The role of electrostatic energy in prediction of obligate protein-protein interactions

Plausible blockers of Spike RBD in SARS-CoV2-molecular design and underlying interaction dynamics from high-level structural descriptors

Abstract

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBD_Spike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBD_Spike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBD_Spike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBD_Spike–ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBD_Spike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently “reaction-prone.” The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic “hydrophobic ↔ polar” mutations serve as a potential breakthrough.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00894-021-04779-0.

High-Throughput Screening for Colloidal Stability of Peptide Formulations Using Dynamic and Static Light Scattering

Selection of an appropriate formulation to stabilize therapeutic proteins against aggregation is one of the most challenging tasks in early-stage drug product development. The amount of aggregates is more difficult to quantify in the case of peptides due to their small molecular size. Here, we investigated the suitability of diffusion self-interaction parameters (k_D) and osmotic second virial coefficients (B₂₂) for high-throughput (HT) screening of peptide formulations regarding their aggregation risk. These parameters were compared to the effect of thermal stress on colloidal stability. The formulation matrix comprised six buffering systems at two selected pH values, four tonicity agents, and a common preservative. The results revealed that electrostatic interactions are the main driver to control colloidal stability. Preferred formulations consisted of acetate and succinate buffer at pH 4.5 combined with glycerol or mannitol and optional m-cresol. k_D proved to be a suitable surrogate for B₂₂ as an indicator of high colloidal stability in the case of peptides as was previously described for globular proteins and antibodies. Formulation assessment solely based on k_D obtained by HT methods offers important insights into the optimization of colloidal stability during the early development of peptide-based liquid formulations and can be performed with a limited amount of peptide (∼360 mg).

A new in silico approach to investigate molecular aspects of factor IX missense causative mutations and their impact on the hemophilia B severity

Factor IX (encoded by F9) is a protein in the coagulation process, where its lack or deficiency leads to hemophilia B. This condition has been much less studied than hemophilia A, especially in Latin America. We analyzed the structural and functional impact of 54 missense mutations (18 reported by us previously, and 36 other mutations from the Factor IX database) through molecular modeling approaches. To accomplish this task, we examine the electrostatic patterns, hydrophobicity/hydrophilicity, disulfide, and H-bond differences of the Factor IX structures harboring the missense mutations found, correlating them with their clinical effects. The 54 mutated sequences were modeled and their physicochemical features were determined and used as input in clusterization tools. The electrostatic pattern seems to influence in disease severity, especially for mutations investigated in epidermal growth factors 1 and 2 (EGF1/2) domains. The combined use of all physicochemical information improved the clustering of structures associated to similar phenotypes, especially for mutations from GLA and EGF1-2 domains. The effect of mutations in the disease phenotype severity seems to be a complex interplay of molecular features, each one contributing to different impacts. This highlights that previous studies and tools analyzing individually single features for single mutations are missing elements that fulfill the whole picture.

Molecular Evidence of Adenosine Deaminase Linking Adenosine A2A Receptor and CD26 Proteins

Adenosine is an endogenous purine nucleoside that acts in all living systems as a homeostatic network regulator through many pathways, which are adenosine receptor (AR)-dependent and -independent. From a metabolic point of view, adenosine deaminase (ADA) is an essential protein in the regulation of the total intracellular and extracellular adenosine in a tissue. In addition to its cytosolic localization, ADA is also expressed as an ecto-enzyme on the surface of different cells. Dipeptidyl peptidase IV (CD26) and some ARs act as binding proteins for extracellular ADA in humans. Since CD26 and ARs interact with ADA at opposite sites, we have investigated if ADA can function as a cell-to-cell communication molecule by bridging the anchoring molecules CD26 and A2AR present on the surfaces of the interacting cells. By combining site-directed mutagenesis of ADA amino acids involved in binding to A2AR and a modification of the bioluminescence resonance energy transfer (BRET) technique that allows detection of interactions between two proteins expressed in different cell populations with low steric hindrance (NanoBRET), we show direct evidence of the specific formation of trimeric complexes CD26-ADA-A2AR involving two cells. By dynamic mass redistribution assays and ligand binding experiments, we also demonstrate that A2AR-NanoLuc fusion proteins are functional. The existence of this ternary complex is in good agreement with the hypothesis that ADA could bridge T-cells (expressing CD26) and dendritic cells (expressing A2AR). This is a new metabolic function for ecto-ADA that, being a single chain protein, it has been considered as an example of moonlighting protein, because it performs more than one functional role (as a catalyst, a costimulator, an allosteric modulator and a cell-to-cell connector) without partitioning these functions in different subunits.

Patterns of amino acid conservation in human and animal immunodeficiency viruses

MOTIVATION

Due to their high genomic variability, RNA viruses and retroviruses present a unique opportunity for detailed study of molecular evolution. Lentiviruses, with HIV being a notable example, are one of the best studied viral groups: hundreds of thousands of sequences are available together with experimentally resolved three-dimensional structures for most viral proteins. In this work, we use these data to study specific patterns of evolution of the viral proteins, and their relationship to protein interactions and immunogenicity.

