The RosettaDock server for local protein-protein docking

Dynamic plasticity of the lipid antigen-binding site of CD1d is crucially favoured by acidic pH and helper proteins

CD1 molecules present lipid antigens for recognition by T-cell receptors (TCRs). Although a reasonably detailed picture of the CD1-lipid-TCR interaction exists, the initial steps regarding lipid loading onto and exchange between CD1 proteins remain elusive. The hydrophobic nature of lipids and the fact that CD1 molecules are unable to extract lipids from membranes raise the need for the assistance of helper proteins in lipid trafficking. However, the experimental study of this traffic in the endosomal compartments at which it occurs is so challenging that computational studies can help provide mechanistic insight into the associated processes. Here we present a multifaceted computational approach to obtain dynamic structural data on the human CD1d isotype. Conformational dynamics analysis shows an intrinsic flexibility associated with the protein architecture. Electrostatic properties together with molecular dynamics results for CD1d complexes with several lipids and helper proteins unravel the high dynamic plasticity of the antigen-binding site that is crucially favoured by acidic pH and the presence of helper proteins.

Primary and secondary anti-viral response captured by the dynamics and phenotype of individual T cell clones

The diverse repertoire of T-cell receptors (TCR) plays a key role in the adaptive immune response to infections. Using TCR alpha and beta repertoire sequencing for T-cell subsets, as well as single-cell RNAseq and TCRseq, we track the concentrations and phenotypes of individual T-cell clones in response to primary and secondary yellow fever immunization — the model for acute infection in humans — showing their large diversity. We confirm the secondary response is an order of magnitude weaker, albeit ∼10 days faster than the primary one. Estimating the fraction of the T-cell response directed against the single immunodominant epitope, we identify the sequence features of TCRs that define the high precursor frequency of the two major TCR motifs specific for this particular epitope. We also show the consistency of clonal expansion dynamics between bulk alpha and beta repertoires, using a new methodology to reconstruct alpha-beta pairings from clonal trajectories.

Challenges and opportunities of automated protein-protein docking: HDOCK server vs human predictions in CAPRI Rounds 38-46

Protein-protein docking plays an important role in the computational prediction of the complex structure between two proteins. For years, a variety of docking algorithms have been developed, as witnessed by the critical assessment of prediction interactions (CAPRI) experiments. However, despite their successes, many docking algorithms often require a series of manual operations like modeling structures from sequences, incorporating biological information, and selecting final models. The difficulties in these manual steps have significantly limited the applications of protein-protein docking, as most of the users in the community are nonexperts in docking. Therefore, automated docking like a web server, which can give a comparable performance to human docking protocol, is pressingly needed. As such, we have participated in the blind CAPRI experiments for Rounds 38-45 and CASP13-CAPRI challenge for Round 46 with both our HDOCK automated docking web server and human docking protocol. It was shown that our HDOCK server achieved an "acceptable" or higher CAPRI-rated model in the top 10 submitted predictions for 65.5% and 59.1% of the targets in the docking experiments of CAPRI and CASP13-CAPRI, respectively, which are comparable to 66.7% and 54.5% for human docking protocol. Similar trends can also be observed in the scoring experiments. These results validated our HDOCK server as an efficient automated docking protocol for nonexpert users. Challenges and opportunities of automated docking are also discussed.

Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry

The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.

Proposing a tandem AND-gate CAR T cell targeting glioblastoma multiforme

CAR T cell therapy is suggested as an effective method to treat hematological malignancies. However, high recurrence rates and in vivo toxicities have limited their widespread use. In order to reduce toxicity and improve tumor specificity, we propose a CAR T cell targeting glioblastoma multiforme utilizing the synNotch receptor pathway linked to a tandem CAR T cell. The extracellular domain of the synNotch receptor is replaced by a single chain fragment variable specific for the EGF receptor variant III (scfv-EGFRvIII), and covalently bonded to a IL-13Rα2-CD133-tandem CAR. This would produce an AND-gate CAR-T cell, which requires activation of both signals from synNotch receptor binding to EGFRvIII and then binding of the tandem CAR to either of the two IL-13Rα2 or CD133 ligands-specific antigens for glioblastoma stem cells. SynNotch receptor activation along with the 4-1BB costimulatory domain results in CAR T cell expression under the TRE promoter, culminating in a tri-specific and effective tumor stem cell recognition and elimination of glioblastoma multiforme.

A Guide for Protein-Protein Docking Using SwarmDock

Many of the biological functions of the cell are driven by protein-protein interactions. However, determining which proteins interact and exactly how they do so to enable their functions, remain major research questions. Functional interactions are dependent on a number of complicated factors; therefore, modeling the three-dimensional structure of protein-protein complexes is still considered a complex endeavor. Nevertheless, the rewards for modeling protein interactions to atomic level detail are substantial, and there are numerous examples of how models can provide useful information for drug design, protein engineering, systems biology, and understanding of the immune system. Here, we provide practical guidelines for docking proteins using the web-server, SwarmDock, a flexible protein-protein docking method. Moreover, we provide an overview of the factors that need to be considered when deciding whether docking is likely to be successful.

The 3.2 Å structure of a bioengineered variant of blood coagulation factor VIII indicates two conformations of the C2 domain

BACKGROUND

Coagulation factor VIII represents one of the oldest protein-based therapeutics, serving as an effective hemophilia A treatment for half a century. Optimal treatment consists of repeated intravenous infusions of blood coagulation factor VIII (FVIII) per week for life. Despite overall treatment success, significant limitations remain, including treatment invasiveness, duration, immunogenicity, and cost. These issues have inspired research into the development of bioengineered FVIII products and gene therapies.

OBJECTIVES

To structurally characterize a bioengineered construct of FVIII, termed ET3i, which is a human/porcine chimeric B domain-deleted heterodimer with improved expression and slower A2 domain dissociation following proteolytic activation by thrombin.

METHODS

The structure of ET3i was characterized with X-ray crystallography and tandem mass spectrometry-based glycoproteomics.

RESULTS

Here, we report the 3.2 Å crystal structure of ET3i and characterize the distribution of N-linked glycans with LC-MS/MS glycoproteomics. This structure shows remarkable conservation with the human FVIII protein and provides a detailed view of the interface between the A2 domain and the remaining FVIII structure. With two FVIII molecules in the crystal, we observe two conformations of the C2 domain relative to the remaining FVIII structure. The improved model and stereochemistry of ET3i served as a scaffold to generate an improved, refined structure of human FVIII. With the original datasets at 3.7 Å and 4.0 Å resolution, this new structure resulted in improved refinement statistics.

