Protein-protein interactions: structurally conserved residues distinguish between binding sites and exposed protein surfaces

Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?

Protein-protein interactions (PPIs) are central to the cellular signaling and regulatory networks that underlie many physiological and pathophysiological processes. It is challenging to target PPIs using traditional small molecule or peptide-based approaches due to the frequent lack of well-defined binding pockets at the large and flat PPI interfaces. Synthetic polymers offer an opportunity to circumvent these challenges by providing unparalleled flexibility in tuning their physiochemical properties to achieve the desired binding properties. In this review, we summarize the current state of the field pertaining to polymer-protein interactions in solution, highlighting various polyelectrolyte systems, their tunable parameters, and their characterization. We provide an outlook on how these architectures can be improved by incorporating sequence control, foldability, and machine learning to mimic proteins at every structural level. Advances in these directions will enable the design of more specific protein-binding polymers and provide an effective strategy for targeting dynamic proteins, such as intrinsically disordered proteins.

Structure-guided Evolutionary Analysis of Interactome Network Rewiring at Single Residue Resolution in Yeasts

Protein-protein interactions (PPIs) are known to rewire extensively during evolution leading to lineage-specific and species-specific changes in molecular processes. However, the detailed molecular evolutionary mechanisms underlying interactome network rewiring are not well-understood. Here, we combine high-confidence PPI data, high-resolution three-dimensional structures of protein complexes, and homology-based structural annotation transfer to construct structurally-resolved interactome networks for the two yeasts S. cerevisiae and S. pombe. We then classify PPIs according to whether they are preserved or different between the two yeast species and compare site-specific evolutionary rates of interfacial versus non-interfacial residues for these different categories of PPIs. We find that residues in PPI interfaces evolve significantly more slowly than non-interfacial residues when using lineage-specific measures of evolutionary rate, but not when using non-lineage-specific measures. Furthermore, both lineage-specific and non-lineage-specific evolutionary rate measures can distinguish interfacial residues from non-interfacial residues for preserved PPIs between the two yeasts, but only the lineage-specific measure is appropriate for rewired PPIs. Finally, both lineage-specific and non-lineage-specific evolutionary rate measures are appropriate for elucidating structural determinants of protein evolution for residues outside of PPI interfaces. Overall, our results demonstrate that unlike tertiary structures of single proteins, PPIs and PPI interfaces can be highly volatile in their evolution, thus requiring the use of lineage-specific measures when studying their evolution. These results yield insight into the evolutionary design principles of PPIs and the mechanisms by which interactions are preserved or rewired between species, improving our understanding of the molecular evolution of PPIs and PPI interfaces at the residue level.

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

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

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

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

Effect of dual modifications with ultrasonication and succinylation on Cicer arietinum protein-iron complexes: Characterization, digestibility, in-vitro cellular mineral uptake and preparation of fortified smoothie

The research aimed to evaluate the effect of ultrasonication and succinylation on the functional, iron binding, physiochemical, and cellular mineral uptake efficacy of chickpea protein concentrate. Succinylation resulted in significant improvements in the water-holding capacity (WHC) (25.47 %), oil-holding capacity (OHC) (31.38 %), and solubility (5.80 %) of the chickpea protein-iron complex. Mineral bioavailability significantly increased by 4.41 %, and there was a significant increase in cellular mineral uptake (64.64 %), retention (36.68 %), and transport (27.96 %). The ferritin content of the succinylated chickpea protein-iron complex showed a substantial increase of 66.31%. Furthermore, the dual modification approach combining ultrasonication and succinylation reduced the particle size of the protein-iron complex with a substantial reduction of 83.25 %. It also resulted in a significant enhancement of 51.5 % in the SH (sulfhydryl) content and 48.92 % in the surface hydrophobicity. Mineral bioavailability and cellular mineral uptake, retention, and transport were further enhanced through dual modification. In terms of application, the addition of single and dual-modified chickpea protein-iron complex to a fruit-based smoothie demonstrated positive acceptance in sensory attributes. Overall, the combined approach of succinylation and ultrasonication to the chickpea protein-iron complex shows a promising strategy for enhancing the physiochemical and techno-functional characteristics, cellular mineral uptake, and the development of vegan food products.

Revealing Propolis Potential Activity on Inhibiting Estrogen Receptor and Heat Shock Protein 90 Overexpressed in Breast Cancer by Bioinformatics Approaches

Breast cancer is the most commonly diagnosed cancer globally, with the highest incidence of breast cancer occurring in Asian countries including Indonesia. Among the types of breast cancer, the estrogen receptor (ER)-positive subtype which is prominent with estrogen receptor alpha (ERα) and heat shock protein 90 (HSP90) overexpression genes becomes the most prevalent than the others, approximately 75% of all breast cancer cases. ERα and HSP90 play a role in breast cancer activities including breast tumor growth, invasion, and metastasis mechanism. Propolis, a natural bee product, has been explored for its anticancer activity. However, there is lack of studies that evaluated the potential inhibitor from propolis compounds to the ERα and HSP90 proteins. Therefore, this article focuses on examining the correlation between ERα and HSP90's role in breast cancer and investigating the potential of 93 unique propolis compositions in inhibiting these genes in breast cancer using in silico approaches. This study revealed the positive correlation between ERα and HSP90 genes in breast cancer disease development. Furthermore, we also found novel potential bioactive compounds of propolis against breast cancer through binding with ERα and HSP90; they were 3',4',7-trihydroxyisoflavone and baicalein-7-O-β-D glucopyranoside, respectively. Further research on these compounds is needed to elucidate deeper mechanisms and activity in the real biological system to develop new breast cancer drug treatments.

Software and Databases for Protein-Protein Docking

Protein-protein interactions (PPIs) provide valuable insights for understanding the principles of biological systems and for elucidating causes of incurable diseases. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. This chapter is a summary of software and databases used for protein-protein docking.

