Enhanced sampling of protein conformational states for dynamic cross-docking within the protein-protein docking server SwarmDock.

The formation of specific protein-protein interactions is often a key to a protein's function. During complex formation, each protein component will undergo a change in the conformational state, for some these changes are relatively small and reside primarily at the sidechain level; however, others may display notable backbone adjustments. One of the classic problems in the protein-docking field is to be able to a priori predict the extent of such conformational changes. In this work, we investigated three protocols to find the most suitable input structure conformations for cross-docking, including a robust sampling approach in normal mode space. Counterintuitively, knowledge of the theoretically best combination of normal modes for unbound-bound transitions does not always lead to the best results. We used a novel spatial partitioning library, Aether Engine (see Supplementary Materials), to efficiently search the conformational states of 56 receptor/ligand pairs, including a recent CAPRI target, in a systematic manner and selected diverse conformations as input to our automated docking server, SwarmDock, a server that allows moderate conformational adjustments during the docking process. In essence, here we present a dynamic cross-docking protocol, which when benchmarked against the simpler approach of just docking the unbound components shows a 10% uplift in the quality of the top docking pose.

IRaPPA: information retrieval based integration of biophysical models for protein assembly selection.

MOTIVATION: In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. RESULTS: Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. AVAILABILITY AND IMPLEMENTATION: IRaPPA has been implemented in the SwarmDock server ( http://bmm.crick.ac.uk/∼SwarmDock/ ), pyDock server ( http://life.bsc.es/pid/pydockrescoring/ ) and ZDOCK server ( http://zdock.umassmed.edu/ ), with code available on request. CONTACT: moal@ebi.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.

SwarmDock: a server for flexible protein-protein docking.

Protein-protein interactions are central to almost all biological functions, and the atomic details of such interactions can yield insights into the mechanisms that underlie these functions. We present a web server that wraps and extends the SwarmDock flexible protein-protein docking algorithm. After uploading PDB files of the binding partners, the server generates low energy conformations and returns a ranked list of clustered docking poses and their corresponding structures. The user can perform full global docking, or focus on particular residues that are implicated in binding. The server is validated in the CAPRI blind docking experiment, against the most current docking benchmark, and against the ClusPro docking server, the highest performing server currently available.

Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization.

Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects.

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.

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

Within the crowded, seemingly chaotic environment of the cell, proteins are still able to find their binding partners. This is achieved via an ensemble of trajectories, which funnel them towards their functional binding sites, the binding funnel. Here, we characterize funnel-like energy structures on the global energy landscape using time-homogeneous finite state Markov chain models. These models are based on the idea that transitions can occur between structurally similar docking solutions, with transition probabilities determined by their difference in binding energy. Funnel-like energy structures are those containing solutions with very high equilibrium populations. Although these are found surrounding both near-native and false positive binding sites, we show that the removal of nonfunnel-like energy structures, by filtering away solutions with low maximum equilibrium population, can significantly improve the ranking of docked poses.

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions.

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

Why 6-Iodouridine Cannot Be Used as a Radiosensitizer of DNA Damage? Computational and Experimental Studies.

Previous density functional theory (DFT) studies on 6-brominated pyrimidine nucleosides suggest that 6-iodo-2'-deoxyuridine (6IdU) should act as a better radiosensitizer than its 5-iodosubstituted 2'-deoxyuridine analogue. In this work, we show that 6IdU is unstable in an aqueous solution. Indeed, a complete disappearance of the 6IdU signal was observed during its isolation by reversed-phase high-performance liquid chromatography (RP-HPLC). As indicated by the thermodynamic characteristics for the SN1-type hydrolysis of 6IdU obtained at the CAM-B3LYP/DGDZVP++ level and the polarizable continuum model (PCM) of water, 6-iodouracil (6IU) was already released quantitatively at ambient temperatures. The simulation of the hydrolysis kinetics demonstrated that a thermodynamic equilibrium was reached within seconds for the title compound. To assess the reliability of the calculations carried out, we synthesized 6-iodouridine (6IUrd), which was, unlike 6IdU, sufficiently stable in an aqueous solution at room temperature. The activation barrier for the N-glycosidic bond dissociation in 6IUrd was estimated experimentally using an Arrhenius plot. The stabilities in water calculated for 6IdU, 6IUrd, and 5-iodo-2'-deoxyuridine (5IdU) could be explained by the electronic and steric effects of the 2'-hydroxy group present in the ribose moiety. Our studies highlight the issue of the hydrolytic stability of potentially radiosensitizing nucleotides which, besides having favorable dissociative electron attachment (DEA) characteristics, must be stable in water to have any practical application.

