Optimal docking area: a new method for predicting protein-protein interaction sites

Proteostatic Remodeling of Small Heat Shock Chaperones─Crystallins by Ran-Binding Protein 2─and the Peptidyl-Prolyl cis-trans Isomerase and Chaperone Activities of Its Cyclophilin Domain

Disturbances in protein phase transitions promote protein aggregation─a neurodegeneration hallmark. The modular Ran-binding protein 2 (Ranbp2) is a cytosolic molecular hub for rate-limiting steps of phase transitions of Ran-GTP-bound protein ensembles exiting nuclear pores. Chaperones also regulate phase transitions and proteostasis by suppressing protein aggregation. Ranbp2 haploinsufficiency promotes the age-dependent neuroprotection of the chorioretina against phototoxicity by proteostatic regulations of neuroprotective substrates of Ranbp2 and by suppressing the buildup of polyubiquitylated substrates. Losses of peptidyl-prolyl cis-trans isomerase (PPIase) and chaperone activities of the cyclophilin domain (CY) of Ranbp2 recapitulate molecular effects of Ranbp2 haploinsufficiency. These CY impairments also stimulate deubiquitylation activities and phase transitions of 19S cap subunits of the 26S proteasome that associates with Ranbp2. However, links between CY moonlighting activity, substrate ubiquitylation, and proteostasis remain incomplete. Here, we reveal the Ranbp2 regulation of small heat shock chaperones─crystallins in the chorioretina by proteomics of mice with total or selective modular deficits of Ranbp2. Specifically, loss of CY PPIase of Ranbp2 upregulates αA-Crystallin, which is repressed in adult nonlenticular tissues. Conversely, impairment of CY's chaperone activity opposite to the PPIase pocket downregulates a subset of αA-Crystallin's substrates, γ-crystallins. These CY-dependent effects cause age-dependent and chorioretinal-selective declines of ubiquitylated substrates without affecting the chorioretinal morphology. A model emerges whereby inhibition of Ranbp2's CY PPIase remodels crystallins' expressions, subdues molecular aging, and preordains the chorioretina to neuroprotection by augmenting the chaperone capacity and the degradation of polyubiquitylated substrates against proteostatic impairments. Further, the druggable Ranbp2 CY holds pan-therapeutic potential against proteotoxicity and neurodegeneration.

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

Electric field induced the changes in structure and function of human transforming growth factor beta receptor type I: from molecular dynamics to docking

The transforming growth factor beta (TGF-β) signaling pathway is believed to play essential roles in several physiological activities, including cancer. TGF-β receptor type I (TBR-I) is a key membrane receptor protein in the TGF-β signaling pathway, which relates to many intracellular biological effects. In recent years, cold atmospheric plasma (CAP) has been found to have promising prospects in selective anticancer therapy and has confirmed its essential role in the TGF-β signaling pathway. However, the ambiguous effect of CAP-induced electric field (EF) on TBR-I still limits the application of CAP in clinical therapy. Molecular dynamics is applied to assess the effect of EF on the structure of the extracellular domain of TBR-I using a series of indicators and methods, and then we discuss the ligand binding ability of TBR-I. Results show that moderate EF intensities' structural restraints may contribute to the structural stability and ligand-binding ability of TBR-I, but an EF higher than 0.1 V/nm will be harmful. What's more, EF induces a change in the docking interface of TBR-I, showing the conformation and position of special sequences of residues decide the ligand binding surface. The relevant results suggest that CAP-induced EF plays a crucial role in receptor-receptor interaction and provides significant guidelines for EF-related anticancer therapy.Communicated by Ramaswamy H. Sarma.

Proteostatic remodeling of small heat shock chaperones - crystallins by Ran-binding protein 2 and the peptidyl-prolyl cis-trans isomerase and chaperone activities of its cyclophilin domain

Disturbances in phase transitions and intracellular partitions of nucleocytoplasmic shuttling substrates promote protein aggregation - a hallmark of neurodegenerative diseases. The modular Ran-binding protein 2 (Ranbp2) is a cytosolic molecular hub for rate-limiting steps of disassembly and phase transitions of Ran-GTP-bound protein ensembles exiting nuclear pores. Chaperones also play central roles in phase transitions and proteostasis by suppressing protein aggregation. Ranbp2 haploinsufficiency promotes the age-dependent neuroprotection of the chorioretina against photo-oxidative stress by proteostatic regulations of Ranbp2 substrates and by countering the build-up of poly-ubiquitylated substrates. Further, the peptidyl-prolyl cis-trans isomerase (PPIase) and chaperone activities of the cyclophilin domain (CY) of Ranbp2 modulate the proteostasis of selective neuroprotective substrates, such as hnRNPA2B1, STAT3, HDAC4 or L/M-opsin, while promoting a decline of ubiquitylated substrates. However, links between CY PPIase activity on client substrates and its effect(s) on ubiquitylated substrates are unclear. Here, proteomics of genetically modified mice with deficits of Ranbp2 uncovered the regulation of the small heat shock chaperones - crystallins by Ranbp2 in the chorioretina. Loss of CY PPIase of Ranbp2 up-regulates αA-crystallin proteostasis, which is repressed in non-lenticular tissues. Conversely, the αA-crystallin's substrates, γ-crystallins, are down-regulated by impairment of CY's C-terminal chaperone activity. These CY-dependent effects cause the age-dependent decline of ubiquitylated substrates without overt chorioretinal morphological changes. A model emerges whereby the Ranbp2 CY-dependent remodeling of crystallins' proteostasis subdues molecular aging and preordains chorioretinal neuroprotection by augmenting the chaperone buffering capacity and the decline of ubiquitylated substrates against proteostatic impairments. Further, CY's moonlighting activity holds pan -therapeutic potential against neurodegeneration.

