Optimal clustering for detecting near-native conformations in protein docking

LIGYSIS-web: a resource for the analysis of protein-ligand binding sites

LIGYSIS-web is a free website accessible to all users without any login requirement for the analysis of protein-ligand binding sites. LIGYSIS-web hosts a database of 65,000 protein-ligand binding sites across 25,000 proteins. LIGYSIS sites are defined by aggregating unique relevant protein-ligand interfaces across different biological assemblies of the same protein deposited on the PDBe. Additionally, users can upload their own structures in PDB or mmCIF format for analysis and subsequent visualisation and download. Ligand sites are characterised using evolutionary divergence from a multiple sequence alignment, human missense genetic variation from gnomAD and relative solvent accessibility to obtain accessibility-based cluster labels and scores indicating likelihood of function. These results are displayed in the LIGYSIS web server, a Python Flask web application with a JavaScript frontend employing Jinja and jQuery to link the 3Dmol.js structure viewer with dynamic tables and Chart.js graphs in an interactive manner. LIGYSIS-web is available at https://www.compbio.dundee.ac.uk/ligysis/, whilst the source code for the analysis pipelines and web application can be accessed at https://github.com/bartongroup/LIGYSIS, https://github.com/bartongroup/LIGYSIS-custom and https://github.com/bartongroup/LIGYSIS-web, respectively.

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

Comparative analysis of canine and human HtrA2 to delineate its role in apoptosis and cancer

Therapeutically, targeting the pro- and anti-apoptotic proteins has been one of the major approaches behind devising strategies to combat associated diseases. Human high-temperature requirement serine protease A2 (hHtrA2), which induces apoptosis through both caspase-dependent and independent pathways is implicated in several diseases including cancer, ischemic heart diseases, and neurodegeneration, thus making it a promising target molecule. In the recent past, the canine model has gained prominence in the understanding of human pathophysiology that was otherwise limited to the rodent system. Moreover, canine models in cancer research provide an opportunity to study spontaneous tumors as their size, lifespan, and environmental exposure are significantly closer to that of humans compared with laboratory rodents. Therefore, using HtrA2 as a model protein, comparative analysis has been done to revisit the hypothesis that canines might be excellent models for cancer research. We have performed evolutionary phylogenetic analyses that confirm a close relationship between canine and human HtrA2s. Molecular modeling demonstrates structural similarities including orientation of the catalytic triad residues, followed by in silico docking and molecular dynamics simulation studies that identify the potential interacting partners for canine HtrA2 (cHtrA2). In vitro biophysical and protease studies depict similarities in interaction with their respective substrates as well as transient transfection of cHtrA2 in mammalian cell culture shows induction of apoptosis. This work, therefore, promises to open a new avenue in cancer research through the study of spontaneous cancer model systems in canines.

Design, Synthesis, and Herbicidal Activity of Natural Naphthoquinone Derivatives Containing Diaryl Ether Structures

Photosynthesis system II (PS II) is an important target for the development of bioherbicides. In this study, a series of natural naphthoquinone derivatives containing diaryl ether were designed and synthesized based on the binding model of lawsone and PS II D1. Bioassays exhibited that most compounds had more than 80% inhibition of Portulaca oleracea and Echinochloa crusgalli roots at a dose of 100 μg/mL and compounds B4, B5, and C3 exhibited superior herbicidal activities against dicotyledonous and monocotyledon weeds to commercial atrazine. In particular, compound B5 exhibited excellent herbicidal activity at a dosage of 150 g a.i./ha. In addition, compared with atrazine, compound B5 causes less damage to crops. Molecular docking studies revealed that compound B5 effectively interacted with Pisum sativum PS II D1 via diverse interaction models, such as π-π stacking and hydrogen bonds. Molecular dynamics simulation studies and chlorophyll fluorescence measurements revealed that compound B5 acted on PS II. This is the first report of natural naphthoquinone derivatives targeting PS II and compound B5 may be a candidate molecule for the development of new herbicides targeting PS II.

Naringenin-4'-glucuronide as a new drug candidate against the COVID-19 Omicron variant: a study based on molecular docking, molecular dynamics, MM/PBSA and MM/GBSA

This study aimed to identify natural bioactive compounds (NBCs) as potential inhibitors of the spike (S1) receptor binding domain (RBD) of the COVID-19 Omicron variant using computer simulations (in silico). NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening, molecular docking, molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), and molecular mechanics/generalized Born surface area (MM/GBSA). Remdesivir was used as a reference drug in docking and MD calculations. A total of 170,906 compounds were analyzed. Molecular docking screening revealed the top four NBCs with a high affinity with the spike (affinity energy <-7 kcal/mol) to be ZINC000045789238, ZINC000004098448, ZINC000008662732, and ZINC000003995616. In the MD analysis, the four ligands formed a complex with the highest dynamic equilibrium S1 (mean RMSD <0.3 nm), lowest fluctuation of the complex amino acid residues (RMSF <1.3), and solvent accessibility stability. However, the ZINC000045789238-spike complex (naringenin-4'-O glucuronide) was the only one that simultaneously had minus signal (-) MM/PBSA and MM/GBSA binding free energy values (-3.74 kcal/mol and -15.65 kcal/mol, respectively), indicating favorable binding. This ligand (naringenin-4'-O glucuronide) was also the one that produced the highest number of hydrogen bonds in the entire dynamic period (average = 4601 bonds per nanosecond). Six mutant amino acid residues formed these hydrogen bonds from the RBD region of S1 in the Omicron variant: Asn417, Ser494, Ser496, Arg403, Arg408, and His505. Naringenin-4'-O-glucuronide showed promising results as a potential drug candidate against COVID-19. In vitro and preclinical studies are needed to confirm these findings.Communicated by Ramaswamy H. Sarma.

