Long-range cooperative binding effects in a T cell receptor variable domain

Bayesian network models identify cooperative GPCR:G protein interactions that contribute to G protein coupling

Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network-like behavior and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance in protein-protein interfaces. Although they are critically important in protein-protein interfaces, it is challenging to determine which amino acid pair interactions are cooperative. In this work, we have used Bayesian network modeling, an interpretable machine learning method, combined with molecular dynamics trajectories to identify the residue pairs that show high cooperativity and their allosteric effect in the interface of G protein-coupled receptor (GPCR) complexes with Gα subunits. Our results reveal six GPCR:Gα contacts that are common to the different Gα subtypes and show strong cooperativity in the formation of interface. Both the C terminus helix5 and the core of the G protein are codependent entities and play an important role in GPCR coupling. We show that a promiscuous GPCR coupling to different Gα subtypes, makes all the GPCR:Gα contacts that are specific to each Gα subtype (Gαs, Gαi, and Gαq). This work underscores the potential of data-driven Bayesian network modeling in elucidating the intricate dependencies and selectivity determinants in GPCR:G protein complexes, offering valuable insights into the dynamic nature of these essential cellular signaling components.

Bayesian network models identify co-operative GPCR:G protein interactions that contribute to G protein coupling

Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network-like behavior of interface interactions and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance in protein-protein interfaces. Although they are critically important in protein-protein interfaces it is challenging to determine which amino acid pair interactions are cooperative. In this work we have used Bayesian network modeling, an interpretable machine learning method, combined with molecular dynamics trajectories to identify the residue pairs that show high cooperativity and their allosteric effect in the interface of G protein-coupled receptor (GPCR) complexes with G proteins. Our results reveal a strong co-dependency in the formation of interface GPCR:G protein contacts. This observation indicates that cooperativity of GPCR:G protein interactions is necessary for the coupling and selectivity of G proteins and is thus critical for receptor function. We have identified subnetworks containing polar and hydrophobic interactions that are common among multiple GPCRs coupling to different G protein subtypes (Gs, Gi and Gq). These common subnetworks along with G protein-specific subnetworks together confer selectivity to the G protein coupling. This work underscores the potential of data-driven Bayesian network modeling in elucidating the intricate dependencies and selectivity determinants in GPCR:G protein complexes, offering valuable insights into the dynamic nature of these essential cellular signaling components.

Glucose Mediates Niche-Specific Repression of Staphylococcus aureus Toxic Shock Syndrome Toxin-1 through the Activity of CcpA in the Vaginal Environment

Staphylococcus aureus chronically colonizes up to 30% of the human population on the skin or mucous membranes, including the nasal tract or vaginal canal. While colonization is often benign, this bacterium also has the capability to cause serious infections. Menstrual toxic shock syndrome (mTSS) is a serious toxinosis associated with improper use of tampons, which can induce an environment that is favorable to the production of the superantigen known as toxic shock syndrome toxin-1 (TSST-1). To better understand environmental signaling that influences TSST-1 production, we analyzed expression in the prototype mTSS strain S. aureus MN8. Using transcriptional and protein-based analysis in two niche-related media, we observed that TSST-1 expression was significantly higher in synthetic nasal medium (SNM) than in vaginally defined medium (VDM). One major divergence in medium composition was high glucose concentration in VDM. The glucose-dependent virulence regulator gene ccpA was deleted in MN8, and, compared with wild-type MN8, we observed increased TSST-1 expression in the ΔccpA mutant when grown in VDM, suggesting that TSST-1 is repressed by catabolite control protein A (CcpA) in the vaginal environment. We were able to relieve CcpA-mediated repression by modifying the glucose level in vaginal conditions, confirming that changes in nutritional conditions contribute to the overexpression of TSST-1 that can lead to mTSS. We also compared CcpA-mediated repression to other key regulators of tst, finding that CcpA regulation is dominant compared to other characterized regulatory mechanisms. This study underlines the importance of environmental signaling for S. aureus pathogenesis in the context of mTSS. IMPORTANCE Menstrual toxic shock syndrome (mTSS) is caused by strains of Staphylococcus aureus that overproduce a toxin known as toxic shock syndrome toxin-1 (TSST-1). This work studied how glucose levels in a model vaginal environment could influence the amount of TSST-1 that is produced by S. aureus. We found that high levels of glucose repress TSST-1 production, and this is done by a regulatory protein called catabolite control protein A (CcpA). The research also demonstrated that, compared with other regulatory proteins, the CcpA regulator appears to be the most important for maintaining low levels of TSST-1 in the vaginal environment, and this information helps to understand how changes in the vaginal environmental can lead to mTSS.

Small Molecule Agents Targeting PD-1 Checkpoint Pathway for Cancer Immunotherapy: Mechanisms of Action and Other Considerations for Their Advanced Development

Pioneering success of antibodies targeting immune checkpoints such as programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) has changed the outlook of cancer therapy. Although these antibodies show impressive durable clinical activity, low response rates and immune-related adverse events are becoming increasingly evident in antibody-based approaches. For further strides in cancer immunotherapy, novel treatment strategies including combination therapies and alternate therapeutic modalities are highly warranted. Towards this discovery and development of small molecule, checkpoint inhibitors are actively being pursued, and the efforts have culminated in the ongoing clinical testing of orally bioavailable checkpoint inhibitors. This review focuses on the small molecule agents targeting PD-1 checkpoint pathway for cancer immunotherapy and highlights various chemotypes/scaffolds and their characterization including binding and functionality along with reported mechanism of action. The learnings from the ongoing small molecule clinical trials and crucial points to be considered for their clinical development are also discussed.

The oncogenic potential of small nuclear ribonucleoprotein polypeptide G: a comprehensive and perspective view

Small nuclear ribonucleoprotein polypeptide G (SNRPG), often referred to as Smith protein G (SmG), is an indispensable component in the biogenesis of spliceosomal uridyl-rich small nuclear ribonucleoprotein particles (U snRNPs; U1, U2, U4 and U5), which are precursors of both the major and minor spliceosome. SNRPG has attracted significant attention because of its implicated roles in tumorigenesis and tumor development. Suggestive evidence of its varying expression levels has been reported in different types of cancers, which include breast cancer, lung cancer, prostate cancer and colon cancer. The accumulating evidence suggests that the splicing machinery component plays a significant role in the initiation and progression of cancers. SNRPG has a wide interaction network, and its functions are predominantly mediated by protein-protein interactions (PPIs), making it a promising anti-cancer therapeutic target in PPI-focused drug technology. Understanding its roles in tumorigenesis and tumor development is an indispensable arsenal in the development of molecular-targeted therapies. Several antitumor drugs linked to splicing machinery components have been reported in different types of cancers and some have already entered the clinic. However, targeting SNRPG as a drug development tool has been an overlooked and underdeveloped strategy in cancer therapy. In this article, we present a comprehensive and perspective view on the oncogenic potential of SNRPG in PPI-focused drug discovery.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Subtle changes at the variable domain interface of the T-cell receptor can strongly increase affinity

Most affinity-maturation campaigns for antibodies and T-cell receptors (TCRs) operate on the residues at the binding site, located within the loops known as complementarity-determining regions (CDRs). Accordingly, mutations in contact residues, or so-called "second shell" residues, that increase affinity are typically identified by directed evolution involving combinatorial libraries. To determine the impact of residues located at a distance from the binding site, here we used single-codon libraries of both CDR and non-CDR residues to generate a deep mutational scan of a human TCR against the cancer antigen MART-1·HLA-A2. Non-CDR residues included those at the interface of the TCR variable domains (Vα and Vβ) and surface-exposed framework residues. Mutational analyses showed that both Vα/Vβ interface and CDR residues were important in maintaining binding to MART-1·HLA-A2, probably due to either structural requirements for proper Vα/Vβ association or direct contact with the ligand. More surprisingly, many Vα/Vβ interface substitutions yielded improved binding to MART-1·HLA-A2. To further explore this finding, we constructed interface libraries and selected them for improved stability or affinity. Among the variants identified, one conservative substitution (F45βY) was most prevalent. Further analysis of F45βY showed that it enhanced thermostability and increased affinity by 60-fold. Thus, introducing a single hydroxyl group at the Vα/Vβ interface, at a significant distance from the TCR·peptide·MHC-binding site, remarkably affected ligand binding. The variant retained a high degree of specificity for MART-1·HLA-A2, indicating that our approach provides a general strategy for engineering improvements in either soluble or cell-based TCRs for therapeutic purposes.

