A Markov-chain model description of binding funnels to enhance the ranking of docked solutions

Computational Analysis of CD46 Protein Interaction with SARS-CoV-2 Structural Proteins: Elucidating a Putative Viral Entry Mechanism into Human Cells

This study examines an unexplored aspect of SARS-CoV-2 entry into host cells, which is widely understood to occur via the viral spike (S) protein's interaction with human ACE2-associated proteins. While vaccines and inhibitors targeting this mechanism are in use, they may not offer complete protection against reinfection. Hence, we investigate putative receptors and their cofactors. Specifically, we propose CD46, a human membrane cofactor protein, as a potential putative receptor and explore its role in cellular invasion, acting possibly as a cofactor with other viral structural proteins. Employing computational techniques, we created full-size 3D models of human CD46 and four key SARS-CoV-2 structural proteins-EP, MP, NP, and SP. We further developed 3D models of CD46 complexes interacting with these proteins. The primary aim is to pinpoint the likely interaction domains between CD46 and these structural proteins to facilitate the identification of molecules that can block these interactions, thus offering a foundation for novel pharmacological treatments for SARS-CoV-2 infection.

Alum and a TLR7 agonist combined with built-in TLR4 and 5 agonists synergistically enhance immune responses against HPV RG1 epitope

To relieve the limitations of the human papillomavirus (HPV) vaccines based on L1 capsid protein, vaccine formulations based on RG1 epitope of HPV L2 using various built-in adjuvants are under study. Herein, we describe design and construction of a rejoined peptide (RP) harboring HPV16 RG1 epitope fused to TLR4/5 agonists and a tetanus toxoid epitope, which were linked by the (GGGS)3 linker in tandem. In silico analyses indicated the proper physicochemical, immunogenic and safety profile of the RP. Docking analyses on predicted 3D model suggested the effective interaction of TLR4/5 agonists within RP with their corresponding TLRs. Expressing the 1206 bp RP-coding DNA in E. coli produced a 46 kDa protein, and immunization of mice by natively-purified RP in different adjuvant formulations indicated the crucial role of the built-in adjuvants for induction of anti-RG1 responses that could be further enhanced by combination of TLR7 agonist/alum adjuvants. While the TLR4/5 agonists contributed in the elicitation of the Th2-polarized immune responses, combination with TLR7 agonist changed the polarization to the balanced Th1/Th2 immune responses. Indeed, RP + TLR7 agonist/alum adjuvants induced the strongest immune responses that could efficiently neutralize the HPV pseudoviruses, and thus might be a promising formulation for an inexpensive and cross-reactive HPV vaccine.

The p.P1127S pathogenic variant lowers von Willebrand factor levels through higher affinity for the macrophagic scavenger receptor LRP1: Clinical phenotype and pathogenic mechanisms

BACKGROUND

The index case is a 21-year-old Italian woman with a mild hemorrhagic syndrome and von Willebrand factor antigen (VWF:Ag) = 34.3 U/dl, VWF recombinant glycoprotein Ib (VWF:GpIbR) = 32.8 U/dl, and factor VIII (FVIII) = 55.3 IU/dl.

AIMS

The aim of this study is to characterize from a genetic and biochemical standpoint this low VWF phenotype.

METHODS

Coagulation and biochemical methods were used to study the structural and functional pattern of VWF multimers in the index case's plasma. Recombinant wild-type and p.P1127S VWF variants were produced using human embryonic kidney (HEK)-293 cells. In addition, genetic screening was carried out to detect single nucleotide variants of some scavenger VWF/FVIII receptor genes such as CLEC4M, STAB2, and ASGR2.

RESULTS

Genetic investigation revealed that the index case inherited from her mother the heterozygous missense mutation c.3379C > T (VWF exon 25), causing the p.P1127S substitution in the VWF D'D3 domain. The index case was also homozygous for the scavenger receptor ASGR2 c.-95 CC-genotype. Desmopressin normalized the VWF level of the patient, although its clearance was faster (t1/2  = 6.7 h) than in normal subjects (t1/2  = 12 ± 0.7 h). FVIII-VWF interaction, A Disintegrin And Metalloprotease with ThromboSpondin type 1 motif-13 levels, ristocetin-induced-platelet-aggregation, and VWF multimeric pattern were normal. The p.P1127S variant was normally synthesized and secreted by HEK-293 cells, and molecular modeling predicts a conformational change showing higher affinity for the macrophagic scavenger receptor lipoprotein receptor-related protein 1 (LRP1), as also experimentally verified.