Classification of likely functional class for ligand binding sites identified from fragment screening

Fragment screening is used to identify binding sites and leads in drug discovery, but it is often unclear which binding sites are functionally important. Here, data from 37 experiments, and 1309 protein structures binding to 1601 ligands were analysed. A method to group ligands by binding sites is introduced and sites clustered according to profiles of relative solvent accessibility. This identified 293 unique ligand binding sites, grouped into four clusters (C1-4). C1 includes larger, buried, conserved, and population missense-depleted sites, enriched in known functional sites. C4 comprises smaller, accessible, divergent, missense-enriched sites, depleted in functional sites. A site in C1 is 28 times more likely to be functional than one in C4. Seventeen sites, which to the best of our knowledge are novel, in 13 proteins are identified as likely to be functionally important with examples from human tenascin and 5-aminolevulinate synthase highlighted. A multi-layer perceptron, and K-nearest neighbours model are presented to predict cluster labels for ligand binding sites with an accuracy of 96% and 100%, respectively, so allowing functional classification of sites for proteins not in this set. Our findings will be of interest to those studying protein-ligand interactions and developing new drugs or function modulators.

A multi-epitope based vaccine against the surface proteins expressed in cyst and trophozoite stages of parasite Entamoeba histolytica

Entamoeba histolytica, an anaerobic parasite, infects humans and other primates and causes fatal diseases, such as amebiasis, amebic liver abscesses, and many others. Thousands of people are infected and dying due to the need for a proper protective cure, especially in poor sanitizing regions, such as Latin America, Asia, and Africa. Around 10% of the world population is infected by E. histolytica every year. Consequently, novel preventive approaches are required to eliminate the threats of the parasite. A designed vaccine targeting the exposed proteins that are common between cyst and trophozoite stages of the parasite's life cycle would be an effective way to repress the impact of the parasite. Therefore, an in silico bioinformatics approach was performed to design an effective vaccine targeting surface proteins common between both stages of the parasite's life cycle using B-cell and T-cell epitopes. The epitopes derived from the conserved portions of the proteins and their corresponding isomers specific to the parasite suggested that the vaccine could benefit cross-protection. Furthermore, the three-dimensional structure of the designed vaccine was modelled, refined, and validated using multiple bioinformatics tools. The physiological properties and solubility were also predicted using different algorithmic tools and found to be highly soluble in nature. The vaccine was found interactcted with TLR immune receptors, and the stability was observed via dynamics simulation. Codon optimization and cloning were performed for expression analysis. Immune simulation prediction anticipated significant immune responses with a high IgG and IgM antibodies expression, Th and Tc cells population, B-cell population, memory cells, INF-γ, and IL-2 cytokines. Therefore, the constructed multi-epitope putative vaccine can effectively neutralize the parasite's harmful effects.

Highly sensitive and label-free electrochemical detection of C-reactive protein on a peptide receptor-gold nanoparticle-black phosphorous nanocomposite modified electrode

C-reactive protein (CRP) is a phylogenetically highly conserved plasma protein found in blood serum, and an enhanced CRP level is indicative of inflammatory conditions such as infection and cancer, among others. In this work, we developed a novel high CRP-affinity peptide-functionalized label-free electrochemical biosensor for the highly sensitive and selective detection of CRP. Throughout biopanning with random peptide libraries, high affinity peptides for CRP was successfully identified, and then a series of synthetic peptide receptor, of which C-terminus was incorporated to gold binding peptide (GBP) as an anchoring motif was covalently immobilized onto gold nanoparticle (AuNPs) tethered polydopamine (PDA)‒black phosphorus (BP) (AuNPs@BP@PDA) nanocomposite electrode. Interaction between the CRP-binding peptide and CRP was confirmed via enzyme-linked immunosorbent assay along with various physicochemical and electrochemical analyses. Under the optimized experimental conditions, the proposed peptide-based biosensor detects CRP in the range of 0-0.036 μg/mL with a detection limit (LOD) of 0.7 ng/mL. The developed sensor effectively detects CRP in the real samples of serum and plasma of Crohn's disease patients. Thus, the fabricated peptide-based biosensor has potential applications in clinical diagnosis and medical applications.

Sensitive Detection of BVDV Using Gold Nanoparticle-Modified Few-Layer Black Phosphorus with Affinity Peptide-Based Electrochemical Sensor

The lethality of the bovine viral diarrhea virus (BVDV) in cattle involves inapparent infection and various, typically subclinical, syndromes. Cattle of all ages are vulnerable to infection with the virus. It also causes considerable economic losses, primarily due to reduced reproductive performance. In the absence of treatment that can completely cure infected animals, detection of BVDV relies on highly sensitive and selective diagnosis methods. In this study, an electrochemical detection system was developed as a useful and sensitive system for the detection of BVDV to suggest the direction of diagnostic technology through the development of conductive nanoparticle synthesis. As a countermeasure, a more sensitive and rapid BVDV detection system was developed using the synthesis of electroconductive nanomaterials black phosphorus (BP) and gold nanoparticle (AuNP). To increase the conductivity effect, AuNP was synthesized on the BP surface, and the stability of BP was improved by using dopamine self-polymerization. Moreover, its characterizations, electrical conductivity, selectivity, and sensitivity toward BVDV also have been investigated. The BP@AuNP-peptide-based BVDV electrochemical sensor exhibited a low detection limit of 0.59 copies mL^-1 with high selectivity and long-term stability (retaining 95% of its initial performance over 30 days).

