Structural characterisation and functional significance of transient protein-protein interactions

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

ProBASS - a language model with sequence and structural features for predicting the effect of mutations on binding affinity

Protein-protein interactions (PPIs) govern virtually all cellular processes. Even a single mutation within PPI can significantly influence overall protein functionality and potentially lead to various types of diseases. To date, numerous approaches have emerged for predicting the change in free energy of binding (ΔΔGbind) resulting from mutations, yet the majority of these methods lack precision. In recent years, protein language models (PLMs) have been developed and shown powerful predictive capabilities by leveraging both sequence and structural data from protein-protein complexes. Yet, PLMs have not been optimized specifically for predicting ΔΔGbind. We developed an approach to predict effects of mutations on PPI binding affinity based on two most advanced protein language models ESM2 and ESM-IF1 that incorporate PPI sequence and structural features, respectively. We used the two models to generate embeddings for each PPI mutant and subsequently fine-tuned our model by training on a large dataset of experimental ΔΔGbind values. Our model, ProBASS (Protein Binding Affinity from Structure and Sequence) achieved a correlation with experimental ΔΔGbind values of 0.83 ± 0.05 for single mutations and 0.69 ± 0.04 for double mutations when model training and testing was done on the same PDB. Moreover, ProBASS exhibited very high correlation (0.81 ± 0.02) between prediction and experiment when training and testing was performed on a dataset containing 2325 single mutations in 132 PPIs. ProBASS surpasses the state-of-the-art methods in correlation with experimental data and could be further trained as more experimental data becomes available. Our results demonstrate that the integration of extensive datasets containing ΔΔGbind values across multiple PPIs to refine the pre-trained PLMs represents a successful approach for achieving a precise and broadly applicable model for ΔΔGbind prediction, greatly facilitating future protein engineering and design studies.

Photoreversible Aggregation of the Biliprotein Containing the First and Second GAF Domains of a Cyanobacteriochrome All2699 in Nostoc sp. PCC7120

As plant photoreceptors, phytochromes are capable of detecting red light and far-red light, thereby governing plant growth. All2699 is a photoreceptor found in Nostoc sp. PCC7120 that specifically responds to red light and far-red light. All2699g1g2 is a truncated protein carrying the first and second GAF (cGMP phosphodiesterase/adenylyl cyclase/FhlA) domains of All2699. In this study, we found that, upon exposure to red light, the protein underwent aggregation, resulting in the formation of protein aggregates. Conversely, under far-red light irradiation, these protein aggregates dissociated. We delved into the factors that impact the aggregation of All2699g1g2, focusing on the protein structure. Our findings showed that the GAF2 domain contains a low-complexity (LC) loop region, which plays a crucial role in mediating protein aggregation. Specifically, phenylalanine at position 239 within the LC loop region was identified as a key site for the aggregation process. Furthermore, our research revealed that various factors, including irradiation time, temperature, concentration, NaCl concentration, and pH value, can impact the aggregation of All2699g1g2. The aggregation led to variations in Pfr concentration depending on temperature, NaCl concentration, and pH value. In contrast, ΔLC did not aggregate and therefore lacked responses to these factors. Consequently, the LC loop region of All2699g1g2 extended and enhanced sensory properties.

Peptide-Based Inhibitors of the Induced Signaling Protein Interactions: Current State and Prospects

Formation of the transient protein complexes in response to activation of cellular receptors is a common mechanism by which cells respond to external stimuli. This article presents the concept of blocking interactions of signaling proteins by the peptide inhibitors, and describes the progress achieved to date in the development of signaling inhibitors that act by blocking the signal-dependent protein interactions.

Role of two modules controlling the interaction between SKAP1 and SRC kinases comparison with SKAP2 architecture and consequences for evolution

SRC kinase associated phosphoprotein 1 (SKAP1), an adaptor for protein assembly, plays an important role in the immune system such as stabilizing immune synapses. Understanding how these functions are controlled at the level of the protein-protein interactions is necessary to describe these processes and to develop therapeutics. Here, we dissected the SKAP1 modular organization to recognize SRC kinases and compared it to that of its paralog SRC kinase associated phosphoprotein 2 (SKAP2). Different conserved motifs common to either both proteins or specific to SKAP2 were found using this comparison. Two modules harboring different binding properties between SKAP1 and SKAP2 were identified: one composed of two conserved motifs located in the second interdomain interacting at least with the SH2 domain of SRC kinases and a second one composed of the DIM domain modulated by the SH3 domain and the activation of SRC kinases. This work suggests a convergent evolution of the binding properties of some SRC kinases interacting specifically with either SKAP1 or SKAP2.

Directly visualizing individual polyorganophosphazenes and their single-chain complexes with proteins

Polyorganophosphazenes are water-soluble macromolecules with immunoadjuvant activity that self-assemble with proteins to enable biological functionality. Direct imaging by cryogenic electron microscopy uncovers the coil structure of those highly charged macromolecules. The successful visualization of individual polymer chains within the vitrified state is achieved in the absence of additives for contrast enhancement and is attributed to the high mass contrast of the inorganic backbone. Upon assembly with proteins, multiple protein copies bind at the single polymer chain level resulting in structures reminiscent of compact spherical complexes or stiffened coils. The outcome depends on protein characteristics and cannot be deduced by commonly used characterization techniques, such as light scattering, thus revealing direct morphological insights crucial for understanding biological activity. Atomic force microscopy supports the morphology outcomes while advanced analytical techniques confirm protein-polymer binding. The chain visualization methodology provides tools for gaining insights into the processes of supramolecular assembly and mechanistic aspects of polymer enabled vaccine delivery.

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity

Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.

Motif-dependent binding on the intervening domain regulates O-GlcNAc transferase

The modification of intracellular proteins with O-linked β-N-acetylglucosamine (O-GlcNAc) moieties is a highly dynamic process that spatiotemporally regulates nearly every important cellular program. Despite its significance, little is known about the substrate recognition and regulation modes of O-GlcNAc transferase (OGT), the primary enzyme responsible for O-GlcNAc addition. In this study, we identified the intervening domain (Int-D), a poorly understood protein fold found only in metazoan OGTs, as a specific regulator of OGT protein-protein interactions and substrate modification. Using proteomic peptide phage display (ProP-PD) coupled with structural, biochemical and cellular characterizations, we discovered a strongly enriched peptide motif, employed by the Int-D to facilitate specific O-GlcNAcylation. We further show that disruption of Int-D binding dysregulates important cellular programs, including response to nutrient deprivation and glucose metabolism. These findings illustrate a mode of OGT substrate recognition and offer key insights into the biological roles of this unique domain.