Emerging Concepts in TCR Specificity: Rationalizing and (Maybe) Predicting Outcomes

T cell specificity emerges from a myriad of processes, ranging from the biological pathways that control T cell signaling to the structural and physical mechanisms that influence how TCRs bind peptides and MHC proteins. Of these processes, the binding specificity of the TCR is a key component. However, TCR specificity is enigmatic: TCRs are at once specific but also cross-reactive. Although long appreciated, this duality continues to puzzle immunologists and has implications for the development of TCR-based therapeutics. In this review, we discuss TCR specificity, emphasizing results that have emerged from structural and physical studies of TCR binding. We show how the TCR specificity/cross-reactivity duality can be rationalized from structural and biophysical principles. There is excellent agreement between predictions from these principles and classic predictions about the scope of TCR cross-reactivity. We demonstrate how these same principles can also explain amino acid preferences in immunogenic epitopes and highlight opportunities for structural considerations in predictive immunology.

Staphylococcus enterotoxin profile of China isolates and the superantigenicity of some novel enterotoxins

The genus of staphylococcus widely distributes in environments and contributes to a variety of animal and human diseases. The enterotoxins (SEs) secreted by this type of pathogen have been the leading cause of bacterial toxic shock syndrome and food poisoning, and thus present a substantial concern to public health. In this study, we analyzed the superantigen profile of 122 staphylococcus strains isolated from diverse sources. When screened for the presence and prevalence of 17 known se or se-like (sel) genes, except selj, all other genes were detected in these isolates. In particular, 95.9% of the isolates harbored at least one se/sel gene. Moreover, 47.5% of them bore at least 5. Remarkably, several non-pathogenic species of animal- and environment-origin were also found to carry multiple se/sels. The most frequent genes detected were tsst (62.3%), sei (54.1%), and seb (46.7%), followed by some sel genes (selo, selu, and selm), which also were present at relatively high frequency (20-30%). The generated data improved understanding of strain-specific differences in enterotoxin expression. The gene products of the latter (selo and selu) were subsequently analyzed for their antigenicity in a mouse model using purified E. coli-based recombinant proteins. The studies revealed a strong activity for SEO in induction of T-lymphocyte proliferation and production of various inflammatory cytokines either in vivo or in vitro. In contrast, SEU exhibited little superantigenic effects. The molecular basis for the difference in antigenicity was analyzed by 3D homology remodeling, which revealed a difference in binding and affinities for MHC-II molecules and TCR Vβ region.

Human scFvs That Counteract Bioactivities of Staphylococcus aureus TSST-1

Some Staphylococcus aureus isolates produced toxic shock syndrome toxin-1 (TSST-1) which is a pyrogenic toxin superantigen (PTSAg). The toxin activates a large fraction of peripheral blood T lymphocytes causing the cells to proliferate and release massive amounts of pro-inflammatory cytokines leading to a life-threatening multisystem disorder: toxic shock syndrome (TSS). PTSAg-mediated-T cell stimulation circumvents the conventional antigenic peptide presentation to T cell receptor (TCR) by the antigen-presenting cell (APC). Instead, intact PTSAg binds directly to MHC-II molecule outside peptide binding cleft and simultaneously cross-links TCR-Vβ region. Currently, there is neither specific TSS treatment nor drug that directly inactivates TSST-1. In this study, human single chain antibodies (HuscFvs) that bound to and neutralized bioactivities of the TSST-1 were generated using phage display technology. Three E. coli clones transfected with TSST-1-bound phages fished-out from the human scFv library using recombinant TSST-1 as bait expressed TSST-1-bound-HuscFvs that inhibited the TSST-1-mediated T cell activation and pro-inflammatory cytokine gene expressions and productions.Computerized simulation, verified by mutations of the residues of HuscFv complementarity determining regions (CDRs),predicted to involve in target binding indicated that the HuscFvs formed interface contact with the toxin residues important for immunopathogenesis. The HuscFvs have high potential for future therapeutic application.

An interdomain boundary in RAG1 facilitates cooperative binding to RAG2 in formation of the V(D)J recombinase complex

V(D)J recombination assembles functional antigen receptor genes during lymphocyte development. Formation of the recombination complex containing the recombination activating proteins, RAG1 and RAG2, is essential for the site-specific DNA cleavage steps in V(D)J recombination. However, little is known concerning how complex formation leads to a catalytically-active complex. Here, we combined limited proteolysis and mass spectrometry methods to identify regions of RAG1 that are sequestered upon association with RAG2. These results show that RAG2 bridges an interdomain boundary in the catalytic region of RAG1. In a second approach, mutation of RAG1 residues within the interdomain boundary were tested for disruption of RAG1:RAG2 complex formation using fluorescence-based pull down assays. The core RAG1 mutants demonstrated varying effects on complex formation with RAG2. Interestingly, two mutants showed opposing results for the ability to interact with core versus full length RAG2, indicating that the non-core region of RAG2 participates in binding to core RAG1. Significantly, all of the RAG1 interdomain mutants demonstrated altered stoichiometries of the RAG complexes, with an increased number of RAG2 per RAG1 subunit compared to the wild type complex. Based on our results, we propose that interaction of RAG2 with RAG1 induces cooperative interactions of multiple binding sites, induced through conformational changes at the RAG1 interdomain boundary, and resulting in formation of the DNA cleavage active site.

Superantigenicity analysis of staphylococcal enterotoxins SElK and SElQ in a mouse model

Staphylococcal enterotoxins (SEs) are superantigenic toxins secreted byStaphylococcus aureusthat is involved in causing food poisoning and human diseases.

Specific increase in potency via structure-based design of a TCR

Adoptive immunotherapy with Ag-specific T lymphocytes is a powerful strategy for cancer treatment. However, most tumor Ags are nonreactive "self" proteins, which presents an immunotherapy design challenge. Recent studies have shown that tumor-specific TCRs can be transduced into normal PBLs, which persist after transfer in ∼30% of patients and effectively destroy tumor cells in vivo. Although encouraging, the limited clinical responses underscore the need for enrichment of T cells with desirable antitumor capabilities prior to patient transfer. In this study, we used structure-based design to predict point mutations of a TCR (DMF5) that enhance its binding affinity for an agonist tumor Ag-MHC (peptide-MHC [pMHC]), Mart-1 (27L)-HLA-A2, which elicits full T cell activation to trigger immune responses. We analyzed the effects of selected TCR point mutations on T cell activation potency and analyzed cross-reactivity with related Ags. Our results showed that the mutated TCRs had improved T cell activation potency while retaining a high degree of specificity. Such affinity-optimized TCRs have demonstrated to be very specific for Mart-1 (27L), the epitope for which they were structurally designed. Although of somewhat limited clinical relevance, these studies open the possibility for future structural-based studies that could potentially be used in adoptive immunotherapy to treat melanoma while avoiding adverse autoimmunity-derived effects.