CONCLUSIONS

These improved structures yield a more confident model for next-generation engineering efforts to develop FVIII therapeutics with longer half-lives, higher expression levels, and lower immunogenicity.

iScore: An MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machines

Computational docking is a promising tool to model three-dimensional (3D) structures of protein-protein complexes, which provides fundamental insights of protein functions in the cellular life. Singling out near-native models from the huge pool of generated docking models (referred to as the scoring problem) remains as a major challenge in computational docking. We recently published iScore, a novel graph kernel based scoring function. iScore ranks docking models based on their interface graph similarities to the training interface graph set. iScore uses a support vector machine approach with random-walk graph kernels to classify and rank protein-protein interfaces. Here, we present the software for iScore. The software provides executable scripts that fully automate the computational workflow. In addition, the creation and analysis of the interface graph can be distributed across different processes using Message Passing interface (MPI) and can be offloaded to GPUs thanks to dedicated CUDA kernels.

Prospects for Discovering the Secondary Metabolites of Cordyceps Sensu Lato by the Integrated Strategy

BACKGROUND

Some species of Cordyceps sensu lato are famous Chinese herbs with significant biological activities, often used as edible food and traditional medicine in China. Cordyceps represents the largest entomopathogenic group of fungi, including 40 genera and 1339 species in three families and incertae sedis of Hypocreales.

OBJECTIVE

Most of the Cordyceps-derivatives have been approved clinically for the treatment of various diseases such as diabetes, cancers, inflammation, cardiovascular, renal and neurological disorders and are used worldwide as supplements and herbal drugs, but there is still need for highly efficient Cordyceps-derived drugs for fatal diseases with approval of the U.S. Food and Drug Administration.

METHODS

Computer-aided drug design concepts could improve the discovery of putative Cordyceps- derived medicine within less time and low budget. The integration of computer-aided drug design methods with experimental validation has contributed to the successful discovery of novel drugs.

RESULTS

This review focused on modern taxonomy, active metabolites, and modern drug design techniques that could accelerate conventional drug design and discovery of Cordyceps s. l. Successful application of computer-aided drug design methods in Cordyceps research has been discussed.

CONCLUSION

It has been concluded that computer-aided drug design techniques could influence the multiple target-focused drug design, because each metabolite of Cordyceps has shown significant activities for the various diseases with very few or no side effects.

The CC' loop of IgV domains of the immune checkpoint receptors, plays a key role in receptor:ligand affinity modulation

Antibodies targeting negative regulators of immune checkpoints have shown unprecedented and durable response against variety of malignancies. While the concept of blocking the negative regulators of the immune checkpoints using mAbs appears to be an outstanding approach, their limited effect and several drawbacks, calls for the rational design of next generation of therapeutics. Soluble isoforms of the negative regulators of immune checkpoint pathways are expressed naturally and regulate immune responses. This suggests, affinity-modified versions of these self-molecules could be effective lead molecules for immunotherapy. To obtain better insights on the hotspot regions for modification, we have analysed structures of 18 immune receptor:ligand complexes containing the IgV domain. Interestingly, this analysis reveals that the CC' loop of IgV domain, a loop which is distinct from CDRs of antibodies, plays a pivotal role in affinity modulation, which was previously not highlighted. It is noteworthy that a ~5-residue long CC' loop in a ~120 residue protein makes significant number of hydrophobic and polar interactions with its cognate ligand. The post-interaction movement of CC' loop to accommodate the incoming ligands, seems to provide additional affinity to the interactions. In silico replacement of the CC' loop of TIGIT with that of Nectin-2 and PVR followed by protein docking trials suggests a key role of the CC' loop in affinity modulation in the TIGIT/Nectin pathway. The CC' loop appears to be a hotspot for the affinity modification without affecting the specificity to their cognate receptors.

Polyelectrolyte character of rigid rod peptide bundlemer chains constructed via hierarchical self-assembly

Short α-helical peptides were computationally designed to self-assemble into robust coiled coils that are antiparallel, homotetrameric bundles. These peptide bundle units, or 'bundlemers', have been utilized as anisotropic building blocks to construct bundlemer-based polymers via a hierarchical, hybrid physical-covalent assembly pathway. The bundlemer chains were constructed using short linker connections via 'click' chemistry reactions between the N-termini of bundlemer constituent peptides. The resulting bundlemer chains appear as extremely rigid, cylindrical rods in transmission electron microscopy (TEM) images. Small angle neutron scattering (SANS) shows that these bundlemer chains exist as individual rods in solution with a cross-section that is equal to that of a single coiled coil bundlemer building block of ≈20 Å. SANS further confirms that the interparticle solution structure of the rigid rod bundlemer chains is heterogeneous and responsive to solution conditions, such as ionic-strength and pH. Due to their peptidic constitution, the bundlemer assemblies behave like polyelectrolytes that carry an average charge density of approximately 3 charges per bundlemer as determined from SANS structure factor data fitting, which describes the repulsion between charged rods in solution. This repulsion manifests as a correlation hole in the scattering profile that is suppressed by dilution or addition of salt. Presence of rod cluster aggregates with a mass fractal dimension of ≈2.5 is also confirmed across all samples. The formation of such dense, fractal-like cluster aggregates in a solution of net repulsive rods is a unique example of the subtle balance between short-range attraction and long-rage repulsion interactions in proteins and other biomaterials. With computational control of constituent peptide sequences, it is further possible to deconvolute the underlying sequence driven structure-property relationships in the modular bundlemer chains.

Structure prediction of biological assemblies using GALAXY in CAPRI rounds 38-45

We participated in CARPI rounds 38-45 both as a server predictor and a human predictor. These CAPRI rounds provided excellent opportunities for testing prediction methods for three classes of protein interactions, that is, protein-protein, protein-peptide, and protein-oligosaccharide interactions. Both template-based methods (GalaxyTBM for monomer protein, GalaxyHomomer for homo-oligomer protein, GalaxyPepDock for protein-peptide complex) and ab initio docking methods (GalaxyTongDock and GalaxyPPDock for protein oligomer, GalaxyPepDock-ab-initio for protein-peptide complex, GalaxyDock2 and Galaxy7TM for protein-oligosaccharide complex) have been tested. Template-based methods depend heavily on the availability of proper templates and template-target similarity, and template-target difference is responsible for inaccuracy of template-based models. Inaccurate template-based models could be improved by our structure refinement and loop modeling methods based on physics-based energy optimization (GalaxyRefineComplex and GalaxyLoop) for several CAPRI targets. Current ab initio docking methods require accurate protein structures as input. Small conformational changes from input structure could be accounted for by our docking methods, producing one of the best models for several CAPRI targets. However, predicting large conformational changes involving protein backbone is still challenging, and full exploration of physics-based methods for such problems is still to come.