A morpheein equilibrium regulates catalysis in phosphoserine phosphatase SerB2 from Mycobacterium tuberculosis

Mycobacterium tuberculosis phosphoserine phosphatase MtSerB2 is of interest as a new antituberculosis target due to its essential metabolic role in L-serine biosynthesis and effector functions in infected cells. Previous works indicated that MtSerB2 is regulated through an oligomeric transition induced by L-Ser that could serve as a basis for the design of selective allosteric inhibitors. However, the mechanism underlying this transition remains highly elusive due to the lack of experimental structural data. Here we describe a structural, biophysical, and enzymological characterisation of MtSerB2 oligomerisation in the presence and absence of L-Ser. We show that MtSerB2 coexists in dimeric, trimeric, and tetrameric forms of different activity levels interconverting through a conformationally flexible monomeric state, which is not observed in two near-identical mycobacterial orthologs. This morpheein behaviour exhibited by MtSerB2 lays the foundation for future allosteric drug discovery and provides a starting point to the understanding of its peculiar multifunctional moonlighting properties.

What's the defect? Using mass defects to study oligomerization of membrane proteins and peptides in nanodiscs with native mass spectrometry

Many membrane proteins form functional complexes that are either homo- or hetero-oligomeric. However, it is challenging to characterize membrane protein oligomerization in intact lipid bilayers, especially for polydisperse mixtures. Native mass spectrometry of membrane proteins and peptides inserted in lipid nanodiscs provides a unique method to study the oligomeric state distribution and lipid preferences of oligomeric assemblies. To interpret these complex spectra, we developed novel data analysis methods using macromolecular mass defect analysis. Here, we provide an overview of how mass defect analysis can be used to study oligomerization in nanodiscs, discuss potential limitations in interpretation, and explore strategies to resolve these ambiguities. Finally, we review recent work applying this technique to studying formation of antimicrobial peptide, amyloid protein, and viroporin complexes with lipid membranes.

Comprehensive quality evaluation of plant-based cheese analogues

BACKGROUND

In recent years, there has been an increasing demand for plant-based cheese analogues, however, the protein content of plant-based cheeses currently on the market is generally low and cannot meet the nutritional needs of consumers.

RESULTS

Based on the ideal value similarity method (TOPSIS) analysis the best recipe for plant-based cheese was 15% tapioca starch, 20% soy protein isolate, 7% gelatine as a quality enhancer and 15% coconut oil. The protein content of this plant-based cheese was170.1 g kg-1 , which was close to commercial dairy-based cheese and significantly higher than commercial plant-based cheese, The fat content was 114.7 g kg-1 , lower than that of commercial dairy-based cheese. The rheology properties show that the viscoelasticity of the plant-based cheese is higher than that of dairy-based cheese and commercial plant-based. The microstructure results show that the type and content of protein has a significant impact on its microstructure. The Fourier-transform infrared (FTIR) spectrum of the microstructure shows a characteristic value at 1700 cm-1 , because the starch was heated and leached to form a complex with lauric acid under the action of hydrogen bond. It can be inferred that in the interaction between plant-based cheese raw materials, fatty acids serve as a bridge between starch and protein.

COUCLUSION

This study described the formula of plant-based cheese and the interaction mechanism between the ingredients, providing a basis for the development of subsequent plant-based cheese related products. © 2023 Society of Chemical Industry.

A Novel Approach for Screening Sericin-Derived Therapeutic Peptides Using Transcriptomics and Immunoprecipitation

With the demand for more efficient and safer therapeutic drugs, targeted therapeutic peptides are well received due to their advantages of high targeting (specificity), low immunogenicity, and minimal side effects. However, the conventional methods of screening targeted therapeutic peptides in natural proteins are tedious, time-consuming, less efficient, and require too many validation experiments, which seriously restricts the innovation and clinical development of peptide drugs. In this study, we established a novel method of screening targeted therapeutic peptides in natural proteins. We also provide details for library construction, transcription assays, receptor selection, therapeutic peptide screening, and biological activity analysis of our proposed method. This method allows us to screen the therapeutic peptides TS263 and TS1000, which have the ability to specifically promote the synthesis of the extracellular matrix. We believe that this method provides a reference for screening other drugs in natural resources, including proteins, peptides, fats, nucleic acids, and small molecules.

Subunit association, and thermal and chemical unfolding of Cucurbitaceae phloem exudate lectins. A review

Detailed characterization of protein (un)folding intermediates is crucial for understanding the (un)folding pathway, aggregation, stability and their functional properties. In recent years, stress-inducible lectins are being investigated with much interest. In plants phloem proteins PP1 and PP2 are major components of the phloem fluid. While PP1 is a structural protein, PP2 exhibits lectin activity, and was proposed to play key roles in wound sealing, anti-pathogenic activity, and transportation of various molecules including RNA within the plant. Cucurbitaceae fruits contain high concentrations of PP2 lectins, which recognize chitooligosaccharides with high specificity. Although the presence of PP2 lectins in the phloem exudate of Cucurbitaceae species was documented over 40 years ago, so far only a few proteins from this family have been purified and characterized in detail. This review summarizes the results of biophysical studies aimed at investigating the oligomeric status of these lectins, their thermal stability, structural perturbations caused by changes in pH and addition of chaotropic agents and characterization of intermediates observed in the unfolding process. The implications of these results in the functional roles played by PP2 type lectins in their native environment are discussed. Finally, perspectives for future biophysical research on these proteins are given.

The red, firm, non-exudative and pale, soft, exudative pork have different in vitro digestive properties of protein

Pale, soft and exudative (PSE) meat has worse edible quality than red, firm and non-exudative (RFN) meat, but their difference in nutritional values is still unclear. In this study, the differences in digestive properties between PSE and RFN pork were explored, and the potential mechanisms were analyzed in terms of protein conformation. The PSE pork showed significantly higher digestibility and smaller particle size compared with RFN pork (P < 0.05) after gastrointestinal digestion. Mechanistically, the lower viscosity was seen in the PSE pork digestion system. The protein structure of PSE pork was disordered with weaker hydrogen bond and ionic bond before and after heating. In addition, the protein (mainly salt-soluble protein) of PSE pork was highly oxidized. The results suggested that higher level of oxidation in PSE pork leads to the destruction of the molecular forces, resulting in the impaired protein conformation and disordered protein structure. The serial changes caused the meat proteins more accessible to digestive enzymes, thus improving the digestibility. The findings provide new insights into the evaluating the quality of PSE meat.