MANF regulates neuronal survival and UPR through its ER-located receptor IRE1α

Mesencephalic astrocyte-derived neurotrophic factor (MANF) is an endoplasmic reticulum (ER)-located protein with cytoprotective effects in neurons and pancreatic β cells in vitro and in models of neurodegeneration and diabetes in vivo. However, the exact mode of MANF action has remained elusive. Here, we show that MANF directly interacts with the ER transmembrane unfolded protein response (UPR) sensor IRE1α, and we identify the binding interface between MANF and IRE1α. The expression of wild-type MANF, but not its IRE1α binding-deficient mutant, attenuates UPR signaling by decreasing IRE1α oligomerization; phosphorylation; splicing of Xbp1, Atf6, and Txnip levels; and protecting neurons from ER stress-induced death. MANF-IRE1α interaction and not MANF-BiP interaction is crucial for MANF pro-survival activity in neurons in vitro and is required to protect dopamine neurons in an animal model of Parkinson's disease. Our data show IRE1α as an intracellular receptor for MANF and regulator of neuronal survival.

Efficient DNA-ligand interaction framework using fuzzy C-means clustering based glowworm swarm optimization (FCMGSO) method

Assessment of DNA and ligand interaction is a great challenge to the medical researchers and drug industries since the accurate mapping of DNA and ligand plays an important role in associating drugs for suitable diseases. The primary objective of this research work is to develop an efficient model for predicting the best DNA and Ligand mapping. In this research work, 500 instances of DNA and drugs used for cancer and non-cancer diseases from the National Centre for Biotechnology Information (NCBI) were considered for analysis. Binding energy is one of the important measures to predict and finalize the best DNA and ligand interaction. Existing methods used for the docking process such as Simulated Annealing (SA), Lamarckian Genetic Algorithm (LGA), Genetic Clustering (GC), Fuzzy C-means clustering (FCM), and Genetic Clustering with Multi swarm Optimization (GCMSO) were applied for all 500 instances. These algorithms failed to produce better binding energy due to a lack of optimization in the existing approaches. Optimization methods play a major role in predicting accurate DNA ligand docking. Hence, this research proposes an efficient architecture using Fuzzy C-Means Clustering with Glowworm Swarm (FCMGSO) optimization method for accurate analysis of the DNA-ligand docking process. Results are proving that the proposed FCMGSO algorithm shows less binding energy than other existing methods in all instances of samples considered from the NCBI dataset.Communicated by Ramaswamy H. Sarma.

Purification, Characterization and Functional Site Prediction of the Vaccinia-related Kinase 2A Small Transmembrane Domain

Vaccinia-related kinases (VRK) are serine-threonine kinases that regulate several signaling pathways. The isoform-VRK2A of one such kinase VRK2 controls cell stress response by interacting with TAK1, a mitogen-activated protein 3 kinase (MAP3K), via its partly cytosolic C-terminal transmembrane domain (VTMD). To establish the driving force and identify the key residues of the VRK2A-TAK1 interaction, we expressed and purified the standalone 3.6 kDa VTMD in the bacterial system using a unique and atypical two-step approach, when the effort to obtain full-length VRK2A remained unsuccessful. Characterization of biophysical properties demonstrated that VTMD domain maintains its structural integrity. Furthermore, dissecting the VRK2A-TAK1 binding interface using in silico tools provided important cues toward engineering the VRK2A-TAK1 interface to modulate its functions with desired characteristics. Most importantly, this novel purification strategy demonstrates its universal applicability in protein biochemistry research by serving as a model system for obtaining difficult-to-purify small proteins or domains.•

VRK2A is a highly disordered transmembrane (TM) kinase, whose TM domain interacts with TAK1 (transforming growth factor-β-activated kinase).

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The standalone VRK2A-TM domain (VTMD) was purified using affinity chromatography followed by two-step centricon based approach.

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Biophysical and in silico analyses confirmed structural integrity of the domain.

A phage virus-based electrochemical biosensor for highly sensitive detection of ovomucoid

Whole peptide-displayed phage particles are promising alternatives to antibodies in sensor development; however, greater control and functionalization of these particles are required. In this study, we aimed to identify and create highly sensitive and selective phage-based electrochemical biosensors for detecting ovomucoid, a known food allergen. Phage display was performed using two different phage libraries (cyclic and linear form of peptides), which displayed affinity peptides capable of binding specifically to ovomucoid. Throughout the biopanning, two phage clones that displayed both peptides (CTDKASSSC and WWQPYSSAPRWL) were selected. After the characterization of their binding affinities, both whole phage particles were covalently attached to a gold electrode using crosslinking chemistry (MUA-EDC/NHS and Sulfo-LC/SPDP); the developed phage sensor was characterized using cyclic voltammetry (CV), square wave voltammetry (SWV), and electrochemical impedance spectroscopy (EIS). The cyclic peptide-displayed phage sensor modified using EDC/NHS chemistry exhibited significantly better binding affinity (K_d = 2.36 ± 0.44 μg/mL) and limit of detection (LOD, 0.12 μg/mL) for ovomucoid than the linear phage sensor, resulting in good reproducibility and recovery, even in an actual egg and white wine samples. This approach may provide an alternative and more efficient way of sensing food allergens with desirable sensitivity, selectivity, and feasibility in food diagnostic applications.

Information-Driven Docking for TCR-pMHC Complex Prediction

T cell receptor (TCR) recognition of peptides presented by major histocompatibility complex (MHC) molecules is a fundamental process in the adaptive immune system. An understanding of this recognition process at the molecular level is crucial for TCR based therapeutics and vaccine design. The broad nature of TCR diversity and cross-reactivity presents a challenge for traditional structural resolution. Computational modelling of TCR-pMHC complexes offers an efficient alternative. This study compares the ability of four general-purpose docking platforms (ClusPro, LightDock, ZDOCK and HADDOCK) to make use of varying levels of binding interface information for accurate TCR-pMHC modelling. Each platform was tested on an expanded benchmark set of 44 TCR-pMHC docking cases. In general, HADDOCK is shown to be the best performer. Docking strategy guidance is provided to obtain the best models for each platform for future research. The TCR-pMHC docking cases used in this study can be downloaded from https://github.com/innate2adaptive/ExpandedBenchmark.

An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants

Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.