Inhibitor discovery for the E. coli meningitis virulence factor IbeA from homology modeling and virtual screening

Escherichia coli (E. coli) K1 is the most common Gram-negative bacteria cause of neonatal meningitis. The penetration of E. coli through the blood-brain barrier is a key step of the meningitis pathogenesis. A host receptor protein, Caspr1, interacts with the E. coli virulence factor IbeA and thus facilitates bacterial penetration through the blood-brain barrier. Based on this result, we have now predicted the binding pattern between Caspr1 and IbeA by an integrated computational protocol. Based on the predicted model, we have identified a putative molecular binding pocket in IbeA, that directly bind with Caspr1. This evidence indicates that the IbeA (229-343aa) region might play a key role in mediating the bacteria invasion. Virtual screening with the molecular model was conducted to search for potential inhibitors from 213,279 commercially available chemical compounds. From the top 50 identified compounds, 9 demonstrated a direct binding ability to the residues within the Caspr1 binding site on IbeA. By using human brain microvascular endothelial cells (hBMEC) with E. coli strain RS218, four molecules were characterized that significantly attenuated the bacteria invasions at concentrations devoid of cell toxicity. Our study provides useful structural information for understanding the pathogenesis of neonatal meningitis, and have identified drug-like compounds that could be used to develop effective anti-meningitis agents.

Protein-assisted RNA fragment docking (RnaX) for modeling RNA-protein interactions using ModelX

Protein–RNA interactions, key in biological processes, remained refractory to prediction algorithms. Here we present a new extension of the ModelX tool suite designed for this purpose. RNA–protein complexes in the Protein Data Bank were decomposed into small peptide–oligonucleotide interacting fragment pairs and used as building blocks to assemble big scaffolds representing complex RNA–protein interactions. This method has already been successful for designing DNA–protein and protein–protein interfaces. Areas under the curve up to 0.86 were achieved on binding site prediction showing the accuracy and coverage of our approach over established and in-house benchmarking sets. Together with FoldX protein design tool suite we were able to engineer backbone- and side chain-compatible interfaces using naked protein structures as input.

RNA–protein interactions are crucial for such key biological processes as regulation of transcription, splicing, translation, and gene silencing, among many others. Knowing where an RNA molecule interacts with a target protein and/or engineering an RNA molecule to specifically bind to a protein could allow for rational interference with these cellular processes and the design of novel therapies. Here we present a robust RNA–protein fragment pair-based method, termed RnaX, to predict RNA-binding sites. This methodology, which is integrated into the ModelX tool suite (http://modelx.crg.es), takes advantage of the structural information present in all released RNA–protein complexes. This information is used to create an exhaustive database for docking and a statistical forcefield for fast discrimination of true backbone-compatible interactions. RnaX, together with the protein design forcefield FoldX, enables us to predict RNA–protein interfaces and, when sufficient crystallographic information is available, to reengineer the interface at the sequence-specificity level by mimicking those conformational changes that occur on protein and RNA mutagenesis. These results, obtained at just a fraction of the computational cost of methods that simulate conformational dynamics, open up perspectives for the engineering of RNA–protein interfaces.

Computational Nanoscopy of Tight Junctions at the Blood-Brain Barrier Interface

The selectivity of the blood-brain barrier (BBB) is primarily maintained by tight junctions (TJs), which act as gatekeepers of the paracellular space by blocking blood-borne toxins, drugs, and pathogens from entering the brain. The BBB presents a significant challenge in designing neurotherapeutics, so a comprehensive understanding of the TJ architecture can aid in the design of novel therapeutics. Unraveling the intricacies of TJs with conventional experimental techniques alone is challenging, but recently developed computational tools can provide a valuable molecular-level understanding of TJ architecture. We employed the computational methods toolkit to investigate claudin-5, a highly expressed TJ protein at the BBB interface. Our approach started with the prediction of claudin-5 structure, evaluation of stable dimer conformations and nanoscale assemblies, followed by the impact of lipid environments, and posttranslational modifications on these claudin-5 assemblies. These led to the study of TJ pores and barriers and finally understanding of ion and small molecule transport through the TJs. Some of these in silico, molecular-level findings, will need to be corroborated by future experiments. The resulting understanding can be advantageous towards the eventual goal of drug delivery across the BBB. This review provides key insights gleaned from a series of state-of-the-art nanoscale simulations (or computational nanoscopy studies) performed on the TJ architecture.

The R882H substitution in the human de novo DNA methyltransferase DNMT3A disrupts allosteric regulation by the tumor supressor p53

A myriad of protein partners modulate the activity of the human DNA methyltransferase 3A (DNMT3A), whose interactions with these other proteins are frequently altered during oncogenesis. We show here that the tumor suppressor p53 decreases DNMT3A activity by forming a heterotetramer complex with DNMT3A. Mutational and modeling experiments suggested that p53 interacts with the same region in DNMT3A as does the structurally characterized DNMT3L. We observed that the p53-mediated repression of DNMT3A activity is blocked by amino acid substitutions within this interface, but surprisingly, also by a distal DNMT3A residue, R882H. DNMT3A R882H occurs frequently in various cancers, including acute myeloid leukemia, and our results suggest that the effects of R882H and other DNMT3A mutations may go beyond changes in DNMT3A methylation activity. To further understand the dynamics of how protein-protein interactions modulate DNMT3A activity, we determined that p53 has a greater affinity for DNMT3A than for DNMT3L and that p53 readily displaces DNMT3L from the DNMT3A:DNMT3L heterotetramer. Interestingly, this occurred even when the preformed DNMT3A:DNMT3L complex was actively methylating DNA. The frequently identified p53 substitutions (R248W and R273H), whereas able to regulate DNMT3A function when forming the DNMT3A:p53 heterotetramer, no longer displaced DNMT3L from the DNMT3A:DNMT3L heterotetramer. The results of our work highlight the complex interplay between DNMT3A, p53, and DNMT3L and how these interactions are further modulated by clinically derived mutations in each of the interacting partners.

