Predicting and Experimentally Validating Hot-Spot Residues at Protein-Protein Interfaces

Understanding β-strand mediated protein-protein interactions: tuning binding behaviour of intrinsically disordered sequences by backbone modification

A significant challenge in chemical biology is to understand and modulate protein-protein interactions (PPIs). Given that many PPIs involve a folded protein domain and a peptide sequence that is intrinsically disordered in isolation, peptides represent powerful tools to understand PPIs. Using the interaction between small ubiquitin-like modifier (SUMO) and SUMO-interacting motifs (SIMs), here we show that N-methylation of the peptide backbone can effectively restrict accessible peptide conformations, predisposing them for protein recognition. Backbone N-methylation in appropriate locations results in faster target binding, and thus higher affinity, as shown by relaxation-based NMR experiments and computational analysis. We show that such higher affinities occur as a consequence of an increase in the energy of the unbound state, and a reduction in the entropic contribution to the binding and activation energies. Thus, backbone N-methylation may represent a useful modification within the peptidomimetic toolbox to probe β-strand mediated interactions.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Building trust in deep learning-based immune response predictors with interpretable explanations

The ability to predict whether a peptide will get presented on Major Histocompatibility Complex (MHC) class I molecules has profound implications in designing vaccines. Numerous deep learning-based predictors for peptide presentation on MHC class I molecules exist with high levels of accuracy. However, these MHC class I predictors are treated as black-box functions, providing little insight into their decision making. To build turst in these predictors, it is crucial to understand the rationale behind their decisions with human-interpretable explanations. We present MHCXAI, eXplainable AI (XAI) techniques to help interpret the outputs from MHC class I predictors in terms of input peptide features. In our experiments, we explain the outputs of four state-of-the-art MHC class I predictors over a large dataset of peptides and MHC alleles. Additionally, we evaluate the reliability of the explanations by comparing against ground truth and checking their robustness. MHCXAI seeks to increase understanding of deep learning-based predictors in the immune response domain and build trust with validated explanations.

Discovery of Nirmatrelvir Resistance Mutations in SARS-CoV-2 3CLpro: A Computational-Experimental Approach

The COVID-19 pandemic has emphasized the urgency for effective antiviral therapies against SARS-CoV-2. Targeting the main protease (3CLpro) of the virus has emerged as a promising approach, and nirmatrelvir (PF-07321332), the active component of Pfizer's oral drug Paxlovid, has demonstrated remarkable clinical efficacy. However, the emergence of resistance mutations poses a challenge to its continued success. In this study, we employed alchemical free energy perturbation (FEP) alanine scanning to identify nirmatrelvir-resistance mutations within SARS-CoV-2 3CLpro. FEP identified several mutations, which were validated through in vitro IC50 experiments and found to result in 8- and 72-fold increases in nirmatrelvir IC50 values. Additionally, we constructed SARS-CoV-2 omicron replicons containing these mutations, and one of the mutants (S144A/E166A) displayed a 20-fold increase in EC50, confirming the role of FEP in identifying drug-resistance mutations. Our findings suggest that FEP can be a valuable tool in proactively monitoring the emergence of resistant strains and guiding the design of future inhibitors with reduced susceptibility to drug resistance. As nirmatrelvir is currently widely used for treating COVID-19, this research has important implications for surveillance efforts and antiviral development.

Visualization of Self-Assembly and Hydration of a β-Hairpin through Integrated Small and Wide-Angle Neutron Scattering

Fundamental understanding of the structure and assembly of nanoscale building blocks is crucial for the development of novel biomaterials with defined architectures and function. However, accessing self-consistent structural information across multiple length scales is challenging. This limits opportunities to exploit atomic scale interactions to achieve emergent macroscale properties. In this work we present an integrative small- and wide-angle neutron scattering approach coupled with computational modeling to reveal the multiscale structure of hierarchically self-assembled β hairpins in aqueous solution across 4 orders of magnitude in length scale from 0.1 Å to 300 nm. Our results demonstrate the power of this self-consistent cross-length scale approach and allows us to model both the large-scale self-assembly and small-scale hairpin hydration of the model β hairpin CLN025. Using this combination of techniques, we map the hydrophobic/hydrophilic character of this model self-assembled biomolecular surface with atomic resolution. These results have important implications for the multiscale investigation of aqueous peptides and proteins, for the prediction of ligand binding and molecular associations for drug design, and for understanding the self-assembly of peptides and proteins for functional biomaterials.

Effect of Spacer Length Modification of the Cationic Side Chain on the Energetics of Antimicrobial Peptide Binding to Membrane-Mimetic Bilayers