Evaluation of CONSRANK-Like Scoring Functions for Rescoring Ensembles of Protein–Protein Docking Poses

Scoring is a challenging step in protein–protein docking, where typically thousands of solutions are generated. In this study, we ought to investigate the contribution of consensus-rescoring, as introduced by Oliva et al. (2013) with the CONSRANK method, where the set of solutions is used to build statistics in order to identify recurrent solutions. We explore several ways to perform consensus-based rescoring on the ZDOCK decoy set for Benchmark 4. We show that the information of the interface size is critical for successful rescoring in this context, but that consensus rescoring in itself performs less well than traditional physics-based evaluation. The results of physics-based and consensus-based rescoring are partially overlapping, supporting the use of a combination of these approaches.

P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking

MOTIVATION

The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy.

RESULTS

In this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3-20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK's decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein-RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html.

AVAILABILITY AND IMPLEMENTATION

www.rnabinding.com/P3DOCK/P3DOCK.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Performance and Its Limits in Rigid Body Protein-Protein Docking

The development of fast Fourier transform (FFT) algorithms enabled the sampling of billions of complex conformations and thus revolutionized protein-protein docking. FFT-based methods are now widely available and have been used in hundreds of thousands of docking calculations. Although the methods perform "soft" docking, which allows for some overlap of component proteins, the rigid body assumption clearly introduces limitations on accuracy and reliability. In addition, the method can work only with energy expressions represented by sums of correlation functions. In this paper we use a well-established protein-protein docking benchmark set to evaluate the results of these limitations by focusing on the performance of the docking server ClusPro, which implements one of the best rigid body methods. Furthermore, we explore the theoretical limits of accuracy when using established energy terms for scoring, provide comparison with flexible docking algorithms, and review the historical performance of servers in the CAPRI docking experiment.

Bayesian Active Learning for Optimization and Uncertainty Quantification in Protein Docking

Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is a lack of rigorous uncertainty quantification (UQ). To fill the gap, we introduce a novel algorithm, Bayesian active learning (BAL), for optimization and UQ of such black-box functions with applications to flexible protein docking. BAL directly models the posterior distribution of the global optimum (i.e., native structures) with active sampling and posterior estimation iteratively feeding each other. Furthermore, it uses complex normal modes to span a homogeneous, Euclidean conformation space suitable for high-dimensional optimization and constructs funnel-like energy models for quality estimation of encounter complexes. Over a protein-docking benchmark set and a CAPRI set including homology docking, we establish that BAL significantly improves against starting points from rigid docking and refinements by particle swarm optimization, providing a top-3 near-native prediction for one third targets. Quality assessment empowered with UQ leads to tight quality intervals with half range around 25% of the actual interface root-mean-square deviation and confidence level at 85%. BAL's estimated probability of a prediction being near-native achieves binary classification AUROC at 0.93 and area under the precision recall curve over 0.60 (compared to 0.50 and 0.14, respectively, by chance), which also improves ranking predictions. This study represents the first UQ solution for protein docking, with rigorous theoretical frameworks and comprehensive empirical assessments.

Protein-Protein and Protein-Peptide Docking with ClusPro Server

The process of creating a model of the structure formed by a pair of interacting molecules is commonly referred to as docking. Protein docking is one of the most studied topics in computational and structural biology with applications to drug design and beyond. In this chapter, we describe ClusPro, a web server for protein-protein and protein-peptide docking. As an input, the server requires two Protein Data Bank (PDB) files (protein-protein mode) or a PDB file for the protein and a sequence for the ligand (protein-peptide mode). Its output consists of ten models of the resulting structure formed by the two objects upon interaction. The server typically produces results in less than 4 h. The server also provides tools (via "Advanced Options" list) for a user to fine-tune the results using any additional knowledge about the interaction process, e.g., small-angle X-ray scattering (SAXS) profile or distance restraints.

Structural modeling and role of HAX-1 as a positive allosteric modulator of human serine protease HtrA2

HAX-1, a multifunctional protein involved in cell proliferation, calcium homeostasis, and regulation of apoptosis, is a promising therapeutic target. It regulates apoptosis through multiple pathways, understanding of which is limited by the obscurity of its structural details and its intricate interaction with its cellular partners. Therefore, using computational modeling, biochemical, functional enzymology and spectroscopic tools, we predicted the structure of HAX-1 as well as delineated its interaction with one of it pro-apoptotic partner, HtrA2. In this study, three-dimensional structure of HAX-1 was predicted by threading and ab initio tools that were validated using limited proteolysis and fluorescence quenching studies. Our pull-down studies distinctly demonstrate that the interaction of HtrA2 with HAX-1 is directly through its protease domain and not via the conventional PDZ domain. Enzymology studies further depicted that HAX-1 acts as an allosteric activator of HtrA2. This 'allosteric regulation' offers promising opportunities for the specific control and functional modulation of a wide range of biological processes associated with HtrA2. Hence, this study for the first time dissects the structural architecture of HAX-1 and elucidates its role in PDZ-independent activation of HtrA2.

Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations

Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play a key role in cellular signaling, protein trafficking, immunology, and oncology. However, it is challenging to predict peptide-protein binding with conventional computational modeling approaches, due to slow dynamics and high peptide flexibility. Here, we present a prototype of the approach which combines global peptide docking using ClusPro PeptiDock and all-atom enhanced simulations using Gaussian accelerated molecular dynamics (GaMD). For three distinct model peptides, the lowest backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures obtained from PeptiDock were 3.3–4.8 Å, being medium quality predictions according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. GaMD simulations refined the peptide-protein complex structures with significantly reduced peptide backbone RMSDs of 0.6–2.7 Å, yielding two high quality (sub-angstrom) and one medium quality models. Furthermore, the GaMD simulations identified important low-energy conformational states and revealed the mechanism of peptide binding to the target proteins. Therefore, PeptiDock+GaMD is a promising approach for exploring peptide-protein interactions.

RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules

Motivation

The root mean square deviation (RMSD) is one of the most used similarity criteria in structural biology and bioinformatics. Standard computation of the RMSD has a linear complexity with respect to the number of atoms in a molecule, making RMSD calculations time-consuming for the large-scale modeling applications, such as assessment of molecular docking predictions or clustering of spatially proximate molecular conformations. Previously, we introduced the RigidRMSD algorithm to compute the RMSD corresponding to the rigid-body motion of a molecule. In this study, we go beyond the limits of the rigid-body approximation by taking into account conformational flexibility of the molecule. We model the flexibility with a reduced set of collective motions computed with e.g. normal modes or principal component analysis.

Results

The initialization of our algorithm is linear in the number of atoms and all the subsequent evaluations of RMSD values between flexible molecular conformations depend only on the number of collective motions that are selected to model the flexibility. Therefore, our algorithm is much faster compared to the standard RMSD computation for large-scale modeling applications. We demonstrate the efficiency of our method on several clustering examples, including clustering of flexible docking results and molecular dynamics (MD) trajectories. We also demonstrate how to use the presented formalism to generate pseudo-random constant-RMSD structural molecular ensembles and how to use these in cross-docking.

Availability and implementation

We provide the algorithm written in C++ as the open-source RapidRMSD library governed by the BSD-compatible license, which is available at http://team.inria.fr/nano-d/software/RapidRMSD/. The constant-RMSD structural ensemble application and clustering of MD trajectories is available at http://team.inria.fr/nano-d/software/nolb-normal-modes/.

Supplementary information

Supplementary data are available at Bioinformatics online.

The structural and biochemical impacts of monomerizing human acetylcholinesterase

Serving a critical role in neurotransmission, human acetylcholinesterase (hAChE) is the target of organophosphate nerve agents. Hence, there is an active interest in studying the mechanism of inhibition and recovery of enzymatic activity, which could lead to better countermeasures against nerve agents. As hAChE is found in different oligomeric assemblies, certain approaches to studying it have been problematic. Herein, we examine the biochemical and structural impact of monomerizing hAChE by using two mutations: L380R/F535K. The activities of monomeric hAChE L380R/F535K and dimeric hAChE were determined to be comparable utilizing a modified Ellman's assay. To investigate the influence of subunit-subunit interactions on the structure of hAChE, a 2.1 Å X-ray crystallographic structure was determined. Apart from minor shifts along the dimer interface, the overall structure of the hAChE L380R/F535K mutant is similar to that of dimeric hAChE. To probe whether the plasticity of the active site was overtly impacted by monomerizing hAChE, the kinetic constants of (PR/S ) - VX (ethyl({2-[bis(propan-2-yl)amino]ethyl}sulfanyl)(methyl)phosphinate) inhibition and subsequent rescue of hAChE L380R/F535K activity with HI-6 (1-(2'-hydroxyiminomethyl-1'-pyridinium)-3-(4'-carbamoyl-1-pyridinium)) were determined and found to be comparable to those of dimeric hAChE. Thus, hAChE L380R/F535K could be used as a substitute for dimeric hAChE when experimentally probing the ability of the hAChE active site to accommodate future nerve agent threats or judge the ability of new therapeutics to access the active site.

P38 and JNK Mitogen-Activated Protein Kinases Interact With Chikungunya Virus Non-structural Protein-2 and Regulate TNF Induction During Viral Infection in Macrophages

Chikungunya virus (CHIKV), a mosquito-borne Alphavirus, is endemic in different parts of the globe. The host macrophages are identified as the major cellular reservoirs of CHIKV during infection and this virus triggers robust TNF production in the host macrophages, which might be a key mediator of virus induced inflammation. However, the molecular mechanism underneath TNF induction is not understood yet. Accordingly, the Raw264.7 cells, a mouse macrophage cell line, were infected with CHIKV to address the above-mentioned question. It was observed that CHIKV induces both p38 and JNK phosphorylation in macrophages in a time-dependent manner and p-p38 inhibitor, SB203580 is effective in reducing infection even at lower concentration as compared to the p-JNK inhibitor, SP600125. However, inhibition of p-p38 and p-JNK decreased CHIKV induced TNF production in the host macrophages. Moreover, CHIKV induced macrophage derived TNF was found to facilitate TCR driven T cell activation. Additionally, it was noticed that the expressions of key transcription factors involved mainly in antiviral responses (p-IRF3) and TNF production (p-c-jun) were induced significantly in the CHIKV infected macrophages as compared to the corresponding mock cells. Further, it was demonstrated that CHIKV mediated TNF production in the macrophages is dependent on p38 and JNK MAPK pathways linking p-c-jun transcription factor. Interestingly, it was found that CHIKV nsP2 interacts with both p-p38 and p-JNK MAPKs in the macrophages. This observation was supported by the in silico protein-protein docking analysis which illustrates the specific amino acids responsible for the nsP2-MAPKs interactions. A strong polar interaction was predicted between Thr-180 (within the phosphorylation lip) of p38 and Gln-273 of nsP2, whereas, no such polar interaction was predicted for the phosphorylation lip of JNK which indicates the differential roles of p-p38 and p-JNK during CHIKV infection in the host macrophages. In summary, for the first time it has been shown that CHIKV triggers robust TNF production in the host macrophages via both p-p38 and p-JNK/p-c-jun pathways and the interaction of viral protein, nsP2 with these MAPKs during infection. Hence, this information might shed light in rationale-based drug designing strategies toward a possible control measure of CHIKV infection in future.

Monte Carlo on the manifold and MD refinement for binding pose prediction of protein-ligand complexes: 2017 D3R Grand Challenge

Manifold representations of rotational/translational motion and conformational space of a ligand were previously shown to be effective for local energy optimization. In this paper we report the development of the Monte-Carlo energy minimization approach (MCM), which uses the same manifold representation. The approach was integrated into the docking pipeline developed for the current round of D3R experiment, and according to D3R assessment produced high accuracy poses for Cathepsin S ligands. Additionally, we have shown that (MD) refinement further improves docking quality. The code of the Monte-Carlo minimization is freely available at https://bitbucket.org/abc-group/mcm-demo .