Hot spot-based design of small-molecule inhibitors for protein-protein interactions

Protein-protein interactions (PPIs) are important targets for the development of chemical probes and therapeutic agents. From the initial discovery of the existence of hot spots at PPI interfaces, it has been proposed that hot spots might provide the key for developing small-molecule PPI inhibitors. However, there has been no review on the ways in which the knowledge of hot spots can be used to achieve inhibitor design, nor critical examination of successful examples. This Digest discusses the characteristics of hot spots and the identification of druggable hot spot pockets. An analysis of four examples of hot spot-based design reveals the importance of this strategy in discovering potent and selective PPI inhibitors. A general procedure for hot spot-based design of PPI inhibitors is outlined.

Computational design of the affinity and specificity of a therapeutic T cell receptor

T cell receptors (TCRs) are key to antigen-specific immunity and are increasingly being explored as therapeutics, most visibly in cancer immunotherapy. As TCRs typically possess only low-to-moderate affinity for their peptide/MHC (pMHC) ligands, there is a recognized need to develop affinity-enhanced TCR variants. Previous in vitro engineering efforts have yielded remarkable improvements in TCR affinity, yet concerns exist about the maintenance of peptide specificity and the biological impacts of ultra-high affinity. As opposed to in vitro engineering, computational design can directly address these issues, in theory permitting the rational control of peptide specificity together with relatively controlled increments in affinity. Here we explored the efficacy of computational design with the clinically relevant TCR DMF5, which recognizes nonameric and decameric epitopes from the melanoma-associated Melan-A/MART-1 protein presented by the class I MHC HLA-A2. We tested multiple mutations selected by flexible and rigid modeling protocols, assessed impacts on affinity and specificity, and utilized the data to examine and improve algorithmic performance. We identified multiple mutations that improved binding affinity, and characterized the structure, affinity, and binding kinetics of a previously reported double mutant that exhibits an impressive 400-fold affinity improvement for the decameric pMHC ligand without detectable binding to non-cognate ligands. The structure of this high affinity mutant indicated very little conformational consequences and emphasized the high fidelity of our modeling procedure. Overall, our work showcases the capability of computational design to generate TCRs with improved pMHC affinities while explicitly accounting for peptide specificity, as well as its potential for generating TCRs with customized antigen targeting capabilities.

Soluble T cell receptor Vβ domains engineered for high-affinity binding to staphylococcal or streptococcal superantigens

Staphylococcus aureus and group A Streptococcus secrete a collection of toxins called superantigens (SAgs), so-called because they stimulate a large fraction of an individual’s T cells. One consequence of this hyperactivity is massive cytokine release leading to severe tissue inflammation and, in some cases, systemic organ failure and death. The molecular basis of action involves the binding of the SAg to both a T cell receptor (TCR) on a T cell and a class II product of the major histocompatibility complex (MHC) on an antigen presenting cell. This cross-linking leads to aggregation of the TCR complex and signaling. A common feature of SAgs is that they bind with relatively low affinity to the variable region (V) of the beta chain of the TCR. Despite this low affinity binding, SAgs are very potent, as each T cell requires only a small fraction of their receptors to be bound in order to trigger cytokine release. To develop high-affinity agents that could neutralize the activity of SAgs, and facilitate the development of detection assays, soluble forms of the Vβ regions have been engineered to affinities that are up to 3 million-fold higher for the SAg. Over the past decade, six different Vβ regions against SAgs from S. aureus (SEA, SEB, SEC3, TSST-1) or S. pyogenes (SpeA and SpeC) have been engineered for high-affinity using yeast display and directed evolution. Here we review the engineering of these high-affinity Vβ proteins, structural features of the six different SAgs and the Vβ proteins, and the specific properties of the engineered Vβ regions that confer high-affinity and specificity for their SAg ligands.

Backbone flexibility of CDR3 and immune recognition of antigens

Conformational entropy is an important component of protein-protein interactions; however, there is no reliable method for computing this parameter. We have developed a statistical measure of residual backbone entropy in folded proteins by using the ϕ-ψ distributions of the 20 amino acids in common secondary structures. The backbone entropy patterns of amino acids within helix, sheet or coil form clusters that recapitulate the branching and hydrogen bonding properties of the side chains in the secondary structure type. The same types of residues in coil and sheet have identical backbone entropies, while helix residues have much smaller conformational entropies. We estimated the backbone entropy change for immunoglobulin complementarity-determining regions (CDRs) from the crystal structures of 34 low-affinity T-cell receptors and 40 high-affinity Fabs as a result of the formation of protein complexes. Surprisingly, we discovered that the computed backbone entropy loss of only the CDR3, but not all CDRs, correlated significantly with the kinetic and affinity constants of the 74 selected complexes. Consequently, we propose a simple algorithm to introduce proline mutations that restrict the conformational flexibility of CDRs and enhance the kinetics and affinity of immunoglobulin interactions. Combining the proline mutations with rationally designed mutants from a previous study led to 2400-fold increase in the affinity of the A6 T-cell receptor for Tax-HLAA2. However, this mutational scheme failed to induce significant binding changes in the already-high-affinity C225-Fab/huEGFR interface. Our results will serve as a roadmap to formulate more effective target functions to design immune complexes with improved biological functions.

Uptake and intracellular trafficking of superantigens in dendritic cells

Bacterial superantigens (SAgs) are exotoxins produced mainly by Staphylococcus aureus and Streptococcus pyogenes that can cause toxic shock syndrome (TSS). According to current paradigm, SAgs interact directly and simultaneously with T cell receptor (TCR) on the T cell and MHC class II (MHC-II) on the antigen-presenting cell (APC), thereby circumventing intracellular processing to trigger T cell activation. Dendritic cells (DCs) are professional APCs that coat nearly all body surfaces and are the most probable candidate to interact with SAgs. We demonstrate that SAgs are taken up by mouse DCs without triggering DC maturation. SAgs were found in intracellular acidic compartment of DCs as biologically active molecules. Moreover, SAgs co-localized with EEA1, RAB-7 and LAMP-2, at different times, and were then recycled to the cell membrane. DCs loaded with SAgs are capable of triggering in vitro lymphocyte proliferation and, injected into mice, stimulate T cells bearing the proper TCR in draining lymph nodes. Transportation and trafficking of SAgs in DCs might increase the local concentration of these exotoxins where they will produce the highest effect by promoting their encounter with both MHC-II and TCR in lymph nodes, and may explain how just a few SAg molecules can induce the severe pathology associated with TSS.

The Missing Heritability in T1D and Potential New Targets for Prevention

Type 1 diabetes (T1D) is a T cell-mediated disease. It is strongly associated with susceptibility haplotypes within the major histocompatibility complex, but this association accounts for an estimated 50% of susceptibility. Other studies have identified as many as 50 additional susceptibility loci, but the effect of most is very modest (odds ratio (OR) <1.5). What accounts for the "missing heritability" is unknown and is often attributed to environmental factors. Here we review new data on the cognate ligand of MHC molecules, the T cell receptor (TCR). In rats, we found that one allele of a TCR variable gene, V β 13A, is strongly associated with T1D (OR >5) and that deletion of V β 13+ T cells prevents diabetes. A role for the TCR is also suspected in NOD mice, but TCR regions have not been associated with human T1D. To investigate this disparity, we tested the hypothesis in silico that previous studies of human T1D genetics were underpowered to detect MHC-contingent TCR susceptibility. We show that stratifying by MHC markedly increases statistical power to detect potential TCR susceptibility alleles. We suggest that the TCR regions are viable candidates for T1D susceptibility genes, could account for "missing heritability," and could be targets for prevention.