CONCLUSIONS

The p.P1127S variant may cause a low VWF phenotype, stemming from an increased VWF affinity for the scavenger receptor LRP1 and, consequently, an accelerated clearance of VWF.

Biophysical Insight into the SARS-CoV2 Spike-ACE2 Interaction and Its Modulation by Hepcidin through a Multifaceted Computational Approach

At the center of the SARS-CoV2 infection, the spike protein and its interaction with the human receptor ACE2 play a central role in the molecular machinery of SARS-CoV2 infection of human cells. Vaccine therapies are a valuable barrier to the worst effects of the virus and to its diffusion, but the need of purposed drugs is emerging as a core target of the fight against COVID19. In this respect, the repurposing of drugs has already led to discovery of drugs thought to reduce the effects of the cytokine storm, but still a drug targeting the spike protein, in the infection stage, is missing. In this work, we present a multifaceted computational approach strongly grounded on a biophysical modeling of biological systems, so to disclose the interaction of the SARS-CoV2 spike protein with ACE2 with a special focus to an allosteric regulation of the spike-ACE2 interaction. Our approach includes the following methodologies: Protein Contact Networks and Network Clustering, Targeted Molecular Dynamics, Elastic Network Modeling, Perturbation Response Scanning, and a computational analysis of energy flow and SEPAS as a protein-softness and monomer-based affinity predictor. We applied this approach to free (closed and open) states of spike protein and spike-ACE2 complexes. Eventually, we analyzed the interactions of free and bound forms of spike with hepcidin (HPC), the major hormone in iron regulation, recently addressed as a central player in the COVID19 pathogenesis, with a special emphasis to the most severe outcomes. Our results demonstrate that, compared with closed and open states, the spike protein in the ACE2-bound state shows higher allosteric potential. The correspondence between hinge sites and the Allosteric Modulation Region (AMR) in the S-ACE complex suggests a molecular basis for hepcidin involvement in COVID19 pathogenesis. We verify the importance of AMR in different states of spike and then study its interactions with HPC and the consequence of the HPC-AMR interaction on spike dynamics and its affinity for ACE2. We propose two complementary mechanisms for HPC effects on spike of SARS-CoV-2; (a) HPC acts as a competitive inhibitor when spike is in a preinfection state (open and with no ACE2), (b) the HPC-AMR interaction pushes the spike structure into the safer closed state. These findings need clear molecular in vivo verification beside clinical observations.

Structural and functional characterization of an auxiliary domain-containing PET hydrolase from Burkholderiales bacterium

Biodegradation of polyethylene terephthalate (PET) is one of fundamental ways to solve plastic pollution. As various microbial hydrolases have an extra domain unlike PETase from Ideonella sakaiensis (IsPETase), research on the role of these extra domain in PET hydrolysis is crucial for the identification and selection of a novel PET hydrolase. Here, we report that a PET hydrolase from Burkholderiales bacterium RIFCSPLOWO2_02_FULL_57_36 (BbPETase) with an additional N-terminal domain (BbPETase^AND) shows a similar hydrolysis activity toward microcrystalline PET and a higher thermal stability than IsPETase. Based on detailed structural comparisons between BbPETase and IsPETase, we generated the BbPETase^{S335N/T338I/M363I/N365G} variant with an enhanced PET-degrading activity and thermal stability. We further revealed that BbPETase^AND contributes to the thermal stability of the enzyme through close contact with the core domain, but the domain might hinder the adhesion of enzyme to PET substrate. We suggest that BbPETase is an enzyme in the evolution of efficient PET degradation and molecular insight into a novel PET hydrolase provides a novel strategy for the development of biodegradation of PET.