Free energy landscape and thermodynamics properties of novel mutations in PncA of pyrazinamide resistance isolates of Mycobacterium tuberculosis

Pyrazinamide (PZA) is one of the first-line antituberculosis therapy, active against non-replicating Mycobacterium tuberculosis (Mtb). The conversion of PZA into pyrazinoic acid (POA), the active form, required the activity of pncA gene product pyrazinamidase (PZase) activity. Mutations occurred in pncA are the primary cause behind the PZA resistance. However, the resistance mechanism is important to explore using high throughput computational approaches. Here we aimed to explore the mechanism of PZA resistance behind novel P62T, L120R, and V130M mutations in PZase using 200 ns molecular dynamics (MD) simulations. MD simulations were performed to observe the structural changes for these three mutants (MTs) compared to the wild types (WT). Root means square fluctuation, the radius of gyration, free energy landscape, root means square deviation, dynamic cross-correlation motion, and pocket volume were found in variation between WT and MTs, revealing the effects of P62T, L120R, and V130M. The free energy conformational landscape of MTs differs significantly from the WT system, lowering the binding of PZA. The geometric shape complementarity of the drug (PZA) and target protein (PZase) further confirmed that P62T, L120R, and V130M affect the protein structure. These effects on PZase may cause vulnerability to convert PZA into POA.Communicated by Ramaswamy H. Sarma.

DeepBSRPred: deep learning-based binding site residue prediction for proteins

MOTIVATION

Proteins-protein interactions (PPIs) are important to govern several cellular activities. Amino acid residues, which are located at the interface are known as the binding sites and the information about binding sites helps to understand the binding affinities and functions of protein-protein complexes.

RESULTS

We have developed a deep neural network-based method, DeepBSRPred, for predicting the binding sites using protein sequence information and predicted structures from AlphaFold2. Specific sequence and structure-based features include position-specific scoring matrix (PSSM), solvent accessible surface area, conservation score and amino acid properties, and residue depth, respectively. Our method predicted the binding sites with an average F1 score of 0.73 in a dataset of 1236 proteins. Further, we compared the performance with other existing methods in the literature using four benchmark datasets and our method outperformed those methods.

AVAILABILITY AND IMPLEMENTATION

The DeepBSRPred web server can be found at https://web.iitm.ac.in/bioinfo2/deepbsrpred/index.html , along with all datasets used in this study. The trained models, the DeepBSRPred standalone source code, and the feature computation pipeline are freely available at https://web.iitm.ac.in/bioinfo2/deepbsrpred/download.html .

Designing Cyclic-Constrained Peptides to Inhibit Human Phosphoglycerate Dehydrogenase

Although loop epitopes at protein-protein binding interfaces often play key roles in mediating oligomer formation and interaction specificity, their binding sites are underexplored as drug targets owing to their high flexibility, relatively few hot spots, and solvent accessibility. Prior attempts to develop molecules that mimic loop epitopes to disrupt protein oligomers have had limited success. In this study, we used structure-based approaches to design and optimize cyclic-constrained peptides based on loop epitopes at the human phosphoglycerate dehydrogenase (PHGDH) dimer interface, which is an obligate homo-dimer with activity strongly dependent on the oligomeric state. The experimental validations showed that these cyclic peptides inhibit PHGDH activity by directly binding to the dimer interface and disrupting the obligate homo-oligomer formation. Our results demonstrate that loop epitope derived cyclic peptides with rationally designed affinity-enhancing substitutions can modulate obligate protein homo-oligomers, which can be used to design peptide inhibitors for other seemingly intractable oligomeric proteins.

Optimal selection of suitable templates in protein interface prediction

MOTIVATION

Molecular-level classification of protein-protein interfaces can greatly assist in functional characterization and rational drug design. The most accurate protein interface predictions rely on finding homologous proteins with known interfaces since most interfaces are conserved within the same protein family. The accuracy of these template-based prediction approaches depends on the correct choice of suitable templates. Choosing the right templates in the immunoglobulin superfamily (IgSF) is challenging because its members share low sequence identity and display a wide range of alternative binding sites despite structural homology.

RESULTS

We present a new approach to predict protein interfaces. First, template-specific, informative evolutionary profiles are established using a mutual information-based approach. Next, based on the similarity of residue level conservation scores derived from the evolutionary profiles, a query protein is hierarchically clustered with all available template proteins in its superfamily with known interface definitions. Once clustered, a subset of the most closely related templates is selected, and an interface prediction is made. These initial interface predictions are subsequently refined by extensive docking. This method was benchmarked on 51 IgSF proteins and can predict nontrivial interfaces of IgSF proteins with an average and median F-score of 0.64 and 0.78, respectively. We also provide a way to assess the confidence of the results. The average and median F-scores increase to 0.8 and 0.81, respectively, if 27% of low confidence cases and 17% of medium confidence cases are removed. Lastly, we provide residue level interface predictions, protein complexes, and confidence measurements for singletons in the IgSF.

AVAILABILITY AND IMPLEMENTATION

Source code is freely available at: https://gitlab.com/fiserlab.org/interdct_with_refinement.

LEVELNET to visualize, explore, and compare protein-protein interaction networks

Physical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in the cell and in what manner relies on partial, noisy, and highly heterogeneous data. Thus, there is a need for methods comprehensively describing and organizing such data. LEVELNET is a versatile and interactive tool for visualizing, exploring, and comparing protein-protein interaction (PPI) networks inferred from different types of evidence. LEVELNET helps to break down the complexity of PPI networks by representing them as multi-layered graphs and by facilitating the direct comparison of their subnetworks toward biological interpretation. It focuses primarily on the protein chains whose 3D structures are available in the Protein Data Bank. We showcase some potential applications, such as investigating the structural evidence supporting PPIs associated to specific biological processes, assessing the co-localization of interaction partners, comparing the PPI networks obtained through computational experiments versus homology transfer, and creating PPI benchmarks with desired properties.

Deploying synthetic coevolution and machine learning to engineer protein-protein interactions

Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.

The Cdc48 N-terminal domain has a molecular switch that mediates the Npl4-Ufd1-Cdc48 complex formation

Cdc48 (VCP/p97) is a major AAA-ATPase involved in protein quality control, along with its canonical cofactors Ufd1 and Npl4 (UN). Here, we present novel structural insights into the interactions within the Cdc48-Npl4-Ufd1 ternary complex. Using integrative modeling, we combine subunit structures with crosslinking mass spectrometry (XL-MS) to map the interaction between Npl4 and Ufd1, alone and in complex with Cdc48. We describe the stabilization of the UN assembly upon binding with the N-terminal-domain (NTD) of Cdc48 and identify a highly conserved cysteine, C115, at the Cdc48-Npl4-binding interface which is central to the stability of the Cdc48-Npl4-Ufd1 complex. Mutation of Cys115 to serine disrupts the interaction between Cdc48-NTD and Npl4-Ufd1 and leads to a moderate decrease in cellular growth and protein quality control in yeast. Our results provide structural insight into the architecture of the Cdc48-Npl4-Ufd1 complex as well as its in vivo implications.