Protein Binding: A Fuzzy Concept

Our understanding of protein binding interactions has matured significantly over the last few years, largely as a result of trying to make sense of the binding interactions of intrinsically disordered proteins. Here, we bring together some disparate ideas that have largely developed independently, and show that they can be linked into a coherent picture that provides insight into quantitative aspects of protein interactions, in particular that transient protein interactions are often optimised for speed, rather than tight binding.

Ensemble-based molecular docking and spectrofluorometric analysis of interaction between cytotoxin and tumor necrosis factor receptor 1

Cytotoxin (CTX) is a three-finger toxin presents predominantly in cobra venom. The functional site of the toxin is located at its three hydrophobic loop tips. Its actual mechanism of cytotoxicity remains inconclusive as few conflicting hypotheses have been proposed in addition to direct cytolytic effects. The present work investigated the interaction between CTX and death receptor families via ensemble-based molecular docking and fluorescence titration analysis. Multiple sequence alignments of different CTX isoforms obtained a conserved CTX sequence. The three-dimensional structure of the conserved CTX was later determined using homology modelling, and its quality was validated. Ensemble-based molecular docking of CTX was performed with different death receptors, such as Fas-ligand and tumor necrosis factor receptor families. Our results showed that tumor necrosis factor receptor 1 (TNFR1) was the best receptor interacting with CTX attributed to the interaction of all three functional loops and evinced with low HADDOCK, Z-score and RMSD value. The interaction between CTX and TNFR1 was also supported by a concentration-dependent reduction of fluorescence intensity with increasing binding affinity. The possible intermolecular interactions between CTX and TNFR1 were Van der Waals forces and hydrogen bonding. Our findings suggest a possibility that CTX triggers apoptosis cell death through non-covalent interactions with TNFR1.Communicated by Ramaswamy H. Sarma.

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications

Protein-protein interactions (PPIs) are essential in normal biological processes, but they can become disrupted or imbalanced in cancer. Various technological advancements have led to an increase in the number of PPI inhibitors, which target hubs in cancer cell's protein networks. However, it remains difficult to develop PPI inhibitors with desired potency and specificity. Supramolecular chemistry has only lately become recognized as a promising method to modify protein activities. In this review, we highlight recent advances in the use of supramolecular modification approaches in cancer therapy. We make special note of efforts to apply supramolecular modifications, such as molecular tweezers, to targeting the nuclear export signal (NES), which can be used to attenuate signaling processes in carcinogenesis. Finally, we discuss the strengths and weaknesses of using supramolecular approaches to targeting PPIs.

A novel binding site on the cryptic intervening domain is a motif-dependent regulator of O-GlcNAc transferase

The modification of intracellular proteins with O-linked β- N -acetylglucosamine (O-GlcNAc) moieties is a highly dynamic process that spatiotemporally regulates nearly every important cellular program. Despite its significance, little is known about the substrate recognition and regulation modes of O-GlcNAc transferase (OGT), the primary enzyme responsible for O-GlcNAc addition. In this study, we have identified the intervening domain (Int-D), a poorly understood protein fold found only in metazoan OGTs, as a specific regulator of OGT protein-protein interactions and substrate modification. Utilizing an innovative proteomic peptide phage display (ProP-PD) coupled with structural, biochemical, and cellular characterizations, we discovered a novel peptide motif, employed by the Int-D to facilitate specific O-GlcNAcylation. We further show that disruption of Int-D binding dysregulates important cellular programs including nutrient stress response and glucose metabolism. These findings illustrate a novel mode of OGT substrate recognition and offer the first insights into the biological roles of this unique domain.

A novel assay that characterizes properdin function shows neutrophil-derived properdin has a distinct oligomeric distribution

Properdin acts as an essential positive regulator of the alternative pathway of complement by stabilizing enzymatic convertases. Identical properdin monomers form head-to-tail associations of oligomers in a reported 20:54:26 ratio (most often described as an approximate 1:2:1 ratio) of tetramers (P₄), trimers (P₃), and dimers (P₂), in blood, under normal physiological conditions. Oligomeric size is proportional to properdin function with tetramers being more active, followed by trimers and dimers. Neutrophils are the most abundant granulocyte, are recruited to inflammatory microenvironments, and are a significant source of properdin, yet the ratio of properdin oligomers released from neutrophils is unknown. The oligomer ratio of neutrophil-derived properdin could have functional consequences in local microenvironments where neutrophils are abundant and complement drives inflammation. We investigated the oligomer properties of neutrophil-derived properdin, as compared to that of normal human sera, using a novel ELISA-based method that detects function of properdin in a way that was proportional to the oligomeric size of properdin (i.e., the larger the oligomer, the higher the detected function). Unexpectedly, neutrophil-derived properdin had 5-fold lower function than donor-matched serum-derived properdin. The lower function was due to a lower percentage of tetramers/trimers and more dimers, indicating a significantly different P₄:P₃:P₂ ratio in neutrophil-derived properdin (18:34:48) as compared to donor-matched serum (29:43:29). Release of lower-order oligomers by neutrophils may constitute a novel regulatory mechanism to control the rate of complement activation in cellular microenvironments. Further studies to determine the factors that affect properdin oligomerization and whether, or how, the predominant dimers in neutrophil-derived properdin, assimilate to the ~1:2:1 ratio found in serum are warranted.

Unravelling the role of transient redox partner complexes in P450 electron transfer mechanics

The molecular evolution of cytochromes P450 and associated redox-driven oxidative catalysis remains a mystery in biology. It is widely believed that sterol 14α-demethylase (CYP51), an essential enzyme of sterol biosynthesis, is the ancestor of the whole P450 superfamily given its conservation across species in different biological kingdoms. Herein we have utilized X-ray crystallography, molecular dynamics simulations, phylogenetics and electron transfer measurements to interrogate the nature of P450-redox partner binding using the naturally occurring fusion protein, CYP51-ferredoxin found in the sterol-producing bacterium Methylococcus capsulatus. Our data advocates that the electron transfer mechanics in the M. capsulatus CYP51-ferredoxin fusion protein involves an ensemble of ferredoxin molecules in various orientations and the interactions are transient. Close proximity of ferredoxin, however, is required to complete the substrate-induced large-scale structural switch in the P450 domain that enables proton-coupled electron transfer and subsequent oxygen scission and catalysis. These results have fundamental implications regarding the early evolution of electron transfer proteins and for the redox reactions in the early steps of sterol biosynthesis. They also shed new light on redox protein mechanics and the subsequent diversification of the P450 electron transfer machinery in nature.