Understanding the mechanism of action of the antimicrobial peptide (AMP) in terms of its structure and energetics is the key to designing new potent and selective AMPs. Recently, we reported a membranolytic 14-residue-long lysine-rich cationic antimicrobial peptide (LL-14: NH3+-LKWLKKLLKWLKKL-CONH2) against Pseudomonas aeruginosa, Klebsiella pneumoniae, and Staphylococcus aureus, which is limited by cytotoxicity and expected to undergo facile protease degradation. Aliphatic side-chain-length modification of the cationic amino-acid residues (Lys and Arg) is a popular strategy for designing protease-resistant AMPs. However, the effect of the peptide side-chain length modifications on the membrane binding affinity and its relation to the atomic structure remain an unsolved problem. We report computer simulations that quantitatively calculated the difference in peptide binding affinity to membrane-mimetic-bilayer models (bacterial: 1-palmitoyl-2-oleoyl-phosphatidylethanolamine (POPE)/1-palmitoyl-2-oleoyl-phosphatidylglycerol (POPG) bilayer and mammalian: 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC) bilayer) upon decreasing or increasing the spacer length of the cationic lysine residues of LL-14 (as well as their arginine analogues). We show that the peptide/bilayer interaction energetics varies drastically in response to spacer length modification. The strength of peptide discrimination depends strongly on the nature of the bilayer (bacterial or mammalian mimetic model). An increase in the lysine spacer length by one carbon (i.e., homolysine analogue of LL-14) is weakly/strongly disfavored by the bacterial/mammalian-membrane-mimetic bilayer. Recently, we have demonstrated an excellent correlation between the antimicrobial activity of the membranolytic cationic peptides and their binding affinity to membrane-mimetic-bilayer models. Thus, the homolysine analogue of LL-14 is a promising noncytotoxic AMP with conserved activity. On the other hand, homoarginine analogue (arginine spacer length increment by a single carbon) was preferred by both the bacteria and the mammalian mimetic bilayers and displayed the strongest affinity for the former among the peptides studied in this work. Thus, the promising most potent homoarginine analogue is likely to be cytotoxic. Shortening the Lys/Arg side chain to a three-carbon spacer (Dab/Agb) improves the binding affinity to bacterial and mammalian-membrane-mimetic bilayers. Arginine and arginine-derivative peptides exhibited stronger binding affinity to the bilayers relative to the lysine analogue. The results provide a plausible explanation to the previous experimental observations, viz., superior antimicrobial activity of the arginine peptides relative to Lys peptides and the improvement of antimicrobial activity upon substitution of Lys with Dab in the cationic peptides. The simulations revealed that the small change in the peptide hydrophobicity by Lys/Arg spacer length modification could drastically alter the energetics of peptide/bilayer binding by fine-tuning the electrostatic interactions. The energetics underlying the peptide selectivity by simple membrane-mimetic bilayer models may be beneficial for designing new selective and protease-resistant AMPs.

New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.

Bioorthogonal activation of prodrugs, for the potential treatment of breast cancer, using the Staudinger reaction

Selective prodrug activation at a tumor site is crucial to maximise the efficiency of chemotherapy approaches and minimise side effects due to off-site activation. In this paper, a new prodrug activation strategy is reported based on the bioorthogonal Staudinger reaction. The feasibility of this prodrug activation strategy was initially demonstrated using 9-azido sialic acid 4 as a trigger and two novel triphenylphosphine-modified N-mustard-PRO 10 and doxorubicin-PRO 12 prodrugs in an HPLC-monitored release study. Then, the azide reporter group was introduced on cancer cells' surfaces through metabolic glycoengineering of sialic acid-rich surface glycans using azide-modified monosaccharides (9-azido sialic acid 4, tetra-O-acetylated-9-azido sialic acid 5 and tetra-O-acetyl azidomannosamine). Next, the N-mustard-PRO 10 and doxorubicin-PRO 12 prodrugs were employed in vitro with the bioengineered cells, and activation of the prodrugs, which allowed selective release of the cytotoxic moiety at the tumour cell, was assessed. Release of the parent drugs from the prodrugs was shown to be dependent on the level of metabolic labelling, where tetra-O-acetyl azidomannosamine allowed the highest level of azide reporter generation in tumor cells and led to full recovery of the parent cytotoxic drug's potency. The selectivity of azide expression on breast cancer MCF-7 cells versus normal fibroblast L929 cells was also probed, with the 9-azido sialic acid and tetra-O-acetylated-9-azido sialic acid showing ∼17-fold higher azide expression on the former. Taken together, these data demonstrate the feasibility of the Staudinger reaction for selective activation of prodrugs targeted to the MCF-7 breast cancer cells.

Elucidating Residue-Level Determinants Affecting Dimerization of Ebola Virus Matrix Protein Using High-Throughput Site Saturation Mutagenesis and Biophysical Approaches

The Ebola virus (EBOV) is a filamentous virus that acquires its lipid envelope from the plasma membrane of the host cell it infects. EBOV assembly and budding from the host cell plasma membrane are mediated by a peripheral protein, known as the matrix protein VP40. VP40 is a 326 amino acid protein with two domains that are loosely linked. The VP40 N-terminal domain (NTD) contains a hydrophobic α-helix, which mediates VP40 dimerization. The VP40 C-terminal domain has a cationic patch, which mediates interactions with anionic lipids and a hydrophobic region that mediates VP40 dimer-dimer interactions. The VP40 dimer is necessary for trafficking to the plasma membrane inner leaflet and interactions with anionic lipids to mediate the VP40 assembly and oligomerization. Despite significant structural information available on the VP40 dimer structure, little is known on how the VP40 dimer is stabilized and how residues outside the NTD hydrophobic portion of the α-helical dimer interface contribute to dimer stability. To better understand how VP40 dimer stability is maintained, we performed computational studies using per-residue energy decomposition and site saturation mutagenesis. These studies revealed a number of novel keystone residues for VP40 dimer stability just adjacent to the α-helical dimer interface as well as distant residues in the VP40 CTD that can stabilize the VP40 dimer form. Experimental studies with representative VP40 mutants in vitro and in cells were performed to test computational predictions that reveal residues that alter VP40 dimer stability. Taken together, these studies provide important biophysical insights into VP40 dimerization and may be useful in strategies to weaken or alter the VP40 dimer structure as a means of inhibiting the EBOV assembly.