Protein-protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction

Molecular glues are a class of small molecules that stabilize the interactions between proteins. Naturally occurring molecular glues are present in many areas of biology where they serve as central regulators of signaling pathways. Importantly, several clinical compounds act as molecular glue degraders that stabilize interactions between E3 ubiquitin ligases and target proteins, leading to their degradation. Molecular glues hold promise as a new generation of therapeutic agents, including those molecular glue degraders that can redirect the protein degradation machinery in a precise way. However, rational discovery of molecular glues is difficult in part due to the lack of understanding of the protein-protein interactions they stabilize. In this review, we summarize the structures of known molecular glue-induced ternary complexes and the interface properties. Detailed analysis shows different mechanisms of ternary structure formation. Additionally, we also review computational approaches for predicting protein-protein interfaces and highlight the promises and challenges. This information will ultimately help inform future approaches for rational molecular glue discovery.

Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

Aromatic Residues on the Side Surface of Cry4Ba-Domain II of Bacillus thuringiensis subsp. israelensis Function in Binding to Their Counterpart Residues on the Aedes aegypti Alkaline Phosphatase Receptor

Receptor binding is a prerequisite process to exert the mosquitocidal activity of the Cry4Ba toxin of Bacillus thuringiensis subsp. israelensis. The beta-sheet prism (domain II) and beta-sheet sandwich (domain III) of the Cry4Ba toxin have been implicated in receptor binding, albeit the precise binding mechanisms of these remain unclear. In this work, alanine scanning was used to determine the contribution to receptor binding of some aromatic and hydrophobic residues on the surface of domains II and III that are predicted to be responsible for binding to the Aedes aegypti membrane-bound alkaline phosphatase (Aa-mALP) receptor. Larvicidal activity assays against A. aegypti larvae revealed that aromatic residues (Trp₃₂₇ on the β2 strand, Tyr₃₄₇ on the β3-β4 loop, and Tyr₃₅₉ on the β4 strand) of domain II were important to the toxicity of the Cry4Ba toxin. Quantitative binding assays using enzyme-linked immunosorbent assay (ELISA) showed similar decreasing trends in binding to the Aa-mALP receptor and in toxicity of the Cry4Ba mutants Trp327Ala, Tyr347Ala, and Tyr359Ala, suggesting that a possible function of these surface-exposed aromatic residues is receptor binding. In addition, binding assays of the Cry4Ba toxin to the mutants of the binding residues Gly₅₁₃, Ser₄₉₀, and Phe₄₉₇ of the Aa-mALP receptor supported the binding function of Trp₃₂₇, Tyr₃₄₇, and Tyr₃₅₉ of the Cry4Ba toxin, respectively. Altogether, our results showed for the first time that aromatic residues on a side surface of the Cry4Ba domain II function in receptor binding. This finding provides greater insight into the possible molecular mechanisms of the Cry4Ba toxin.

Mutagenesis studies of TRPV1 subunit interfaces informed by genomic variant analysis

Protein structures and mutagenesis studies have been instrumental in elucidating molecular mechanisms of ion channel function, but making informed choices about which residues to target for mutagenesis can be challenging. Therefore, we investigated the potential for using human population genomic data to further refine our selection of mutagenesis sites in TRPV1. Single nucleotide polymorphism data of TRPV1 from gnomAD 2.1.1 revealed a lower number of missense variants within buried residues of the ankyrin repeat domain and an increased number of variants between secondary structure elements of the transmembrane segments. We hypothesized that residues critical to interactions at interfaces between subunits or domains in the channel would exhibit a similar reduction in variants. We identified in the structure of ground squirrel TRPV1 (PDB: 7LQY) a possible electrostatic network between K155 and K160 in the N-terminal ankyrin repeat domain and E761 and D762 in the C-terminus (K-KED). Consistent with our hypothesis for residues at key interface sites, none of the four residues have any variants reported in gnomAD 2.1.1. Ca2+ imaging of TRPV1 K-KED mutants confirmed significant roles for these residues, but we found that the electrostatic interaction is not essential since channel function is still observed in total charge reversals on the C-terminal side of the interface (E761K/D762K). Interestingly, Ca2+ imaging responses for a charge swap experiment with K155D/D762K showed partially restored wild-type responses. Using electrophysiology, we found that charge reversals on either K155 or D762 increased the baseline currents of TRPV1, and the charge swapped double mutant, K155D/D762K, partially restored baseline currents to wild-type levels. We interpret these results to mean that contacts across residues in the K-KED interface shift the equilibria of conformations to closed pore states. Our study demonstrates the utility and applicability of a combined missense variant and structure targeted investigation of residues at TRPV1 subunit interfaces.

In Silico Analyses of All STAT3 Missense Variants Leading to Explore Divergent AD-HIES Clinical Phenotypes

Autosomal dominant hyper-IgE syndrome (AD-HIES) is linked to dominant negative mutations of the STAT3 protein whose molecular basis for dysfunction is unclear and presenting with a variety of clinical manifestations with only supportive treatment. To establish the relationship between the impact of STAT3 mutations in different domains and the severity of the clinical manifestations, 105 STAT3 mutations were analyzed for their impact on protein stability, flexibility, function, and binding affinity using in Silico approaches. Our results showed that 73% of the studied mutations have an impact on the physicochemical properties of the protein, altering the stability, flexibility and function to varying degrees. In particular, mutations affecting the DNA binding domain (DBD) and the Src Homology 2 (SH2) have a significant impact on the protein structure and disrupt its interaction either with DNA or other STAT3 to form a heterodomain complex, leading to severe clinical phenotypes. Collectively, this study suggests that there is a close relationship between the domain involving the mutation, the degree of variation in the properties of the protein and the degree of loss of function ranging from partial loss to complete loss, explaining the variability of clinical manifestations between mild and severe.

