular knowledge of virus–antibody interactions is essential for the development of better vaccines and for a timely assessment of the spread and severity of epidemics. For foot-and-mouth disease virus (FMDV) research, in particular, computational methods for antigen–antibody (Ag–Ab) interaction, and cross-antigenicity characterization and prediction are critical to design engineered vaccines with robust, long-lasting, and wider response against different strains. We integrated existing structural modeling and prediction algorithms to study the surface properties of FMDV Ags and Abs and their interaction. First, we explored four modeling and two Ag–Ab docking methods and implemented a computational pipeline based on a reference Ag–Ab structure for FMDV of serotype C, to be used as a source protocol for the study of unknown interaction pairs of Ag–Ab. Next, we obtained the variable region sequence of two monoclonal IgM and IgG antibodies that recognize and neutralize antigenic site A (AgSA) epitopes from South America serotype A FMDV and developed two peptide ELISAs for their fine epitope mapping. Then, we applied the previous Ag–Ab molecular structure modeling and docking protocol further scored by functional peptide ELISA data. This work highlights a possible different behavior in the immune response of IgG and IgM Ab isotypes. The present method yielded reliable Ab models with differential paratopes and Ag interaction topologies in concordance with their isotype classes. Moreover, it demonstrates the applicability of computational prediction techniques to the interaction phenomena between the FMDV immunodominant AgSA and Abs, and points out their potential utility as a metric for virus-related, massive Ab repertoire analysis or as a starting point for recombinant vaccine design.

Exploring effectiveness of ab-initio protein-protein docking methods on a novel antibacterial protein complex dataset

Diseases caused by bacterial infections become a critical problem in public heath. Antibiotic, the traditional treatment, gradually loses their effectiveness due to the resistance. Meanwhile, antibacterial proteins attract more attention because of broad spectrum and little harm to host cells. Therefore, exploring new effective antibacterial proteins is urgent and necessary. In this paper, we are committed to evaluating the effectiveness of ab-initio docking methods in antibacterial protein-protein docking. For this purpose, we constructed a three-dimensional (3D) structure dataset of antibacterial protein complex, called APCset, which contained $19$ protein complexes whose receptors or ligands are homologous to antibacterial peptides from Antimicrobial Peptide Database. Then we selected five representative ab-initio protein-protein docking tools including ZDOCK3.0.2, FRODOCK3.0, ATTRACT, PatchDock and Rosetta to identify these complexes' structure, whose performance differences were obtained by analyzing from five aspects, including top/best pose, first hit, success rate, average hit count and running time. Finally, according to different requirements, we assessed and recommended relatively efficient protein-protein docking tools. In terms of computational efficiency and performance, ZDOCK was more suitable as preferred computational tool, with average running time of $6.144$ minutes, average Fnat of best pose of $0.953$ and average rank of best pose of $4.158$. Meanwhile, ZDOCK still yielded better performance on Benchmark 5.0, which proved ZDOCK was effective in performing docking on large-scale dataset. Our survey can offer insights into the research on the treatment of bacterial infections by utilizing the appropriate docking methods.

Insights into the Molecular Inhibition of the Oncogenic Channel KV10.1 by Globular Toxins

Inhibition of the expression of the human ether-à-go-go (hEAG1 or hK_V10.1) channel is associated with a dramatic reduction in the growth of several cancerous tumors. The modulation of this channel's activity is a promising target for the development of new anticancer drugs. Although some small molecules have shown inhibitory activity against K_V10.1, their lack of specificity has prevented their use in humans. In vitro studies have recently identified a limited number of peptide toxins with proven specificity in their hK_V10.1 channel inhibitory effect. These peptide toxins have become desirable candidates to use as lead compounds to design more potent and specific hK_V10.1 inhibitors. However, the currently available studies lack the atomic resolution needed to characterize the molecular features that favor their binding to hK_V10.1. In this work, we present the first attempt to locate the possible hK_V10.1 binding sites of the animal peptide toxins APETx4, Aa1a, Ap1a, and k-hefutoxin 1, all of which described as hK_V10.1 inhibitors. Our studies incorporated homology modeling to construct a robust three-dimensional (3D) model of hK_V10.1, applied protein docking, and multiscale molecular dynamics techniques to reveal in atomic resolution the toxin-channel interactions. Our approach suggests that some peptide toxins bind in the outer vestibule surrounding the pore of hK_V10.1; it also identified the channel residues Met397 and Asp398 as possible anchors that stabilize the binding of the evaluated toxins. Finally, a description of the possible mechanism for inhibition and gating is presented.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Optimizing the First TPR Domain of the Human SPAG1 Protein Provides Insight into the HSP70 and HSP90 Binding Properties