Software and Databases for Protein-Protein Docking

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

Toll-Like Receptor 3 Mediates Aortic Stenosis Through a Conserved Mechanism of Calcification

BACKGROUND

Calcific aortic valve disease (CAVD) is characterized by a phenotypic switch of valvular interstitial cells to bone-forming cells. Toll-like receptors (TLRs) are evolutionarily conserved pattern recognition receptors at the interface between innate immunity and tissue repair. Type I interferons (IFNs) are not only crucial for an adequate antiviral response but also implicated in bone formation. We hypothesized that the accumulation of endogenous TLR3 ligands in the valvular leaflets may promote the generation of osteoblast-like cells through enhanced type I IFN signaling.

METHODS

Human valvular interstitial cells isolated from aortic valves were challenged with mechanical strain or synthetic TLR3 agonists and analyzed for bone formation, gene expression profiles, and IFN signaling pathways. Different inhibitors were used to delineate the engaged signaling pathways. Moreover, we screened a variety of potential lipids and proteoglycans known to accumulate in CAVD lesions as potential TLR3 ligands. Ligand-receptor interactions were characterized by in silico modeling and verified through immunoprecipitation experiments. Biglycan (Bgn), Tlr3, and IFN-α/β receptor alpha chain (Ifnar1)-deficient mice and a specific zebrafish model were used to study the implication of the biglycan (BGN)-TLR3-IFN axis in both CAVD and bone formation in vivo. Two large-scale cohorts (GERA [Genetic Epidemiology Research on Adult Health and Aging], n=55 192 with 3469 aortic stenosis cases; UK Biobank, n=257 231 with 2213 aortic stenosis cases) were examined for genetic variation at genes implicated in BGN-TLR3-IFN signaling associating with CAVD in humans.

RESULTS

Here, we identify TLR3 as a central molecular regulator of calcification in valvular interstitial cells and unravel BGN as a new endogenous agonist of TLR3. Posttranslational BGN maturation by xylosyltransferase 1 (XYLT1) is required for TLR3 activation. Moreover, BGN induces the transdifferentiation of valvular interstitial cells into bone-forming osteoblasts through the TLR3-dependent induction of type I IFNs. It is intriguing that Bgn-/-, Tlr3-/-, and Ifnar1-/- mice are protected against CAVD and display impaired bone formation. Meta-analysis of 2 large-scale cohorts with >300 000 individuals reveals that genetic variation at loci relevant to the XYLT1-BGN-TLR3-interferon-α/β receptor alpha chain (IFNAR) 1 pathway is associated with CAVD in humans.

CONCLUSIONS

This study identifies the BGN-TLR3-IFNAR1 axis as an evolutionarily conserved pathway governing calcification of the aortic valve and reveals a potential therapeutic target to prevent CAVD.

Porphyromonas gingivalis induction of TLR2 association with Vinculin enables PI3K activation and immune evasion

Porphyromonas gingivalis is a Gram-negative anaerobic bacterium that thrives in the inflamed environment of the gingival crevice, and is strongly associated with periodontal disease. The host response to P. gingivalis requires TLR2, however P. gingivalis benefits from TLR2-driven signaling via activation of PI3K. We studied TLR2 protein-protein interactions induced in response to P. gingivalis, and identified an interaction between TLR2 and the cytoskeletal protein vinculin (VCL), confirmed using a split-ubiquitin system. Computational modeling predicted critical TLR2 residues governing the physical association with VCL, and mutagenesis of interface residues W684 and F719, abrogated the TLR2-VCL interaction. In macrophages, VCL knock-down led to increased cytokine production, and enhanced PI3K signaling in response to P. gingivalis infection, effects that correlated with increased intracellular bacterial survival. Mechanistically, VCL suppressed TLR2 activation of PI3K by associating with its substrate PIP2. P. gingivalis induction of TLR2-VCL led to PIP2 release from VCL, enabling PI3K activation via TLR2. These results highlight the complexity of TLR signaling, and the importance of discovering protein-protein interactions that contribute to the outcome of infection.

A hotspot for posttranslational modifications on the androgen receptor dimer interface drives pathology and anti-androgen resistance

Mutations of the androgen receptor (AR) associated with prostate cancer and androgen insensitivity syndrome may profoundly influence its structure, protein interaction network, and binding to chromatin, resulting in altered transcription signatures and drug responses. Current structural information fails to explain the effect of pathological mutations on AR structure-function relationship. Here, we have thoroughly studied the effects of selected mutations that span the complete dimer interface of AR ligand-binding domain (AR-LBD) using x-ray crystallography in combination with in vitro, in silico, and cell-based assays. We show that these variants alter AR-dependent transcription and responses to anti-androgens by inducing a previously undescribed allosteric switch in the AR-LBD that increases exposure of a major methylation target, Arg⁷⁶¹. We also corroborate the relevance of residues Arg⁷⁶¹ and Tyr⁷⁶⁴ for AR dimerization and function. Together, our results reveal allosteric coupling of AR dimerization and posttranslational modifications as a disease mechanism with implications for precision medicine.

The multivalency of the glucocorticoid receptor ligand-binding domain explains its manifold physiological activities

The glucocorticoid receptor (GR) is a ubiquitously expressed transcription factor that controls metabolic and homeostatic processes essential for life. Although numerous crystal structures of the GR ligand-binding domain (GR-LBD) have been reported, the functional oligomeric state of the full-length receptor, which is essential for its transcriptional activity, remains disputed. Here we present five new crystal structures of agonist-bound GR-LBD, along with a thorough analysis of previous structural work. We identify four distinct homodimerization interfaces on the GR-LBD surface, which can associate into 20 topologically different homodimers. Biologically relevant homodimers were identified by studying a battery of GR point mutants including crosslinking assays in solution, quantitative fluorescence microscopy in living cells, and transcriptomic analyses. Our results highlight the relevance of non-canonical dimerization modes for GR, especially of contacts made by loop L1-3 residues such as Tyr545. Our work illustrates the unique flexibility of GR's LBD and suggests different dimeric conformations within cells. In addition, we unveil pathophysiologically relevant quaternary assemblies of the receptor with important implications for glucocorticoid action and drug design.