Structural Determinants of Yeast Protein-Protein Interaction Interface Evolution at the Residue Level

Interfaces of contact between proteins play important roles in determining the proper structure and function of protein-protein interactions (PPIs). Therefore, to fully understand PPIs, we need to better understand the evolutionary design principles of PPI interfaces. Previous studies have uncovered that interfacial sites are more evolutionarily conserved than other surface protein sites. Yet, little is known about the nature and relative importance of evolutionary constraints in PPI interfaces. Here, we explore constraints imposed by the structure of the microenvironment surrounding interfacial residues on residue evolutionary rate using a large dataset of over 700 structural models of baker's yeast PPIs. We find that interfacial residues are, on average, systematically more conserved than all other residues with a similar degree of total burial as measured by relative solvent accessibility (RSA). Besides, we find that RSA of the residue when the PPI is formed is a better predictor of interfacial residue evolutionary rate than RSA in the monomer state. Furthermore, we investigate four structure-based measures of residue interfacial involvement, including change in RSA upon binding (ΔRSA), number of residue-residue contacts across the interface, and distance from the center or the periphery of the interface. Integrated modeling for evolutionary rate prediction in interfaces shows that ΔRSA plays a dominant role among the four measures of interfacial involvement, with minor, but independent contributions from other measures. These results yield insight into the evolutionary design of interfaces, improving our understanding of the role that structure plays in the molecular evolution of PPIs at the residue level.

Identification of 121 variants of honey bee Vitellogenin protein sequences with structural differences at functional sites

Proteins are under selection to maintain central functions and to accommodate needs that arise in ever-changing environments. The positive selection and neutral drift that preserve functions result in a diversity of protein variants. The amount of diversity differs between proteins: multifunctional or disease-related proteins tend to have fewer variants than proteins involved in some aspects of immunity. Our work focuses on the extensively studied protein Vitellogenin (Vg), which in honey bees (Apis mellifera) is multifunctional and highly expressed and plays roles in immunity. Yet, almost nothing is known about the natural variation in the coding sequences of this protein or how amino acid-altering variants might impact structure-function relationships. Here, we map out allelic variation in honey bee Vg using biological samples from 15 countries. The successful barcoded amplicon Nanopore sequencing of 543 bees revealed 121 protein variants, indicating a high level of diversity in Vg. We find that the distribution of non-synonymous single nucleotide polymorphisms (nsSNPs) differs between protein regions with different functions; domains involved in DNA and protein-protein interactions contain fewer nsSNPs than the protein's lipid binding cavities. We outline how the central functions of the protein can be maintained in different variants and how the variation pattern may inform about selection from pathogens and nutrition.

Multi-task learning to leverage partially annotated data for PPI interface prediction

Protein protein interactions (PPI) are crucial for protein functioning, nevertheless predicting residues in PPI interfaces from the protein sequence remains a challenging problem. In addition, structure-based functional annotations, such as the PPI interface annotations, are scarce: only for about one-third of all protein structures residue-based PPI interface annotations are available. If we want to use a deep learning strategy, we have to overcome the problem of limited data availability. Here we use a multi-task learning strategy that can handle missing data. We start with the multi-task model architecture, and adapted it to carefully handle missing data in the cost function. As related learning tasks we include prediction of secondary structure, solvent accessibility, and buried residue. Our results show that the multi-task learning strategy significantly outperforms single task approaches. Moreover, only the multi-task strategy is able to effectively learn over a dataset extended with structural feature data, without additional PPI annotations. The multi-task setup becomes even more important, if the fraction of PPI annotations becomes very small: the multi-task learner trained on only one-eighth of the PPI annotations—with data extension—reaches the same performances as the single-task learner on all PPI annotations. Thus, we show that the multi-task learning strategy can be beneficial for a small training dataset where the protein’s functional properties of interest are only partially annotated.

BioAdhere: tailor-made bioadhesives for epiretinal visual prostheses

Introduction: Visual prostheses, i.e. epiretinal stimulating arrays, are a promising therapy in treating retinal dystrophies and degenerations. In the wake of a new generation of devices, an innovative method for epiretinal fixation of stimulator arrays is required. We present the development of tailor-made bioadhesive peptides (peptesives) for fixating epiretinal stimulating arrays omitting the use of traumatic retinal tacks. Materials and methods: Binding motifs on the stimulating array (poly[chloro-p-xylylene] (Parylene C)) and in the extracellular matrix of the retinal surface (collagens I and IV, laminin, fibronectin) were identified. The anchor peptides cecropin A (CecA), KH1, KH2 (author's initials) and osteopontin (OPN) were genetically fused to reporter proteins to assess their binding behavior to coated microtiter plates via fluorescence-based assays. Domain Z (DZ) of staphylococcal protein A was used as a separator to generate a bioadhesive peptide. Following ISO 10993 "biological evaluation of medical materials", direct and non-direct cytotoxicity testing (L-929 and R28 retinal progenitor cells) was performed. Lastly, the fixating capabilities of the peptesives were tested in proof-of-principle experiments. Results: The generation of the bioadhesive peptide required evaluation of the N- and C-anchoring of investigated APs. The YmPh-CecA construct showed the highest activity on Parylene C in comparison with the wildtype phytase without the anchor peptide. eGFP-OPN was binding to all four investigated ECM proteins (collagen I, laminin > collagen IV, fibronectin). The strongest binding to collagen I was observed for eGFP-KH1, while the strongest binding to fibronectin was observed for eGFP-KH2. The selectivity of binding was checked by incubating eGFP-CecA and eGFP-OPN on ECM proteins and on Parylene C, respectively. Direct and non-direct cytotoxicity testing of the peptide cecropin-A-DZ-OPN using L-929 and R28 cells showed good biocompatibility properties. Proof-of-concept experiments in post-mortem rabbit eyes suggested an increased adhesion of CecA-DZ-OPN-coated stimulating arrays. Conclusion: This is the first study to prove the applicability and biocompatibility of peptesives for the fixation of macroscopic objects.