Doubly Constrained C-terminal of Roc (COR) Domain-Derived Peptides Inhibit Leucine-Rich Repeat Kinase 2 (LRRK2) Dimerization

Missense mutations along the leucine-rich repeat kinase 2 (LRRK2) protein are a major contributor to Parkinson's Disease (PD), the second most commonly occurring neurodegenerative disorder worldwide. We recently reported the development of allosteric constrained peptide inhibitors that target and downregulate LRRK2 activity through disruption of LRRK2 dimerization. In this study, we designed doubly constrained peptides with the objective of inhibiting C-terminal of Roc (COR)-COR mediated dimerization at the LRRK2 dimer interface. We show that the doubly constrained peptides are cell-permeant, bind wild-type and pathogenic LRRK2, inhibit LRRK2 dimerization and kinase activity, and inhibit LRRK2-mediated neuronal apoptosis, and in contrast to ATP-competitive LRRK2 kinase inhibitors, they do not induce the mislocalization of LRRK2 to skein-like structures in cells. This work highlights the significance of COR-mediated dimerization in LRRK2 activity while also highlighting the use of doubly constrained peptides to stabilize discrete secondary structural folds within a peptide sequence.

Design of human immunodeficiency virus-1 neutralizing peptides targeting CD4-binding site: An integrative computational biologics approach

Peptide therapeutics have recently gained momentum in antiviral therapy due to their increased potency and cost-effectiveness. Interaction of the HIV-1 envelope gp120 with the host CD4 receptor is a critical step for viral entry, and therefore the CD4-binding site (CD4bs) of gp120 is a potential hotspot for blocking HIV-1 infection. The present study aimed to design short peptides from well-characterized CD4bs targeting broadly neutralizing antibodies (bNAbs), which could be utilized as bNAb mimetics for viral neutralization. Co-crystallized structures of HIV-1 gp120 in complex with CD4bs-directed bNAbs were used to derive hexameric peptides using the Rosetta Peptiderive protocol. Based on empirical insights into co-crystallized structures, peptides derived from the heavy chain alone were considered. The peptides were docked with both HIV-1 subtype B and C gp120, and the stability of the peptide–antigen complexes was validated using extensive Molecular Dynamics (MD) simulations. Two peptides identified in the study demonstrated stable intermolecular interactions with SER365, GLY366, and GLY367 of the PHE43 cavity in the CD4 binding pocket, and with ASP368 of HIV-1 gp120, thereby mimicking the natural interaction between ASP368gp120 and ARG59CD4–RECEPTOR. Furthermore, the peptides featured favorable physico-chemical properties for virus neutralization suggesting that these peptides may be highly promising bNAb mimetic candidates that may be taken up for experimental validation.

Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions

The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes.

In Silico Identification of Potential Sites for a Plastic-Degrading Enzyme by a Reverse Screening through the Protein Sequence Space and Molecular Dynamics Simulations

The accumulation of polyethylene terephthalate (PET) seriously harms the environment because of its high resistance to degradation. The recent discovery of the bacteria-secreted biodegradation enzyme, PETase, sheds light on PET recycling; however, the degradation efficiency is far from practical use. Here, in silico alanine scanning mutagenesis (ASM) and site-saturation mutagenesis (SSM) were employed to construct the protein sequence space from binding energy of the PETase–PET interaction to identify the number and position of mutation sites and their appropriate side-chain properties that could improve the PETase–PET interaction. The binding mechanisms of the potential PETase variant were investigated through atomistic molecular dynamics simulations. The results show that up to two mutation sites of PETase are preferable for use in protein engineering to enhance the PETase activity, and the proper side chain property depends on the mutation sites. The predicted variants agree well with prior experimental studies. Particularly, the PETase variants with S238C or Q119F could be a potential candidate for improving PETase. Our combination of in silico ASM and SSM could serve as an alternative protocol for protein engineering because of its simplicity and reliability. In addition, our findings could lead to PETase improvement, offering an important contribution towards a sustainable future.

Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions

Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.

Functional and Structural Roles of the Dimer Interface in the Activity and Stability of Clostridium butyricum 1,3-Propanediol Oxidoreductase

Biosynthesis of 1,3-propanediol (1,3-PD) by 1,3-propanediol oxidoreductase (PDOR) is often limited by the stability issues. To address this issue, the goal of the present study was to engineer the Clostridium butyricum PDOR dimeric interface. The interface exists between the chains and plays a role in the synthesis of 1,3-PD, which is hindered by the increased temperature and pH. Herein, we engineered PDOR by HotSpot Wizard 3.0 and molecular dynamics simulations, improving its thermal stability, pH tolerance, and catalytic properties with respect to the wild-type PDOR activity at 37 °C. Compared to the activity of the wild-type PDOR, the N298C mutant showed 0.5-fold greater activity at pH 8.0, while the P299E mutant showed significantly increased activity of over five fold at pH 4.0. Further structural comparisons between the wild-type and P299E mutant revealed that the extraordinary stability of the P299E mutant could be due to the formation of additional hydrogen bonds and salt bridges. The N298C mutant also exhibits thermal stability at a broad range of temperature at pH 8 with respect to wild-type PDOR and other mutants. The molecular dynamics simulations revealed that stability profiles of P299E mutants at pH 4.0 are attributed to identical root mean square deviation values and stable conformations in the motif region present in the dimer interface of the enzyme. These findings suggest that the dimer interface motifs are essential for the compactness and stability of the PDOR enzyme; therefore, engineering the PDOR using a structure-guided approach could aid in improving its activity and stability under various physiological conditions (pH and temperature).