RESULTS

We propose a method for identification of two types of surface residues clusters with abnormal conservation: extremely conserved and extremely variable clusters. We identify them on the surface of proteins from HIV and other animal immunodeficiency viruses. Both types of clusters are overrepresented on the interaction interfaces of viral proteins with other proteins, nucleic acids or low molecular-weight ligands, both in the viral particle and between the virus and its host. In the immunodeficiency viruses, the interaction interfaces are not more conserved than the corresponding proteins on an average, and we show that extremely conserved clusters coincide with protein-protein interaction hotspots, predicted as the residues with the largest energetic contribution to the interaction. Extremely variable clusters have been identified here for the first time. In the HIV-1 envelope protein gp120, they overlap with known antigenic sites. These antigenic sites also contain many residues from extremely conserved clusters, hence representing a unique interacting interface enriched both in extremely conserved and in extremely variable clusters of residues. This observation may have important implication for antiretroviral vaccine development.

AVAILABILITY AND IMPLEMENTATION

A Python package is available at https://bioinf.mpi-inf.mpg.de/publications/viral-ppi-pred/

CONTACT

voitenko@mpi-inf.mpg.de or kalinina@mpi-inf.mpg.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mutations and interactions in human ERα and bZIP proteins: An in silico approach for cell signaling in breast oncology

I.

BACKGROUND

Metastasis of breast cancer serves the most aggravating cause for transience in breast cancer patients. Accumulating evidences suggest that signal transduction in human breast cancers commences in estrogen-reliant pattern via signaling of the estrogen-receptor α-subunit (ERα) and XBP-1 (bZIP-domain) proteins. Furthermore, earlier investigations from SAGE and GST pull-down assay, also state that a point mutation in ERα leads to a risky factor by resulting into hyper-responsiveness towards estrogen and increased proliferation of breast cancer cells. So, a molecular-level exploration into the signaling mechanism is a prime requisite for future clinical and therapeutic progress. II.

METHODS AND RESULTS

Present study explores primarily the residual participation of the two essential proteins from humans to boost the signaling mechanism in malignant breast tumors. So, 3D structures of the respective monomer proteins were demonstrated and mutated protein was homology modeled after the satisfaction of the stereo-chemical features. The functionality was observed to be conserved after mutation. Abrupt increment in protein-protein interactions was studied for the individual optimized and Molecular Dynamics simulated protein complexes. Revelation from supportive statistical significances for several energy calculations, solvent accessibility areas, electrostatic surface potentials and interaction studies led to confer that after mutation, the complex and the individual protein were the most stable and the best interactive one. For metastasis in breast cancer cells, polar charged residues hold a significant contribution. III.

CONCLUSION

Therefore, this investigation provides a cogent framework for the interactive studies associated with breast cancer and an exposure towards the lethal impact on mutation.

Competition-cooperation relationship networks characterize the competition and cooperation between proteins

By analyzing protein-protein interaction (PPI) networks, one can find that a protein may have multiple binding partners. However, it is difficult to determine whether the interactions with these partners occur simultaneously from binary PPIs alone. Here, we construct the yeast and human competition-cooperation relationship networks (CCRNs) based on protein structural interactomes to clearly exhibit the relationship (competition or cooperation) between two partners of the same protein. If two partners compete for the same interaction interface, they would be connected by a competitive edge; otherwise, they would be connected by a cooperative edge. The properties of three kinds of hubs (i.e., competitive, modest, and cooperative hubs) are analyzed in the CCRNs. Our results show that competitive hubs have higher clustering coefficients and form clusters in the human CCRN, but these tendencies are not observed in the yeast CCRN. We find that the human-specific proteins contribute significantly to these differences. Subsequently, we conduct a series of computational experiments to investigate the regulatory mechanisms that avoid competition between proteins. Our comprehensive analyses reveal that for most yeast and human protein competitors, transcriptional regulation plays an important role. Moreover, the human-specific proteins have a particular preference for other regulatory mechanisms, such as alternative splicing.

Algorithmic approaches to protein-protein interaction site prediction

Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.

Structural evaluation of BTK and PKCδ mediated phosphorylation of MAL at positions Tyr86 and Tyr106

A number of diseases including sepsis, rheumatoid arthritis, diabetes, cardiovascular diseases and hyperinflammatory immune disorders have been associated with Toll like receptor (TLR) 2 and TLR4. Endogenous adaptor protein known as MyD88 adapter-like protein (MAL) bind exclusively to the cytosolic portions of TLR2 and TLR4 to initiate downstream signalling. Brutons tyrosine kinase (BTK) and protein kinase C delta (PKCδ) have been implicated to phosphorylate MAL and activate it to initiate downstream signalling. BTK has been associated with phosphorylation at positions Tyr86 and Tyr106, necessary for the activation of MAL but definite residual target of PKCδ in MAL is still to be explored. To produce a better understanding of the functional domains involved in the formation of MAL-kinase complexes, computer-aided studies were used to characterize the protein-protein interactions (PPIs) of phosphorylated BTK and PKCδ with MAL. Docking and physicochemical studies indicated that BTK was involved in close contact with Tyr86 and Tyr106 of MAL whereas PKCδ may phosphorylate Tyr106 only. Moreover, the electrostatics charge distribution of binding interfaces of BTK and PKCδ were distinct but compatible with respective regions of MAL. Our results implicate that position of Tyr86 is specifically phosphorylated by BTK whereas Tyr106 can be phosphorylated by competitive action of both BTK and PKCδ. Additionally, the residues of MAL which are necessary for interaction with TLR2, TLR4, MyD88 and SOCS-1 also play their roles in maintaining interaction with kinases and can be targeted in future to reduce TLR2 and TLR4 induced pathological responses.