An Evolutionary Conservation and Druggability Analysis of Enzymes Belonging to the Bacterial Shikimate Pathway

Enzymes belonging to the shikimate pathway have long been considered promising targets for antibacterial drugs because they have no counterpart in mammals and are essential for bacterial growth and virulence. However, despite decades of research, there are currently no clinically relevant antibacterial drugs targeting any of these enzymes, and there are legitimate concerns about whether they are sufficiently druggable, i.e., whether they can be adequately modulated by small and potent drug-like molecules. In the present work, in silico analyses combining evolutionary conservation and druggability are performed to determine whether these enzymes are candidates for broad-spectrum antibacterial therapy. The results presented here indicate that the substrate-binding sites of most enzymes in this pathway are suitable drug targets because of their reasonable conservation and druggability scores. An exception was the substrate-binding site of 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase, which was found to be undruggable because of its high content of charged residues and extremely high overall polarity. Although the presented study was designed from the perspective of broad-spectrum antibacterial drug development, this workflow can be readily applied to any antimicrobial target analysis, whether narrow- or broad-spectrum. Moreover, this research also contributes to a deeper understanding of these enzymes and provides valuable insights into their properties.

Donor-Acceptor Pyridinium Salts for Photo-Induced Electron-Transfer-Driven Modification of Tryptophan in Peptides, Proteins, and Proteomes Using Visible Light

Tryptophan (Trp) plays a variety of critical functional roles in protein biochemistry; however, owing to its low natural frequency and poor nucleophilicity, the design of effective methods for both single protein bioconjugation at Trp as well as for in situ chemoproteomic profiling remains a challenge. Here, we report a method for covalent Trp modification that is suitable for both scenarios by invoking photo-induced electron transfer (PET) as a means of driving efficient reactivity. We have engineered biaryl N-carbamoyl pyridinium salts that possess a donor-acceptor relationship that enables optical triggering with visible light whilst simultaneously attenuating the probe's photo-oxidation potential in order to prevent photodegradation. This probe was assayed against a small bank of eight peptides and proteins, where it was found that micromolar concentrations of the probe and short irradiation times (10-60 min) with violet light enabled efficient reactivity toward surface exposed Trp residues. The carbamate transferring group can be used to transfer useful functional groups to proteins including affinity tags and click handles. DFT calculations and other mechanistic analyses reveal correlations between excited state lifetimes, relative fluorescence quantum yields, and chemical reactivity. Biotinylated and azide-functionalized pyridinium salts were used for Trp profiling in HEK293T lysates and in situ in HEK293T cells using 440 nm LED irradiation. Peptide-level enrichment from live cell labeling experiments identified 290 Trp modifications, with 82% selectivity for Trp modification over other π-amino acids, demonstrating the ability of this method to identify and quantify reactive Trp residues from live cells.

Structure-guided affinity maturation of a single-chain variable fragment antibody against the Fu-bc epitope of the dengue virus envelope protein

Dengue is one of the most dominant arthropod-borne viral diseases, infecting at least 390 million people every year throughout the world. Despite this, there is no effective treatment against dengue, and the only available vaccine has already been withdrawn owing to the significant adverse effects. Therefore, passive immunotherapy using monoclonal antibodies is now being sought as a therapeutic option. To date, many dengue monoclonal antibodies have been identified, most of which are serotype-specific, and only a few of which are cross-reactive. Furthermore, antibodies that cross-react within serotypes are weakly neutralizing and frequently induce antibody-dependent enhancement, which promotes viral entry and replication. Therefore, broadly neutralizing antibodies with no risk of antibody-dependent enhancement are required for the treatment of dengue. Here, we developed a single-chain variable fragment (scFv) antibody from an anti-fusion loop E53 antibody (PDB: 2IGF). We introduced previously predicted favorable complementarity-determining region (CDR) mutations into the gene encoding the scFv antibody for affinity maturation, and the resultant variants were tested in vitro against the highly conserved fusion and bc epitope of the dengue virus envelope protein. We show some of these scFv variants with two to three substitution mutations in three different CDRs possess affinity constants (K_D) ranging from 20 to 200 nM. The scFv-mutant15, containing D31L, Y105W, and S227W substitutions, showed the lowest affinity constant, (K_D = 24 ± 7 nM), approximately 100-fold lower than its parental construct. We propose that the scFv-derivative antibody may be a good candidate for the development of an effective and safe immunotherapy.

Peptide stapling by late-stage Suzuki–Miyaura cross-coupling

The development of peptide stapling techniques to stabilise α-helical secondary structure motifs of peptides led to the design of modulators of protein–protein interactions, which had been considered undruggable for a long time. We disclose a novel approach towards peptide stapling utilising macrocyclisation by late-stage Suzuki–Miyaura cross-coupling of bromotryptophan-containing peptides of the catenin-binding domain of axin. Optimisation of the linker length in order to find a compromise between both sufficient linker rigidity and flexibility resulted in a peptide with an increased α-helicity and enhanced binding affinity to its native binding partner β-catenin. An increased proteolytic stability against proteinase K has been demonstrated.

From complete cross-docking to partners identification and binding sites predictions

Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a "blind" protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. The docking algorithm uses a coarse-grained representation of the protein structures and treats them as rigid bodies. We applied the approach to a few hundred of proteins, in the unbound conformations, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces, and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. We provide a readout of the contributions of shape and physico-chemical complementarity, interface matching, and specificity, in the predictions. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.

Computational Alanine Scanning Reveals Common Features of TCR/pMHC Recognition in HLA-DQ8-Associated Celiac Disease

In HLA-DQ8-associated celiac disease, Gliadin-γ1 or Gliadin-α1 peptide is presented to the cell surface and recognized by several types of T-cell receptor (TCR), but it is still unclear how the TCR, peptide, and the major histocompatibility complex (MHC) act together to trigger celiac disease. For now, most of the analysis is based on static crystal structures. And the detailed information about these structures based on energetic interaction is still lacking. Here, we took four types of celiac disease-related MHC-peptide-TCR structures from three patients to perform computational alanine scanning calculations using the molecular mechanics generalized born surface area (MM/GBSA) approach combined with a recently developed interaction entropy (IE) method to identify the key residues on TCR, peptide, and MHC. Our study aims to shed some light on the interaction mechanism of this complex protein interaction system. Based on detailed computational analysis and mutational calculations, important binding interactions in these triple-interaction complexes are analyzed, and critical residues responsible for TCR/pMHC recognition pattern in HLA-DQ8-associated celiac disease are presented. These detailed analysis and computational result should help shed light on our understanding of the celiac disease and the development of the medical treatment.