Prediction of contact matrix for protein-protein interaction

MOTIVATION

Prediction of protein-protein interaction has become an important part of systems biology in reverse engineering the biological networks for better understanding the molecular biology of the cell. Although significant progress has been made in terms of prediction accuracy, most computational methods only predict whether two proteins interact but not their interacting residues-the information that can be very valuable for understanding the interaction mechanisms and designing modulation of the interaction. In this work, we developed a computational method to predict the interacting residue pairs-contact matrix for interacting protein domains, whose rows and columns correspond to the residues in the two interacting domains respectively and whose values (1 or 0) indicate whether the corresponding residues (do or do not) interact.

RESULTS

Our method is based on supervised learning using support vector machines. For each domain involved in a given domain-domain interaction (DDI), an interaction profile hidden Markov model (ipHMM) is first built for the domain family, and then each residue position for a member domain sequence is represented as a 20-dimension vector of Fisher scores, characterizing how similar it is as compared with the family profile at that position. Each element of the contact matrix for a sequence pair is now represented by a feature vector from concatenating the vectors of the two corresponding residues, and the task is to predict the element value (1 or 0) from the feature vector. A support vector machine is trained for a given DDI, using either a consensus contact matrix or contact matrices for individual sequence pairs, and is tested by leave-one-out cross validation. The performance averaged over a set of 115 DDIs collected from the 3 DID database shows significant improvement (sensitivity up to 85%, and specificity up to 85%), as compared with a multiple sequence alignment-based method (sensitivity 57%, and specificity 78%) previously reported in the literature.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Engineering a soluble high-affinity receptor domain that neutralizes staphylococcal enterotoxin C in rabbit models of disease

Superantigens (SAgs) are a class of immunostimulatory exotoxins that activate large numbers of T cells, leading to overproduction of cytokines and subsequent inflammatory reactions and systemic toxicity. Staphylococcal enterotoxin C (SEC), a SAg secreted by Staphylococcus aureus, has been implicated in various illnesses including non-menstrual toxic shock syndrome (TSS) and necrotizing pneumonia. SEC has been shown to cause TSS illness in rabbits and the toxin contributes to lethality associated with methicillin-resistant S.aureus (MRSA) in a rabbit model of pneumonia. With the goal of reducing morbidity and mortality associated with SEC, a high-affinity variant of the extracellular variable domain of the T-cell receptor beta-chain for SEC (~14 kDa) was generated by directed evolution using yeast display. This protein was characterized biochemically and shown to cross-react with the homologous (65% identical) SAg staphylococcal enterotoxin B (SEB). The soluble, high-affinity T-cell receptor protein neutralized SEC and SEB in vitro and also significantly reduced the bacterial burden of an SEC-positive strain of MRSA (USA400 MW2) in an infective endocarditis model. The neutralizing agent also prevented lethality due to MW2 in a necrotizing pneumonia rabbit model. These studies characterize a soluble high-affinity neutralizing agent against SEC, which is cross-reactive with SEB, and that has potential to be used intravenously with antibiotics to manage staphylococcal diseases that involve these SAgs.

Staphylococcal superantigens in colonization and disease

Superantigens (SAgs) are a family of potent immunostimulatory exotoxins known to be produced by only a few bacterial pathogens, including Staphylococcus aureus. More than 20 distinct SAgs have been characterized from different S. aureus strains and at least 80% of clinical strains harbor at least one SAg gene, although most strains encode many. SAgs have been classically associated with food poisoning and toxic shock syndrome (TSS), for which these toxins are the causative agent. TSS is a potentially fatal disease whereby SAg-mediated activation of T cells results in overproduction of cytokines and results in systemic inflammation and shock. Numerous studies have also shown a possible role for SAgs in other diseases such as Kawasaki disease (KD), atopic dermatitis (AD), and chronic rhinosinusitis (CRS). There is also now a rich understanding of the mechanisms of action of SAgs, as well as their structures and function. However, we have yet to discover what purpose SAgs play in the life cycle of S. aureus, and why such a wide array of these toxins exists. This review will focus on recent developments within the SAg field in terms of the molecular biology of these toxins and their role in both colonization and disease.

HotRegion: a database of predicted hot spot clusters

Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. HotRegion is accessible at http://prism.ccbb.ku.edu.tr/hotregion.

DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein-protein docking

Background

Computational approaches to protein-protein docking typically include scoring aimed at improving the rank of the near-native structure relative to the false-positive matches. Knowledge-based potentials improve modeling of protein complexes by taking advantage of the rapidly increasing amount of experimentally derived information on protein-protein association. An essential element of knowledge-based potentials is defining the reference state for an optimal description of the residue-residue (or atom-atom) pairs in the non-interaction state.

Results

The study presents a new Distance- and Environment-dependent, Coarse-grained, Knowledge-based (DECK) potential for scoring of protein-protein docking predictions. Training sets of protein-protein matches were generated based on bound and unbound forms of proteins taken from the DOCKGROUND resource. Each residue was represented by a pseudo-atom in the geometric center of the side chain. To capture the long-range and the multi-body interactions, residues in different secondary structure elements at protein-protein interfaces were considered as different residue types. Five reference states for the potentials were defined and tested. The optimal reference state was selected and the cutoff effect on the distance-dependent potentials investigated. The potentials were validated on the docking decoys sets, showing better performance than the existing potentials used in scoring of protein-protein docking results.

Conclusions

A novel residue-based statistical potential for protein-protein docking was developed and validated on docking decoy sets. The results show that the scoring function DECK can successfully identify near-native protein-protein matches and thus is useful in protein docking. In addition to the practical application of the potentials, the study provides insights into the relative utility of the reference states, the scope of the distance dependence, and the coarse-graining of the potentials.

Specificity and cooperativity at β-lactamase position 104 in TEM-1/BLIP and SHV-1/BLIP interactions

Establishing a quantitative understanding of the determinants of affinity in protein-protein interactions remains challenging. For example, TEM-1/β-lactamase inhibitor protein (BLIP) and SHV-1/BLIP are homologous β-lactamase/β-lactamase inhibitor protein complexes with disparate K(d) values (3 nM and 2 μM, respectively), and a single substitution, D104E in SHV-1, results in a 1000-fold enhancement in binding affinity. In TEM-1, E104 participates in a salt bridge with BLIP K74, whereas the corresponding SHV-1 D104 does not in the wild type SHV-1/BLIP co-structure. Here, we present a 1.6 Å crystal structure of the SHV-1 D104E/BLIP complex that demonstrates that this point mutation restores this salt bridge. Additionally, mutation of a neighboring residue, BLIP E73M, results in salt bridge formation between SHV-1 D104 and BLIP K74 and a 400-fold increase in binding affinity. To understand how this salt bridge contributes to complex affinity, the cooperativity between the E/K or D/K salt bridge pair and a neighboring hot spot residue (BLIP F142) was investigated using double mutant cycle analyses in the background of the E73M mutation. We find that BLIP F142 cooperatively stabilizes both interactions, illustrating how a single mutation at a hot spot position can drive large perturbations in interface stability and specificity through a cooperative interaction network.