Bi-allelic variants in neuronal cell adhesion molecule cause a neurodevelopmental disorder characterized by developmental delay, hypotonia, neuropathy/spasticity

Cell adhesion molecules are membrane-bound proteins predominantly expressed in the central nervous system along principal axonal pathways with key roles in nervous system development, neural cell differentiation and migration, axonal growth and guidance, myelination, and synapse formation. Here, we describe ten affected individuals with bi-allelic variants in the neuronal cell adhesion molecule NRCAM that lead to a neurodevelopmental syndrome of varying severity; the individuals are from eight families. This syndrome is characterized by developmental delay/intellectual disability, hypotonia, peripheral neuropathy, and/or spasticity. Computational analyses of NRCAM variants, many of which cluster in the third fibronectin type III (Fn-III) domain, strongly suggest a deleterious effect on NRCAM structure and function, including possible disruption of its interactions with other proteins. These findings are corroborated by previous in vitro studies of murine Nrcam-deficient cells, revealing abnormal neurite outgrowth, synaptogenesis, and formation of nodes of Ranvier on myelinated axons. Our studies on zebrafish nrcama^Δ mutants lacking the third Fn-III domain revealed that mutant larvae displayed significantly altered swimming behavior compared to wild-type larvae (p < 0.03). Moreover, nrcama^Δ mutants displayed a trend toward increased amounts of α-tubulin fibers in the dorsal telencephalon, demonstrating an alteration in white matter tracts and projections. Taken together, our study provides evidence that NRCAM disruption causes a variable form of a neurodevelopmental disorder and broadens the knowledge on the growing role of the cell adhesion molecule family in the nervous system.

ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab-based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.

A two-site flexible clamp mechanism for RET-GDNF-GFRα1 assembly reveals both conformational adaptation and strict geometric spacing

RET receptor tyrosine kinase plays vital developmental and neuroprotective roles in metazoans. GDNF family ligands (GFLs) when bound to cognate GFRα co-receptors recognize and activate RET stimulating its cytoplasmic kinase function. The principles for RET ligand-co-receptor recognition are incompletely understood. Here, we report a crystal structure of the cadherin-like module (CLD1-4) from zebrafish RET revealing interdomain flexibility between CLD2 and CLD3. Comparison with a cryo-electron microscopy structure of a ligand-engaged zebrafish RETECD-GDNF-GFRα1a complex indicates conformational changes within a clade-specific CLD3 loop adjacent to the co-receptor. Our observations indicate that RET is a molecular clamp with a flexible calcium-dependent arm that adapts to different GFRα co-receptors, while its rigid arm recognizes a GFL dimer to align both membrane-proximal cysteine-rich domains. We also visualize linear arrays of RETECD-GDNF-GFRα1a suggesting that a conserved contact stabilizes higher-order species. Our study reveals that ligand-co-receptor recognition by RET involves both receptor plasticity and strict spacing of receptor dimers by GFL ligands.

Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors-S/Ace2, Tmprss2, Cathepsins L and K, and Mpro-to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.

Biophysical Compatibility of a Heterotrimeric Tyrosinase-TYRP1-TYRP2 Metalloenzyme Complex

Tyrosinase (TYR) is a copper-containing monooxygenase central to the function of melanocytes. Alterations in its expression or activity contribute to variations in skin, hair and eye color, and underlie a variety of pathogenic pigmentary phenotypes, including several forms of oculocutaneous albinism (OCA). Many of these phenotypes are linked to individual missense mutations causing single nucleotide variants and polymorphisms (SNVs) in TYR. We previously showed that two TYR homologues, TYRP1 and TYRP2, modulate TYR activity and stabilize the TYR protein. Accordingly, to investigate whether TYR, TYRP1, and TYRP2 are biophysically compatible with various heterocomplexes, we computationally docked a high-quality 3D model of TYR to the crystal structure of TYRP1 and to a high-quality 3D model of TYRP2. Remarkably, the resulting TYR-TYRP1 heterodimer was complementary in structure and energy with the TYR-TYRP2 heterodimer, with TYRP1 and TYRP2 docking to different adjacent surfaces on TYR that apposed a third realistic protein interface between TYRP1-TYRP2. Hence, the 3D models are compatible with a heterotrimeric TYR-TYRP1-TYRP2 complex. In addition, this heterotrimeric TYR-TYRP1-TYRP2 positioned the C-terminus of each folded enzymatic domain in an ideal position to allow their C-terminal transmembrane helices to form a putative membrane embedded three-helix bundle. Finally, pathogenic TYR mutations causing OCA1A, which also destabilize TYR biochemically, cluster on an unoccupied protein interface at the periphery of the heterotrimeric complex, suggesting that this may be a docking site for OCA2, an anion channel. Pathogenic OCA2 mutations result in similar phenotypes to those produced by OCA1A TYR mutations. While this complex may be difficult to detect in vitro, due to the complex environment of the vertebrate cellular membranous system, our results support the existence of a heterotrimeric complex in melanogenesis.