Artificial intelligence based methods for hot spot prediction

Proteins interact through their interfaces to fulfill essential functions in the cell. They bind to their partners in a highly specific manner and form complexes that have a profound effect on understanding the biological pathways they are involved in. Any abnormal interactions may cause diseases. Therefore, the identification of small molecules which modulate protein interactions through their interfaces has high therapeutic potential. However, discovering such molecules is challenging. Most protein-protein binding affinity is attributed to a small set of amino acids found in protein interfaces known as hot spots. Recent studies demonstrate that drug-like small molecules specifically may bind to hot spots. Therefore, hot spot prediction is crucial. As experimental data accumulates, artificial intelligence begins to be used for computational hot spot prediction. First, we review machine learning and deep learning for computational hot spot prediction and then explain the significance of hot spots toward drug design.

Geometrical and electro-static determinants of protein-protein interactions

Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redundant database of over 2,265 interaction interfaces. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the offorces interactions. The residue interaction propensity and all of the rest of the parametric scores converged to a statistical indistinguishable common sub-range and followed the similar distribution trends for all three classes of sequence-based classifications PPInS. This indicates that the principles of molecular recognition are dependent on the preciseness of the fit in the interaction interfaces. Thus, it reinforces the importance of geometrical and electrostatic complementarity as the main determinants for PPIs.

Protein-like particles through nanoprecipitation of mixtures of polymers of opposite charge

HYPOTHESIS

Polymer nanoparticles (NPs) have a very high potential for applications notably in the biomedical field. However, synthetic polymer NPs cannot yet concurrence the functionalities of proteins, their natural counterparts, notably in terms of size, control over internal structure and interactions with biological environments. We hypothesize that kinetic trapping of polymers bearing oppositely charged groups in NPs could bring a new level of control and allow mimicking the surfaces of proteins.

EXPERIMENTS

Here, the assembly of mixed-charge polymer NPs through nanoprecipitation of mixtures of oppositely charged polymers is studied. Two series of copolymers made of ethyl methacrylate and 1 to 25 mol% of either methacrylic acid or a trimethylammonium bearing methacrylate are synthesized. These carboxylic acid or trimethylammonium bearing polymers are then mixed in different ratios and nanoprecipitated. The influence of the charge fraction, mixing ratio of the polymers, and precipitation conditions on NP size and surface charge is studied.

FINDINGS

Using this approach, NPs of less than 25 nm with tunable surface charge from +40 mV to -40 mV are assembled. The resulting NPs are sensitive to pH and certain NP formulations have an isoelectric point allowing repeated charge reversal. Encapsulation of fluorescent dyes yields very bright fluorescent NPs, whose interactions with cells are studied through fluorescence microscopy. The obtained results show the potential of nanoprecipitation of oppositely charged polymers for the design of NPs with precisely tuned surface properties.

Protein music of enhanced musicality by music style guided exploration of diverse amino acid properties

Inspired by the traceable analogies between protein sequences and music notes, protein music has been composed from amino acid sequences for popularizing science and sourcing melodies. Despite the continuous development of protein-to-music algorithms, the musicality of protein music lags far behind human music. Musicality may be enhanced by fine-tuned protein-to-music mapping to the features of a specific music style. We analyzed the features of a music style (Fantasy-Impromptu style), and used the quantized musical features to guide broad exploration of diverse amino acid properties (104 properties, sequence patterns and variations) for developing a novel protein-to-music algorithm of enhanced musicality. This algorithm was applied to 18 proteins of various biological functions. The derived music pieces consistently exhibited enhanced musicality with respect to existing protein music. Music style guided exploration of diverse amino acid properties enable protein music composition of enhanced musicality, which may be further developed and applied to a wider variety of music styles.

Applying Bioinformatic Platforms, In Vitro, and In Vivo Functional Assays in the Characterization of Genetic Variants in the GH/IGF Pathway Affecting Growth and Development

Heritability accounts for over 80% of adult human height, indicating that genetic variability is the main determinant of stature. The rapid technological development of Next-Generation Sequencing (NGS), particularly Whole Exome Sequencing (WES), has resulted in the characterization of several genetic conditions affecting growth and development. The greatest challenge of NGS remains the high number of candidate variants identified. In silico bioinformatic tools represent the first approach for classifying these variants. However, solving the complicated problem of variant interpretation requires the use of experimental approaches such as in vitro and, when needed, in vivo functional assays. In this review, we will discuss a rational approach to apply to the gene variants identified in children with growth and developmental defects including: (i) bioinformatic tools; (ii) in silico modeling tools; (iii) in vitro functional assays; and (iv) the development of in vivo models. While bioinformatic tools are useful for a preliminary selection of potentially pathogenic variants, in vitro—and sometimes also in vivo—functional assays are further required to unequivocally determine the pathogenicity of a novel genetic variant. This long, time-consuming, and expensive process is the only scientifically proven method to determine causality between a genetic variant and a human genetic disease.

Biased anti-idiotype response in rabbits leads to high-affinity monoclonal antibodies to biologics

Antibody formation to human(ized) therapeutic antibodies in humans is highly skewed toward anti-idiotype responses, probably because the idiotype is the only 'foreign' part of the antibody molecule. Here, we analyzed antibody responses to F(ab')2 fragments of a panel of 17 human(ized) therapeutic antibodies in rabbits. Homology between the rabbit germline and the human(ized) antibodies is moderate not only for the variable domains (both the complementarity-determining regions and the framework regions), but also for the constant domains (66% or less). Nevertheless, we observed a highly skewed anti-idiotype response in all cases, with up to >90% of the antibodies directed toward the idiotype. These results indicate that the idiotype may be inherently immunodominant. We used these biased responses to raise monoclonal rabbit anti-idiotype antibodies against secukinumab, ustekinumab, reslizumab, mepolizumab, palivizumab, and dupilumab and demonstrate the potential to develop sensitive pharmacokinetic assays with these antibodies.

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).