Tetratricopeptide repeat domains, or TPR domains, are protein domains that mediate protein:protein interaction. As they allow contacts between proteins, they are of particular interest in transient steps of the assembly process of macromolecular complexes, such as the ribosome or the dynein arms. In this study, we focused on the first TPR domain of the human SPAG1 protein. SPAG1 is a multidomain protein that is important for ciliogenesis whose known mutations are linked to primary ciliary dyskinesia syndrome. It can interact with the chaperones RUVBL1/2, HSP70, and HSP90. Using protein sequence optimization in combination with structural and biophysical approaches, we analyzed, with atomistic precision, how the C-terminal tails of HSPs bind a variant form of SPAG1-TPR1 that mimics the wild-type domain. We discuss our results with regard to other complex three-dimensional structures with the aim of highlighting the motifs in the TPR sequences that could drive the positioning of the HSP peptides. These data could be important for the druggability of TPR regulators.

Structural basis of a chemokine heterodimer binding to glycosaminoglycans

Chemokines Cxcl1/KC and Cxcl2/MIP2 play a crucial role in coordinating neutrophil migration to the insult site. Chemokines' recruitment activity is regulated by monomer-dimer equilibrium and binding to glycosaminoglycans (GAGs). GAG chains exist as covalently linked to core proteins of proteoglycans (PGs) and also as free chains due to cleavage by heparanases during the inflammatory response. Compared with free GAGs, binding to GAGs in a PG is influenced by their fixed directionality due to covalent linkage and restricted mobility. GAG interactions impact chemokine monomer/dimer levels, chemotactic and haptotactic gradients, life time, and presentation for receptor binding. Here, we show that Cxcl1 and Cxcl2 also form heterodimers. Using a disulfide-trapped Cxcl1-Cxcl2 heterodimer, we characterized its binding to free heparin using nuclear magnetic resonance and isothermal titration calorimetry, and to immobilized heparin and heparan sulfate using surface plasmon resonance. These data, in conjunction with molecular docking, indicate that the binding characteristics such as geometry and stoichiometry of the heterodimer are different between free and immobilized GAGs and are also distinctly different from those of the homodimers. We propose that the intrinsic asymmetry of the heterodimer structure, along with differences in its binding to PG GAGs and free GAGs, regulate chemokine function.

Structural analysis of alternate sigma factor ComX with RpoC, RpoB and its cognate CIN promoter reveals a distinctive promoter melting mechanism

Alternate sigma factors play a major role in the survival of pathogenic bacteria such as Streptococcus pyogenes in adverse environment conditions. Stress induced sigma factors mediate gene expression under conditions of pathogenesis, dormancy and unusual environmental cues. In the present work, ComX, an alternate sigma factor from S. pyogenes has been characterized. The structures of ComX, RpoB β subunit and RpoC β' subunit of RNA polymerase have been predicted using comparative and homology modelling respectively and validated. Attempts have been made to study RpoB-RpoC-ComX complex interactions with Double Strand (DS) and Single Strand (SS) promoter regions. Stability of these complexes and the promoter melting mechanism have been analysed using Molecular Dynamic (MD) simulations. This study suggests that ComX, although identifies promoter analogous to the alternate sigma factor SigH of M. tuberculosis, follows a distinctive promoter flip out mechanism.Communicated by Ramaswamy H. Sarma.