Protein structure prediction

Predicting 3D structure of protein from its amino acid sequence is one of the most important unsolved problems in biophysics and computational biology. This paper attempts to give a comprehensive introduction of the most recent effort and progress on protein structure prediction. Following the general flowchart of structure prediction, related concepts and methods are presented and discussed. Moreover, brief introductions are made to several widely-used prediction methods and the community-wide critical assessment of protein structure prediction (CASP) experiments.

Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes

We propose a novel stochastic global optimization algorithm with applications to the refinement stage of protein docking prediction methods. Our approach can process conformations sampled from multiple clusters, each roughly corresponding to a different binding energy funnel. These clusters are obtained using a density-based clustering method. In each cluster, we identify a smooth “permissive” subspace which avoids high-energy barriers and then underestimate the binding energy function using general convex polynomials in this subspace. We use the underestimator to bias sampling towards its global minimum. Sampling and subspace underestimation are repeated several times and the conformations sampled at the last iteration form a refined ensemble. We report computational results on a comprehensive benchmark of 224 protein complexes, establishing that our refined ensemble significantly improves the quality of the conformations of the original set given to the algorithm. We also devise a method to enhance the ensemble from which near-native models are selected.

Chikungunya virus nsP1 interacts directly with nsP2 and modulates its ATPase activity

Chikungunya virus (CHIKV) is a mosquito-borne virus, which has created an alarming threat in the world due to unavailability of vaccine and antiviral compounds. The CHIKV nsP2 contains ATPase, RTPase, helicase and protease activities, whereas, nsP1 is a viral capping enzyme. In alphaviruses, the four non-structural proteins form the replication complex in the cytoplasm and this study characterizes the interaction between CHIKV nsP1 and nsP2. It was observed that, both the proteins co-localize in the cytoplasm and interact in the CHIKV infected cells by confocal microscopy and immunoprecipitation assay. Further, it was demonstrated through mutational analysis that, the amino acids 1-95 of nsP2 and 170-288 of nsP1 are responsible for their direct interaction. Additionally, it was noticed that, the ATPase activity of nsP2 is enhanced in the presence of nsP1, indicating the functional significance of this interaction. In silico analysis showed close (≤1.7 Å) polar interaction (hydrogen bond) between Glu⁴, Arg^{7, 96, 225} of nsP2 with Lys^{256, 206}, Val³⁶⁷ and Phe³¹² of nsP1 respectively. Hence, this investigation provides molecular characterization of CHIKV nsP1-nsP2 interaction which might be a useful target for rational designing of antiviral drugs.

How Molecular Size Impacts RMSD Applications in Molecular Dynamics Simulations

The root-mean-square deviation (RMSD) is a similarity measure widely used in analysis of macromolecular structures and dynamics. As increasingly larger macromolecular systems are being studied, dimensionality effects such as the "curse of dimensionality" (a diminishing ability to discriminate pairwise differences between conformations with increasing system size) may exist and significantly impact RMSD-based analyses. For such large bimolecular systems, whether the RMSD or other alternative similarity measures might suffer from this "curse" and lose the ability to discriminate different macromolecular structures had not been explicitly addressed. Here, we show such dimensionality effects for both weighted and nonweighted RMSD schemes. We also provide a mechanism for the emergence of the "curse of dimensionality" for RMSD from the law of large numbers by showing that the conformational distributions from which RMSDs are calculated become increasingly similar as the system size increases. Our findings suggest the use of weighted RMSD schemes for small proteins (less than 200 residues) and nonweighted RMSD for larger proteins when analyzing molecular dynamics trajectories.

The ClusPro web server for protein-protein docking

The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.

The ClusPro web server for protein-protein docking

The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.

New additions to the ClusPro server motivated by CAPRI

The heavily used protein-protein docking server ClusPro performs three computational steps as follows: (1) rigid body docking, (2) RMSD based clustering of the 1000 lowest energy structures, and (3) the removal of steric clashes by energy minimization. In response to challenges encountered in recent CAPRI targets, we added three new options to ClusPro. These are (1) accounting for small angle X-ray scattering data in docking; (2) considering pairwise interaction data as restraints; and (3) enabling discrimination between biological and crystallographic dimers. In addition, we have developed an extremely fast docking algorithm based on 5D rotational manifold FFT, and an algorithm for docking flexible peptides that include known sequence motifs. We feel that these developments will further improve the utility of ClusPro. However, CAPRI emphasized several shortcomings of the current server, including the problem of selecting the right energy parameters among the five options provided, and the problem of selecting the best models among the 10 generated for each parameter set. In addition, results convinced us that further development is needed for docking homology models. Finally, we discuss the difficulties we have encountered when attempting to develop a refinement algorithm that would be computationally efficient enough for inclusion in a heavily used server. Proteins 2017; 85:435-444. © 2016 Wiley Periodicals, Inc.

Structural quality of unrefined models in protein docking

Structural characterization of protein-protein interactions is essential for understanding life processes at the molecular level. However, only a fraction of protein interactions have experimentally resolved structures. Thus, reliable computational methods for structural modeling of protein interactions (protein docking) are important for generating such structures and understanding the principles of protein recognition. Template-based docking techniques that utilize structural similarity between target protein-protein interaction and cocrystallized protein-protein complexes (templates) are gaining popularity due to generally higher reliability than that of the template-free docking. However, the template-based approach lacks explicit penalties for intermolecular penetration, as opposed to the typical free docking where such penalty is inherent due to the shape complementarity paradigm. Thus, template-based docking models are commonly assumed to require special treatment to remove large structural penetrations. In this study, we compared clashes in the template-based and free docking of the same proteins, with crystallographically determined and modeled structures. The results show that for the less accurate protein models, free docking produces fewer clashes than the template-based approach. However, contrary to the common expectation, in acceptable and better quality docking models of unbound crystallographically determined proteins, the clashes in the template-based docking are comparable to those in the free docking, due to the overall higher quality of the template-based docking predictions. This suggests that the free docking refinement protocols can in principle be applied to the template-based docking predictions as well. Proteins 2016; 85:39-45. © 2016 Wiley Periodicals, Inc.

ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution

Accurate sampling of conformational space and, in particular, the transitions between functional substates has been a challenge in molecular dynamic (MD) simulations of large biomolecular systems. We developed an Elastic Network Model (ENM)-based computational method, ClustENM, for sampling large conformational changes of biomolecules with various sizes and oligomerization states. ClustENM is an iterative method that combines ENM with energy minimization and clustering steps. It is an unbiased technique, which requires only an initial structure as input, and no information about the target conformation. To test the performance of ClustENM, we applied it to six biomolecular systems: adenylate kinase (AK), calmodulin, p38 MAP kinase, HIV-1 reverse transcriptase (RT), triosephosphate isomerase (TIM), and the 70S ribosomal complex. The generated ensembles of conformers determined at atomic resolution show good agreement with experimental data (979 structures resolved by X-ray and/or NMR) and encompass the subspaces covered in independent MD simulations for TIM, p38, and RT. ClustENM emerges as a computationally efficient tool for characterizing the conformational space of large systems at atomic detail, in addition to generating a representative ensemble of conformers that can be advantageously used in simulating substrate/ligand-binding events.

Current state of theoretical and experimental studies of the voltage-dependent anion channel (VDAC)

Voltage-dependent anion channel (VDAC), the major channel of the mitochondrial outer membrane provides a controlled pathway for respiratory metabolites in and out of the mitochondria. In spite of the wealth of experimental data from structural, biochemical, and biophysical investigations, the exact mechanisms governing selective ion and metabolite transport, especially the role of titratable charged residues and interactions with soluble cytosolic proteins, remain hotly debated in the field. The computational advances hold a promise to provide a much sought-after solution to many of the scientific disputes around solute and ion transport through VDAC and hence, across the mitochondrial outer membrane. In this review, we examine how Molecular Dynamics, Free Energy, and Brownian Dynamics simulations of the large β-barrel channel, VDAC, advanced our understanding. We will provide a short overview of non-conventional techniques and also discuss examples of how the modeling excursions into VDAC biophysics prospectively aid experimental efforts. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

Focused grid-based resampling for protein docking and mapping

The fast Fourier transform (FFT) sampling algorithm has been used with success in application to protein-protein docking and for protein mapping, the latter docking a variety of small organic molecules for the identification of binding hot spots on the target protein. Here we explore the local rather than global usage of the FFT sampling approach in docking applications. If the global FFT based search yields a near-native cluster of docked structures for a protein complex, then focused resampling of the cluster generally leads to a substantial increase in the number of conformations close to the native structure. In protein mapping, focused resampling of the selected hot spot regions generally reveals further hot spots that, while not as strong as the primary hot spots, also contribute to ligand binding. The detection of additional ligand binding regions is shown by the improved overlap between hot spots and bound ligands.

InterEvDock: a docking server to predict the structure of protein-protein interactions using evolutionary information

The structural modeling of protein–protein interactions is key in understanding how cell machineries cross-talk with each other. Molecular docking simulations provide efficient means to explore how two unbound protein structures interact. InterEvDock is a server for protein docking based on a free rigid-body docking strategy. A systematic rigid-body docking search is performed using the FRODOCK program and the resulting models are re-scored with InterEvScore and SOAP-PP statistical potentials. The InterEvScore potential was specifically designed to integrate co-evolutionary information in the docking process. InterEvDock server is thus particularly well suited in case homologous sequences are available for both binding partners. The server returns 10 structures of the most likely consensus models together with 10 predicted residues most likely involved in the interface. In 91% of all complexes tested in the benchmark, at least one residue out of the 10 predicted is involved in the interface, providing useful guidelines for mutagenesis. InterEvDock is able to identify a correct model among the top10 models for 49% of the rigid-body cases with evolutionary information, making it a unique and efficient tool to explore structural interactomes under an evolutionary perspective. The InterEvDock web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock/.

Evaluation of the coarse-grained OPEP force field for protein-protein docking

Background

Knowing the binding site of protein–protein complexes helps understand their function and shows possible regulation sites. The ultimate goal of protein–protein docking is the prediction of the three-dimensional structure of a protein–protein complex. Docking itself only produces plausible candidate structures, which must be ranked using scoring functions to identify the structures that are most likely to occur in nature.

Methods

In this work, we rescore rigid body protein–protein predictions using the optimized potential for efficient structure prediction (OPEP), which is a coarse-grained force field. Using a force field based on continuous functions rather than a grid-based scoring function allows the introduction of protein flexibility during the docking procedure. First, we produce protein–protein predictions using ZDOCK, and after energy minimization via OPEP we rank them using an OPEP-based soft rescoring function. We also train the rescoring function for different complex classes and demonstrate its improved performance for an independent dataset.

Results

The trained rescoring function produces a better ranking than ZDOCK for more than 50 % of targets, rising to over 70 % when considering only enzyme/inhibitor complexes.

Conclusions

This study demonstrates for the first time that energy functions derived from the coarse-grained OPEP force field can be employed to rescore predictions for protein–protein complexes.