InterEvDock2: an expanded server for protein docking using evolutionary and biological information from homology models and multimeric inputs

Computational protein docking is a powerful strategy to predict structures of protein-protein interactions and provides crucial insights for the functional characterization of macromolecular cross-talks. We previously developed InterEvDock, a server for ab initio protein docking based on rigid-body sampling followed by consensus scoring using physics-based and statistical potentials, including the InterEvScore function specifically developed to incorporate co-evolutionary information in docking. InterEvDock2 is a major evolution of InterEvDock which allows users to submit input sequences – not only structures – and multimeric inputs and to specify constraints for the pairwise docking process based on previous knowledge about the interaction. For this purpose, we added modules in InterEvDock2 for automatic template search and comparative modeling of the input proteins. The InterEvDock2 pipeline was benchmarked on 812 complexes for which unbound homology models of the two partners and co-evolutionary information are available in the PPI4DOCK database. InterEvDock2 identified a correct model among the top 10 consensus in 29% of these cases (compared to 15–24% for individual scoring functions) and at least one correct interface residue among 10 predicted in 91% of these cases. InterEvDock2 is thus a unique protein docking server, designed to be useful for the experimental biology community. The InterEvDock2 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock2/.

Lamin A molecular compression and sliding as mechanisms behind nucleoskeleton elasticity

Lamin A is a nuclear intermediate filament protein critical for nuclear architecture and mechanics and mutated in a wide range of human diseases. Yet little is known about the molecular architecture of lamins and mechanisms of their assembly. Here we use SILAC cross-linking mass spectrometry to determine interactions within lamin dimers and between dimers in higher-order polymers. We find evidence for a compression mechanism where coiled coils in the lamin A rod can slide onto each other to contract rod length, likely driven by a wide range of electrostatic interactions with the flexible linkers between coiled coils. Similar interactions occur with unstructured regions flanking the rod domain during oligomeric assembly. Mutations linked to human disease block these interactions, suggesting that this spring-like contraction can explain in part the dynamic mechanical stretch and flexibility properties of the lamin polymer and other intermediate filament networks.

Unintended specificity of an engineered ligand-binding protein facilitated by unpredicted plasticity of the protein fold

Attempts to create novel ligand-binding proteins often focus on formation of a binding pocket with shape complementarity against the desired ligand (particularly for compounds that lack distinct polar moieties). Although designed proteins often exhibit binding of the desired ligand, in some cases they display unintended recognition behavior. One such designed protein, that was originally intended to bind tetrahydrocannabinol (THC), was found instead to display binding of 25-hydroxy-cholecalciferol (25-D3) and was subjected to biochemical characterization, further selections for enhanced 25-D3 binding affinity and crystallographic analyses. The deviation in specificity is due in part to unexpected altertion of its conformation, corresponding to a significant change of the orientation of an α-helix and an equally large movement of a loop, both of which flank the designed ligand-binding pocket. Those changes led to engineered protein constructs that exhibit significantly more contacts and complementarity towards the 25-D3 ligand than the initial designed protein had been predicted to form towards its intended THC ligand. Molecular dynamics simulations imply that the initial computationally designed mutations may contribute to the movement of the helix. These analyses collectively indicate that accurate prediction and control of backbone dynamics conformation, through a combination of improved conformational sampling and/or de novo structure design, represents a key area of further development for the design and optimization of engineered ligand-binding proteins.

Targeting Kinase Interaction Networks: A New Paradigm in PPI Based Design of Kinase Inhibitors

BACKGROUND

Kinases are key modulators in regulating diverse range of cellular activities and are an essential part of the protein-protein interactome. Understanding the interaction of kinases with different substrates and other proteins is vital to decode the cell signaling machinery as well as causative mechanism for disease onset and progression.

OBJECTIVE

The objective of this review is to present all studies on the structure and function of few important kinases and highlight the protein-protein interaction (PPI) mechanism of kinases and the kinase specific interactome databases and how such studies could be utilized to develop anticancer drugs.

METHODS

The article is a review of the detailed description of the various domains in kinases that are involved in protein-protein interactions and specific inhibitors developed targeting these PPI domains.

RESULTS

The review has surfaced in depth the interacting domains in key kinases and their features and the roles of PPI in the human kinome and the various signaling cascades that are involved in certain types of cancer.

CONCLUSION

The insight availed into the mechanism of existing peptide inhibitors and peptidomimetics against kinases will pave way for the design and generation of domain specific peptide inhibitors with better productivity and efficiency and the various software and servers available can be of great use for the identification and analysis of protein-protein interactions.

HDOCK: a web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy

Protein–protein and protein–DNA/RNA interactions play a fundamental role in a variety of biological processes. Determining the complex structures of these interactions is valuable, in which molecular docking has played an important role. To automatically make use of the binding information from the PDB in docking, here we have presented HDOCK, a novel web server of our hybrid docking algorithm of template-based modeling and free docking, in which cases with misleading templates can be rescued by the free docking protocol. The server supports protein–protein and protein–DNA/RNA docking and accepts both sequence and structure inputs for proteins. The docking process is fast and consumes about 10–20 min for a docking run. Tested on the cases with weakly homologous complexes of <30% sequence identity from five docking benchmarks, the HDOCK pipeline tied with template-based modeling on the protein–protein and protein–DNA benchmarks and performed better than template-based modeling on the three protein–RNA benchmarks when the top 10 predictions were considered. The performance of HDOCK became better when more predictions were considered. Combining the results of HDOCK and template-based modeling by ranking first of the template-based model further improved the predictive power of the server. The HDOCK web server is available at http://hdock.phys.hust.edu.cn/.

GalaxyTongDock: Symmetric and asymmetric ab initio protein-protein docking web server with improved energy parameters

Protein-protein docking methods are spotlighted for their roles in providing insights into protein-protein interactions in the absence of full structural information by experiment. GalaxyTongDock is an ab initio protein-protein docking web server that performs rigid-body docking just like ZDOCK but with improved energy parameters. The energy parameters were trained by iterative docking and parameter search so that more native-like structures are selected as top rankers. GalaxyTongDock performs asymmetric docking of two different proteins (GalaxyTongDock_A) and symmetric docking of homo-oligomeric proteins with C_n and D_n symmetries (GalaxyTongDock_C and GalaxyTongDock_D). Performance tests on an unbound docking benchmark set for asymmetric docking and a model docking benchmark set for symmetric docking showed that GalaxyTongDock is better or comparable to other state-of-the-art methods. Experimental and/or evolutionary information on binding interfaces can be easily incorporated by using block and interface options. GalaxyTongDock web server is freely available at http://galaxy.seoklab.org/tongdock. © 2019 Wiley Periodicals, Inc.