PEA-15 engages in allosteric interactions using a common scaffold in a phosphorylation-dependent manner

Phosphoprotein enriched in astrocytes, 15 kDa (PEA-15) is a death-effector domain (DED) containing protein involved in regulating mitogen-activated protein kinase and apoptosis pathways. In this molecular dynamics study, we examined how phosphorylation of the PEA-15 C-terminal tail residues, Ser-104 and Ser-116, allosterically mediates conformational changes of the DED and alters the binding specificity from extracellular-regulated kinase (ERK) to Fas-associated death domain (FADD) protein. We delineated that the binding interfaces between the unphosphorylated PEA-15 and ERK2 and between the doubly phosphorylated PEA-15 and FADD are similarly composed of a scaffold that includes both the DED and the C-terminal tail residues of PEA-15. While the unphosphorylated serine residues do not directly interact with ERK2, the phosphorylated Ser-116 engages in strong electrostatic interactions with arginine residues on FADD DED. Upon PEA-15 binding, FADD repositions its death domain (DD) relative to the DED, an essential conformational change to allow the death-inducing signaling complex (DISC) assembly.

Paratope states in solution improve structure prediction and docking

Structure-based antibody design and accurate predictions of antibody-antigen interactions remain major challenges in computational biology. By using molecular dynamics simulations, we show that a single static X-ray structure is not sufficient to identify determinants of antibody-antigen recognition. Here, we investigate antibodies that undergo substantial conformational changes upon antigen binding and have been classified as difficult cases in an extensive benchmark for antibody-antigen docking. We present thermodynamics and transition kinetics of these conformational rearrangements and show that paratope states can be used to improve antibody-antigen docking. By using the unbound antibody X-ray structure as starting structure for molecular dynamics simulations, we retain a binding competent conformation substantially different to the unbound antibody X-ray structure. We also observe that the kinetically dominant antibody paratope conformations are chosen by the bound antigen conformation with the highest probability. Thus, we show that paratope states in solution can improve antibody-antigen docking and structure prediction.

Alpha-Synuclein defects autophagy by impairing SNAP29-mediated autophagosome-lysosome fusion

Dopaminergic (DA) cell death in Parkinson's disease (PD) is associated with the gradual appearance of neuronal protein aggregates termed Lewy bodies (LBs) that are comprised of vesicular membrane structures and dysmorphic organelles in conjunction with the protein alpha-Synuclein (α-Syn). Although the exact mechanism of neuronal aggregate formation and death remains elusive, recent research suggests α-Syn-mediated alterations in the lysosomal degradation of aggregated proteins and organelles - a process termed autophagy. Here, we used a combination of molecular biology and immunochemistry to investigate the effect of α-Syn on autophagy turnover in cultured human DA neurons and in human post-mortem brain tissue. We found α-Syn overexpression to reduce autophagy turnover by compromising the fusion of autophagosomes with lysosomes, thus leading to a decrease in the formation of autolysosomes. In accord with a compensatory increase in the plasma membrane fusion of autophagosomes, α-Syn enhanced the number of extracellular vesicles (EV) and the abundance of autophagy-associated proteins in these EVs. Mechanistically, α-Syn decreased the abundance of the v-SNARE protein SNAP29, a member of the SNARE complex mediating autophagolysosome fusion. In line, SNAP29 knockdown mimicked the effect of α-Syn on autophagy whereas SNAP29 co-expression reversed the α-Syn-induced changes on autophagy turnover and EV release and ameliorated DA neuronal cell death. In accord with our results from cultured neurons, we found a stage-dependent reduction of SNAP29 in SNc DA neurons from human post-mortem brain tissue of Lewy body pathology (LBP) cases. In summary, our results thus demonstrate a previously unknown effect of α-Syn on intracellular autophagy-associated SNARE proteins and, as a consequence, a reduced autolysosome fusion. As such, our findings will therefore support the investigation of autophagy-associated pathological changes in PD.