PPI-HotspotDB: Database of Protein-Protein Interaction Hot Spots

Single-point mutations of certain residues (so-called hot spots) impair/disrupt protein-protein interactions (PPIs), leading to pathogenesis and drug resistance. Conventionally, a PPI-hot spot is identified when its replacement decreased the binding free energy significantly, generally by ≥2 kcal/mol. The relatively few mutations with such a significant binding free energy drop limited the number of distinct PPI-hot spots. By defining PPI-hot spots based on mutations that have been manually curated in UniProtKB to significantly impair/disrupt PPIs in addition to binding free energy changes, we have greatly expanded the number of distinct PPI-hot spots by an order of magnitude. These experimentally determined PPI-hot spots along with available structures have been collected in a database called PPI-Hotspot^DB. We have applied the PPI-Hotspot^DB to create a nonredundant benchmark, PPI-Hotspot+PDB^BM, for assessing methods to predict PPI-hot spots using the free structure as input. PPI-Hotspot^DB will benefit the design of mutagenesis experiments and development of PPI-hot spot prediction methods. The database and benchmark are freely available at https://ppihotspot.limlab.dnsalias.org.

Computational Screening for the Anticancer Potential of Seed-Derived Antioxidant Peptides: A Cheminformatic Approach

Some seed-derived antioxidant peptides are known to regulate cellular modulators of ROS production, including those proposed to be promising targets of anticancer therapy. Nevertheless, research in this direction is relatively slow owing to the inevitable time-consuming nature of wet-lab experimentations. To help expedite such explorations, we performed structure-based virtual screening on seed-derived antioxidant peptides in the literature for anticancer potential. The ability of the peptides to interact with myeloperoxidase, xanthine oxidase, Keap1, and p47^phox was examined. We generated a virtual library of 677 peptides based on a database and literature search. Screening for anticancer potential, non-toxicity, non-allergenicity, non-hemolyticity narrowed down the collection to five candidates. Molecular docking found LYSPH as the most promising in targeting myeloperoxidase, xanthine oxidase, and Keap1, whereas PSYLNTPLL was the best candidate to bind stably to key residues in p47^phox. Stability of the four peptide-target complexes was supported by molecular dynamics simulation. LYSPH and PSYLNTPLL were predicted to have cell- and blood-brain barrier penetrating potential, although intolerant to gastrointestinal digestion. Computational alanine scanning found tyrosine residues in both peptides as crucial to stable binding to the targets. Overall, LYSPH and PSYLNTPLL are two potential anticancer peptides that deserve deeper exploration in future.

Discovery of SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding

The main protease (Mpro) of SARS-CoV-2 is central to viral maturation and is a promising drug target, but little is known about structural aspects of how it binds to its 11 natural cleavage sites. We used biophysical and crystallographic data and an array of biomolecular simulation techniques, including automated docking, molecular dynamics (MD) and interactive MD in virtual reality, QM/MM, and linear-scaling DFT, to investigate the molecular features underlying recognition of the natural Mpro substrates. We extensively analysed the subsite interactions of modelled 11-residue cleavage site peptides, crystallographic ligands, and docked COVID Moonshot-designed covalent inhibitors. Our modelling studies reveal remarkable consistency in the hydrogen bonding patterns of the natural Mpro substrates, particularly on the N-terminal side of the scissile bond. They highlight the critical role of interactions beyond the immediate active site in recognition and catalysis, in particular plasticity at the S2 site. Building on our initial Mpro-substrate models, we used predictive saturation variation scanning (PreSaVS) to design peptides with improved affinity. Non-denaturing mass spectrometry and other biophysical analyses confirm these new and effective 'peptibitors' inhibit Mpro competitively. Our combined results provide new insights and highlight opportunities for the development of Mpro inhibitors as anti-COVID-19 drugs.

Towards optimizing peptide-based inhibitors of protein-protein interactions: predictive saturation variation scanning (PreSaVS)

A simple-to-implement and experimentally validated computational workflow for sequence modification of peptide inhibitors of protein–protein interactions (PPIs) is described.

An experimentally validated approach for in silico modification of peptide based protein–protein interaction inhibitors is described.

Multi-modal adaptor-clathrin contacts drive coated vesicle assembly

Clathrin‐coated pits are formed by the recognition of membrane and cargo by the AP2 complex and the subsequent recruitment of clathrin triskelia. A role for AP2 in coated‐pit assembly beyond initial clathrin recruitment has not been explored. Clathrin binds the β2 subunit of AP2, and several binding sites have been identified, but our structural knowledge of these interactions is incomplete and their functional importance during endocytosis is unclear. Here, we analysed the cryo‐EM structure of clathrin cages assembled in the presence of β2 hinge‐appendage (β2HA). We find that the β2‐appendage binds in at least two positions in the cage, demonstrating that multi‐modal binding is a fundamental property of clathrin‐AP2 interactions. In one position, β2‐appendage cross‐links two adjacent terminal domains from different triskelia. Functional analysis of β2HA‐clathrin interactions reveals that endocytosis requires two clathrin interaction sites: a clathrin‐box motif on the hinge and the “sandwich site” on the appendage. We propose that β2‐appendage binding to more than one triskelion is a key feature of the system and likely explains why assembly is driven by AP2.