Searching Hit Potential Antimicrobials in Natural Compounds Space against Biofilm Formation

Biofilms are communities of microorganisms that can colonize biotic and abiotic surfaces and thus play a significant role in the persistence of bacterial infection and resistance to antimicrobial. About 65% and 80% of microbial and chronic infections are associated with biofilm formation, respectively. The increase in infections by multi-resistant bacteria instigates the need for the discovery of novel natural-based drugs that act as inhibitory molecules. The inhibition of diguanylate cyclases (DGCs), the enzyme implicated in the synthesis of the second messenger, cyclic diguanylate (c-di-GMP), involved in the biofilm formation, represents a potential approach for preventing the biofilm development. It has been extensively studied using PleD protein as a model of DGC for in silico studies as virtual screening and as a model for in vitro studies in biofilms formation. This study aimed to search for natural products capable of inhibiting the Caulobacter crescentus enzyme PleD. For this purpose, 224,205 molecules from the natural products ZINC15 database, have been evaluated through molecular docking and molecular dynamic simulation. Our results suggest trans-Aconitic acid (TAA) as a possible starting point for hit-to-lead methodologies to obtain new inhibitors of the PleD protein and hence blocking the biofilm formation.

AFP-LSE: Antifreeze Proteins Prediction Using Latent Space Encoding of Composition of k-Spaced Amino Acid Pairs

Species living in extremely cold environments resist the freezing conditions through antifreeze proteins (AFPs). Apart from being essential proteins for various organisms living in sub-zero temperatures, AFPs have numerous applications in different industries. They possess very small resemblance to each other and cannot be easily identified using simple search algorithms such as BLAST and PSI-BLAST. Diverse AFPs found in fishes (Type I, II, III, IV and antifreeze glycoproteins (AFGPs)), are sub-types and show low sequence and structural similarity, making their accurate prediction challenging. Although several machine-learning methods have been proposed for the classification of AFPs, prediction methods that have greater reliability are required. In this paper, we propose a novel machine-learning-based approach for the prediction of AFP sequences using latent space learning through a deep auto-encoder method. For latent space pruning, we use the output of the auto-encoder with a deep neural network classifier to learn the non-linear mapping of the protein sequence descriptor and class label. The proposed method outperformed the existing methods, yielding excellent results in comparison. A comprehensive ablation study is performed, and the proposed method is evaluated in terms of widely used performance measures. In particular, the proposed method demonstrated a high Matthews correlation coefficient of 0.52, F-score of 0.49, and Youden’s index of 0.81 on an independent test dataset, thereby outperforming the existing methods for AFP prediction.

User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation

Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate-L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.

Identifying protein-protein interface via a novel multi-scale local sequence and structural representation

Background

Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface.

Results

In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average I_rmsd value of 3.28Å and overall F_nat value of 63%, which improves upon I_rmsd of 3.89Å and F_nat of 49% for ZRANK, and I_rmsd of 3.99Å and F_nat of 46% for ClusPro. On CAPRI targets, our method achieves average I_rmsd value of 3.45Å and overall F_nat value of 46%, which improves upon I_rmsd of 4.18Å and F_nat of 40% for ZRANK, and I_rmsd of 5.12Å and F_nat of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively.

Conclusion

Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.

Mapping Ethanol Tolerance in Budding Yeast Reveals High Genetic Variation in a Wild Isolate

Ethanol tolerance, a polygenic trait of the yeast Saccharomyces cerevisiae, is the primary factor determining industrial bioethanol productivity. Until now, genomic elements affecting ethanol tolerance have been mapped only at low resolution, hindering their identification. Here, we explore the genetic architecture of ethanol tolerance, in the F6 generation of an Advanced Intercrossed Line (AIL) mapping population between two phylogenetically distinct, but phenotypically similar, S. cerevisiae strains (a common laboratory strain and a wild strain isolated from nature). Under ethanol stress, 51 quantitative trait loci (QTLs) affecting growth and 96 QTLs affecting survival, most of them novel, were identified, with high resolution, in some cases to single genes, using a High-Resolution Mapping Package of methodologies that provided high power and high resolution. We confirmed our results experimentally by showing the effects of the novel mapped genes: MOG1, MGS1, and YJR154W. The mapped QTLs explained 34% of phenotypic variation for growth and 72% for survival. High statistical power provided by our analysis allowed detection of many loci with small, but mappable effects, uncovering a novel “quasi-infinitesimal” genetic architecture. These results are striking demonstration of tremendous amounts of hidden genetic variation exposed in crosses between phylogenetically separated strains with similar phenotypes; as opposed to the more common design where strains with distinct phenotypes are crossed. Our findings suggest that ethanol tolerance is under natural evolutionary fitness-selection for an optimum phenotype that would tend to eliminate alleles of large effect. The study provides a platform for development of superior ethanol-tolerant strains using genome editing or selection.

Predicting the Affinity of Peptides to Major Histocompatibility Complex Class II by Scoring Molecular Dynamics Simulations

Predicting the binding affinity of peptides able to interact with major histocompatibility complex (MHC) molecules is a priority for researchers working in the identification of novel vaccines candidates. Most available approaches are based on the analysis of the sequence of peptides of known experimental affinity. However, for MHC class II receptors, these approaches are not very accurate, due to the intrinsic flexibility of the complex. To overcome these limitations, we propose to estimate the binding affinity of peptides bound to an MHC class II by averaging the score of the configurations from finite-temperature molecular dynamics simulations. The score is estimated for 18 different scoring functions, and we explored the optimal manner for combining them. To test the predictions, we considered eight peptides of known binding affinity. We found that six scoring functions correlate with the experimental ranking of the peptides significantly better than the others. We then assessed a set of techniques for combining the scoring functions by linear regression and logistic regression. We obtained a maximum accuracy of 82% for the predicted sign of the binding affinity using a logistic regression with optimized weights. These results are potentially useful to improve the reliability of in silico protocols to design high-affinity binding peptides for MHC class II receptors.