Altered functional properties of the codling moth Orco mutagenized in the intracellular loop-3

Amino acid substitutions within the conserved polypeptide sequence of the insect olfactory receptor co-receptor (Orco) have been demonstrated to influence its pharmacological properties. By sequence analysis and phylogenetic investigation, in the Lepidopteran subgroup Ditrysia we identified a fixed substitution in the intracellular loop-3 (ICL-3) of a conserved histidine to glutamine. By means of HEK293 cells as a heterologous system, we functionally expressed Orco from the Ditrysian model Cydia pomonella (CpomOrco) and compared its functional properties with a site-directed mutagenized version where this ICL-3-glutamine was reverted to histidine (CpomOrco^Q417H). The mutagenized CpomOrco^Q417H displayed decreased responsiveness to VUAA1 and reduced response efficacy to an odorant agonist was observed, when co-transfected with the respective OR subunit. Evidence of reduced responsiveness and sensitivity to ligands for the mutagenized Orco suggest the fixed glutamine substitution to be optimized for functionality of the cation channel within Ditrysia. In addition, contrary to the wild type, the mutagenized CpomOrco^Q417H preserved characteristics of VUAA-binding when physiologic conditions turned to acidic. Taken together, our findings provide further evidence of the importance of ICL-3 in forming basic functional properties of insect Orco- and Orco/OR-channels, and suggest involvement of ICL-3 in the potential functional adaptation of Ditrysian Orcos to acidified extra-/intracellular environment.

Why are ACE2 binding coronavirus strains SARS-CoV/SARS-CoV-2 wild and NL63 mild?

Coronaviruses are responsible for several epidemics, including the 2002 SARS, 2012 MERS, and COVID‐19. The emergence of recent COVID‐19 pandemic due to SARS‐CoV‐2 virus in December 2019 has resulted in considerable research efforts to design antiviral drugs and other therapeutics against coronaviruses. In this context, it is crucial to understand the biophysical and structural features of the major proteins that are involved in virus‐host interactions. In the current study, we have compared spike proteins from three strains of coronaviruses NL63, SARS‐CoV, and SARS‐CoV, known to bind human angiotensin‐converting enzyme 2 (ACE2), in terms of sequence/structure conservation, hydrophobic cluster formation and importance of binding site residues. The study reveals that the severity of coronavirus strains correlates positively with the interaction area, surrounding hydrophobicity and interaction energy and inversely correlate with the flexibility of the binding interface. Also, we identify the conserved residues in the binding interface of spike proteins in all three strains. The systematic point mutations show that these conserved residues in the respective strains are evolutionarily favored at their respective positions. The similarities and differences in the spike proteins of the three viruses indicated in this study may help researchers to deeply understand the structural behavior, binding site properties and etiology of ACE2 binding, accelerating the screening of potential lead molecules and the development/repurposing of therapeutic drugs.

Mechanistic analysis of A46V, H57Y, and D129N in pyrazinamidase associated with pyrazinamide resistance

Pyrazinamide (PZA) is a component of first-line drugs, active against latent Mycobacterium tuberculosis (MTB) isolates. The prodrug is activated into the active form, pyrazinoic acid (POA) via pncA gene-encoded pyrazinamidase (PZase). Mutations in pncA have been reported, most commonly responsible for PZA-resistance in more than 70% of the resistant cases. In our previous study, we detected many mutations in PZase among PZA-resistance MTB isolates including A46V, H71Y, and D129N. The current study was aimed to investigate the molecular mechanism of PZA-resistance behind mutants (MTs) A46V, H71Y, and D129N in comparison with the wild type (WT) through molecular dynamic (MD) simulation. MTB positive samples were subjected to PZA drug susceptibility testing (DST) against critical concentration (100ug/ml). The resistant samples were subjected to pncA sequencing. Thirty-six various mutations have been observed in the coding region of pncA of PZA-resistant isolates (GenBank accession No. MH461111) including A46V, H71Y, and D129N. The post-simulation analysis revealed a significant variation in MTs structural dynamics as compared to the WT. Root means square deviations (RMSD) and Root means square fluctuation (RMSF) has been found in variation between WT and MTs. Folding effect and pocket volume were altered in MTs when compared with WT. Geometric matching supports the effect of mutation A46V, H71Y, and D129N on PZase structure that may have an insight effect on PZase dynamics, making them vulnerable to convert pro-PZA into active form, POA. In conclusion, the current analyses will provide useful information behind PZA-resistance for better management of drug-resistant TB.

Diameter Is a Key 3D Characteristic for Assessments of Efficient Inhibitors of Protein-Protein Interactions

Three-dimensional (3D) molecular descriptors, including physicochemical and shape properties, for protein-protein interaction (PPI) interface inhibitors have become a topic of discussion. However, the relationships between such properties and binding free energy have not been adequately investigated. In this study, we focused on identifying key 3D molecular descriptors related to the binding free energy and/or the ligand efficiency (LE) of PPI interface inhibitors. A positive correlation was found between the binding free energy and the diameter (D) of cylindrical 3D molecules, in addition to a correlation between LE and D/heavy atom count (HAC). In addition, we showed a correlation between LE and D/HAC for macrocyclic compounds, suggesting that the present findings could be applied during assessments of the potential of macrocyclic PPI interface inhibitors in drug discovery processes.

Molecular modeling and interaction between Arabidopsis sulfite oxidase and the GW motif of Turnip crinkle virus coat protein

Previous study has shown that Hibiscus sulfite oxidase (SO) interacts with Hibiscus chlorotic ringspot virus (HCRSV) coat protein (CP) and triggers sulfur enhanced defense (SED). In this study, we show the interaction of Arabidopsis SO (AtSO) and Turnip crinkle virus (TCV) CP in Arabidopsis thaliana plants. We identified the binding sites of TCV CP (W274) and AtSO (D223) using bioinformatics and confirmed it experimentally. Mutation of binding site W274 to A274 in TCV CP resulted in failure of TCV infection. TCV accumulation in SO over-expression (SO_OE) plants was lower than that in wild-type (WT) and SO knock-out (SO_KO) plants at 7 dpi but reached a level similar to that of WT and SO_KO plants at 10 dpi. AtSO competed with Argonaute 1 (AGO1) for TCV CP binding in vitro. AtSO may serve as an anti-viral factor through sequestering TCV CP for binding with AGO1 and confers virus resistance.