Classification and prediction of protein-protein interaction interface using machine learning algorithm

Structural insight of the protein-protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimental lag in solving protein-protein complex structures, three-dimensional (3D) knowledge of the PPI interfaces can be gained via computational approaches like molecular docking and post-docking analyses. Despite development of numerous docking tools and techniques, success in identification of native like interfaces based on docking score functions is limited. Hence, we employed an in-depth investigation of the structural features of the interface that might successfully delineate native complexes from non-native ones. We identify interface properties, which show statistically significant difference between native and non-native interfaces belonging to homo and hetero, protein-protein complexes. Utilizing these properties, a support vector machine (SVM) based classification scheme has been implemented to differentiate native and non-native like complexes generated using docking decoys. Benchmarking and comparative analyses suggest very good performance of our SVM classifiers. Further, protein interactions, which are proven via experimental findings but not resolved structurally, were subjected to this approach where 3D-models of the complexes were generated and most likely interfaces were predicted. A web server called Protein Complex Prediction by Interface Properties (PCPIP) is developed to predict whether interface of a given protein-protein dimer complex resembles known protein interfaces. The server is freely available at http://www.hpppi.iicb.res.in/pcpip/ .

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.

Evaluation of specificity determinants in Mycobacterium tuberculosis σ/anti-σ factor interactions

Extra Cytoplasmic Function (ECF) σ factor/regulatory protein (anti-σ factor) pairs govern environment mediated changes in gene expression in bacteria. The release of the ECF σ factor from an inactive σ/anti-σ factor complex is triggered by specific environmental stimuli. The free σ factor then associates with the RNA polymerase and drives the expression of genes in its target regulon. Multiple ECF σ/anti-σ pairs ensure calibrated changes in the expression profile by correlating diverse environmental stimuli with changes in the intracellular levels of different ECF σ factors. Specificity in σ/anti-σ factor interaction is thus essential for accurate signal transduction. Here we describe experiments to evaluate interactions between different M. tuberculosis σ and anti-σ proteins in vitro. The interaction parameters suggest that cross-talk between non-cognate σ/anti-σ pairs is likely. The sequence and conformational determinants that govern interaction specificity in a σ/anti-σ complex are not immediately evident due to substantial structural conservation. Sequence-structure analysis of all σ/anti-σ pairs suggest that conserved residues are not the primary determinants of σ/anti-σ interactions-a finding that suggests a potential route to set tolerance limits in interaction specificity. Non-specific σ/anti-σ interactions are likely to be biologically significant as it can contribute to heterogeneity in cellular responses in a bacterial population under less stringent requirements. This finding is relevant for synthetic biology approaches to engineer bacteria using σ/anti-σ transcription initiation modules for diverse applications in biotechnology.

Prediction of GABARAP interaction with the GABA type A receptor

We have performed docking simulations on GABARAP interacting with the GABA type A receptor using SwarmDock. We have also used a novel method to study hydration sites on the surface of these two proteins; this method identifies regions around proteins where desolvation is relatively easy, and these are possible locations where proteins can bind each other. There is a high degree of consistency between the predictions of these two methods. Moreover, we have also identified binding sites on GABARAP for other proteins, and listed possible binding sites for as yet unknown proteins on both GABARAP and the GABA type A receptor intracellular domain.

Properties that rank protein:protein docking poses with high accuracy

The development of docking algorithms to predict near-native structures of protein:protein complexes from the structure of the isolated monomers is of paramount importance for molecular biology and drug discovery.

Flexible Protein-Protein Docking with SwarmDock

The atomic structures of protein complexes can provide useful information for drug design, protein engineering, systems biology, and understanding pathology. Obtaining this information experimentally can be challenging. However, if the structures of the subunits are known, then it is often possible to model the complex computationally. This chapter provide practical guidelines for docking proteins using the SwarmDock flexible protein-protein docking method, providing an overview of the factors that need to be considered when deciding whether docking is likely to be successful, the preparation of structural input, generation of docked poses, analysis and ranking of docked poses, and the validation of models using external data.

GENESIS - The GENEric SImulation System for Modelling State Transitions

This software implements a discrete time Markov chain model, used to model transitions between states when the transition probabilities are known a priori. It is highly configurable; the user supplies two text files, a "state transition table" and a "config file", to the Perl script genesis.pl. Given the content of these files, the script generates a set of C++ classes based on the State design pattern, and a main program, which can then be compiled and run. The C++ code generated is based on the specification in the text files. Both multiple branching and bi-directional transitions are allowed. The software has been used to model the natural histories of colorectal cancer in Mexico. Although written primarily to model such disease processes, it can be used in any process which depends on discrete states with known transition probabilities between those states. One suitable area may be in environmental modelling. A test suite is supplied with the distribution. Due to its high degree of configurability and flexibility, this software has good re-use potential. It is stored on the Figshare repository.