Protein-protein interaction specificity is captured by contact preferences and interface composition

Motivation

Large-scale computational docking will be increasingly used in future years to discriminate protein–protein interactions at the residue resolution. Complete cross-docking experiments make in silico reconstruction of protein–protein interaction networks a feasible goal. They ask for efficient and accurate screening of the millions structural conformations issued by the calculations.

Results

We propose CIPS (Combined Interface Propensity for decoy Scoring), a new pair potential combining interface composition with residue–residue contact preference. CIPS outperforms several other methods on screening docking solutions obtained either with all-atom or with coarse-grain rigid docking. Further testing on 28 CAPRI targets corroborates CIPS predictive power over existing methods. By combining CIPS with atomic potentials, discrimination of correct conformations in all-atom structures reaches optimal accuracy. The drastic reduction of candidate solutions produced by thousands of proteins docked against each other makes large-scale docking accessible to analysis.

Availability and implementation

CIPS source code is freely available at http://www.lcqb.upmc.fr/CIPS.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

Genistein inhibits African swine fever virus replication in vitro by disrupting viral DNA synthesis

African swine fever virus (ASFV) is the causal agent of a highly-contagious and fatal disease of domestic pigs, leading to serious socio-economic consequences in affected countries. Once, neither an anti-viral drug nor an effective vaccines are available, studies on new anti-ASFV molecules are urgently need. Recently, it has been shown that ASFV type II topoisomerase (ASFV-topo II) is inhibited by several fluoroquinolones (bacterial DNA topoisomerase inhibitors), raising the idea that this viral enzyme can be a potential target for drug development against ASFV. Here, we report that genistein hampers ASFV infection at non-cytotoxic concentrations in Vero cells and porcine macrophages. Interestingly, the antiviral activity of this isoflavone, previously described as a topo II poison in eukaryotes, is maximal when it is added to cells at middle-phase of infection (8 hpi), disrupting viral DNA replication, blocking the transcription of late viral genes as well as the synthesis of late viral proteins, reducing viral progeny. Further, the single cell electrophoresis analysis revealed the presence of fragmented ASFV genomes in cells exposed to genistein, suggesting that this molecule also acts as an ASFV-topo II poison and not as a reversible inhibitor. No antiviral effects were detected when genistein was added before or at entry phase of ASFV infection. Molecular docking studies demonstrated that genistein may interact with four residues of the ATP-binding site of ASFV-topo II (Asn-144, Val-146, Gly-147 and Leu-148), showing more binding affinity (−4.62 kcal/mol) than ATP⁴⁻ (−3.02 kcal/mol), emphasizing the idea that this viral enzyme has an essential role during viral genome replication and can be a good target for drug development against ASFV.

•

Genistein shows potent anti-ASFV activity at non-cytotoxic concentrations.

•

Genistein disrupts viral genome replication and viral protein synthesis.

•

It acts as an ASFV-topo II poison promoting irreversible viral genome breaks.

•

Docking studies revealed that genistein interacts with the ATP-binding site of ASFV-topo II with more affinity than ATP⁴⁻.

Energy Landscape of the Substrate Translocation Equilibrium of Plasma-Membrane Glutamate Transporters

Glutamate transporters maintain a large glutamate concentration gradient across synaptic membranes and are, thus, critical for functioning of the excitatory synapse. Mammalian glutamate transporters concentrate glutamate inside cells through energetic coupling of glutamate flux to the transmembrane concentration gradient of Na⁺. Structural models based on an archeal homologue, GltPh, suggest an elevator-like carrier mechanism. However, the energetic determinants of this carrier-based movement are not well understood. Although electrostatics play an important role in governing these energetics, their implication on transport dynamics has not been studied. Here, we combine a pre-steady-state kinetic analysis of the translocation equilibrium with electrostatic computations to gain insight into the energetics of the translocation process. Our results show the biphasic nature of translocation, consistent with the existence of an intermediate on the translocation pathway. In the absence of voltage, the equilibrium is shifted to the outward-facing configuration. Electrostatic computations confirm the intermediate state and show that the elevator-like movement is energetically feasible in the presence of bound Na⁺ ions, whereas a substrate-hopping model is energetically prohibitive. Our results highlight the critical contribution of charge compensation to transport and add to results from previous molecular dynamics simulations for improved understanding of the glutamate translocation process.

Ferritin is secreted via 2 distinct nonclassical vesicular pathways

Ferritin turnover plays a major role in tissue iron homeostasis, and ferritin malfunction is associated with impaired iron homeostasis and neurodegenerative diseases. In most eukaryotes, ferritin is considered an intracellular protein that stores iron in a nontoxic and bioavailable form. In insects, ferritin is a classically secreted protein and plays a major role in systemic iron distribution. Mammalian ferritin lacks the signal peptide for classical endoplasmic reticulum-Golgi secretion but is found in serum and is secreted via a nonclassical lysosomal secretion pathway. This study applied bioinformatics and biochemical tools, alongside a protein trafficking mouse models, to characterize the mechanisms of ferritin secretion. Ferritin trafficking via the classical secretion pathway was ruled out, and a 2:1 distribution of intracellular ferritin between membrane-bound compartments and the cytosol was observed, suggesting a role for ferritin in the vesicular compartments of the cell. Focusing on nonclassical secretion, we analyzed mouse models of impaired endolysosomal trafficking and found that ferritin secretion was decreased by a BLOC-1 mutation but increased by BLOC-2, BLOC-3, and Rab27A mutations of the cellular trafficking machinery, suggesting multiple export routes. A 13-amino-acid motif unique to ferritins that lack the secretion signal peptide was identified on the BC-loop of both subunits and plays a role in the regulation of ferritin secretion. Finally, we provide evidence that secretion of iron-rich ferritin was mediated via the multivesicular body-exosome pathway. These results enhance our understanding of the mechanism of ferritin secretion, which is an important piece in the puzzle of tissue iron homeostasis.