Insights into the properties of the two enantiomers of trans-δ-viniferin, a resveratrol derivative: antioxidant activity, biochemical and molecular modeling studies of its interactions with hemoglobin

Resveratrol is widely known as an antioxidant and anti-inflammatory molecule. The present study first reports the effects of trans-δ-viniferin (TVN), a dimer of resveratrol, on human erythrocytes. The antioxidant activity of TVN was tested using in vitro model systems such as hydroxy radical scavenging, DPPH and lipid peroxidation. In addition we have examined the influence of the 15R,22R- and 15S,22S-enantiomers (abbreviated R,R-TVN, and S,S-TVN, respectively) on anion transport, ATP release, caspase 3 activation. Given that hemoglobin (Hb) redox reactions are the major source of RBC oxidative stress, we also explored the effects of TVN on hemoglobin function. TVN showed moderate antioxidant properties and good protective activity from hemoglobin oxidation. Potential binding sites of R,R-TVN and S,S-TVN with oxy- and deoxy-Hb were also investigated through an extensive in silico docking approach and molecular dynamics calculations. The whole molecular modeling studies indicate that binding of R,R-TVN and S,S-TVN to Hb lacks of specific ligand-target interactions. This is the first report on the biological activity of the individual enantiomers of a resveratrol-related dimer.

The impact of side-chain packing on protein docking refinement

We study the impact of optimizing the side-chain positions in the interface region between two proteins during the process of binding. Mathematically, the problem is similar to side-chain prediction, which has been extensively explored in the process of protein structure prediction. The protein-protein docking application, however, has a number of characteristics that necessitate different algorithmic and implementation choices. In this work, we implement a distributed approximate algorithm that can be implemented on multiprocessor architectures and enables a trade-off between accuracy and running speed. We report computational results on benchmarks of enzyme-inhibitor and other types of complexes, establishing that the side-chain flexibility our algorithm introduces substantially improves the performance of docking protocols. Furthermore, we establish that the inclusion of unbound side-chain conformers in the side-chain positioning problem is critical in these performance improvements. The code is available to the community under open source license.

A computational design approach for virtual screening of peptide interactions across K(+) channel families

Ion channels represent a large family of membrane proteins with many being well established targets in pharmacotherapy. The ‘druggability’ of heteromeric channels comprised of different subunits remains obscure, due largely to a lack of channel-specific probes necessary to delineate their therapeutic potential in vivo. Our initial studies reported here, investigated the family of inwardly rectifying potassium (Kir) channels given the availability of high resolution crystal structures for the eukaryotic constitutively active Kir2.2 channel. We describe a ‘limited’ homology modeling approach that can yield chimeric Kir channels having an outer vestibule structure representing nearly any known vertebrate or invertebrate channel. These computationally-derived channel structures were tested ""in silico for ‘docking’ to NMR structures of tertiapin (TPN), a 21 amino acid peptide found in bee venom. TPN is a highly selective and potent blocker for the epithelial rat Kir1.1 channel, but does not block human or zebrafish Kir1.1 channel isoforms. Our Kir1.1 channel-TPN docking experiments recapitulated published in vitro ""findings for TPN-sensitive and TPN-insensitive channels. Additionally, in silico site-directed mutagenesis identified ‘hot spots’ within the channel outer vestibule that mediate energetically favorable docking scores and correlate with sites previously identified with in vitro thermodynamic mutant-cycle analysis. These ‘proof-of-principle’ results establish a framework for virtual screening of re-engineered peptide toxins for interactions with computationally derived Kir channels that currently lack channel-specific blockers. When coupled with electrophysiological validation, this virtual screening approach may accelerate the drug discovery process, and can be readily applied to other ion channels families where high resolution structures are available.

Docking server for the identification of heparin binding sites on proteins

Many proteins of widely differing functionality and structure are capable of binding heparin and heparan sulfate. Since crystallizing protein-heparin complexes for structure determination is generally difficult, computational docking can be a useful approach for understanding specific interactions. Previous studies used programs originally developed for docking small molecules to well-defined pockets, rather than for docking polysaccharides to highly charged shallow crevices that usually bind heparin. We have extended the program PIPER and the automated protein-protein docking server ClusPro to heparin docking. Using a molecular mechanics energy function for scoring and the fast Fourier transform correlation approach, the method generates and evaluates close to a billion poses of a heparin tetrasaccharide probe. The docked structures are clustered using pairwise root-mean-square deviations as the distance measure. It was shown that clustering of heparin molecules close to each other but having different orientations and selecting the clusters with the highest protein-ligand contacts reliably predicts the heparin binding site. In addition, the centers of the five most populated clusters include structures close to the native orientation of the heparin. These structures can provide starting points for further refinement by methods that account for flexibility such as molecular dynamics. The heparin docking method is available as an advanced option of the ClusPro server at http://cluspro.bu.edu/ .

Insights into the architecture of the eIF2Bα/β/δ regulatory subcomplex

Eukaryotic translation initiation factor 2B (eIF2B), the guanine nucleotide exchange factor for the G-protein eIF2, is one of the main targets for the regulation of protein synthesis. The eIF2B activity is inhibited in response to a wide range of stress factors and diseases, including viral infections, hypoxia, nutrient starvation, and heme deficiency, collectively known as the integrated stress response. eIF2B has five subunits (α-ε). The α, β, and δ subunits are homologous to each other and form the eIF2B regulatory subcomplex, which is believed to be a trimer consisting of monomeric α, β, and δ subunits. Here we use a combination of biophysical methods, site-directed mutagenesis, and bioinformatics to show that the human eIF2Bα subunit is in fact a homodimer, at odds with the current trimeric model for the eIF2Bα/β/δ regulatory complex. eIF2Bα dimerizes using the same interface that is found in the homodimeric archaeal eIF2Bα/β/δ homolog aIF2B and related metabolic enzymes. We also present evidence that the eIF2Bβ/δ binding interface is similar to that in the eIF2Bα2 homodimer. Mutations at the predicted eIF2Bβ/δ dimer interface cause genetic neurological disorders in humans. We propose that the eIF2B regulatory subcomplex is an α2β2δ2 hexamer, composed of one α2 homodimer and two βδ heterodimers. Our results offer novel insights into the architecture of eIF2B and its interactions with the G-protein eIF2.