Chimeric Cellobiose Dehydrogenases Reveal the Function of Cytochrome Domain Mobility for the Electron Transfer to Lytic Polysaccharide Monooxygenase

The natural function of cellobiose dehydrogenase (CDH) to donate electrons from its catalytic flavodehydrogenase (DH) domain via its cytochrome (CYT) domain to lytic polysaccharide monooxygenase (LPMO) is an example of a highly efficient extracellular electron transfer chain. To investigate the function of the CYT domain movement in the two occurring electron transfer steps, two CDHs from the ascomycete Neurospora crassa (NcCDHIIA and NcCDHIIB) and five chimeric CDH enzymes created by domain swapping were studied in combination with the fungus’ own LPMOs (NcLPMO9C and NcLPMO9F). Kinetic and electrochemical methods and hydrogen/deuterium exchange mass spectrometry were used to study the domain movement, interaction, and electron transfer kinetics. Molecular docking provided insights into the protein–protein interface, the orientation of domains, and binding energies. We find that the first, interdomain electron transfer step from the catalytic site in the DH domain to the CYT domain depends on steric and electrostatic interface complementarity and the length of the protein linker between both domains but not on the redox potential difference between the FAD and heme b cofactors. After CYT reduction, a conformational change of CDH from its closed state to an open state allows the second, interprotein electron transfer (IPET) step from CYT to LPMO to occur by direct interaction of the b-type heme and the type-2 copper center. Chimeric CDH enzymes favor the open state and achieve higher IPET rates by exposing the heme b cofactor to LPMO. The IPET, which is influenced by interface complementarity and the heme b redox potential, is very efficient with bimolecular rates between 2.9 × 10⁵ and 1.1 × 10⁶ M^–1 s^–1.

Interaction-induced structural transformation of lysozyme and kappa-carrageenan in binary complexes

The interactions between κ-carrageenan and hen egg-white lysozyme have been studied. In dilute solutions, the insoluble complexes with constant κ-carrageenan/lysozyme ratio of 0.3, or 12 disaccharide units per mole of protein are formed. FTIR-spectroscopy revealed that κ-carrageenan retains its unordered conformation and induces the rise of β-structure in lysozyme. In the complexes formed in concentrated mixtures, κ-carrageenan adopts helical conformation and lysozyme retains its native-like structure. These complexes contain 21 disaccharide units per mole of protein. Molecular modeling showed that flexible coil and rigid double helix of κ-carrageenan have different binding patterns to lysozyme surface. The latter has a strong preference to positively charged spots in lysozyme α-domain while the former also interacts to protein β-domain and stabilizes short-living β-structures. The obtained results confirm the preference of unordered κ-carrageenan to β-structure rich protein regions, which can be further used in the development of carrageenan-based protection of amyloid-like aggregation of proteins.

ATP biphasically modulates LLPS of SARS-CoV-2 nucleocapsid protein and specifically binds its RNA-binding domain

SARS-CoV-2 is a highly contagious coronavirus causing the ongoing pandemic. Very recently its genomic RNA of ∼30 kb was decoded to be packaged with nucleocapsid (N) protein into phase separated condensates. Interestingly, viruses have no ability to generate ATP but host cells have very high ATP concentrations of 2–12 mM. A key question thus arises whether ATP modulates liquid-liquid phase separation (LLPS) of the N protein. Here we discovered that ATP not only biphasically modulates LLPS of the viral N protein as we previously found on human FUS and TDP-43, but also dissolves the droplets induced by oligonucleic acid. Residue-specific NMR characterization showed ATP specifically binds the RNA-binding domain (RBD) of the N protein with the average Kd of 3.3 ± 0.4 mM. The ATP-RBD complex structure was constructed by NMR-derived constraints, in which ATP occupies a pocket within the positive-charged surface utilized for binding nucleic acids. Our study suggests that ATP appears to be exploited by SARS-CoV-2 to promote its life cycle by facilitating the uncoating, localizing and packing of its genomic RNA. Therefore the interactions of ATP with the viral RNA and N protein might represent promising targets for design of drugs and vaccines to terminate the pandemic.

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SARS-CoV-2 genomic RNA is packaged with its N protein via phase separation.

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Here we discovered that ATP biphasically modulates phase separation of N protein.

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ATP also specifically bind the RNA-binding domain of N protein with Kd of 3.3 mM.

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ATP is thus critical for uncoating, localizing and packing of the viral genomic RNA.

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ATP-Nprotein-RNA interactions represent key targets for drug of drugs and vaccines.