Large-scale prediction of key dynamic interacting proteins in multiple cancers

Tracking cancer dynamic protein-protein interactions (PPIs) and deciphering their pathogenesis remain a challenge. We presented a dynamic PPIs' hypothesis: permanent and transient interactions might achieve dynamic switchings from normal cells to malignancy, which could cause maintenance functions to be interrupted and transient functions to be sustained. Based on the hypothesis, we first predicted >1400 key cancer genes (KCG) by applying PPI-express we proposed to 18 cancer gene expression datasets. We then further screened out key dynamic interactions (KDI) of cancer based on KCG and transient and permanent interactions under both conditions. Two prominent functional characteristics, "Cell cycle-related" and "Immune-related", were presented for KCG, suggesting that these might be their general characteristics. We found that, compared to permanent to transient KDI pairs (P2T) in the network, transient to permanent (T2P) have significantly higher edge betweenness (EB), and P2T pairs tending to locate intra-functional modules may play roles in maintaining normal biological functions, while T2P KDI pairs tending to locate inter-modules may play roles in biological signal transduction. It was consistent with our hypothesis. Also, we analyzed network characteristics of KDI pairs and their functions. Our findings of KDI may serve to understand and explain a few hallmarks of cancer.

Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants

High‐resolution experimental structural determination of protein–protein interactions has led to valuable mechanistic insights, yet due to the massive number of interactions and experimental limitations there is a need for computational methods that can accurately model their structures. Here we explore the use of the recently developed deep learning method, AlphaFold, to predict structures of protein complexes from sequence. With a benchmark of 152 diverse heterodimeric protein complexes, multiple implementations and parameters of AlphaFold were tested for accuracy. Remarkably, many cases (43%) had near‐native models (medium or high critical assessment of predicted interactions accuracy) generated as top‐ranked predictions by AlphaFold, greatly surpassing the performance of unbound protein–protein docking (9% success rate for near‐native top‐ranked models), however AlphaFold modeling of antibody–antigen complexes within our set was unsuccessful. We identified sequence and structural features associated with lack of AlphaFold success, and we also investigated the impact of multiple sequence alignment input. Benchmarking of a multimer‐optimized version of AlphaFold (AlphaFold‐Multimer) with a set of recently released antibody–antigen structures confirmed a low rate of success for antibody–antigen complexes (11% success), and we found that T cell receptor–antigen complexes are likewise not accurately modeled by that algorithm, showing that adaptive immune recognition poses a challenge for the current AlphaFold algorithm and model. Overall, our study demonstrates that end‐to‐end deep learning can accurately model many transient protein complexes, and highlights areas of improvement for future developments to reliably model any protein–protein interaction of interest.

Spatial organization of hydrophobic and charged residues affects protein thermal stability and binding affinity

What are the molecular determinants of protein–protein binding affinity and whether they are similar to those regulating fold stability are two major questions of molecular biology, whose answers bring important implications both from a theoretical and applicative point of view. Here, we analyze chemical and physical features on a large dataset of protein–protein complexes with reliable experimental binding affinity data and compare them with a set of monomeric proteins for which melting temperature data was available. In particular, we probed the spatial organization of protein (1) intramolecular and intermolecular interaction energies among residues, (2) amino acidic composition, and (3) their hydropathy features. Analyzing the interaction energies, we found that strong Coulombic interactions are preferentially associated with a high protein thermal stability, while strong intermolecular van der Waals energies correlate with stronger protein–protein binding affinity. Statistical analysis of amino acids abundances, exposed to the molecular surface and/or in interaction with the molecular partner, confirmed that hydrophobic residues present on the protein surfaces are preferentially located in the binding regions, while charged residues behave oppositely. Leveraging on the important role of van der Waals interface interactions in binding affinity, we focused on the molecular surfaces in the binding regions and evaluated their shape complementarity, decomposing the molecular patches in the 2D Zernike basis. For the first time, we quantified the correlation between local shape complementarity and binding affinity via the Zernike formalism. In addition, considering the solvent interactions via the residue hydropathy, we found that the hydrophobicity of the binding regions dictates their shape complementary as much as the correlation between van der Waals energy and binding affinity. In turn, these relationships pave the way to the fast and accurate prediction and design of optimal binding regions as the 2D Zernike formalism allows a rapid and superposition-free comparison between possible binding surfaces.

Evolution of homo-oligomerization of methionine S-adenosyltransferases is replete with structure-function constrains

Homomers are prevalent in bacterial proteomes, particularly among core metabolic enzymes. Homomerization is often key to function and regulation, and interfaces that facilitate the formation of homomeric enzymes are subject to intense evolutionary change. However, our understanding of the molecular mechanisms that drive evolutionary variation in homomeric complexes is still lacking. How is the diversification of protein interfaces linked to variation in functional regulation and structural integrity of homomeric complexes? To address this question, we studied quaternary structure evolution of bacterial methionine S‐adenosyltransferases (MATs)—dihedral homotetramers formed along a large and conserved dimeric interface harboring two active sites, and a small, recently evolved, interdimeric interface. Here, we show that diversity in the physicochemical properties of small interfaces is directly linked to variability in the kinetic stability of MAT quaternary complexes and in modes of their functional regulation. Specifically, hydrophobic interactions within the small interface of Escherichia coli MAT render the functional homotetramer kinetically stable yet impose severe aggregation constraints on complex assembly. These constraints are alleviated by electrostatic interactions that accelerate dimer‐dimer assembly. In contrast, Neisseria gonorrhoeae MAT adopts a nonfunctional dimeric state due to the low hydrophobicity of its small interface and the high flexibility of its active site loops, which perturbs small interface integrity. Remarkably, in the presence of methionine and ATP, N. gonorrhoeae MAT undergoes substrate‐induced assembly into a functional tetrameric state. We suggest that evolution acts on the interdimeric interfaces of MATs to tailor the regulation of their activity and stability to unique organismal needs.

PDB Code(s): 7R2W;7R3B;

Amplifying Intermolecular Events by Streptavidin-Induced Proximity

Weak interactions between biomolecules play important roles in many cellular functions. Structural and kinetic analyses of these interactions, however, have been hindered by the transient nature of such events. Here, we pointed out a general approach to overcome this obstacle─anchoring the molecular partners to streptavidin hosts─and achieved constrained proximity and stoichiometry for the sought-after molecular coupling. We elaborated this idea through a series of DNA hybridization reactions and quantitatively characterized them using single-molecule experiments. Compared to a nominally 1 μM solution, for example, the streptavidin-induced proximity (SIP) amounted to an effective molarity of ∼10-30 μM for the binding partners. There was also a significantly increased proportion of molecular association, manifested in both ensemble population and single-molecule residence time. As an application example, we showed how SIP enabled the observation and quantitative characterization of an unstable complex between Cas9-RNA and noncognate DNA substrates, interactions that had been challenging to characterize previously. Conceptually simple and implementationally robust, SIP was shown to considerably enhance the efficacy in capturing weak interactions and, as demonstrated here, could empower scientists to see the otherwise unseeable.