Association of BUD13-ZNF259-APOA5-APOA1-SIK3 cluster polymorphism in 11q23.3 and structure of APOA5 with increased plasma triglyceride levels in a Korean population

In this association study on chromosome 11, the data from 12,537 Korean individuals within the Health Examinee (HEXA) and the Korea Association Resource (KARE) projects were analysed to identify genetic loci correlating with increased and decreased plasma triglyceride (TG) levels. We identified a locus in chromosomal region 11q23.3 that harbours genes BUD13, ZNF259, APOA5, APOA1, and SIK3, which may be associated with plasma TG levels. In this locus, 13 relevant single-nucleotide polymorphisms (SNPs) were found: rs184616707, rs118175510, rs60954647, rs79408961, and rs180373 (near BUD13); rs11604424 (in ZNF259); rs2075291, rs651821, and rs7123666 (in or near APOA5); rs525028 (near APOA1), and rs645258, rs10160754, and rs142395187 (in or near SIK3). All 13 SNPs satisfied the genome-wide significance level (P < 5.0 × 10^-8) in both meta-analysis and conditional analysis. Haplotype analysis of six SNPs (rs79408961, rs180373, rs2075291, rs651821, rs525028, and rs10160754) that were selected based on the β coefficient and conditional P values, revealed nine common haplotypes (with frequency 0.02-0.34) associated with both increased and reduced TG levels. Furthermore, to shed light on possible structural implications, we modelled and simulated the G185C variant of APOA5 (corresponding to rs2075291), which showed the strongest association. Molecular dynamics simulation results showed that this polymorphic variant of APOA5 has a different hydrogen bond network, increased average distance between chains, and an ability to form distinct clusters. Owing to the orientation of cysteine, the possibility of disulphide bond formation with other proteins is evident. In summary, our association and modelling analyses provided evidence that genetic variations in chromosomal region 11q23.3 are associated with elevated TG levels.

Proteome-wide, Structure-Based Prediction of Protein-Protein Interactions/New Molecular Interactions Viewer

Determining the complete Arabidopsis (Arabidopsis thaliana) protein-protein interaction network is essential for understanding the functional organization of the proteome. Numerous small-scale studies and a couple of large-scale ones have elucidated a fraction of the estimated 300,000 binary protein-protein interactions in Arabidopsis. In this study, we provide evidence that a docking algorithm has the ability to identify real interactions using both experimentally determined and predicted protein structures. We ranked 0.91 million interactions generated by all possible pairwise combinations of 1,346 predicted structure models from an Arabidopsis predicted "structure-ome" and found a significant enrichment of real interactions for the top-ranking predicted interactions, as shown by cosubcellular enrichment analysis and yeast two-hybrid validation. Our success rate for computationally predicted, structure-based interactions was 63% of the success rate for published interactions naively tested using the yeast two-hybrid system and 2.7 times better than for randomly picked pairs of proteins. This study provides another perspective in interactome exploration and biological network reconstruction using protein structural information. We have made these interactions freely accessible through an improved Arabidopsis Interactions Viewer and have created community tools for accessing these and ∼2.8 million other protein-protein and protein-DNA interactions for hypothesis generation by researchers worldwide. The Arabidopsis Interactions Viewer is freely available at http://bar.utoronto.ca/interactions2/.

DNA-protein interaction: identification, prediction and data analysis

Life in living organisms is dependent on specific and purposeful interaction between other molecules. Such purposeful interactions make the various processes inside the cells and the bodies of living organisms possible. DNA-protein interactions, among all the types of interactions between different molecules, are of considerable importance. Currently, with the development of numerous experimental techniques, diverse methods are convenient for recognition and investigating such interactions. While the traditional experimental techniques to identify DNA-protein complexes are time-consuming and are unsuitable for genome-scale studies, the current high throughput approaches are more efficient in determining such interaction at a large-scale, but they are clearly too costly to be practice for daily applications. Hence, according to the availability of much information related to different biological sequences and clearing different dimensions of conditions in which such interactions are formed, with the developments related to the computer, mathematics, and statistics motivate scientists to develop bioinformatics tools for prediction the interaction site(s). Until now, there has been much progress in this field. In this review, the factors and conditions governing the interaction and the laboratory techniques for examining such interactions are addressed. In addition, developed bioinformatics tools are introduced and compared for this reason and, in the end, several suggestions are offered for the promotion of such tools in prediction with much more precision.

Lactate dehydrogenase A regulates autophagy and tamoxifen resistance in breast cancer

Estrogen receptor (ER) antagonist, tamoxifen has been universally used for the treatment of the ER-positive breast cancer; however, the inevitable emergence of resistance to tamoxifen obstructs the successful treatment of this cancer. So, there is an immediate requirement for the search of a novel therapeutic target for treatment of this cancer. Acquired tamoxifen-resistant breast cancer cell lines MCF-7 (MCF-7/TAM-R) and T47D (T47D/TAM-R) showed higher apoptotic resistance accompanied by induction of pro-survival autophagy compared to their parental cells. Besides, tamoxifen resistance was associated with reduced production of ATP and with overexpression of glycolytic pathways, leading to induced autophagy to meet the energy demand. Further, our study revealed that LDHA; one of the key molecules of glycolysis in association with Beclin-1 induced pro-survival autophagy in tamoxifen-resistant breast cancer. Mechanistically, pharmacological and genetic inhibition of LDHA reduced the pro-survival autophagy, with the restoration of apoptosis and reverting back the EMT like phenomena noticed in tamoxifen-resistant breast cancer. In total, targeting LDHA opened a novel strategy to interrupt autophagy and tamoxifen resistance in breast cancer.

Constitutively active BRS3 is a genuinely orphan GPCR in placental mammals

G protein-coupled receptors (GPCRs) play an important role in physiology and disease and represent the most productive drug targets. Orphan GPCRs, with their endogenous ligands unknown, were considered a source of drug targets and consequently attract great interest to identify their endogenous cognate ligands for deorphanization. However, a contrary view to the ubiquitous existence of endogenous ligands for every GPCR is that there might be a significant overlooked fraction of orphan GPCRs that function constitutively in a ligand-independent manner only. Here, we investigated the evolution of the bombesin receptor-ligand family in vertebrates in which one member-bombesin receptor subtype-3 (BRS3)-is a potential orphan GPCR. With analysis of 17 vertebrate BRS3 structures and 10 vertebrate BRS3 functional data, our results demonstrated that nonplacental vertebrate BRS3 still connects to the original ligands-neuromedin B (NMB) and gastrin-releasing peptide (GRP)-because of adaptive evolution, with significantly changed protein structure, especially in three altered key residues (Q127R, P205S, and R294H) originally involved in ligand binding/activation, whereas the placental mammalian BRS3 lost the binding affinity to NMB/GRP and constitutively activates Gs/Gq/G12 signaling in a ligand-independent manner. Moreover, the N terminus of placental mammalian BRS3 underwent positive selection, exhibiting significant structural differences compared to nonplacental vertebrate BRS3, and this domain plays an important role in constitutive activity of placental mammalian BRS3. In conclusion, constitutively active BRS3 is a genuinely orphan GPCR in placental mammals, including human. To our knowledge, this study identified the first example that might represent a new group of genuinely orphan GPCRs that will never be deorphanized by the discovery of a natural ligand and provided new perspectives in addition to the current ligand-driven GPCR deorphanization.