Protein interaction evolution from promiscuity to specificity with reduced flexibility in an increasingly complex network

A key question regarding protein evolution is how proteins adapt to the dynamic environment in which they function and how in turn their evolution shapes the protein interaction network. We used extant and resurrected ancestral plant MADS-domain transcription factors to understand how SEPALLATA3, a protein with hub and glue properties, evolved and takes part in network organization. Although the density of dimeric interactions was saturated in the network, many new interactions became mediated by SEPALLATA3 after a whole genome triplication event. By swapping SEPALLATA3 and its ancestors between dimeric networks of different ages, we found that the protein lost the capacity of promiscuous interaction and acquired specificity in evolution. This was accompanied with constraints on conformations through proline residue accumulation, which made the protein less flexible. SHORT VEGETATIVE PHASE on the other hand (non-hub) was able to gain protein-protein interactions due to a C-terminal domain insertion, allowing for a larger interaction interface. These findings illustrate that protein interaction evolution occurs at the level of conformational dynamics, when the binding mechanism concerns an induced fit or conformational selection. Proteins can evolve towards increased specificity with reduced flexibility when the complexity of the protein interaction network requires specificity.

Human and server docking prediction for CAPRI round 30-35 using LZerD with combined scoring functions

We report the performance of protein-protein docking predictions by our group for recent rounds of the Critical Assessment of Prediction of Interactions (CAPRI), a community-wide assessment of state-of-the-art docking methods. Our prediction procedure uses a protein-protein docking program named LZerD developed in our group. LZerD represents a protein surface with 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. The appropriate soft representation of protein surface with 3DZD makes the method more tolerant to conformational change of proteins upon docking, which adds an advantage for unbound docking. Docking was guided by interface residue prediction performed with BindML and cons-PPISP as well as literature information when available. The generated docking models were ranked by a combination of scoring functions, including PRESCO, which evaluates the native-likeness of residues' spatial environments in structure models. First, we discuss the overall performance of our group in the CAPRI prediction rounds and investigate the reasons for unsuccessful cases. Then, we examine the performance of several knowledge-based scoring functions and their combinations for ranking docking models. It was found that the quality of a pool of docking models generated by LZerD, that is whether or not the pool includes near-native models, can be predicted by the correlation of multiple scores. Although the current analysis used docking models generated by LZerD, findings on scoring functions are expected to be universally applicable to other docking methods. Proteins 2017; 85:513-527. © 2016 Wiley Periodicals, Inc.

Designing the Sniper: Improving Targeted Human Cytolytic Fusion Proteins for Anti-Cancer Therapy via Molecular Simulation

Targeted human cytolytic fusion proteins (hCFPs) are humanized immunotoxins for selective treatment of different diseases including cancer. They are composed of a ligand specifically binding to target cells genetically linked to a human apoptosis-inducing enzyme. hCFPs target cancer cells via an antibody or derivative (scFv) specifically binding to e.g., tumor associated antigens (TAAs). After internalization and translocation of the enzyme from endocytosed endosomes, the human enzymes introduced into the cytosol are efficiently inducing apoptosis. Under in vivo conditions such enzymes are subject to tight regulation by native inhibitors in order to prevent inappropriate induction of cell death in healthy cells. Tumor cells are known to upregulate these inhibitors as a survival mechanism resulting in escape of malignant cells from elimination by immune effector cells. Cytosolic inhibitors of Granzyme B and Angiogenin (Serpin P9 and RNH1, respectively), reduce the efficacy of hCFPs with these enzymes as effector domains, requiring detrimentally high doses in order to saturate inhibitor binding and rescue cytolytic activity. Variants of Granzyme B and Angiogenin might feature reduced affinity for their respective inhibitors, while retaining or even enhancing their catalytic activity. A powerful tool to design hCFPs mutants with improved potency is given by in silico methods. These include molecular dynamics (MD) simulations and enhanced sampling methods (ESM). MD and ESM allow predicting the enzyme-protein inhibitor binding stability and the associated conformational changes, provided that structural information is available. Such "high-resolution" detailed description enables the elucidation of interaction domains and the identification of sites where particular point mutations may modify those interactions. This review discusses recent advances in the use of MD and ESM for hCFP development from the viewpoints of scientists involved in both fields.

Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking

Protein-protein docking is an important computational tool for predicting protein-protein interactions. With the rapid development of proteomics projects, more and more experimental binding information ranging from mutagenesis data to three-dimensional structures of protein complexes are becoming available. Therefore, how to appropriately incorporate the biological information into traditional ab initio docking has been an important issue and challenge in the field of protein-protein docking. To address these challenges, we have developed a Hybrid DOCKing protocol of template-based and template-free approaches, referred to as HDOCK. The basic procedure of HDOCK is to model the structures of individual components based on the template complex by a template-based method if a template is available; otherwise, the component structures will be modeled based on monomer proteins by regular homology modeling. Then, the complex structure of the component models is predicted by traditional protein-protein docking. With the HDOCK protocol, we have participated in the CPARI experiment for rounds 28-35. Out of the 25 CASP-CAPRI targets for oligomer modeling, our HDOCK protocol predicted correct models for 16 targets, ranking one of the top algorithms in this challenge. Our docking method also made correct predictions on other CAPRI challenges such as protein-peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets. The advantage of our hybrid docking approach over pure template-based docking was further confirmed by a comparative evaluation on 20 CASP-CAPRI targets. Proteins 2017; 85:497-512. © 2016 Wiley Periodicals, Inc.

GP0.4 from bacteriophage T7: in silico characterisation of its structure and interaction with E. coli FtsZ

Background

Proteins produced by bacteriophages can have potent antimicrobial activity. The study of phage-host interactions can therefore inform small molecule drug discovery by revealing and characterising new drug targets. Here we characterise in silico the predicted interaction of gene protein 0.4 (GP0.4) from the Escherichia coli (E. coli) phage T7 with E. coli filamenting temperature-sensitive mutant Z division protein (FtsZ). FtsZ is a tubulin homolog which plays a key role in bacterial cell division and that has been proposed as a drug target.

Results

Using ab initio, fragment assembly structure modelling, we predicted the structure of GP0.4 with two programs. A structure similarity-based network was used to identify a U-shaped helix-turn-helix candidate fold as being favoured. ClusPro was used to dock this structure prediction to a homology model of E. coli FtsZ resulting in a favourable predicted interaction mode. Alternative docking methods supported the proposed mode which offered an immediate explanation for the anti-filamenting activity of GP0.4. Importantly, further strong support derived from a previously characterised insertion mutation, known to abolish GP0.4 activity, that is positioned in close proximity to the proposed GP0.4/FtsZ interface.

Conclusions

The mode of interaction predicted by bioinformatics techniques strongly suggests a mechanism through which GP0.4 inhibits FtsZ and further establishes the latter’s druggable intrafilament interface as a potential drug target.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-016-2149-5) contains supplementary material, which is available to authorized users.

Rigid-Docking Approaches to Explore Protein-Protein Interaction Space

Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.

Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps

In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions.

NPPD: A Protein-Protein Docking Scoring Function Based on Dyadic Differences in Networks of Hydrophobic and Hydrophilic Amino Acid Residues

Protein-protein docking (PPD) predictions usually rely on the use of a scoring function to rank docking models generated by exhaustive sampling. To rank good models higher than bad ones, a large number of scoring functions have been developed and evaluated, but the methods used for the computation of PPD predictions remain largely unsatisfactory. Here, we report a network-based PPD scoring function, the NPPD, in which the network consists of two types of network nodes, one for hydrophobic and the other for hydrophilic amino acid residues, and the nodes are connected when the residues they represent are within a certain contact distance. We showed that network parameters that compute dyadic interactions and those that compute heterophilic interactions of the amino acid networks thus constructed allowed NPPD to perform well in a benchmark evaluation of 115 PPD scoring functions, most of which, unlike NPPD, are based on some sort of protein-protein interaction energy. We also showed that NPPD was highly complementary to these energy-based scoring functions, suggesting that the combined use of conventional scoring functions and NPPD might significantly improve the accuracy of current PPD predictions.