Improving the Performance of MM/PBSA in Protein-Protein Interactions via the Screening Electrostatic Energy

Accurate calculation of protein-protein binding free energy is of great importance in biological and medical science, yet it remains a hugely challenging problem. In this work, we develop a new strategy in which a screened electrostatic energy (i.e., adding an exponential damping factor to the Coulombic interaction energy) is used within the framework of the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method. Our results show that the Pearson correlation coefficient in the modified MM/PBSA is over 0.70, which is much better than that in the standard MM/PBSA, especially in the Amber14SB force field. In particular, the performance of the standard MM/PBSA is very poor in a system where the proteins carry like charges. Moreover, we also calculated the mean absolute error (MAE) between the calculated and experimental ΔG values and found that the MAE in the modified MM/PBSA was indeed much smaller than that in the standard MM/PBSA. Furthermore, the effect of the dielectric constant of the proteins and the salt conditions on the results was also investigated. The present study highlights the potential power of the modified MM/PBSA for accurately predicting the binding energy in highly charged biosystems.

In Silico Identification of Multi-target Anti-SARS-CoV-2 Peptides from Quinoa Seed Proteins

Peptides are promising antagonists against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). To expedite drug discovery, a computational approach is widely employed for the initial screening of anti-SARS-CoV-2 candidates. This study aimed to investigate the potential of peptides from quinoa seed proteins as multi-target antagonists against SARS-CoV-2 spike glycoprotein receptor-binding domain, main protease, and papain-like protease. Five quinoa proteins were hydrolyzed in silico by papain and subtilisin. Among the 1465 peptides generated, seven could interact stably with the key binding residues and catalytic residues of the viral targets, mainly via hydrogen bonds and hydrophobic interactions. The seven peptides were comparable or superior to previously reported anti-SARS-CoV-2 peptides based on docking scores. Key residues in the seven peptides contributing to binding to viral targets were determined by computational alanine scanning. The seven peptides were predicted in silico to be non-toxic and non-allergenic. The peptides ranged between 546.66 and 3974.87 g/mol in molecular mass, besides exhibiting basic and cationic properties (isoelectric points: 8.26-12.10; net charges: 0.1-4.0). Among the seven peptides, VEDKGMMHQQRMMEKAMNIPRMCGTMQRKCRMS was found to bind the largest number of key residues on the targets. In conclusion, seven putative non-toxic, non-allergenic, multi-target anti-SARS-CoV-2 peptides were identified from quinoa seed proteins. The in vitro and in vivo efficacies of the seven peptides against SARS-CoV-2 deserve attention in future bench-top testing.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10989-021-10214-y.

Scanning Protein Surfaces with DNA-Encoded Libraries

Understanding the ligandability of a target protein, defined as the capability of a protein to bind drug-like compounds on any site, can give important stimuli to drug-development projects. For instance, inhibition of protein-protein interactions usually depends on the identification of protein surface binders. DNA-encoded chemical libraries (DELs) allow scanning of protein surfaces with large chemical space. Encoded library selection screens uncovered several protein-protein interaction inhibitors and compounds binding to the surface of G protein-coupled receptors (GPCRs) and kinases. The protein surface-binding chemotypes from DELs are predominantly chemically modified and cyclized peptides, and functional small-molecule peptidomimetics. Peptoid libraries and structural peptidomimetics have been less studied in the DEL field, hinting at hitherto less populated chemical space and suggesting alternative library designs. Roughly a third of bioactive molecules evolved from smaller, target-focused libraries. They showcase the potential of encoded libraries to identify more potent molecules from weak, for example, fragment-like, starting points.

Structural Characterization of Receptor-Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers

G protein-coupled receptor (GPCR) oligomerization, while contentious, continues to attract the attention of researchers. Numerous experimental investigations have validated the presence of GPCR dimers, and the relevance of dimerization in the effectuation of physiological functions intensifies the attractiveness of this concept as a potential therapeutic target. GPCRs, as a single entity, have been the main source of scrutiny for drug design objectives for multiple diseases such as cancer, inflammation, cardiac, and respiratory diseases. The existence of dimers broadens the research scope of GPCR functions, revealing new signaling pathways that can be targeted for disease pathogenesis that have not previously been reported when GPCRs were only viewed in their monomeric form. This review will highlight several aspects of GPCR dimerization, which include a summary of the structural elucidation of the allosteric modulation of class C GPCR activation offered through recent solutions to the three-dimensional, full-length structures of metabotropic glutamate receptor and γ-aminobutyric acid B receptor as well as the role of dimerization in the modification of GPCR function and allostery. With the growing influence of computational methods in the study of GPCRs, we will also be reviewing recent computational tools that have been utilized to map protein-protein interactions (PPI).