Integrative Structure Modeling: Overview and Assessment

Integrative structure modeling computationally combines data from multiple sources of information with the aim of obtaining structural insights that are not revealed by any single approach alone. In the first part of this review, we survey the commonly used sources of structural information and the computational aspects of model building. Throughout the past decade, integrative modeling was applied to various biological systems, with a focus on large protein complexes. Recent progress in the field of cryo-electron microscopy (cryo-EM) has resolved many of these complexes to near-atomic resolution. In the second part of this review, we compare a range of published integrative models with their higher-resolution counterparts with the aim of critically assessing their accuracy. This comparison gives a favorable view of integrative modeling and demonstrates its ability to yield accurate and informative results. We discuss possible roles of integrative modeling in the new era of cryo-EM and highlight future challenges and directions.

Benchmarking of different molecular docking methods for protein-peptide docking

Background

Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD.

Result

Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively.

Conclusion

The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench (http://webs.iiitd.edu.in/raghava/ppdbench/).

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2449-y) contains supplementary material, which is available to authorized users.

The human transketolase-like proteins TKTL1 and TKTL2 are bona fide transketolases

Background

Three transketolase genes have been identified in the human genome to date: transketolase (TKT), transketolase-like 1 (TKTL1) and transketolase-like 2 (TKTL2). Altered TKT functionality is strongly implicated in the development of diabetes and various cancers, thus offering possible therapeutic utility. It will be of great value to know whether TKTL1 and TKTL2 are, similarly, potential therapeutic targets. However, it remains unclear whether TKTL1 and TKTL2 are functional transketolases.

Results

Homology modelling of TKTL1 and TKTL2 using TKT as template, revealed that both TKTL1 and TKTL2 could assume a folded structure like TKT. TKTL1/2 presented a cleft of suitable dimensions between the homodimer surfaces that could accommodate the co-factor-substrate. An appropriate cavity and a hydrophobic nodule were also present in TKTL1/2, into which the diphosphate group fitted, and that was implicated in aminopyrimidine and thiazole ring binding in TKT, respectively. The presence of several identical residues at structurally equivalent positions in TKTL1/2 and TKT identified a network of interactions between the protein and co-factor-substrate, suggesting the functional fidelity of TKTL1/2 as transketolases.

Conclusions

Our data support the hypothesis that TKTL1 and TKTL2 are functional transketolases and represent novel therapeutic targets for diabetes and cancer.

Electronic supplementary material

The online version of this article (10.1186/s12900-018-0099-y) contains supplementary material, which is available to authorized users.

How Can Interleukin-1 Receptor Antagonist Modulate Distinct Cell Death Pathways?

Multiple mechanisms of cell death exist (apoptosis, necroptosis, pyroptosis) and the subtle balance of several distinct proteins and inhibitors tightly regulates the cell fate toward one or the other pathway. Here, by combining coimmunoprecipitation, enzyme assays, and molecular simulations, we ascribe a new role, within this entangled regulatory network, to the interleukin-1 receptor antagonist (IL-1Ra). Our study enlightens that IL-1Ra, which usually inhibits the inflammatory effects of IL-1α/β by binding to IL-1 receptor, under advanced pathological states prevents apoptosis and/or necroptosis by noncompetitively inhibiting the activity of caspase-8 and -9. Consensus docking, followed by cumulative 10 μs of molecular dynamics simulations unprecedentedly reveal that IL-1Ra binds both caspases at their dimeric interface, preventing, in this manner, the formation of their catalytically/signaling active form. The resulting IL-1Ra/caspase-8(9) adducts are stabilized by hydrophobic and by few key hydrogen bonding interactions, formed by residues fully conserved across distinct caspases (-3, -6, -7, -8, and -9), and closely resemble the binding mode of the caspases inhibitors XIAP (X-linked inhibitor of apoptosis) and c-FLIP (cellular FLICE-like inhibitory protein). Tight regulation of the different forms of cell death has a major impact on distinct human illnesses (i.e., cancer, neurodegeneration, ischemic injury, atherosclerosis, viral/bacterial infections, and immune reaction). Hence, our study, pinpointing IL-1Ra as new actor of the intricate cell death regulatory network and gaining an atomic-scale understanding of its mechanism may open new avenues toward innovative therapeutic strategies to tackle major human diseases.

Refinement of protein-protein complexes in contact map space with metadynamics simulations

Accurate protein-protein complex prediction, to atomic detail, is a challenging problem. For flexible docking cases, current state-of-the-art docking methods are limited in their ability to exhaustively search the high dimensionality of the problem space. In this study, to obtain more accurate models, an investigation into the local optimization of initial docked solutions is presented with respect to a reference crystal structure. We show how physics-based refinement of protein-protein complexes in contact map space (CMS), within a metadynamics protocol, can be performed. The method uses 5 times replicated 10 ns simulations for sampling and ranks the generated conformational snapshots with ZRANK to identify an ensemble of n snapshots for final model building. Furthermore, we investigated whether the reconstructed free energy surface (FES), or a combination of both FES and ZRANK, referred to as CSα , can help to reduce snapshot ranking error.

Web Services for Molecular Docking Simulations

Docking process is one of the most significant activities for the analysis of protein-protein or protein-ligand complexes. These tools have become of unique importance when allocated in web services, collaborating scientifically with several areas of knowledge in an interdisciplinary way. Among the several web services dedicated to carrying out molecular docking simulations, we selected the DockThor web service. To illustrate the application of DockThor to protein-ligand docking simulations, we analyzed the docking of a ligand against the structure of epidermal growth factor receptor, an essential molecular marker in cancer research.

Advances in distributed computing with modern drug discovery

INTRODUCTION

Computational chemistry dramatically accelerates the drug discovery process and high-performance computing (HPC) can be used to speed up the most expensive calculations. Supporting a local HPC infrastructure is both costly and time-consuming, and, therefore, many research groups are moving from in-house solutions to remote-distributed computing platforms. Areas covered: The authors focus on the use of distributed technologies, solutions, and infrastructures to gain access to HPC capabilities, software tools, and datasets to run the complex simulations required in computational drug discovery (CDD). Expert opinion: The use of computational tools can decrease the time to market of new drugs. HPC has a crucial role in handling the complex algorithms and large volumes of data required to achieve specificity and avoid undesirable side-effects. Distributed computing environments have clear advantages over in-house solutions in terms of cost and sustainability. The use of infrastructures relying on virtualization reduces set-up costs. Distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available. There is a trade-off between cost-effectiveness and accessibility in using on-demand computing resources rather than free/academic resources. Graphics processing unit computing, with its outstanding parallel computing power, is becoming increasingly important.