A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison

Reliable identification of near-native poses of docked protein-protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein-protein interactions is challenging for traditional biophysical or knowledge based potentials and the identification of many false positive binding sites is not unusual. Often, ranking protocols are based on initial clustering of docked poses followed by the application of an energy function to rank each cluster according to its lowest energy member. Here, we present an approach of cluster ranking based not only on one molecular descriptor (e.g., an energy function) but also employing a large number of descriptors that are integrated in a machine learning model, whereby, an extremely randomized tree classifier based on 109 molecular descriptors is trained. The protocol is based on first locally enriching clusters with additional poses, the clusters are then characterized using features describing the distribution of molecular descriptors within the cluster, which are combined into a pairwise cluster comparison model to discriminate near-native from incorrect clusters. The results show that our approach is able to identify clusters containing near-native protein-protein complexes. In addition, we present an analysis of the descriptors with respect to their power to discriminate near native from incorrect clusters and how data transformations and recursive feature elimination can improve the ranking performance. Proteins 2017; 85:528-543. © 2016 Wiley Periodicals, Inc.

Mapping the Energetic Epitope of an Antibody/Interleukin-23 Interaction with Hydrogen/Deuterium Exchange, Fast Photochemical Oxidation of Proteins Mass Spectrometry, and Alanine Shave Mutagenesis

Epitope mapping the specific residues of an antibody/antigen interaction can be used to support mechanistic interpretation, antibody optimization, and epitope novelty assessment. Thus, there is a strong need for mapping methods, particularly integrative ones. Here, we report the identification of an energetic epitope by determining the interfacial hot-spot that dominates the binding affinity for an anti-interleukin-23 (anti-IL-23) antibody by using the complementary approaches of hydrogen/deuterium exchange mass spectrometry (HDX-MS), fast photochemical oxidation of proteins (FPOP), alanine shave mutagenesis, and binding analytics. Five peptide regions on IL-23 with reduced backbone amide solvent accessibility upon antibody binding were identified by HDX-MS, and five different peptides over the same three regions were identified by FPOP. In addition, FPOP analysis at the residue level reveals potentially key interacting residues. Mutants with 3-5 residues changed to alanine have no measurable differences from wild-type IL-23 except for binding of and signaling blockade by the 7B7 anti-IL-23 antibody. The M5 IL-23 mutant differs from wild-type by five alanine substitutions and represents the dominant energetic epitope of 7B7. M5 shows a dramatic decrease in binding to BMS-986010 (which contains the 7B7 Fab, where Fab is fragment antigen-binding region of an antibody), yet it maintains functional activity, binding to p40 and p19 specific reagents, and maintains biophysical properties similar to wild-type IL-23 (monomeric state, thermal stability, and secondary structural features).

BindML/BindML+: Detecting Protein-Protein Interaction Interface Propensity from Amino Acid Substitution Patterns

Prediction of protein-protein interaction sites in a protein structure provides important information for elucidating the mechanism of protein function and can also be useful in guiding a modeling or design procedures of protein complex structures. Since prediction methods essentially assess the propensity of amino acids that are likely to be part of a protein docking interface, they can help in designing protein-protein interactions. Here, we introduce BindML and BindML+ protein-protein interaction sites prediction methods. BindML predicts protein-protein interaction sites by identifying mutation patterns found in known protein-protein complexes using phylogenetic substitution models. BindML+ is an extension of BindML for distinguishing permanent and transient types of protein-protein interaction sites. We developed an interactive web-server that provides a convenient interface to assist in structural visualization of protein-protein interactions site predictions. The input data for the web-server are a tertiary structure of interest. BindML and BindML+ are available at http://kiharalab.org/bindml/ and http://kiharalab.org/bindml/plus/ .

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes

Studying protein-protein interactions leads to a better understanding of the underlying principles of several biological pathways. Cost and labor-intensive experimental techniques suggest the need for computational methods to complement them. Several such state-of-the-art methods have been reported for analyzing diverse aspects such as predicting binding partners, interface residues, and binding affinity for protein-protein complexes with reliable performance. However, there are specific drawbacks for different methods that indicate the need for their improvement. This review highlights various available computational algorithms for analyzing diverse aspects of protein-protein interactions and endorses the necessity for developing new robust methods for gaining deep insights about protein-protein interactions.

Protein social behavior makes a stronger signal for partner identification than surface geometry

Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico‐chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross‐docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S‐index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface‐based (ranking) score to discriminate partners from non‐interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137–154. © 2016 Wiley Periodicals, Inc.

A transgenic approach for controlling Lygus in cotton

Lygus species of plant-feeding insects have emerged as economically important pests of cotton in the United States. These species are not controlled by commercial Bacillus thuringiensis (Bt) cotton varieties resulting in economic losses and increased application of insecticide. Previously, a Bt crystal protein (Cry51Aa2) was reported with insecticidal activity against Lygus spp. However, transgenic cotton plants expressing this protein did not exhibit effective protection from Lygus feeding damage. Here we employ various optimization strategies, informed in part by protein crystallography and modelling, to identify limited amino-acid substitutions in Cry51Aa2 that increase insecticidal activity towards Lygus spp. by >200-fold. Transgenic cotton expressing the variant protein, Cry51Aa2.834_16, reduce populations of Lygus spp. up to 30-fold in whole-plant caged field trials. One transgenic event, designated MON88702, has been selected for further development of cotton varieties that could potentially reduce or eliminate insecticide application for control of Lygus and the associated environmental impacts.

Plant-feeding insects of the Lygus genus have emerged as a major pest effecting cotton crops in the USA. Here the authors optimize the insecticidal activity of a Bacillus thuringiensis crystal protein and produce transgenic plants that are resistant to feeding damage by Lygus species.