Full genome sequences and molecular characterization of tick-borne encephalitis virus strains isolated from human patients

Tick-borne encephalitis virus (TBEV) causes tick-borne encephalitis (TBE), one of the most important human neuroinfections across Eurasia. Up to date, only three full genome sequences of human European TBEV isolates are available, mostly due to difficulties with isolation of the virus from human patients. Here we present full genome characterization of an additional five low-passage TBEV strains isolated from human patients with severe forms of TBE. These strains were isolated in 1953 within Central Bohemia in the former Czechoslovakia, and belong to the historically oldest human TBEV isolates in Europe. We demonstrate here that all analyzed isolates are distantly phylogenetically related, indicating that the emergence of TBE in Central Europe was not caused by one predominant strain, but rather a pool of distantly related TBEV strains. Nucleotide identity between individual sequenced TBEV strains ranged from 97.5% to 99.6% and all strains shared large deletions in the 3' non-coding region, which has been recently suggested to be an important determinant of virulence. The number of unique amino acid substitutions varied from 3 to 9 in individual isolates, but no characteristic amino acid substitution typical exclusively for all human TBEV isolates was identified when compared to the isolates from ticks. We did, however, correlate that the exploration of the TBEV envelope glycoprotein by specific antibodies were in close proximity to these unique amino acid substitutions. Taken together, we report here the largest number of patient-derived European TBEV full genome sequences to date and provide a platform for further studies on evolution of TBEV since the first emergence of human TBE in Europe.

Predicting the structure of protein-protein complexes using the SwarmDock Web Server

Protein-protein interactions drive many of the biological functions of the cell. Any two proteins have the potential to interact; however, whether the interactions are of biological significance is dependent on a number of complicated factors. Thus, modelling the three-dimensional structure of protein-protein complexes is still considered to be a complex endeavor. Nevertheless, many experimentalists now wish to boost their knowledge of protein-protein interactions, well beyond complexes resolved experimentally, and for them to be able to do so it is important they are able to effectively and confidently predict protein-protein interactions. The main aim of this chapter is to acquaint the reader, particularly one from a non-computational background, how to use a state-of-the-art protein docking tool. In particular, we describe here the SwarmDock Server (SDS), a web service for the flexible modelling of protein-protein complexes; this server is freely available at: http://bmm.cancerresearchuk.org/~SwarmDock/. Supplementary files for Case Studies are provided with the chapter and available at extras.springer.com.

RaTrav: a tool for calculating mean first-passage times on biochemical networks

BACKGROUND

The concept of mean first-passage times (MFPTs) occupies an important place in the theory of stochastic processes, with the methods of their calculation being equally important in theoretical physics, chemistry and biology. We present here a software tool designed to support computational biology studies where Markovian dynamics takes place and MFPTs between initial and single or multiple final states in network-like systems are used. Two methods are made available for which their efficiency is strongly dependent on the topology of the defined network: the combinatorial Hill technique and the Monte Carlo simulation method.

RESULTS

After a brief introduction to RaTrav, we highlight the utility of MFPT calculations by providing two examples (accompanied by Additional file 1) where they are deemed to be of importance: analysis of a protein-protein docking funnel and interpretation of the free energy transduction between two coupled enzymatic reactions controlled by the dynamics of transition between enzyme conformational states.

CONCLUSIONS

RaTrav is a versatile and easy to use software tool for calculating MFPTs across biochemical networks. The user simply prepares a text file with the structure of a given network, along with some additional basic parameters such as transition probabilities, waiting probabilities (if any) and local times (weights of edges), which define explicitly the stochastic dynamics on the network. The RaTrav tool can then be applied in order to compute desired MFPTs. For the provided examples, we were able to find the favourable binding path within a protein-protein docking funnel and to calculate the degree of coupling for two chemical reactions catalysed simultaneously by the same protein enzyme. However, the list of possible applications is much wider.

The scoring of poses in protein-protein docking: current capabilities and future directions

BACKGROUND

Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling.

RESULTS

We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically.

CONCLUSIONS

All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.