Expanding the Scope of Orthogonal Translation with Pyrrolysyl-tRNA Synthetases Dedicated to Aromatic Amino Acids

In protein engineering and synthetic biology, Methanosarcina mazei pyrrolysyl-tRNA synthetase (MmPylRS), with its cognate tRNAPyl, is one of the most popular tools for site-specific incorporation of non-canonical amino acids (ncAAs). Numerous orthogonal pairs based on engineered MmPylRS variants have been developed during the last decade, enabling a substantial genetic code expansion, mainly with aliphatic pyrrolysine analogs. However, comparatively less progress has been made to expand the substrate range of MmPylRS towards aromatic amino acid residues. Therefore, we set to further expand the substrate scope of orthogonal translation by a semi-rational approach; redesigning the MmPylRS efficiency. Based on the randomization of residues from the binding pocket and tRNA binding domain, we identify three positions (V401, W417 and S193) crucial for ncAA specificity and enzyme activity. Their systematic mutagenesis enabled us to generate MmPylRS variants dedicated to tryptophan (such as β-(1-Azulenyl)-l-alanine or 1-methyl-l-tryptophan) and tyrosine (mainly halogenated) analogs. Moreover, our strategy also significantly improves the orthogonal translation efficiency with the previously activated analog 3-benzothienyl-l-alanine. Our study revealed the engineering of both first shell and distant residues to modify substrate specificity as an important strategy to further expand our ability to discover and recruit new ncAAs for orthogonal translation.

Chemometric Models of Differential Amino Acids at the Navα and Navβ Interface of Mammalian Sodium Channel Isoforms

(1) Background: voltage-gated sodium channels (Na_vs) are integral membrane proteins that allow the sodium ion flux into the excitable cells and initiate the action potential. They comprise an α (Na_vα) subunit that forms the channel pore and are coupled to one or more auxiliary β (Na_vβ) subunits that modulate the gating to a variable extent. (2) Methods: after performing homology in silico modeling for all nine isoforms (Na_v1.1α to Na_v1.9α), the Na_vα and Na_vβ protein-protein interaction (PPI) was analyzed chemometrically based on the primary and secondary structures as well as topological or spatial mapping. (3) Results: our findings reveal a unique isoform-specific correspondence between certain segments of the extracellular loops of the Na_vα subunits. Precisely, loop S5 in domain I forms part of the PPI and assists Na_vβ1 or Na_vβ3 on all nine mammalian isoforms. The implied molecular movements resemble macroscopic springs, all of which explains published voltage sensor effects on sodium channel fast inactivation in gating. (4) Conclusions: currently, the specific functions exerted by the Na_vβ1 or Na_vβ3 subunits on the modulation of Na_vα gating remain unknown. Our work determined functional interaction in the extracellular domains on theoretical grounds and we propose a schematic model of the gating mechanism of fast channel sodium current inactivation by educated guessing.

Mapping of Recognition Sites of Monoclonal Antibodies Responsible for the Inhibition of Pneumolysin Functional Activity

The pathogenicity of many bacteria, including Streptococcus pneumoniae, depends on pore-forming toxins (PFTs) that cause host cell lysis by forming large pores in cholesterol-containing cell membranes. Therefore, PFTs-neutralising antibodies may provide useful tools for reducing S. pneumoniae pathogenic effects. This study aimed at the development and characterisation of monoclonal antibodies (MAbs) with neutralising activity to S. pneumoniae PFT pneumolysin (PLY). Five out of 10 produced MAbs were able to neutralise the cytolytic activity of PLY on a lung epithelial cell line. Epitope mapping with a series of recombinant overlapping PLY fragments revealed that neutralising MAbs are directed against PLY loops L1 and L3 within domain 4. The epitopes of MAbs 3A9, 6E5 and 12F11 located at L1 loop (aa 454–471) were crucial for PLY binding to the immobilised cholesterol. In contrast, the MAb 12D10 recognising L3 (aa 403–423) and the MAb 3F3 against the conformational epitope did not interfere with PLY-cholesterol interaction. Due to conformation-dependent binding, the approach to use overlapping peptides for fine epitope mapping of the neutralising MAbs was unsuccessful. Therefore, the epitopes recognised by the MAbs were analysed using computational methods. This study provides new data on PLY sites involved in functional activity.

Aromatic clusters in protein-protein and protein-drug complexes

Aromatic rings are important residues for biological interactions and appear to a large extent as part of protein-drug and protein-protein interactions. They are relevant for both protein stability and molecular recognition processes due to their natural occurrence in aromatic aminoacids (Trp, Phe, Tyr and His) as well as in designed drugs since they are believed to contribute to optimizing both affinity and specificity of drug-like molecules. Despite the mentioned relevance, the impact of aromatic clusters on protein-protein and protein-drug complexes is still poorly characterized, especially in those that go beyond a dimer. In this work, we studied protein-drug and protein-protein complexes and systematically analyzed the presence and structure of their aromatic clusters. Our results show that aromatic clusters are highly prevalent in both protein-protein and protein-drug complexes, and suggest that protein-protein aromatic clusters have idealized interactions, probably because they were optimized by evolution, as compared to protein-drug clusters that were manually designed. Interestingly, the configuration, solvent accessibility and secondary structure of aromatic residues in protein-drug complexes shed light on the relation between these properties and compound affinity, allowing researchers to better design new molecules.

Diffusive protein interactions in human versus bacterial cells

Random encounters between proteins in crowded cells are by no means passive, but found to be under selective control. This control enables proteome solubility, helps to optimise the diffusive search for interaction partners, and allows for adaptation to environmental extremes. Interestingly, the residues that modulate the encounters act mesoscopically through protein surface hydrophobicity and net charge, meaning that their detailed signatures vary across organisms with different intracellular constraints. To examine such variations, we use in-cell NMR relaxation to compare the diffusive behaviour of bacterial and human proteins in both human and Escherichia coli cytosols. We find that proteins that ‘stick’ in E. coli are generally less restricted in mammalian cells. Furthermore, the rotational diffusion in the mammalian cytosol is less sensitive to surface-charge mutations. This implies that, in terms of protein motions, the mammalian cytosol is more forgiving to surface alterations than E. coli cells. The cellular differences seem not linked to the proteome properties per se, but rather to a 6-fold difference in protein concentrations. Our results outline a scenario in which the tolerant cytosol of mammalian cells, found in long-lived multicellular organisms, provides an enlarged evolutionary playground, where random protein-surface mutations are less deleterious than in short-generational bacteria.

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Random protein encounters and diffusibility in cells are controlled by surface charge.

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Protein rotational diffusion is less restricted in human cells than in E. coli.

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Human cells are less sensitive to alterations of protein charge than Escherichia coli cells.