Tethering-induced destabilization and ATP-binding for tandem RRM domains of ALS-causing TDP-43 and hnRNPA1

TDP-43 and hnRNPA1 contain tandemly-tethered RNA-recognition-motif (RRM) domains, which not only functionally bind an array of nucleic acids, but also participate in aggregation/fibrillation, a pathological hallmark of various human diseases including amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), alzheimer's disease (AD) and Multisystem proteinopathy (MSP). Here, by DSF, NMR and MD simulations we systematically characterized stability, ATP-binding and conformational dynamics of TDP-43 and hnRNPA1 RRM domains in both tethered and isolated forms. The results reveal three key findings: (1) upon tethering TDP-43 RRM domains become dramatically coupled and destabilized with Tm reduced to only 49 °C. (2) ATP specifically binds TDP-43 and hnRNPA1 RRM domains, in which ATP occupies the similar pockets within the conserved nucleic-acid-binding surfaces, with the affinity slightly higher to the tethered than isolated forms. (3) MD simulations indicate that the tethered RRM domains of TDP-43 and hnRNPA1 have higher conformational dynamics than the isolated forms. Two RRM domains become coupled as shown by NMR characterization and analysis of inter-domain correlation motions. The study explains the long-standing puzzle that the tethered TDP-43 RRM1–RRM2 is particularly prone to aggregation/fibrillation, and underscores the general role of ATP in inhibiting aggregation/fibrillation of RRM-containing proteins. The results also rationalize the observation that the risk of aggregation-causing diseases increases with aging.

Structural insights in interactions between RNase from Bacillus Intermedius and rhamnogalacturonan I from potato

Being biocompatible and biodegradable polymers, polysaccharides present a perspective material for drug delivery systems. This study aimed at unraveling the molecular details of interactions between rhamnogalacturonan I, brunched with galactan side chains, and RNase from Bacillus Intermedius, binase. FTIR- and NMR-spectroscopic analyses showed that binase interacts with side chains of the polysaccharide. In complexes with polysaccharide, the protein retains its native structure. The 2D-NMR techniques revealed eight protein residues responsive to polysaccharide binding. Further, computer simulations were carried out to provide the atomistic details of binase-polysaccharide complexes. Both blind and knowledge-based docking procedures elucidate the existence of epitopes on the binase surface with the preferential binding of galactan fragments. The refinement of these complexes by molecular dynamics simulations confirmed stable protein-polysaccharide interactions. The results of this study strengthen the knowledge on non-specific protein-carbohydrate interactions and outline the rhamnogalacturonan I as a possible matrix material for protein delivery systems.

Cathepsins L and B in Dysdercus peruvianus, Rhodnius prolixus, and Mahanarva fimbriolata. Looking for enzyme adaptations to digestion

Cysteine peptidases (CP) play a role as digestive enzymes in hemipterans similar to serine peptidases in most other insects. There are two major CPs: cathepsin L (CAL), which is an endopeptidase and cathepsin B (CAB) that is both an exopeptidase and a minor endopeptidase. There are thirteen putative CALs in Dysdercus peruvianus, which in some cases were confirmed by cloning their encoding genes. RNA-seq data showed that DpCAL5 is mainly expressed in the anterior midgut (AM), DpCAL10 in carcass (whole body less midgut), suggesting it is a lysosomal enzyme, and the other DpCALs are expressed in middle (MM) and posterior (PM) midgut. The expression data were confirmed by qPCR and enzyme secretion to midgut lumen by a proteomic approach. Two CAL activities were isolated by chromatography from midgut samples with similar kinetic properties toward small substrates. Docking analysis of a long peptide with several DpCALs modeled with digestive Tenebrio molitor CAL (TmCAL3) as template showed that on adapting to luminal digestion DpCALs (chiefly DpCAL5) changed in relation to their ancestral lysosomal enzyme (DpCAL10) mainly at its S2 subsite. A similar conclusion arrived from structure alignment-based clustering of DpCALs based on structural similarity of the modeled structures. Changes mostly on S2 subsite could mean the enzymes turn out less peptide-bond selective, as described in TmCALs. R. prolixus CALs changed on adapting to luminal digestion, although less than DpCALs. Both D. peruvianus and R. prolixus have two digestive CABs which are expressed in the same extension as CALs, in the first digestive section of the midgut, but less than in the other midgut sections. Mahanarva fimbriolata does not seem to have digestive CALs and their digestive CABs are mainly expressed in the first digestive section of the midgut and do not diverge much from their lysosomal counterparts. The data suggest that CABs are necessary at the initial stage of digestion in CP-dependent Hemipterans, which action is completed by CALs with low peptide-bond selectivity in Heteroptera species. In M. fimbriolata protein digestion is supposed to be associated with the inactivation of sap noxious proteins, making CAB sufficient as digestive CP. Hemipteran genomes and transcriptome data showed that CALs have been recruited as digestive enzymes only in heteropterans, whereas digestive CABs occur in all hemipterans.