Physicochemical classification of organisms

The hypervariable residues that compose the major part of proteins’ surfaces are generally considered outside evolutionary control. Yet, these “nonconserved” residues determine the outcome of stochastic encounters in crowded cells. It has recently become apparent that these encounters are not as random as one might imagine, but carefully orchestrated by the intracellular electrostatics to optimize protein diffusion, interactivity, and partner search. The most influential factor here is the protein surface-charge density, which takes different optimal values across organisms with different intracellular conditions. In this study, we examine how far the net-charge density and other physicochemical properties of proteomes will take us in terms of distinguishing organisms in general. The results show that these global proteome properties not only follow the established taxonomical hierarchy, but also provide clues to functional adaptation. In many cases, the proteome–property divergence is even resolved at species level. Accordingly, the variable parts of the genes are not as free to drift as they seem in sequence alignment, but present a complementary tool for functional, taxonomic, and evolutionary assignment.

Impact of oligomerization on the allergenicity of allergens

Type I hypersensitivity (allergic reaction) is an unsuitable or overreactive immune response to an allergen due to cross-link immunoglobulin E (IgE) antibodies bound to its high-affinity IgE receptors (FcεRIs) on effector cells. It is needless to say that at least two epitopes on allergens are required to the successful and effective cross-linking. There are some reports pointing to small proteins with only one IgE epitope could cross-link FcεRI-bound IgE through homo-oligomerization which provides two same IgE epitopes. Therefore, oligomerization of allergens plays an indisputable role in the allergenic feature and stability of allergens. In this regard, we review the signaling capacity of the B cell receptor (BCR) complex and cross-linking of FcεRI which results in the synthesis of allergen-specific IgE. This review also discusses the protein-protein interactions involved in the oligomerization of allergens and provide some explanations about the oligomerization of some well-known allergens, such as calcium-binding allergens, Alt a 1, Bet v 1, Der p 1, Per a3, and Fel d 1, along with the effects of their concentrations on dimerization.

Design of Protein Segments and Peptides for Binding to Protein Targets

Recent years have witnessed a rise in methods for accurate prediction of structure and design of novel functional proteins. Design of functional protein fragments and peptides occupy a small, albeit unique, space within the general field of protein design. While the smaller size of these peptides allows for more exhaustive computational methods, flexibility in their structure and sparsity of data compared to proteins, as well as presence of noncanonical building blocks, add additional challenges to their design. This review summarizes the current advances in the design of protein fragments and peptides for binding to targets and discusses the challenges in the field, with an eye toward future directions.

A novel computational strategy for defining the minimal protein molecular surface representation

Most proteins perform their biological function by interacting with one or more molecular partners. In this respect, characterizing local features of the molecular surface, that can potentially be involved in the interaction with other molecules, represents a step forward in the investigation of the mechanisms of recognition and binding between molecules. Predictive methods often rely on extensive samplings of molecular patches with the aim to identify hot spots on the surface. In this framework, analysis of large proteins and/or many molecular dynamics frames is often unfeasible due to the high computational cost. Thus, finding optimal ways to reduce the number of points to be sampled maintaining the biological information (including the surface shape) carried by the molecular surface is pivotal. In this perspective, we here present a new theoretical and computational algorithm with the aim of defining a set of molecular surfaces composed of points not uniformly distributed in space, in such a way as to maximize the information of the overall shape of the molecule by minimizing the number of total points. We test our procedure’s ability in recognizing hot-spots by describing the local shape properties of portions of molecular surfaces through a recently developed method based on the formalism of 2D Zernike polynomials. The results of this work show the ability of the proposed algorithm to preserve the key information of the molecular surface using a reduced number of points compared to the complete surface, where all points of the surface are used for the description. In fact, the methodology shows a significant gain of the information stored in the sampling procedure compared to uniform random sampling.

Calculation and Visualization of Binding Equilibria in Protein Studies

A set of simulation applets has been developed for visualizing the behavior of the association and dissociation reactions in protein studies. These reactions are simple equilibrium reactions, and the equilibrium constants, most often dissociation constant K _D, are useful measures of affinity. Equilibria, even in simple systems, may not behave intuitively, which can cause misconceptions and mistakes. These applets can be utilized for planning experiments, for verifying experimental results, and for visualization of the equilibria in education. The considered reactions include protein homodimerization, ligand binding to a receptor (or heterodimerization), and competitive ligand binding. The latter one can be considered as either a ligand binding to two receptors or a binding of two ligands to a single receptor. In general, the user is required to input the total concentrations of all proteins and ligands and the dissociation constants of all complexes, and the applets output the equilibrium concentrations of all protein species graphically as functions of concentration and as numerical values at a specified point. Also, a curve fitting tool is provided which roughly estimates the concentrations or the dissociation constants based on the experimental data. The applets are freely available online (URL: https://protsim.github.io/protsim) and readily hackable for custom purposes if necessary.

Gated Transient Dissipative Dimerization of DNA Tetrahedra Nanostructures for Programmed DNAzymes Catalysis

Transient dissipative dimerization and transient gated dimerization of DNA tetrahedra nanostructures are introduced as functional modules to emulate transient and gated protein-protein interactions and emergent protein-protein guided transient catalytic functions, operating in nature. Four tetrahedra are engineered to yield functional modules that, in the presence of pre-engineered auxiliary nucleic acids and the nicking enzyme Nt.BbvCI, lead to the fueled transient dimerization of two pairs of tetrahedra. The dynamic transient formation and depletion of DNA tetrahedra are followed by transient FRET signals generated by fluorophore-labeled tetrahedra. The integration of two inhibitors within the mixture of the four tetrahedra and two auxiliary modules, fueling the transient dimerization, results in selective inhibitor-guided gated transient dimerization of two different DNA tetrahedra dimers. Kinetic models for the dynamic transient dimerization and gated transient dimerization of the DNA tetrahedra are formulated and computationally simulated. The derived rate-constants allow the prediction and subsequent experimental validation of the performance of the systems under different auxiliary conditions. In addition, by appropriate modification of the four tetrahedra structures, the triggered gated emergence of selective transient catalytic functions driven by the two pairs of DNA tetrahedra dimers is demonstrated.