Distinct Residues Contribute to Motility Repression and Autoregulation in the Proteus mirabilis Fimbria-Associated Transcriptional Regulator AtfJ

Proteus mirabilis contributes to a significant number of catheter-associated urinary tract infections, where coordinated regulation of adherence and motility is critical for ascending disease progression. Previously, the mannose-resistant Proteus-like (MR/P) fimbria-associated transcriptional regulator MrpJ has been shown to both repress motility and directly induce the transcription of its own operon; in addition, it affects the expression of a wide range of cellular processes. Interestingly, 14 additional mrpJ paralogs are included in the P. mirabilis genome. Looking at a selection of MrpJ paralogs, we discovered that these proteins, which consistently repress motility, also have nonidentical functions that include cross-regulation of fimbrial operons. A subset of paralogs, including AtfJ (encoded by the ambient temperature fimbrial operon), Fim8J, and MrpJ, are capable of autoinduction. We identified an element of the atf promoter extending from 487 to 655 nucleotides upstream of the transcriptional start site that is responsive to AtfJ, and we found that AtfJ directly binds this fragment. Mutational analysis of AtfJ revealed that its two identified functions, autoregulation and motility repression, are not invariably linked. Residues within the DNA-binding helix-turn-helix domain are required for motility repression but not necessarily autoregulation. Likewise, the C-terminal domain is dispensable for motility repression but is essential for autoregulation. Supported by a three-dimensional (3D) structural model, we hypothesize that the C-terminal domain confers unique regulatory capacities on the AtfJ family of regulators.

IMPORTANCE

Balancing adherence with motility is essential for uropathogens to successfully establish a foothold in their host. Proteus mirabilis uses a fimbria-associated transcriptional regulator to switch between these antagonistic processes by increasing fimbrial adherence while simultaneously downregulating flagella. The discovery of multiple related proteins, many of which also function as motility repressors, encoded in the P. mirabilis genome has raised considerable interest as to their functionality and potential redundancy in this organism. This study provides an important advance in this field by elucidating the nonidentical effects of these paralogs on a molecular level. Our mechanistic studies of one member of this group, AtfJ, shed light on how these differing functions may be conferred despite the limited sequence variety exhibited by the paralogous proteins.

Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes. The method can be used to analyse other viral genomes and to predict the associated protein interaction networks.

Progress and challenges in predicting protein interfaces

The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.

Local Geometry and Evolutionary Conservation of Protein Surfaces Reveal the Multiple Recognition Patches in Protein-Protein Interactions

Protein-protein interactions (PPIs) are essential to all biological processes and they represent increasingly important therapeutic targets. Here, we present a new method for accurately predicting protein-protein interfaces, understanding their properties, origins and binding to multiple partners. Contrary to machine learning approaches, our method combines in a rational and very straightforward way three sequence- and structure-based descriptors of protein residues: evolutionary conservation, physico-chemical properties and local geometry. The implemented strategy yields very precise predictions for a wide range of protein-protein interfaces and discriminates them from small-molecule binding sites. Beyond its predictive power, the approach permits to dissect interaction surfaces and unravel their complexity. We show how the analysis of the predicted patches can foster new strategies for PPIs modulation and interaction surface redesign. The approach is implemented in JET2, an automated tool based on the Joint Evolutionary Trees (JET) method for sequence-based protein interface prediction. JET2 is freely available at www.lcqb.upmc.fr/JET2.

Membrane and Protein Interactions of the Pleckstrin Homology Domain Superfamily

The human genome encodes about 285 proteins that contain at least one annotated pleckstrin homology (PH) domain. As the first phosphoinositide binding module domain to be discovered, the PH domain recruits diverse protein architectures to cellular membranes. PH domains constitute one of the largest protein superfamilies, and have diverged to regulate many different signaling proteins and modules such as Dbl homology (DH) and Tec homology (TH) domains. The ligands of approximately 70 PH domains have been validated by binding assays and complexed structures, allowing meaningful extrapolation across the entire superfamily. Here the Membrane Optimal Docking Area (MODA) program is used at a genome-wide level to identify all membrane docking PH structures and map their lipid-binding determinants. In addition to the linear sequence motifs which are employed for phosphoinositide recognition, the three dimensional structural features that allow peripheral membrane domains to approach and insert into the bilayer are pinpointed and can be predicted ab initio. The analysis shows that conserved structural surfaces distinguish which PH domains associate with membrane from those that do not. Moreover, the results indicate that lipid-binding PH domains can be classified into different functional subgroups based on the type of membrane insertion elements they project towards the bilayer.

Understanding the functional difference between growth arrest-specific protein 6 and protein S: an evolutionary approach

Although protein S (PROS1) and growth arrest-specific protein 6 (GAS6) proteins are homologous with a high degree of structural similarity, they are functionally different. The objectives of this study were to identify the evolutionary origins from which these functional differences arose. Bioinformatics methods were used to estimate the evolutionary divergence time and to detect the amino acid residues under functional divergence between GAS6 and PROS1. The properties of these residues were analysed in the light of their three-dimensional structures, such as their stability effects, the identification of electrostatic patches and the identification potential protein-protein interaction. The divergence between GAS6 and PROS1 probably occurred during the whole-genome duplications in vertebrates. A total of 78 amino acid sites were identified to be under functional divergence. One of these sites, Asn463, is involved in N-glycosylation in GAS6, but is mutated in PROS1, preventing this post-translational modification. Sites experiencing functional divergence tend to express a greater diversity of stabilizing/destabilizing effects than sites that do not experience such functional divergence. Three electrostatic patches in the LG1/LG2 domains were found to differ between GAS6 and PROS1. Finally, a surface responsible for protein-protein interactions was identified. These results may help researchers to analyse disease-causing mutations in the light of evolutionary and structural constraints, and link genetic pathology to clinical phenotypes.