Dissecting protein-protein interactions using directed evolution

Protein-protein interactions are essential for life. They are responsible for most cellular functions and when they go awry often lead to disease. Proteins are inherently complex. They are flexible macromolecules whose constituent amino acid components act in combinatorial and networked ways when they engage one another in binding interactions. It is just this complexity that allows them to conduct such a broad array of biological functions. Despite decades of intense study of the molecular basis of protein-protein interactions, key gaps in our understanding remain, hindering our ability to accurately predict the specificities and affinities of their interactions. Until recently, most protein-protein investigations have been probed experimentally at the single-amino acid level, making them, by definition, incapable of capturing the combinatorial nature of, and networked communications between, the numerous residues within and outside of the protein-protein interface. This aspect of protein-protein interactions, however, is emerging as a major driving force for protein affinity and specificity. Understanding a combinatorial process necessarily requires a combinatorial experimental tool. Much like the organisms in which they reside, proteins naturally evolve over time, through a combinatorial process of mutagenesis and selection, to functionally associate. Elucidating the process by which proteins have evolved may be one of the keys to deciphering the molecular rules that govern their interactions with one another. Directed evolution is a technique performed in the laboratory that mimics natural evolution on a tractable time scale that has been utilized widely to engineer proteins with novel capabilities, including altered binding properties. In this review, we discuss directed evolution as an emerging tool for dissecting protein-protein interactions.

The T cell receptor beta-chain second complementarity determining region loop (CDR2beta governs T cell activation and Vbeta specificity by bacterial superantigens

Superantigens (SAgs) are microbial toxins defined by their ability to activate T lymphocytes in a T cell receptor (TCR) β-chain variable domain (Vβ)-specific manner. Although existing structural information indicates that diverse bacterial SAgs all uniformly engage the Vβ second complementarity determining region (CDR2β) loop, the molecular rules that dictate SAg-mediated T cell activation and Vβ specificity are not fully understood. Herein we report the crystal structure of human Vβ2.1 (hVβ2.1) in complex with the toxic shock syndrome toxin-1 (TSST-1) SAg, and mutagenesis of hVβ2.1 indicates that the non-canonical length of CDR2β is a critical determinant for recognition by TSST-1 as well as the distantly related SAg streptococcal pyrogenic exotoxin C. Frame work (FR) region 3 is uniquely critical for TSST-1 function explaining the fine Vβ-specificity exhibited by this SAg. Furthermore, domain swapping experiments with SAgs, which use distinct domains to engage both CDR2β and FR3/4β revealed that the CDR2β contacts dictate T lymphocyte Vβ-specificity. These findings demonstrate that the TCR CDR2β loop is the critical determinant for functional recognition and Vβ-specificity by diverse bacterial SAgs.

Assessing energetic contributions to binding from a disordered region in a protein-protein interaction

Many functional proteins are at least partially disordered prior to binding. Although the structural transitions upon binding of disordered protein regions can influence the affinity and specificity of protein complexes, their precise energetic contributions to binding are unknown. Here, we use a model protein-protein interaction system in which a locally disordered region has been modified by directed evolution to quantitatively assess the thermodynamic and structural contributions to binding of disorder-to-order transitions. Through X-ray structure determination of the protein binding partners before and after complex formation and isothermal titration calorimetry of the interactions, we observe a correlation between protein ordering and binding affinity for complexes along this affinity maturation pathway. Additionally, we show that discrepancies between observed and calculated heat capacities based on buried surface area changes in the protein complexes can be explained largely by heat capacity changes that would result solely from folding the locally disordered region. Previously developed algorithms for predicting binding energies of protein-protein interactions, however, are unable to correctly model the energetic contributions of the structural transitions in our model system. While this highlights the shortcomings of current computational methods in modeling conformational flexibility, it suggests that the experimental methods used here could provide training sets of molecular interactions for improving these algorithms and further rationalizing molecular recognition in protein-protein interactions.

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

Background

Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles.

Results

In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure.

Conclusions

We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows.

Combinations of affinity-enhancing mutations in a T cell receptor reveal highly nonadditive effects within and between complementarity determining regions and chains

Understanding the energetic and structural response to multiple mutations in a protein-protein interface is a key aspect of rational protein design. Here we investigate the cooperativity of combinations of point mutations of a T cell receptor (TCR) that binds in vivo to HLA-A2 MHC and a viral peptide. The mutations were obtained from two sources: a structure-based design study on the TCR alpha chain (nine mutations) and an in vitro selection study on the TCR beta chain (four mutations). In addition to combining the highest-affinity variants from each chain, we tested other combinations of mutations within and among the chains, for a total of 23 TCR mutants that we measured for binding kinetics to the peptide and major histocompatibility complex. A wide range of binding affinities was observed, from 2- to 1000-fold binding improvement versus that of the wild type, with significant nonadditive effects observed within and between TCR chains. This included an amino acid-dependent cooperative interaction between CDR1 and CDR3 residues that are separated by more than 9 A in the wild-type complex. When analyzing the kinetics of the mutations, we found that the association rates were primarily responsible for the cooperativity, while the dissociation rates were responsible for the anticooperativity (less-than-additive energetics). On the basis of structural modeling of anticooperative mutants, we determined that side chain clash between proximal mutants likely led to nonadditive binding energies. These results highlight the complex nature of TCR association and binding and will be informative in future design efforts that combine multiple mutant residues.

Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing

BACKGROUND

Weak intermolecular interactions such as hydrogen bonding and hydrophobic interactions are key players in stabilizing energetically-favored ligands, in an open conformational environment of protein structures. However, it is still poorly understood how the binding parameters associated with these interactions facilitate a drug-lead to recognize a specific target and improve drugs efficacy. To understand this, comprehensive analysis of hydrophobic interactions, hydrogen bonding and binding affinity have been analyzed at the interface of c-Src and c-Abl kinases and 4-amino substituted 1H-pyrazolo [3, 4-d] pyrimidine compounds.

METHODOLOGY

In-silico docking studies were performed, using Discovery Studio software modules LigandFit, CDOCKER and ZDOCK, to investigate the role of ligand binding affinity at the hydrophobic pocket of c-Src and c-Abl kinase. Hydrophobic and hydrogen bonding interactions of docked molecules were compared using LigPlot program. Furthermore, 3D-QSAR and MFA calculations were scrutinized to quantify the role of weak interactions in binding affinity and drug efficacy.

CONCLUSIONS

The in-silico method has enabled us to reveal that a multi-targeted small molecule binds with low affinity to its respective targets. But its binding affinity can be altered by integrating the conformationally favored functional groups at the active site of the ligand-target interface. Docking studies of 4-amino-substituted molecules at the bioactive cascade of the c-Src and c-Abl have concluded that 3D structural folding at the protein-ligand groove is also a hallmark for molecular recognition of multi-targeted compounds and for predicting their biological activity. The results presented here demonstrate that hydrogen bonding and optimized hydrophobic interactions both stabilize the ligands at the target site, and help alter binding affinity and drug efficacy.