The Effect of Recombinant Human Erythropoietin on Bacterial Growth: A Dual-Edged Sword

Background

Hypererythropoietinemia is associated with common diseases like non-uremic anaemia where infection burden is high. Erythropoietin (EPO) is also given as therapy for anaemia associated with chronic kidney disease and cancer and in those who are at a higher risk of infections. EPO is known to have an effect on macrophages by which it helps in the growth of some intracellular pathogens. However, its direct role on bacterial growth is currently unknown.

Summary

Here, we investigated the direct effect of recombinant human erythropoietin (rhuEPO) on the growth of pathogenic Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa. In silico experiments were designed to gain insight into the mechanisms. We found that 30 IU/L rhuEPO promoted the growth of E. coli and S. aureus and inhibited the growth of P. aeruginosa. In silico observations suggest that bacterial cell surface proteins may interact with the EPO and may cause the observed effects.

Key Message

It appears that some pathogens can explore EPO to proliferate and growth of others are inhibited by the same. The consequence of such observation is a matter of widespread concern for future research.

Cryptic protein interactions regulate DNA replication initiation

DNA replication is a fundamental biological process that is tightly regulated in all cells. In bacteria, DnaA controls when and where replication begins by building a step-wise complex that loads the replicative helicase onto chromosomal DNA. In many low-GC Gram-positive species, DnaA recruits the DnaD and DnaB proteins to function as adaptors to assist in helicase loading. How DnaA, its adaptors and the helicase form a complex at the origin is unclear. We addressed this question using the bacterial two-hybrid assay to determine how the initiation proteins from Bacillus subtilis interact with each other. We show that cryptic interaction sites play a key role in this process and we map these regions for the entire pathway. In addition, we found that the SirA regulator that blocks initiation in sporulating cells binds to a surface on DnaA that overlaps with DnaD. The interaction between DnaA and DnaD was also mapped to the same DnaA surface in the human pathogen Staphylococcus aureus, demonstrating the broad conservation of this surface. Therefore, our study has unveiled key protein interactions essential for initiation and our approach is widely applicable for mapping interactions in other signaling pathways that are governed by cryptic binding surfaces.

The Diversity of Mammalian Hemoproteins and Microbial Heme Scavengers Is Shaped by an Arms Race for Iron Piracy

Iron is an essential micronutrient for most living species. In mammals, hemoglobin (Hb) stores more than two thirds of the body's iron content. In the bloodstream, haptoglobin (Hp) and hemopexin (Hpx) sequester free Hb or heme. Pathogenic microorganisms usually acquire iron from their hosts and have evolved complex systems of iron piracy to circumvent nutritional immunity. Herein, we performed an evolutionary analysis of genes coding for mammalian heme-binding proteins and heme-scavengers in pathogen species. The underlying hypothesis is that these molecules are engaged in a molecular arms race. We show that positive selection drove the evolution of mammalian Hb and Hpx. Positively selected sites in Hb are located at the interaction surface with Neisseria meningitidis heme scavenger HpuA and with Staphylococcus aureus iron-regulated surface determinant B (IsdB). In turn, positively selected sites in HpuA and IsdB are located in the flexible protein regions that contact Hb. A residue in Hb (S45H) was also selected on the Caprinae branch. This site stabilizes the interaction with Trypanosoma brucei hemoglobin-haptoglobin (HbHp) receptor (TbHpHbR), a molecule that also mediates trypanosome lytic factor (TLF) entry. In TbHpHbR, positive selection drove the evolution of a variant (L210S) which allows evasion from TLF but reduces affinity for HbHp. Finally, selected sites in Hpx are located at the interaction surface with the Haemophilus influenzae hemophore HxuA, which in turn displays fast evolving sites at the Hpx-binding interface. These results shed light into host-pathogens conflicts and establish the importance of nutritional immunity as an evolutionary force.

An Evaluation of the Potential of NMR Spectroscopy and Computational Modelling Methods to Inform Biopharmaceutical Formulations

Protein-based therapeutics are considered to be one of the most important classes of pharmaceuticals on the market. The growing need to prolong stability of high protein concentrations in liquid form has proven to be challenging. Therefore, significant effort is being made to design formulations which can enable the storage of these highly concentrated protein therapies for up to 2 years. Currently, the excipient selection approach involves empirical high-throughput screening, but does not reveal details on aggregation mechanisms or the molecular-level effects of the formulations under storage conditions. Computational modelling approaches have the potential to elucidate such mechanisms, and rapidly screen in silico prior to experimental testing. Nuclear Magnetic Resonance (NMR) spectroscopy can also provide complementary insights into excipient–protein interactions. This review will highlight the underpinning principles of molecular modelling and NMR spectroscopy. It will also discuss the advancements in the applications of computational and NMR approaches in investigating excipient–protein interactions.

Targeting detoxification genes by phloem-mediated RNAi: A new approach for controlling phloem-feeding insect pests

Many phloem-feeding insects are considered severe pests of agriculture and are controlled mainly by chemical insecticides. Continued extensive use of these inputs is environmentally undesirable, and also leads to the development of insecticide resistance. Here, we used a plant-mediated RNA interference (RNAi) approach, to develop a new control strategy for phloem-feeding insects. The approach aims to silence "key" detoxification genes, involved in the insect's ability to neutralize defensive and toxic plant chemistry. We targeted a glutathione S-transferase (GST) gene, BtGSTs5, in the phloem-feeding whitefly Bemisia tabaci, a devastating global agricultural pest. We report three major findings. First, significant down regulation of the BtGSTs5 gene was obtained in the gut of B. tabaci when the insects were fed on Arabidopsis thaliana transgenic plants expressing dsRNA against BtGSTs5 under a phloem-specific promoter. This brings evidence that phloem-feeding insects can be efficiently targeted by plant-mediated RNAi. Second, in-silico and in-vitro analyses indicated that the BtGSTs5 enzyme can accept as substrates, hydrolyzed aliphatic- and indolic-glucosinolates, and produce their corresponding detoxified conjugates. Third, performance assays suggested that the BtGSTs5 gene silencing prolongs the developmental period of B. tabaci nymphs. Taken together, these findings suggest that BtGSTs5 is likely to play an important role in enabling B. tabaci to successfully feed on glucosinolate-producing plants. Targeting the gene by RNAi in Brassicaceae cropping systems, will likely not eliminate the pest populations from the fields but will significantly reduce their success over the growing season, support prominent activity of natural enemies, eventually allowing the establishment of stable and sustainable agroecosystem.