A potential interaction between the SARS-CoV-2 spike protein and nicotinic acetylcholine receptors

Changeux et al. (Changeux et al. C. R. Biol. 343:33-39.) recently suggested that the SARS-CoV-2 spike protein may interact with nicotinic acetylcholine receptors (nAChRs) and that such interactions may be involved in pathology and infectivity. This hypothesis is based on the fact that the SARS-CoV-2 spike protein contains a sequence motif similar to known nAChR antagonists. Here, we use molecular simulations of validated atomically detailed structures of nAChRs and of the spike to investigate the possible binding of the Y674-R685 region of the spike to nAChRs. We examine the binding of the Y674-R685 loop to three nAChRs, namely the human α4β2 and α7 subtypes and the muscle-like αβγδ receptor from Tetronarce californica. Our results predict that Y674-R685 has affinity for nAChRs. The region of the spike responsible for binding contains a PRRA motif, a four-residue insertion not found in other SARS-like coronaviruses. The conformational behavior of the bound Y674-R685 is highly dependent on the receptor subtype; it adopts extended conformations in the α4β2 and α7 complexes but is more compact when bound to the muscle-like receptor. In the α4β2 and αβγδ complexes, the interaction of Y674-R685 with the receptors forces the loop C region to adopt an open conformation, similar to other known nAChR antagonists. In contrast, in the α7 complex, Y674-R685 penetrates deeply into the binding pocket in which it forms interactions with the residues lining the aromatic box, namely with TrpB, TyrC1, and TyrC2. Estimates of binding energy suggest that Y674-R685 forms stable complexes with all three nAChR subtypes. Analyses of simulations of the glycosylated spike show that the Y674-R685 region is accessible for binding. We suggest a potential binding orientation of the spike protein with nAChRs, in which they are in a nonparallel arrangement to one another.

Recent Progress Using De Novo Design to Study Protein Structure, Design and Binding Interactions

De novo protein design is a powerful methodology used to study natural functions in an artificial-protein context. Since its inception, it has been used to reproduce a plethora of reactions and uncover biophysical principles that are often difficult to extract from direct studies of natural proteins. Natural proteins are capable of assuming a variety of different structures and subsequently binding ligands at impressively high levels of both specificity and affinity. Here, we will review recent examples of de novo design studies on binding reactions for small molecules, nucleic acids, and the formation of protein-protein interactions. We will then discuss some new structural advances in the field. Finally, we will discuss some advancements in computational modeling and design approaches and provide an overview of some modern algorithmic tools being used to design these proteins.

Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization

This work measures baseline sampling characteristics that highlight fundamental differences between sampling methods for assembly driven by short-ranged pair potentials. Such granular comparison is essential for fast, flexible, and accurate hybridization of complementary methods. Besides sampling speed, efficiency, and accuracy of uniform grid coverage, other sampling characteristics measured are (i) accuracy of covering narrow low energy regions that have low effective dimension (ii) ability to localize sampling to specific basins, and (iii) flexibility in sampling distributions. As a proof of concept, we compare a recently developed geometric methodology EASAL (Efficient Atlasing and Search of Assembly Landscapes) and the traditional Monte Carlo (MC) method for sampling the energy landscape of two assembling trans-membrane helices, driven by short-range pair potentials. By measuring the above-mentioned sampling characteristics, we demonstrate that EASAL provides localized and accurate coverage of crucial regions of the energy landscape of low effective dimension, under flexible sampling distributions, with much fewer samples and computational resources than MC sampling. EASAL's empirically validated theoretical guarantees permit credible extrapolation of these measurements and comparisons to arbitrary number and size of assembling units. Promising avenues for hybridizing the complementary advantages of the two methods are discussed.

Query-guided protein-protein interaction inhibitor discovery

Protein-protein interactions (PPIs) are central to biological mechanisms, and can serve as compelling targets for drug discovery. Yet, the discovery of small molecule inhibitors of PPIs remains challenging given the large and typically shallow topography of the interacting protein surfaces. Here, we describe a general approach to the discovery of orthosteric PPI inhibitors that mimic specific secondary protein structures. Initially, hot residues at protein-protein interfaces are identified in silico or from experimental data, and incorporated into secondary structure-based queries. Virtual libraries of small molecules are then shape-matched against the queries, and promising ligands docked to target proteins. The approach is exemplified experimentally using two unrelated PPIs that are mediated by an α-helix (p53/hDM2) and a β-strand (GKAP/SHANK1-PDZ). In each case, selective PPI inhibitors are discovered with low μM activity as determined by a combination of fluorescence anisotropy and 1H-15N HSQC experiments. In addition, hit expansion yields a series of PPI inhibitors with defined structure-activity relationships. It is envisaged that the generality of the approach will enable discovery of inhibitors of a wide range of unrelated secondary structure-mediated PPIs.

Overcoming challenges in developing small molecule inhibitors for GPVI and CLEC-2

GPVI and CLEC-2 have emerged as promising targets for long-term prevention of both arterial thrombosis and thrombo-inflammation with a decreased bleeding risk relative to current drugs. However, while there are potent blocking antibodies of both receptors, their protein nature comes with decreased bioavailability, making formulation for oral medication challenging. Small molecules are able to overcome these limitations, but there are many challenges in developing antagonists of nanomolar potency, which is necessary when considering the structural features that underlie the interaction of CLEC-2 and GPVI with their protein ligands. In this review, we describe current small-molecule inhibitors for both receptors and strategies to overcome such limitations, including considerations when it comes to in silico drug design and the importance of complex compound library selection.