Energetic determinants of protein binding specificity: insights into protein interaction networks

One of the challenges of the postgenomic era is to provide a more realistic representation of cellular processes by combining a systems biology description of functional networks with information on their interacting components. Here we carried out a systematic large-scale computational study on a structural protein-protein interaction network dataset in order to dissect thermodynamic characteristics of binding determining the interplay between protein affinity and specificity. As expected, interactions involving specific binding sites display higher affinities than those of promiscuous binding sites. Next, in order to investigate a possible role of modular distribution of hot spots in binding specificity, we divided binding sites into modules previously shown to be energetically independent. In general, hot spots that interact with different partners are located in different modules. We further observed that common hot spots tend to interact with partners exhibiting common binding motifs, whereas different hot spots tend to interact with partners with different motifs. Thus, energetic properties of binding sites provide insights into the way proteins modulate interactions with different partners. Knowledge of those factors playing a role in protein specificity is important for understanding how proteins acquire additional partners during evolution. It should also be useful in drug design.

Structure-based design of a T-cell receptor leads to nearly 100-fold improvement in binding affinity for pepMHC

T-cell receptors (TCRs) are proteins that recognize peptides from foreign proteins bound to the major histocompatibility complex (MHC) on the surface of an antigen-presenting cell. This interaction enables the T cells to initiate a cell-mediated immune response to terminate cells displaying the foreign peptide on their MHC. Naturally occurring TCRs have high specificity but low affinity toward the peptide-MHC (pepMHC) complex. This prevents the usage of solubilized TCRs for diagnosis and treatment of viral infections or cancers. Efforts to enhance the binding affinity of several TCRs have been reported in recent years, through randomized libraries and in vitro selection. However, there have been no reported efforts to enhance the affinity via structure-based design, which allows more control and understanding of the mechanism of improvement. Here, we have applied structure-based design to a human TCR to improve its pepMHC binding. Our design method evolved based on iterative steps of prediction, testing, and generating more predictions based on the new data. The final design function, named ZAFFI, has a correlation of 0.77 and average error of 0.35 kcal/mol with the binding free energies of 26 point mutations for this system that we measured by surface plasmon resonance (SPR). Applying the filter that we developed to remove nonbinding predictions, this correlation increases to 0.85, and the average error decreases to 0.3 kcal/mol. Using this algorithm, we predicted and tested several point mutations that improved binding, with one giving over sixfold binding improvement. Four of the point mutations that improved binding were then combined to give a mutant TCR that binds the pepMHC 99 times more strongly than the wild-type TCR.

Molecular recognition of diverse ligands by T-cell receptors

T-cell receptors (TCRs) are structurally related to antibodies, and also interact with a diverse set of ligands. TCRs recognize foreign peptide antigens displayed by major histocompatibility complex (MHC) molecules and foreign lipid-based antigens presented by CD1. These interactions initiate an immune response through T-cell activation. These critical surveillance and response initiation functions of the adaptive immune system are not perfect, though, as TCR interactions with self antigens can lead to autoimmune disease. Mutated peptides can also be recognized specifically by TCRs, and may be important in tumor immunity. TCRs are also bound specifically by a family of bacterial toxins called superantigens, which over-stimulate the immune system to cause numerous human diseases.

An integrative approach for predicting interactions of protein regions

MOTIVATION

Protein-protein interactions are commonly mediated by the physical contact of distinct protein regions. Computational identification of interacting protein regions aids in the detailed understanding of protein networks and supports the prediction of novel protein interactions and the reconstruction of protein complexes.

RESULTS

We introduce an integrative approach for predicting protein region interactions using a probabilistic model fitted to an observed protein network. In particular, we consider globular domains, short linear motifs and coiled-coil regions as potential protein-binding regions. Possible cooperations between multiple regions within the same protein are taken into account. A.negrained confidence system allows for varying the impact of specific protein interactions and region annotations on the modeling process. We apply our prediction approach to a large training set using a maximum likelihood method, compare different scoring functions for region interactions and validate the predicted interactions against a collection of experimentally observed interactions. In addition, we analyze prediction performance with respect to the inclusion of different region types, the incorporation of confidence values for training data and the utilization of predicted protein interactions.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The modular organization of domain structures: insights into protein-protein binding

Domains are the building blocks of proteins and play a crucial role in protein–protein interactions. Here, we propose a new approach for the analysis and prediction of domain–domain interfaces. Our method, which relies on the representation of domains as residue-interacting networks, finds an optimal decomposition of domain structures into modules. The resulting modules comprise highly cooperative residues, which exhibit few connections with other modules. We found that non-overlapping binding sites in a domain, involved in different domain–domain interactions, are generally contained in different modules. This observation indicates that our modular decomposition is able to separate protein domains into regions with specialized functions. Our results show that modules with high modularity values identify binding site regions, demonstrating the predictive character of modularity. Furthermore, the combination of modularity with other characteristics, such as sequence conservation or surface patches, was found to improve our predictions. In an attempt to give a physical interpretation to the modular architecture of domains, we analyzed in detail six examples of protein domains with available experimental binding data. The modular configuration of the TEM1-β-lactamase binding site illustrates the energetic independence of hotspots located in different modules and the cooperativity of those sited within the same modules. The energetic and structural cooperativity between intramodular residues is also clearly shown in the example of the chymotrypsin inhibitor, where non–binding site residues have a synergistic effect on binding. Interestingly, the binding site of the T cell receptor β chain variable domain 2.1 is contained in one module, which includes structurally distant hot regions displaying positive cooperativity. These findings support the idea that modules possess certain functional and energetic independence. A modular organization of binding sites confers robustness and flexibility to the performance of the functional activity, and facilitates the evolution of protein interactions.

Proteins are built by domains, which mediate protein–protein interactions involved in different biological activities. A challenging problem in computational biology is the understanding of the domain–domain interaction mechanism. Here, we propose a new approach for the analysis and prediction of domain–domain binding sites. Our computational approach, which relies on the modular division of 3-D domain structures, identifies modular regions involved in binding and can complement previously introduced predictive methods. Further results illustrate that binding sites display a modular configuration. A detailed analysis of protein domains with available experimental binding data revealed that modules are energetically independent from each other, whereas residues within modules contribute cooperatively to the binding energy. The modular composition of binding surfaces may generate high binding affinity and specificity, and facilitate the appearance of new domain binding partners. This advantageous organization of protein structures has been conserved by evolution and may be used to design an effective drug strategy.

Molecular architecture of the major histocompatibility complex class I-binding site of Ly49 natural killer cell receptors

Natural killer (NK) cells play a vital role in the detection and destruction of virally infected and tumor cells during innate immune responses. The highly polymorphic Ly49 family of NK receptors regulates NK cell function by sensing major histocompatibility complex class I (MHC-I) molecules on target cells. Despite the determination of two Ly49-MHC-I complex structures, the molecular features of Ly49 receptors that confer specificity for particular MHC-I alleles have not been identified. To understand the functional architecture of Ly49-binding sites, we determined the crystal structures of Ly49C and Ly49G and completed refinement of the Ly49C-H-2K(b) complex. This information, combined with mutational analysis of Ly49A, permitted a structure-based classification of Ly49s that we used to dissect the binding site into three distinct regions, each having different roles in MHC recognition. One region, located at the center of the binding site, has a similar structure across the Ly49 family and mediates conserved interactions with MHC-I that contribute most to binding. However, the preference of individual Ly49s for particular MHC-I molecules is governed by two regions that flank the central region and are structurally more variable. One of the flanking regions divides Ly49s into those that recognize both H-2D and H-2K versus only H-2D ligands, whereas the other discriminates among H-2D or H-2K alleles. The modular design of Ly49-binding sites provides a framework for predicting the MHC-binding specificity of Ly49s that have not been characterized experimentally.