Structural rearrangements in the C-terminal domain homolog of Orange Carotenoid Protein are crucial for carotenoid transfer

A recently reported family of soluble cyanobacterial carotenoproteins, homologs of the C-terminal domain (CTDH) of the photoprotective Orange Carotenoid Protein, is suggested to mediate carotenoid transfer from the thylakoid membrane to the Helical Carotenoid Proteins, which are paralogs of the N-terminal domain of the OCP. Here we present the three-dimensional structure of a carotenoid-free CTDH variant from Anabaena (Nostoc) PCC 7120. This CTDH contains a cysteine residue at position 103. Two dimer-forming interfaces were identified, one stabilized by a disulfide bond between monomers and the second between each monomer's β-sheets, both compatible with small-angle X-ray scattering data and likely representing intermediates of carotenoid transfer processes. The crystal structure revealed a major positional change of the C-terminal tail. Further mutational analysis revealed the importance of the C-terminal tail in both carotenoid uptake and delivery. These results have allowed us to suggest a detailed model for carotenoid transfer via these soluble proteins.

EPO does not promote interaction between the erythropoietin and beta-common receptors

A direct interaction between the erythropoietin (EPOR) and the beta-common (βc) receptors to form an Innate Repair Receptor (IRR) is controversial. On one hand, studies have shown a functional link between EPOR and βc receptor in tissue protection while others have shown no involvement of the βc receptor in tissue repair. To date there is no biophysical evidence to confirm a direct association of the two receptors either in vitro or in vivo. We investigated the existence of an interaction between the extracellular regions of EPOR and the βc receptor in silico and in vitro (either in the presence or absence of EPO or EPO-derived peptide ARA290). Although a possible interaction between EPOR and βc was suggested by our computational and genomic studies, our in vitro biophysical analysis demonstrates that the extracellular regions of the two receptors do not specifically associate. We also explored the involvement of the βc receptor gene (Csf2rb) under anaemic stress conditions and found no requirement for the βc receptor in mice. In light of these studies, we conclude that the extracellular regions of the EPOR and the βc receptor do not directly interact and that the IRR is not involved in anaemic stress.

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.

Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

Molecular characterization of second tomato α1,3/4-fucosidase (α-Fuc'ase Sl-2), a member of glycosyl hydrolase family 29 active toward the core α1,3-fucosyl residue in plant N-glycans

In a previous study, we molecular-characterized a tomato (Solanum lycopersicum) α1, 3/4-fucosidase (α-Fuc'ase Sl-1) encoded in a tomato gene (Solyc03g006980), indicating that α-Fuc'ase Sl-1 is involved in the turnover of Lea epitope-containing N-glycans. In this study, we have characterized another tomato gene (Solyc11g069010) encoding α1, 3/4-fucosidase (α-Fuc'ase Sl-2), which is also active toward the complex type N-glycans containing Lea epitope(s). The baculovirus-insect cell expression system was used to express that α-Fuc'ase Sl-2 with anti-FLAG tag, and the expression product (rFuc'ase Sl-2), was found as a 65 kDa protein using SDS-PAGE and has an optimum pH of around 5.0. Similarly to rFuc'ase Sl-1, rFuc'ase Sl-2 hydrolyzed the non-reducing terminal α1, 3-fucose residue on LNFP III and α1, 4-fucose residues of Lea epitopes on plant complex type N-glycans, but not the core α1, 3-fucose residue on Manβ1-4GlcNAcβ1-4(Fucα1-3)GlcNAc or Fucα1-3GlcNAc. However, we found that both α-Fuc'ases Sl-1 and Sl-2 were specifically active toward α1, 3-fucose residue on GlcNAcβ1-4(Fucα1-3)GlcNAc, indicating that the non-substituted β-GlcNAc linked to the proximal GlcNAc residue of the core tri-saccharide moiety of plant specific N-glycans must be a pre-requisite for α-Fuc'ase activity. A 3 D modelled structure of the catalytic sites of α-Fuc'ase Sl-2 suggested that Asp192 and Glu236 may be important for binding to the α1, 3/4 fucose residue.

HSYMDOCK: a docking web server for predicting the structure of protein homo-oligomers with Cn or Dn symmetry

A major subclass of protein-protein interactions is formed by homo-oligomers with certain symmetry. Therefore, computational modeling of the symmetric protein complexes is important for understanding the molecular mechanism of related biological processes. Although several symmetric docking algorithms have been developed for Cn symmetry, few docking servers have been proposed for Dn symmetry. Here, we present HSYMDOCK, a web server of our hierarchical symmetric docking algorithm that supports both Cn and Dn symmetry. The HSYMDOCK server was extensively evaluated on three benchmarks of symmetric protein complexes, including the 20 CASP11-CAPRI30 homo-oligomer targets, the symmetric docking benchmark of 213 Cn targets and 35 Dn targets, and a nonredundant test set of 55 transmembrane proteins. It was shown that HSYMDOCK obtained a significantly better performance than other similar docking algorithms. The server supports both sequence and structure inputs for the monomer/subunit. Users have an option to provide the symmetry type of the complex, or the server can predict the symmetry type automatically. The docking process is fast and on average consumes 10∼20 min for a docking job. The HSYMDOCK web server is available at http://huanglab.phys.hust.edu.cn/hsymdock/.

Direct Phosphorylation of SRC Homology 3 Domains by Tyrosine Kinase Receptors Disassembles Ligand-Induced Signaling Networks

Phosphotyrosine (pTyr) signaling has evolved into a key cell-to-cell communication system. Activated receptor tyrosine kinases (RTKs) initiate several pTyr-dependent signaling networks by creating the docking sites required for the assembly of protein complexes. However, the mechanisms leading to network disassembly and its consequence on signal transduction remain essentially unknown. We show that activated RTKs terminate downstream signaling via the direct phosphorylation of an evolutionarily conserved Tyr present in most SRC homology (SH) 3 domains, which are often part of key hub proteins for RTK-dependent signaling. We demonstrate that the direct EPHA4 RTK phosphorylation of adaptor protein NCK SH3s at these sites results in the collapse of signaling networks and abrogates their function. We also reveal that this negative regulation mechanism is shared by other RTKs. Our findings uncover a conserved mechanism through which RTKs rapidly and reversibly terminate downstream signaling while remaining in a catalytically active state on the plasma membrane.