Identification of potential interleukin-8 inhibitors acting on the interactive site between chemokine and CXCR2 receptor: A computational approach

Interactions between interleukin (IL)-8 and its receptors, CXCR1, and CXCR2, serve crucial roles in inflammatory conditions and various types of cancers. Inhibition of this signaling pathway has been exploited as a promising strategy in treating these diseases. However, most studies only focused on the design of allosteric antagonists-bound receptors on the intracellular side of IL-8 receptors. Recently, the first cryo-EM structures of IL-8-CXCR2-Gi complexes have been solved, revealing the unique binding and activation modes of the endogenous chemokine IL-8. Hence, we set to identify small molecule inhibitors for IL-8 using critical protein-protein interaction between IL-8 and CXCR2 at the orthosteric binding site. The pharmacophore models and molecular docking screened compounds from DrugBank and NCI databases. The oral bioavailability of the top 23 ligands from the screening was then predicted by the SwissAMDE tool. Molecular dynamics simulation and free binding energy calculation were performed for the best compounds. The result indicated that DB14770, DB12121, and DB03916 could form strong interactions and stable protein-ligand complexes with IL-8. These three candidates are potential IL-8 inhibitors that can be further evaluated by in vitro experiments in the next stage.

The properties of human disease mutations at protein interfaces

The assembly of proteins into complexes and their interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions in proteins with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in three-dimensional space, show a significant disease enrichment. Finally, we observe that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors.

Nearly all proteins interact with other molecules as part of their biological function. For example, proteins can interact with other copies of the same type of protein, with different proteins, with DNA, or with small ligand molecules. Many mutations at protein interfaces, the regions of proteins that interact with other molecules, are known to cause human genetic disease. In this study, we first investigate how different types of protein interfaces have different tendencies to be associated with disease. We also show that the closer a mutation is to an interface, the more likely it is to cause disease. Finally, we study how mutations at different types of interfaces tend to be associated with different changes in amino acid properties, which appears to influence our ability to computationally predict the effects of mutations. Ultimately, we hope that consideration of protein interface properties will eventually improve our ability to identify new disease-causing mutations.

X-ray diffraction and in vivo studies reveal the quinary structure of Trypanosoma cruzi nucleoside diphosphate kinase 1: a novel helical oligomer structure

Trypanosoma cruzi is a flagellated protozoan parasite that causes Chagas disease, which represents a serious health problem in the Americas. Nucleoside diphosphate kinases (NDPKs) are key enzymes that are implicated in cellular energy management. TcNDPK1 is the canonical isoform in the T. cruzi parasite. TcNDPK1 has a cytosolic, perinuclear and nuclear distribution. It is also found in non-membrane-bound filaments adjacent to the nucleus. In the present work, X-ray diffraction and in vivo studies of TcNDPK1 are described. The structure reveals a novel, multi-hexameric, left-handed helical oligomer structure. The results of directed mutagenesis studies led to the conclusion that the microscopic TcNDPK1 granules observed in vivo in T. cruzi parasites are made up by the association of TcNDPK1 oligomers. In the absence of experimental data, analysis of the interactions in the X-ray structure of the TcNDPK1 oligomer suggests the probable assembly and disassembly steps: dimerization, assembly of the hexamer as a trimer of dimers, hexamer association to generate the left-handed helical oligomer structure and finally oligomer association in a parallel manner to form the microscopic TcNDPK1 filaments that are observed in vivo in T. cruzi parasites. Oligomer disassembly takes place on the binding of substrate in the active site of TcNDPK1, leading to dissociation of the hexamers. This study constitutes the first report of such a protein arrangement, which has never previously been seen for any protein or NDPK. Further studies are needed to determine its physiological role. However, it may suggest a paradigm for protein storage reflecting the complex mechanism of action of TcNDPK1.

A Quantitative Systems Approach to Define Novel Effects of Tumour p53 Mutations on Binding Oncoprotein MDM2

Understanding transient protein interactions biochemically at the proteome scale remains a long-standing challenge. Current tools developed to study protein interactions in high-throughput measure stable protein complexes and provide binary readouts; they do not elucidate dynamic and weak protein interactions in a proteome. The majority of protein interactions are transient and cover a wide range of affinities. Nucleic acid programmable protein arrays (NAPPA) are self-assembling protein microarrays produced by freshly translating full-length proteins in situ on the array surface. Herein, we have coupled NAPPA to surface plasmon resonance imaging (SPRi) to produce a novel label-free platform that measures many protein interactions in real-time allowing the determination of the KDs and rate constants. The developed novel NAPPA-SPRi technique showed excellent ability to study protein-protein interactions of clinical mutants of p53 with its regulator MDM2. Furthermore, this method was employed to identify mutant p53 proteins insensitive to the drug nutlin-3, currently in clinical practice, which usually disrupts the p53-MDM2 interactions. Thus, significant differences in the interactions were observed for p53 mutants on the DNA binding domain (Arg-273-Cys, Arg-273-His, Arg-248-Glu, Arg-280-Lys), on the structural domain (His-179-Tyr, Cys-176-Phe), on hydrophobic moieties in the DNA binding domain (Arg-280-Thr, Pro-151-Ser, Cys-176-Phe) and hot spot mutants (Gly-245-Cys, Arg-273-Leu, Arg-248-Glu, Arg-248-Gly), which signifies the importance of point mutations on the MDM2 interaction and nutlin3 effect, even in molecular locations related to other protein activities.

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.

End-Binding E3 Ubiquitin Ligases Enable Protease Signaling

Post-translational modifications (PTMs) direct the assembly of protein complexes. In this context, proteolysis is a unique PTM because it is irreversible; the hydrolysis of the peptide backbone generates separate fragments bearing a new N and C terminus. Proteolysis can "re-wire" protein-protein interactions (PPIs) via the recruitment of end-binding proteins to new termini. In this review, we focus on the role of proteolysis in specifically creating complexes by recruiting E3 ubiquitin ligases to new N and C termini. These complexes potentiate proteolytic signaling by "erasing" proteolytic modifications. This activity tunes the duration and magnitude of protease signaling events. Recent work has shown that the stepwise process of proteolysis, end-binding by E3 ubiquitin ligases, and fragment turnover is associated with both the nascent N terminus (i.e., N-degron pathways) and the nascent C terminus (i.e., the C-degron pathways). Here, we discuss how these pathways might harmonize protease signaling with protein homeostasis (i.e., proteostasis).

Enhanced Molecular Dynamics Method to Efficiently Increase the Discrimination Capability of Computational Protein-Protein Docking

Protein–protein docking typically consists of the generation of putative binding conformations, which are subsequently ranked by fast heuristic scoring functions. The simplicity of these functions allows for computational efficiency but has severe repercussions on their discrimination capabilities. In this work, we show the effectiveness of suitable descriptors calculated along short scaled molecular dynamics runs in recognizing the nearest-native bound conformation among a set of putative structures generated by the HADDOCK tool for eight protein–protein systems.