Identification of β-strand mediated protein-protein interaction inhibitors using ligand-directed fragment ligation

β-Strand mediated protein-protein interactions (PPIs) represent underexploited targets for chemical probe development despite representing a significant proportion of known and therapeutically relevant PPI targets. β-Strand mimicry is challenging given that both amino acid side-chains and backbone hydrogen-bonds are typically required for molecular recognition, yet these are oriented along perpendicular vectors. This paper describes an alternative approach, using GKAP/SHANK1 PDZ as a model and dynamic ligation screening to identify small-molecule replacements for tranches of peptide sequence. A peptide truncation of GKAP functionalized at the N- and C-termini with acylhydrazone groups was used as an anchor. Reversible acylhydrazone bond exchange with a library of aldehyde fragments in the presence of the protein as template and in situ screening using a fluorescence anisotropy (FA) assay identified peptide hybrid hits with comparable affinity to the GKAP peptide binding sequence. Identified hits were validated using FA, ITC, NMR and X-ray crystallography to confirm selective inhibition of the target PDZ-mediated PPI and mode of binding. These analyses together with molecular dynamics simulations demonstrated the ligands make transient interactions with an unoccupied basic patch through electrostatic interactions, establishing proof-of-concept that this unbiased approach to ligand discovery represents a powerful addition to the armory of tools that can be used to identify PPI modulators.

Computational Hot-Spot Analysis of the SARS-CoV-2 Receptor Binding Domain/ACE2 Complex*

Infection and replication of SARS CoV-2 (the virus that causes COVID-19) requires entry to the interior of host cells. In humans, a protein-protein interaction (PPI) between the SARS CoV-2 receptor-binding domain (RBD) and the extracellular peptidase domain of ACE2 on the surface of cells in the lower respiratory tract is an initial step in the entry pathway. Inhibition of the SARS CoV-2 RBD/ACE2 PPI is currently being evaluated as a target for therapeutic and/or prophylactic intervention. However, relatively little is known about the molecular underpinnings of this complex. Employing multiple computational platforms, we predicted "hot-spot" residues in a positive-control PPI (PMI/MDM2) and the CoV-2 RBD/ACE2 complex. Computational alanine scanning mutagenesis was performed to predict changes in Gibbs' free energy that are associated with mutating residues at the positive control (PMI/MDM2) or SARS RBD/ACE2 binding interface to alanine. Additionally, we used the Adaptive Poisson-Boltzmann Solver to calculate macromolecular electrostatic surfaces at the interface of the positive-control PPI and SARS CoV-2/ACE2 PPI. Finally, a comparative analysis of hot-spot residues for SARS-CoV and SARS-CoV-2, in complex with ACE2, is provided. Collectively, this study illuminates predicted hot-spot residues, and clusters, at the SARS CoV-2 RBD/ACE2 binding interface, potentially guiding the development of reagents capable of disrupting this complex and halting COVID-19.

A Coarse-Grained Methodology Identifies Intrinsic Mechanisms That Dissociate Interacting Protein Pairs

We address the problem of triggering dissociation events between proteins that have formed a complex. We have collected a set of 25 non-redundant, functionally diverse protein complexes having high-resolution three-dimensional structures in both the unbound and bound forms. We unify elastic network models with perturbation response scanning (PRS) methodology as an efficient approach for predicting residues that have the propensity to trigger dissociation of an interacting protein pair, using the three-dimensional structures of the bound and unbound proteins as input. PRS reveals that while for a group of protein pairs, residues involved in the conformational shifts are confined to regions with large motions, there are others where they originate from parts of the protein unaffected structurally by binding. Strikingly, only a few of the complexes have interface residues responsible for dissociation. We find two main modes of response: In one mode, remote control of dissociation in which disruption of the electrostatic potential distribution along protein surfaces play the major role; in the alternative mode, mechanical control of dissociation by remote residues prevail. In the former, dissociation is triggered by changes in the local environment of the protein, e.g., pH or ionic strength, while in the latter, specific perturbations arriving at the controlling residues, e.g., via binding to a third interacting partner is required for decomplexation. We resolve the observations by relying on an electromechanical coupling model which reduces to the usual elastic network result in the limit of the lack of coupling. We validate the approach by illustrating the biological significance of top residues selected by PRS on select cases where we show that the residues whose perturbation leads to the observed conformational changes correspond to either functionally important or highly conserved residues in the complex.

Rapid prediction of crucial hotspot interactions for icosahedral viral capsid self-assembly by energy landscape atlasing validated by mutagenesis

Icosahedral viruses are under a micrometer in diameter, their infectious genome encapsulated by a shell assembled by a multiscale process, starting from an integer multiple of 60 viral capsid or coat protein (VP) monomers. We predict and validate inter-atomic hotspot interactions between VP monomers that are important for the assembly of 3 types of icosahedral viral capsids: Adeno Associated Virus serotype 2 (AAV2) and Minute Virus of Mice (MVM), both T = 1 single stranded DNA viruses, and Bromo Mosaic Virus (BMV), a T = 3 single stranded RNA virus. Experimental validation is by in-vitro, site-directed mutagenesis data found in literature. We combine ab-initio predictions at two scales: at the interface-scale, we predict the importance (cruciality) of an interaction for successful subassembly across each interface between symmetry-related VP monomers; and at the capsid-scale, we predict the cruciality of an interface for successful capsid assembly. At the interface-scale, we measure cruciality by changes in the capsid free-energy landscape partition function when an interaction is removed. The partition function computation uses atlases of interface subassembly landscapes, rapidly generated by a novel geometric method and curated opensource software EASAL (efficient atlasing and search of assembly landscapes). At the capsid-scale, cruciality of an interface for successful assembly of the capsid is based on combinatorial entropy. Our study goes all the way from resource-light, multiscale computational predictions of crucial hotspot inter-atomic interactions to validation using data on site-directed mutagenesis' effect on capsid assembly. By reliably and rapidly narrowing down target interactions, (no more than 1.5 hours per interface on a laptop with Intel Core i5-2500K @ 3.2 Ghz CPU and 8GB of RAM) our predictions can inform and reduce time-consuming in-vitro and in-vivo experiments, or more computationally intensive in-silico analyses.