Structure based peptide design, molecular dynamics and MM-PBSA studies for targeting C terminal dimerization of NFAT5 DNA binding domain

NFAT5 as a transcription factor with an established role in osmotic stress response, has also been revealed to be active under numerous settings, including pathological conditions such as diabetic microvascular complications, chronic arthritis and cancer. Despite these links, current strategies for downregulating NFAT5 activity only relies on indirect modulators, not directly targeting NFAT5, itself. With this study, through using a computational approach, an original peptide was explored to directly target C terminal dimerization of NFAT5 RHR, located in its DNA binding domain. At first, homodimeric NFAT5 RHR bound to its consensus DNA was used for prediction of a preliminary peptide sequence. Possible amino acid replacements for this preliminary peptide were predicted for optimization, which was followed by addition of a cell penetrating peptide sequence. These attempts yielded a small peptide library, which was further investigated for peptide affinities towards C terminal of NFAT5 RHR through molecular docking, 50 ns and 250 ns molecular dynamics simulations, followed by estimation of MM-PBSA based relative binding free energies. Results indicated that after receiving mutations on the preliminary peptide sequence for optimization, a unique peptide could target C terminal dimerization region of NFAT5 RHR through using its cell penetrating peptide sequence. In conclusion, this is the first study presenting computational evidence on identification of a novel peptide capable of directly targeting NFAT5 dimerization. Besides, future implications of these observations were also discussed in terms of methodology and possible applications.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

Peptides targeting dengue viral nonstructural protein 1 inhibit dengue virus production

Viruses manipulate the life cycle in host cells via the use of viral properties and host machineries. Development of antiviral peptides against dengue virus (DENV) infection has previously been concentrated on blocking the actions of viral structural proteins and enzymes in virus entry and viral RNA processing in host cells. In this study, we proposed DENV NS1, which is a multifunctional non-structural protein indispensable for virus production, as a new target for inhibition of DENV infection by specific peptides. We performed biopanning assays using a phage-displayed peptide library and identified 11 different sequences of 12-mer peptides binding to DENV NS1. In silico analyses of peptide-protein interactions revealed 4 peptides most likely to bind to DENV NS1 at specific positions and their association was analysed by surface plasmon resonance. Treatment of Huh7 cells with these 4 peptides conjugated with N-terminal fluorescent tag and C-terminal cell penetrating tag at varying time-of-addition post-DENV infection could inhibit the production of DENV-2 in a time- and dose-dependent manner. The inhibitory effects of the peptides were also observed in other virus serotypes (DENV-1 and DENV-4), but not in DENV-3. These findings indicate the potential application of peptides targeting DENV NS1 as antiviral agents against DENV infection.

Refinement of pairwise potentials via logistic regression to score protein-protein interactions

Protein-protein interactions (PPIs) are ubiquitous and functionally of great importance in biological systems. Hence, the accurate prediction of PPIs by protein-protein docking and scoring tools is highly desirable in order to characterize their structure and biological function. Ab initio docking protocols are divided into the sampling of docking poses to produce at least one near-native structure, and then to evaluate the vast candidate structures by scoring. Concurrent development in both sampling and scoring is crucial for the deployment of protein-protein docking software. In the present work, we apply a machine learning model on pairwise potentials to refine the task of protein quaternary structure native structure detection among decoys. A decoy set was featurized using the Knowledge and Empirical Combined Scoring Algorithm 2 (KECSA2) pairwise potential. The highly unbalanced decoy set was then balanced using a comparison concept between native and decoy structures. The resultant comparison descriptors were used to train a logistic regression (LR) classifier. The LR model yielded the optimal performance for native detection among decoys compared with conventional scoring functions, while exhibiting lesser performance for the detection of low root mean square deviation decoy structures. Its deployment on an independent benchmark set confirms that the scoring function performs competitively relative to other scoring functions. The scripts used are available at https://github.com/TanemuraKiyoto/PPI-native-detection-via-LR.