A viral CTL escape mutation leading to immunoglobulin-like transcript 4-mediated functional inhibition of myelomonocytic cells

Viral mutational escape can reduce or abrogate recognition by the T cell receptor (TCR) of virus-specific CD8⁺ T cells. However, very little is known about the impact of cytotoxic T lymphocyte (CTL) epitope mutations on interactions between peptide–major histocompatibility complex (MHC) class I complexes and MHC class I receptors expressed on other cell types. Here, we analyzed a variant of the immunodominant human leukocyte antigen (HLA)-B2705–restricted HIV-1 Gag KK10 epitope (KRWIILGLNK) with an L to M amino acid substitution at position 6 (L6M), which arises as a CTL escape variant after primary infection but is sufficiently immunogenic to elicit a secondary, de novo HIV-1–specific CD8⁺ T cell response with an alternative TCR repertoire in chronic infection. In addition to altering recognition by HIV-1–specific CD8⁺ T cells, the HLA-B2705–KK10 L6M complex also exhibits substantially increased binding to the immunoglobulin-like transcript (ILT) receptor 4, an inhibitory MHC class I–specific receptor expressed on myelomonocytic cells. Binding of the B2705–KK10 L6M complex to ILT4 leads to a tolerogenic phenotype of myelomonocytic cells with lower surface expression of dendritic cell (DC) maturation markers and co-stimulatory molecules. These data suggest a link between CTL-driven mutational escape, altered recognition by innate MHC class I receptors on myelomonocytic cells, and functional impairment of DCs, and thus provide important new insight into biological consequences of viral sequence diversification.

Analysis on multi-domain cooperation for predicting protein-protein interactions

Background

Domains are the basic functional units of proteins. It is believed that protein-protein interactions are realized through domain interactions. Revealing multi-domain cooperation can provide deep insights into the essential mechanism of protein-protein interactions at the domain level and be further exploited to improve the accuracy of protein interaction prediction.

Results

In this paper, we aim to identify cooperative domains for protein interactions by extending two-domain interactions to multi-domain interactions. Based on the high-throughput experimental data from multiple organisms with different reliabilities, the interactions of domains were inferred by a Linear Programming algorithm with Multi-domain pairs (LPM) and an Association Probabilistic Method with Multi-domain pairs (APMM). Experimental results demonstrate that our approach not only can find cooperative domains effectively but also has a higher accuracy for predicting protein interaction than the existing methods. Cooperative domains, including strongly cooperative domains and superdomains, were detected from major interaction databases MIPS and DIP, and many of them were verified by physical interactions from the crystal structures of protein complexes in PDB which provide intuitive evidences for such cooperation. Comparison experiments in terms of protein/domain interaction prediction justified the benefit of considering multi-domain cooperation.

Conclusion

From the computational viewpoint, this paper gives a general framework to predict protein interactions in a more accurate manner by considering the information of both multi-domains and multiple organisms, which can also be applied to identify cooperative domains, to reconstruct large complexes and further to annotate functions of domains. Supplementary information and software are provided in and .

Spatial chemical conservation of hot spot interactions in protein-protein complexes

Background

Conservation of the spatial binding organizations at the level of physico-chemical interactions is important for the formation and stability of protein-protein complexes as well as protein and drug design. Due to the lack of computational tools for recognition of spatial patterns of interactions shared by a set of protein-protein complexes, the conservation of such interactions has not been addressed previously.

Results

We performed extensive spatial comparisons of physico-chemical interactions common to different types of protein-protein complexes. We observed that 80% of these interactions correspond to known hot spots. Moreover, we show that spatially conserved interactions allow prediction of hot spots with a success rate higher than obtained by methods based on sequence or backbone similarity. Detection of spatially conserved interaction patterns was performed by our novel MAPPIS algorithm. MAPPIS performs multiple alignments of the physico-chemical interactions and the binding properties in three dimensional space. It is independent of the overall similarity in the protein sequences, folds or amino acid identities. We present examples of interactions shared between complexes of colicins with immunity proteins, serine proteases with inhibitors and T-cell receptors with superantigens. We unravel previously overlooked similarities, such as the interactions shared by the structurally different RNase-inhibitor families.

Conclusion

The key contribution of MAPPIS is in discovering the 3D patterns of physico-chemical interactions. The detected patterns describe the conserved binding organizations that involve energetically important hot spot residues and are crucial for the protein-protein associations.

Thermodynamically reengineering the listerial invasion complex InlA/E-cadherin

Biological processes essentially all depend on the specific recognition between macromolecules and their interaction partners. Although many such interactions have been characterized both structurally and biophysically, the thermodynamic effects of small atomic changes remain poorly understood. Based on the crystal structure of the bacterial invasion protein internalin (InlA) of Listeria monocytogenes in complex with its human receptor E-cadherin (hEC1), we analyzed the interface to identify single amino acid substitutions in InlA that would potentially improve the overall quality of interaction and hence increase the weak binding affinity of the complex. Dissociation constants of InlA-variant/hEC1 complexes, as well as enthalpy and entropy of binding, were quantified by isothermal titration calorimetry. All single substitutions indeed significantly increase binding affinity. Structural changes were verified crystallographically at < or =2.0-A resolution, allowing thermodynamic characteristics of single substitutions to be rationalized structurally and providing unique insights into atomic contributions to binding enthalpy and entropy. Structural and thermodynamic data of all combinations of individual substitutions result in a thermodynamic network, allowing the source of cooperativity between distant recognition sites to be identified. One such pair of single substitutions improves affinity 5,000-fold. We thus demonstrate that rational reengineering of protein complexes is possible by making use of physically distant hot spots of recognition.

TCR recognition of peptide/MHC class II complexes and superantigens

Major histocompatibility complex (MHC) class II molecules display peptides to the T cell receptor (TCR). The ability of the TCR to discriminate foreign from self-peptides presented by MHC molecules is a requirement of an effective adaptive immune response. Dysregulation of this molecular recognition event often leads to a disease state. Recently, a number of structural studies have provided significant insight into several such dysregulated interactions between peptide/MHC complexes and TCR molecules. These include TCR recognition of self-peptides, which results in autoimmune reactions, and of mutant self-peptides, common in the immunosurveillance of tumors, as well as the engagement of TCRs by superantigens, a family of bacterial toxins responsible for toxic shock syndrome.

A novel loop domain in superantigens extends their T cell receptor recognition site

Superantigens (SAGs) interact with host immune receptors to induce a massive release of inflammatory cytokines that can lead to toxic shock syndrome and death. Bacterial SAGs can be classified into five distinct evolutionary groups. Group V SAGs are characterized by the alpha3-beta8 loop, a unique approximately 15 amino acid residue extension that is required for optimal T cell activation. Here, we report the X-ray crystal structures of the group V SAG staphylococcal enterotoxin K (SEK) alone and in complex with the TCR hVbeta5.1 domain. SEK adopts a unique TCR binding orientation relative to other SAG-TCR complexes, which results in the alpha3-beta8 loop contacting the apical loop of framework region 4, thereby extending the known TCR recognition site of SAGs. These interactions are absolutely required for TCR binding and T cell activation by SEK, and dictate the TCR Vbeta domain specificity of SEK and other group V SAGs.