Protein complexes and neighborhoods driving autophagy

Autophagy summarizes evolutionarily conserved, intracellular degradation processes targeting cytoplasmic material for lysosomal degradation. These encompass constitutive processes as well as stress responses, which are often found dysregulated in diseases. Autophagy pathways help in the clearance of damaged organelles, protein aggregates and macromolecules, mediating their recycling and maintaining cellular homeostasis. Protein-protein interaction networks contribute to autophagosome biogenesis, substrate loading, vesicular trafficking and fusion, protein translocations across membranes and degradation in lysosomes. Hypothesis-free proteomic approaches tremendously helped in the functional characterization of protein-protein interactions to uncover molecular mechanisms regulating autophagy. In this review, we elaborate on the importance of understanding protein-protein-interactions of varying affinities and on the strengths of mass spectrometry-based proteomic approaches to study these, generating new mechanistic insights into autophagy regulation. We discuss in detail affinity purification approaches and recent developments in proximity labeling coupled to mass spectrometry, which uncovered molecular principles of autophagy mechanisms.Abbreviations: AMPK: AMP-activated protein kinase; AP-MS: affinity purification-mass spectrometry; APEX2: ascorbate peroxidase-2; ATG: autophagy related; BioID: proximity-dependent biotin identification; ER: endoplasmic reticulum; GFP: green fluorescent protein; iTRAQ: isobaric tag for relative and absolute quantification; MS: mass spectrometry; PCA: protein-fragment complementation assay; PL-MS: proximity labeling-mass spectrometry; PtdIns3P: phosphatidylinositol-3-phosphate; PTM: posttranslational modification; PUP-IT: pupylation-based interaction tagging; RFP: red fluorescent protein; SILAC: stable isotope labeling by amino acids in cell culture; TAP: tandem affinity purification; TMT: tandem mass tag.

Human FoxP Transcription Factors as Tractable Models of the Evolution and Functional Outcomes of Three-Dimensional Domain Swapping

The association of two or more proteins to adopt a quaternary complex is one of the most widespread mechanisms by which protein function is modulated. In this scenario, three-dimensional domain swapping (3D-DS) constitutes one plausible pathway for the evolution of protein oligomerization that exploits readily available intramolecular contacts to be established in an intermolecular fashion. However, analysis of the oligomerization kinetics and thermodynamics of most extant 3D-DS proteins shows its dependence on protein unfolding, obscuring the elucidation of the emergence of 3D-DS during evolution, its occurrence under physiological conditions, and its biological relevance. Here, we describe the human FoxP subfamily of transcription factors as a feasible model to study the evolution of 3D-DS, due to their significantly faster dissociation and dimerization kinetics and lower dissociation constants in comparison to most 3D-DS models. Through the biophysical and functional characterization of FoxP proteins, relevant structural aspects highlighting the evolutionary adaptations of these proteins to enable efficient 3D-DS have been ascertained. Most biophysical studies on FoxP suggest that the dynamics of the polypeptide chain are crucial to decrease the energy barrier of 3D-DS, enabling its fast oligomerization under physiological conditions. Moreover, comparison of biophysical parameters between human FoxP proteins in the context of their minute sequence differences suggests differential evolutionary strategies to favor homoassociation and presages the possibility of heteroassociations, with direct impacts in their gene regulation function.

Drug-resistant epilepsy: Drug target hypothesis and beyond the receptors

Epilepsy is a chronic neurological disorder that affects more than 50 million people worldwide. Despite a recent introduction of antiseizure drugs for the treatment of epileptic seizures, one-third of these patients suffer from drug-resistant epilepsy (DRE). The therapeutic target hypothesis is a cited theory to explain DRE. According to the target hypothesis, the failure to achieve seizure freedom leads to alteration of the structure and/or function of the antiseizure medication (ASM) target. However, this hypothesis fails to explain why patients with DRE do not respond to antiseizure medications of different targets. This review presents different conditions, such as epigenetic mechanisms and protein-protein interactions that may result in alterations of diverse drug targets using different mechanisms. These novel conditions represent new targets to control DRE.

In silico drug discovery for a complex immunotherapeutic target - human c-Rel protein

Target evaluation and rational drug design rely on identifying and characterising small-molecule binding sites on therapeutically relevant target proteins. Immunotherapeutics development is especially challenging because of complex disease etiology and heterogenous nature of targets. c-Rel protein, a promising target in many human inflammatory and cancer pathologies, was selected as a case study for an effective in silico screening platform development since this transcription factor currently has no successful therapeutic inhibitors or modulators. This study introduces a novel in silico screening approach to probe binding sites using structural validation sets, molecular modelling and describes a method of a computer-aided drug design when a crystal structure is not available for the target of interest. In addition, we showed that binding sites can be analysed with the machine learning as well as molecular simulation approaches to help assess and systematically analyse how drug candidates can exert their mode of action. Finally, this cutting-edge approach was subjected to a high through-put virtual screen of selected 34 M drug-like compounds filtered from a library of 659 M compounds by identifying the most promising structures and proposing potential action mechanisms for the future development of highly selective human c-Rel inhibitors and/or modulators.

Protein Complex Organization Imposes Constraints on Proteome Dysregulation in Cancer

Protein assembly is a highly dynamic process and proteins can interact in different ways and stoichiometries within a complex. The importance of maintaining protein stoichiometry for complex function and avoiding aggregation of orphan subunits has been demonstrated. However, how exactly the organization of proteins into complexes constrains differential protein abundance in extreme cellular conditions like cancer, where a lot of protein abundance changes occur, has not been systematically investigated. To study this, we collected proteomic data made available by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) to quantify proteomic changes during carcinogenesis and systematically tested five interaction types in complexes to investigate which of these features impact on protein abundance correlation patterns in cancer. We found that higher than expected fraction of protein complex subunits does not show changes in their abundances compared to those in the normal samples. Furthermore, we found that the way proteins interact in complexes indeed constrains their co-abundance patterns. Our results highlight the role of the interactions between the proteins and the need of cancer cells to deal with aberrant changes in protein abundance.