Native Electrophoresis-Coupled Activity Assays Reveal Catalytically-Active Protein Aggregates of Escherichia coli β-Glucuronidase

β-glucuronidase is found as a functional homotetramer in a variety of organisms, including humans and other animals, as well as a number of bacteria. This enzyme is important in these organisms, catalyzing the hydrolytic removal of a glucuronide moiety from substrate molecules. This process serves to break down sugar conjugates in animals and provide sugars for metabolism in bacteria. While β-glucuronidase is primarily found as a homotetramer, previous studies have indicated that the human form of the protein is also catalytically active as a dimer. Here we present evidence for not only an active dimer of the E. coli form of the protein, but also for several larger active complexes, including an octomer and a 16-mer. Additionally, we propose a model for the structures of these large complexes, based on computationally-derived molecular modeling studies. These structures may have application in the study of human disease, as several diseases have been associated with the aggregation of proteins.

Structure of the bovine COPI δ subunit μ homology domain at 2.15 Å resolution

The heptameric COPI coat (coatomer) plays an essential role in vesicular transport in the early secretory system of eukaryotic cells. While the structures of some of the subunits have been determined, that of the δ-COP subunit has not been reported to date. The δ-COP subunit is part of a subcomplex with structural similarity to tetrameric clathrin adaptors (APs), where δ-COP is the structural homologue of the AP μ subunit. Here, the crystal structure of the μ homology domain (MHD) of δ-COP (δ-MHD) obtained by phasing using a combined SAD-MR method is presented at 2.15 Å resolution. The crystallographic asymmetric unit contains two monomers that exhibit short sections of disorder, which may allude to flexible regions of the protein. The δ-MHD is composed of two subdomains connected by unstructured linkers. Comparison between this structure and those of known MHD domains from the APs shows significant differences in the positions of specific loops and β-sheets, as well as a more general change in the relative positions of the protein subdomains. The identified difference may be the major source of cargo-binding specificity. Finally, the crystal structure is used to analyze the potential effect of the I422T mutation in δ-COP previously reported to cause a neurodegenerative phenotype in mice.

Predicting protein interface residues using easily accessible on-line resources

It has been more than a decade since the completion of the Human Genome Project that provided us with a complete list of human proteins. The next obvious task is to figure out how various parts interact with each other. On that account, we review 10 methods for protein interface prediction, which are freely available as web servers. In addition, we comparatively evaluate their performance on a common data set comprising different quality target structures. We find that using experimental structures and high-quality homology models, structure-based methods outperform those using only protein sequences, with global template-based approaches providing the best performance. For moderate-quality models, sequence-based methods often perform better than those structure-based techniques that rely on fine atomic details. We note that post-processing protocols implemented in several methods quantitatively improve the results only for experimental structures, suggesting that these procedures should be tuned up for computer-generated models. Finally, we anticipate that advanced meta-prediction protocols are likely to enhance interface residue prediction. Notwithstanding further improvements, easily accessible web servers already provide the scientific community with convenient resources for the identification of protein-protein interaction sites.

Algorithmic approaches to protein-protein interaction site prediction

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

The structure function of the death domain of human IRAK-M

BACKGROUND

IRAK-M is an inhibitor of Toll-like receptor signaling that acts by re-directing IRAK-4 activity to TAK1 independent NF-κB activation and by inhibition of IRAK-1/IRAK-2 activity. IRAK-M is expressed in monocytes/macrophages and lung epithelial cells. Lack of IRAK-M in mice greatly improves the resistance to nosocomial pneumonia and lung tumors, which entices IRAK-M as a potential therapeutic target. IRAK-M consists of an N-terminal death domain (DD), a dysfunctional kinase domain and unstructured C-terminal domain. Little is known however on IRAK-M's structure-function relationships.

RESULTS

Since death domains provide the important interactions of IRAK-1, IRAK-2 and IRAK-4 molecules, we generated a 3D structure model of the human IRAK-M-DD (residues C5-G119) to guide mutagenesis studies and predict protein-protein interaction points. First we identified the DD residues involved in the endogenous capacity of IRAK-M to activate NF-κB that is displayed upon overexpression in 293T cells. W74 and R97, at distinct interfaces of the IRAK-M-DD, were crucial for this endogenous NF-κB activating capacity, as well as the C-terminal domain (S445-E596) of IRAK-M. Resulting anti-inflammatory A20 and pro-inflammatory IL-8 transcription in 293T cells was W74 dependent, while IL-8 protein expression was dependent on R97 and the TRAF6 binding motif at P478. The IRAK-M-DD W74 and R97 binding interfaces are predicted to interact with opposite sides of IRAK-4-DD's. Secondly we identified DD residues important for the inhibitory action of IRAK-M by stable overexpression of mutants in THP-1 macrophages and H292 lung epithelial cells. IRAK-M inhibited TLR2/4-mediated cytokine production in macrophages in a manner that is largely dependent on W74. R97 was not involved in inhibition of TNF production but was engaged in IL-6 down-regulation by IRAK-M. Protein-interactive residues D19-A23, located in between W74 and R97, were also observed to be crucial for inhibition of TLR2/4 mediated cytokine induction in macrophages. Remarkably, IRAK-M inhibited TLR5 mediated IL-8 production by lung epithelial cells independent of W74 and R97, but dependent on D19-A23 and R70, two surface-exposed regions that harbor predicted IRAK-2-DD interaction points of IRAK-M.

CONCLUSION

IRAK-M employs alternate residues of its DD to inhibit the different inflammatory mediators induced by varying TLRs and cells.