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.

Underdamped vibrations control the primary electron transfer in photosynthesis at low temperatures.

Measurements performed with the help of ultrafast laser spectroscopy have clearly shown that the primary electron transfer in photosynthesis lasts no longer than a few picoseconds. Equally fast are processes of vibrational relaxation which have to be taken into account in the correct description of the phenomenon. Here, we consider a simple theory combining the electron transfer process with diffusion in the energy space of a chosen underdamped vibrational mode of protein environment. Analytical formulas for effective transition rate constants are derived, and a transient kinetics is considered. The quality of analytical approximations is verified by numerical simulations for various physical conditions. Efficient parallel computations have been applied. The model can explain a peculiar temperature dependence of pump-probe spectra observed.

Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2.

We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall and r=0.72 for the rigid complexes.

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.

Output-input ratio in thermally fluctuating biomolecular machines.

Biological molecular machines are proteins that operate under isothermal conditions and hence are referred to as free energy transducers. They can be formally considered as enzymes that simultaneously catalyze two chemical reactions: the free energy-donating (input) reaction and the free energy-accepting (output) one. Most if not all biologically active proteins display a slow stochastic dynamics of transitions between a variety of conformational substates composing their native state. This makes the description of the enzymatic reaction kinetics in terms of conventional rate constants insufficient. In the steady state, upon taking advantage of the assumption that each reaction proceeds through a single pair (the gate) of transition conformational substates of the enzyme-substrates complex, the degree of coupling between the output and the input reaction fluxes has been expressed in terms of the mean first-passage times on a conformational transition network between the distinguished substates. The theory is confronted with the results of random-walk simulations on the five-dimensional hypercube. The formal proof is given that, for single input and output gates, the output-input degree of coupling cannot exceed unity. As some experiments suggest such exceeding, looking for the conditions for increasing the degree of coupling value over unity challenges the theory. Performed simulations of random walks on several model networks involving more extended gates indicate that the case of the degree of coupling value higher than 1 is realized in a natural way on critical branching trees extended by long-range shortcuts. Such networks are scale-free and display the property of the small world. For short-range shortcuts, the networks are scale-free and fractal, representing a reasonable model for biomolecular machines displaying tight coupling, i.e., the degree of coupling equal exactly to unity. A hypothesis is stated that the protein conformational transition networks, as just as higher-level biological networks, the protein interaction network, and the metabolic network, have evolved in the process of self-organized criticality.

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.

Search for inhibitors of aminoacyl-tRNA synthases by virtual click chemistry.

The increase of multidrug-resistant strains of bacteria to known classes of antibiotics present a severe challenge for modern medicine. The most promising strategy to combat pathogenic bacteria is to discover new drug targets. In this regard, aminoacyl-tRNA synthetases are particularly well suited to develop novel drugs that show no cross-resistance to other classical antibiotics. To date various chemical structures that inhibit AA-RS have been identified. In this report we present an interesting approach towards generating of Leu-RS inhibitors by virtual click chemistry. That is we identified key fragments for ligand binding within catalytic pocket of Leu-RS, generated the collection of similar fragments with the use of Ligand.Info, identified the fragments that are most strongly bound in different areas within the catalytic pocket, and finally with the use of virtual click chemistry we generated a set of molecules which are most likely to act as highly potent bacterial Leu-RS inhibitors.

IA, database of known ligands of aminoacyl-tRNA synthetases.

The IA database contains 240 structures of known inhibitors of aminoacyl-tRNA synthetases. Structures can be downloaded in different file formats (mol, sdf, smile, png). The search engine offers possibility of searching for the ligands with a given functional group. Additionally, one can search for ligands that act on selected synthetases and from particular references. The data include information which synthetase a given ligand inhibits together with the inhibition constant (IC50) if known.