An interpretable machine learning method for homo-trimeric protein interface residue-residue interaction prediction

Protein-protein interaction plays an important role in life activities. A more fine-grained analysis, such as residues and atoms level, will better benefit us to understand the mechanism for inter-protein interaction and drug design. The development of efficient computational methods to reduce trials and errors, as well as assisting experimental researchers to determine the complex structure are some of the ongoing studies in the field. The research of trimer protein interface, especially homotrimer, has been rarely studied. In this paper, we proposed an interpretable machine learning method for homo-trimeric protein interface residue pairs prediction. The structure, sequence, and physicochemical information are intergraded as feature input fed to model for training. Graph model is utilized to present spatial information for intra-protein. Matrix factorization captures the different features' interactions. Kernel function is designed to auto-acquire the adjacent information of our target residue pairs. The accuracy rate achieves 54.5% in an independent test set. Sequence and structure alignment exhibit the ability of model self-study. Our model indicates the biological significance between sequence and structure, and could be auxiliary for reducing trials and errors in the fields of protein complex determination and protein-protein docking, etc. SIGNIFICANCE: Protein complex structures are significant for understanding protein function and promising functional protein design. With data increasing, some computational tools have been developed for protein complex residue contact prediction, which is one of the most significant steps for complex structure prediction. But for homo-trimeric protein, the sequence-based deep learning predictors are infeasible for homologous sequences, and the algorithm black box prevents us from understanding of each step operation. In this way, we propose an interpreting machine learning method for homo-trimeric protein interface residue-residue interaction prediction, and the predictor shows a good performance. Our work provides a computational auxiliary way for determining the homo-trimeric proteins interface residue pairs which will be further verified by wet experiments, and and gives a hand for the downstream works, such as protein-protein docking, protein complex structure prediction and drug design.

Unfolding Mechanisms and Conformational Stability of the Dimeric Endophilin N-BAR Domain

Endophilin, which is a member of the Bin-amphiphysin-Rvs (BAR) domain protein superfamily, contains a homodimeric N-BAR domain of a characteristic crescent shape. The N-BAR domain comprises a six-helix bundle and is known to sense and generate membrane curvature. Here, we characterize aspects of the unfolding mechanism of the endophilin A1 N-BAR domain during thermal denaturation and examine factors that influence the thermal stability of this domain. Far-UV circular dichroism (CD) spectroscopy was applied to monitor changes in the secondary structure above room temperature. The protein's conformational changes were further characterized through Foerster resonance energy transfer and cross-linking experiments at varying temperatures. Our results indicate that thermal unfolding of the endophilin N-BAR is (minimally) a two-step process, with a dimeric intermediate that displays partial helicity loss. Furthermore, a thermal shift assay and temperature-dependent CD were applied to compare the unfolding processes of several truncated versions of endophilin. The melting temperature of the N-BAR domain decreased when we deleted either the N-terminal H0 helix or the unstructured linker of endophilin. This result suggests that these intrinsically disordered domains may play a role in structurally stabilizing the functional N-BAR domain in vivo. Finally, we show that single-site mutations can also compromise endophilin's thermal stability.

A survey of human histone H1 subtypes interaction networks: Implications for histones H1 functioning

To show a spectrum of histone H1 subtypes (H1.1-H1.5) activity realized through the protein-protein interactions, data selected from APID resources were processed with sequence-based bioinformatics approaches. Histone H1 subtypes participate in over half a thousand interactions with nuclear and cytosolic proteins (ComPPI database) engaged in the enzymatic activity and binding of nucleic acids and proteins (SIFTER tool). Small-scale networks of H1 subtypes (STRING network) have similar topological parameters (P > .05) which are, however, different for networks hubs between subtype H1.1 and H1.4 and subtype H1.3 and H1.5 (P < .05) (Cytoscape software). Based on enriched GO terms (g:Profiler toolset) of interacting proteins, molecular function and biological process of networks hubs is related to RNA binding and ribosome biogenesis (subtype H1.1 and H1.4), cell cycle and cell division (subtype H1.3 and H1.5) and protein ubiquitination and degradation (subtype H1.2). The residue propensity (BIPSPI predictor) and secondary structures of interacting surfaces (GOR algorithm) as well as a value of equilibrium dissociation constant (ISLAND predictor) indicate that a type of H1 subtypes interactions is transient in term of the stability and medium-strong in relation to the strength of binding. Histone H1 subtypes bind interacting partners in the intrinsic disorder-dependent mode (FoldIndex, PrDOS predictor), according to the coupled folding and binding and mutual synergistic folding mechanism. These results evidence that multifunctional H1 subtypes operate via protein interactions in the networks of crucial cellular processes and, therefore, confirm a new histone H1 paradigm relating to its functioning in the protein-protein interaction networks.

Current Methods for Detecting Cell Membrane Transient Interactions

Short-lived cell membrane complexes play a key role in regulating cell signaling and communication. Many of these complexes are formed based on low-affinity and transient interactions among various lipids and proteins. New techniques have emerged to study these previously overlooked membrane transient interactions. Exciting functions of these transient interactions have been discovered in cellular events such as immune signaling, host-pathogen interactions, and diseases such as cancer. In this review, we have summarized current experimental methods that allow us to detect and analyze short-lived cell membrane protein-protein, lipid-protein, and lipid-lipid interactions. These methods can provide useful information about the strengths, kinetics, and/or spatial patterns of membrane transient interactions. However, each method also has its own limitations. We hope this review can be used as a guideline to help the audience to choose proper approaches for studying membrane transient interactions in different membrane trafficking and cell signaling events.

Post-translational Regulation of DNA Polymerase η, a Connection to Damage-Induced Cohesion in Saccharomyces cerevisiae

Double-strand breaks that are induced postreplication trigger establishment of damage-induced cohesion in Saccharomyces cerevisiae, locally at the break site and genome-wide on undamaged chromosomes. The translesion synthesis polymerase, polymerase η, is required for generation of damage-induced cohesion genome-wide. However, its precise role and regulation in this process is unclear. Here, we investigated the possibility that the cyclin-dependent kinase Cdc28 and the acetyltransferase Eco1 modulate polymerase η activity. Through in vitro phosphorylation and structure modeling, we showed that polymerase η is an attractive substrate for Cdc28 Mutation of the putative Cdc28-phosphorylation site Ser14 to Ala not only affected polymerase η protein level, but also prevented generation of damage-induced cohesion in vivo We also demonstrated that Eco1 acetylated polymerase η in vitro Certain nonacetylatable polymerase η mutants showed reduced protein level, deficient nuclear accumulation, and increased ultraviolet irradiation sensitivity. In addition, we found that both Eco1 and subunits of the cohesin network are required for cell survival after ultraviolet irradiation. Our findings support functionally important Cdc28-mediated phosphorylation, as well as post-translational modifications of multiple lysine residues that modulate polymerase η activity, and provide new insights into understanding the regulation of polymerase η for damage-induced cohesion.