Preventing the Interaction between Coronaviruses Spike Protein and Angiotensin I Converting Enzyme 2: An In Silico Mechanistic Case Study on Emodin as a Potential Model Compound

Emodin, a widespread natural anthraquinone, has many biological activities including health-protective and adverse effects. Amongst beneficial effects, potential antiviral activity against coronavirus responsible for the severe acute respiratory syndrome outbreak in 2002–2003 has been described associated with the inhibition of the host cells target receptors recognition by the viral Spike protein. However, the inhibition mechanisms have not been fully characterized, hindering the rational use of emodin as a model compound to develop more effective analogues. This work investigates emodin interaction with the Spike protein to provide a mechanistic explanation of such inhibition. A 3D molecular modeling approach consisting of docking simulations, pharmacophoric analysis and molecular dynamics was used. The plausible mechanism is described as an interaction of emodin at the protein–protein interface which destabilizes the viral protein-target receptor complex. This analysis has been extended to the Spike protein of the coronavirus responsible for the current pandemic hypothesizing emodin’s functional conservation. This solid knowledge-based foothold provides a possible mechanistic rationale of the antiviral activity of emodin as a future basis for the potential development of efficient antiviral cognate compounds. Data gaps and future work on emodin-related adverse effects in parallel to its antiviral pharmacology are explored.

Advances in Molecular Dynamics Simulations and Enhanced Sampling Methods for the Study of Protein Systems

Molecular dynamics (MD) simulation is a rigorous theoretical tool that when used efficiently could provide reliable answers to questions pertaining to the structure-function relationship of proteins. Data collated from protein dynamics can be translated into useful statistics that can be exploited to sieve thermodynamics and kinetics crucial for the elucidation of mechanisms responsible for the modulation of biological processes such as protein-ligand binding and protein-protein association. Continuous modernization of simulation tools enables accurate prediction and characterization of the aforementioned mechanisms and these qualities are highly beneficial for the expedition of drug development when effectively applied to structure-based drug design (SBDD). In this review, current all-atom MD simulation methods, with focus on enhanced sampling techniques, utilized to examine protein structure, dynamics, and functions are discussed. This review will pivot around computer calculations of protein-ligand and protein-protein systems with applications to SBDD. In addition, we will also be highlighting limitations faced by current simulation tools as well as the improvements that have been made to ameliorate their efficiency.

SARS-CoV-2 nucleocapsid and Nsp3 binding: an in silico study

Severe acute respiratory syndrome virus 2 (SARS-CoV-2) belongs to the single-stranded positive-sense RNA family. The virus contains a large genome that encodes four structural proteins, small envelope (E), matrix (M), nucleocapsid phosphoprotein (N), spike (S), and 16 nonstructural proteins (nsp1-16) that together, ensure replication of the virus in the host cell. Among these proteins, the interactions of N and Nsp3 are essential that links the viral genome for processing. The N proteins reside at CoV RNA synthesis sites known as the replication-transcription complexes (RTCs). The N-terminal of N has RNA-binding domain (N-NTD), capturing the RNA genome while the C-terminal domain (N-CTD) anchors the viral Nsp3, a component of RTCs. Although the structural information has been recently released, the residues involved in contacts between N-CTD with Nsp3 are still unknown. To find the residues involved in interactions between two proteins, three-dimensional structures of both proteins were retrieved and docked using HADDOCK. Residues at N-CTD were detected in interaction with L499, R500, K501, V502, P503, T504, D505, N506, Y507, I508, T509, K529, K530K532, S533 of Nsp3 and N-NTD to synthesize SARS-CoV-2 RNA. The interaction between Nsp3 and CTD of N protein may be a potential drug target. The current study provides information for better understanding the interaction between N protein and Nsp3 that could be a possible target for future inhibitors.

SARS-CoV-2 nucleocapsid and Nsp3 binding: an in silico study

Severe acute respiratory syndrome virus 2 (SARS-CoV-2) belongs to the single-stranded positive-sense RNA family. The virus contains a large genome that encodes four structural proteins, small envelope (E), matrix (M), nucleocapsid phosphoprotein (N), spike (S), and 16 nonstructural proteins (nsp1-16) that together, ensure replication of the virus in the host cell. Among these proteins, the interactions of N and Nsp3 are essential that links the viral genome for processing. The N proteins reside at CoV RNA synthesis sites known as the replication-transcription complexes (RTCs). The N-terminal of N has RNA-binding domain (N-NTD), capturing the RNA genome while the C-terminal domain (N-CTD) anchors the viral Nsp3, a component of RTCs. Although the structural information has been recently released, the residues involved in contacts between N-CTD with Nsp3 are still unknown. To find the residues involved in interactions between two proteins, three-dimensional structures of both proteins were retrieved and docked using HADDOCK. Residues at N-CTD were detected in interaction with L499, R500, K501, V502, P503, T504, D505, N506, Y507, I508, T509, K529, K530K532, S533 of Nsp3 and N-NTD to synthesize SARS-CoV-2 RNA. The interaction between Nsp3 and CTD of N protein may be a potential drug target. The current study provides information for better understanding the interaction between N protein and Nsp3 that could be a possible target for future inhibitors.

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.