Display, engineering, and applications of antigen-specific T cell receptors

The use of T cell receptors (TCRs) as potential therapeutic agents provides an opportunity to target a greatly expanded array of antigens, compared to those now targeted with monoclonal antibodies. With the advent of new display technologies and TCR formats for in vitro engineering, it should be possible to generate high-affinity TCRs against virtually any peptide antigen that is shown to bind to a major histocompatibility complex (MHC) molecule (e.g. peptides derived from viral antigens or from self proteins that are associated with the transformed phenotype). What remains, however, are challenges associated with effective targeting of very low numbers of cell surface antigens (pepMHC), fewer than the case for conventional monoclonal antibody-based therapies. This hurdle might be overcome with the attachment of more effective payloads for soluble TCR approaches, or by using TCR gene transfer into T cells that can then be adoptively transferred into patients. There is considerable work to be done on the physiological aspects of either approach, including pharmacokinetic studies in the case of soluble TCRs, and T cell trafficking, persistence, and autoreactivity studies in the case of adoptively transferred T cells. As with the field of monoclonal antibodies, it will take time to explore these issues, but the potential benefits of TCR-based therapies make these challenges worth the effort.

Structural basis of T-cell specificity and activation by the bacterial superantigen TSST-1

Superantigens (SAGs) bind simultaneously to major histocompatibility complex (MHC) and T-cell receptor (TCR) molecules, resulting in the massive release of inflammatory cytokines that can lead to toxic shock syndrome (TSS) and death. A major causative agent of TSS is toxic shock syndrome toxin-1 (TSST-1), which is unique relative to other bacterial SAGs owing to its structural divergence and its stringent TCR specificity. Here, we report the crystal structure of TSST-1 in complex with an affinity-matured variant of its wild-type TCR ligand, human T-cell receptor beta chain variable domain 2.1. From this structure and a model of the wild-type complex, we show that TSST-1 engages TCR ligands in a markedly different way than do other SAGs. We provide a structural basis for the high TCR specificity of TSST-1 and present a model of the TSST-1-dependent MHC-SAG-TCR T-cell signaling complex that is structurally and energetically unique relative to those formed by other SAGs. Our data also suggest that protein plasticity plays an exceptionally significant role in this affinity maturation process that results in more than a 3000-fold increase in affinity.

The molecular architecture of protein-protein binding sites

The formation of specific protein interactions plays a crucial role in most, if not all, biological processes, including signal transduction, cell regulation, the immune response and others. Recent advances in our understanding of the molecular architecture of protein-protein binding sites, which facilitates such diversity in binding affinity and specificity, are enabling us to address key questions. What is the amino acid composition of binding sites? What are interface hotspots? How are binding sites organized? What are the differences between tight and weak interacting complexes? How does water contribute to binding? Can the knowledge gained be translated into protein design? And does a universal code for binding exist, or is it the architecture and chemistry of the interface that enable diverse but specific binding solutions?

Crystal structure of the streptococcal superantigen SpeI and functional role of a novel loop domain in T cell activation by group V superantigens

Superantigens (SAgs) are potent microbial toxins that bind simultaneously to T cell receptors (TCRs) and class II major histocompatibility complex molecules, resulting in the activation and expansion of large T cell subsets and the onset of numerous human diseases. Within the bacterial SAg family, streptococcal pyrogenic exotoxin I (SpeI) has been classified as belonging to the group V SAg subclass, which are characterized by a unique, relatively conserved approximately 15 amino acid extension (amino acid residues 154 to 170 in SpeI; herein referred to as the alpha3-beta8 loop), absent in SAg groups I through IV. Here, we report the crystal structure of SpeI at 1.56 A resolution. Although the alpha3-beta8 loop in SpeI is several residues shorter than that of another group V SAg, staphylococcal enterotoxin serotype I, the C-terminal portions of these loops, which are located adjacent to the putative TCR binding site, are structurally similar. Mutagenesis and subsequent functional analysis of SpeI indicates that TCR beta-chains are likely engaged in a similar general orientation as other characterized SAgs. We show, however, that the alpha3-beta8 loop length, and the presence of key glycine residues, are necessary for optimal activation of T cells. Based on Vbeta-skewing analysis of human T cells activated with SpeI and structural models, we propose that the alpha3-beta8 loop is positioned to form productive intermolecular contacts with the TCR beta-chain, likely in framework region 3, and that these contacts are required for optimal TCR recognition by SpeI, and likely all other group V SAgs.

Recent advances in surface plasmon resonance based techniques for bioanalysis

Surface plasmon resonance (SPR) is a powerful and versatile spectroscopic method for biomolecular interaction analysis (BIA) and has been well reviewed in previous years. This updated 2006 review of SPR, SPR spectroscopy, and SPR imaging explores cutting-edge technology with a focus on material, method, and instrument development. A number of recent SPR developments and interesting applications for bioanalysis are provided. Three focus topics are discussed in more detail to exemplify recent progress. They include surface plasmon fluorescence spectroscopy, nanoscale glassification of SPR substrates, and enzymatic amplification in SPR imaging. Through these examples it is clear to us that the development of SPR-based methods continues to grow, while the applications continue to diversify. Major trends appear to be present in the development of combined techniques, use of new materials, and development of new methodologies. Together, these works constitute a major thrust that could eventually make SPR a common tool for surface interaction analysis and biosensing. The future outlook for SPR and SPR-associated BIA studies, in our opinion, is very bright. Surface plasmon resonance (SPR) is a powerful and versatile spectroscopic method for biomolecular interaction analysis (BIA) and has been well reviewed in previous years. This updated 2006 review of SPR, SPR spectroscopy, and SPR imaging explores cutting-edge technology with a focus on material, method, and instrument development. A number of recent SPR developments and interesting applications for bioanalysis are provided. Three focus topics are discussed in more detail to exemplify recent progress. They include surface plasmon fluorescence spectroscopy, nanoscale glassification of SPR substrates, and enzymatic amplification in SPR imaging. Through these examples it is clear to us that the development of SPR-based methods continues to grow, while the applications continue to diversify. Major trends appear to be present in the development of combined techniques, use of new materials, and development of new methodologies. Together, these works constitute a major thrust that could eventually make SPR a common tool for surface interaction analysis and biosensing. The future outlook for SPR and SPR-associated BIA studies, in our opinion, is very bright.

Binding Hot Spots in the TEM1–BLIP Interface in Light of its Modular Architecture

Proteins bind one another in aqua's solution to form tight and specific complexes. Previously we have shown that this is achieved through the modular architecture of the interaction network formed by the interface residues, where tight cooperative interactions are found within modules but not between them. Here we extend this study to cover the entire interface of TEM1 beta-lactamase and its protein inhibitor BLIP using an improved method for deriving interaction maps based on REDUCE to add hydrogen atoms and then by evaluating the interactions using modifications of the programs PROBE, NCI and PARE. An extensive mutagenesis study of the interface residues indeed showed that each module is energetically independent on other modules, and that cooperativity is found only within a module. By solving the X-ray structure of two interface mutations affecting two different modules, we demonstrated that protein-protein binding occur via the structural reorganization of the binding modules, either by a "lock and key" or an induced fit mechanism. To explain the cooperativity within a module, we performed multiple-mutant cycle analysis of cluster 2 resulting in a high-resolution energy map of this module. Mutant studies are usually done in reference to alanine, which can be regarded as a deletion of a side-chain. However, from a biological perspective, there is a major interest to understand non-Ala substitutions, as they are most common. Using X-ray crystallography and multiple-mutant cycle analysis we demonstrated the added complexity in understanding non-Ala mutations. Here, a double mutation replacing the wild-type Glu,Tyr to Tyr,Asn on TEM1 (res id 104,105) caused a major backbone structural rearrangement of BLIP, changing the composition of two modules but not of other modules within the interface. This shows the robustness of the modular approach, yet demonstrates the complexity of in silico protein design.