Web application for studying the free energy of binding and protonation states of protein-ligand complexes based on HINT

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

Computational reconstruction of atomistic protein structures from coarse-grained models

Three-dimensional protein structures, whether determined experimentally or theoretically, are often too low resolution. In this mini-review, we outline the computational methods for protein structure reconstruction from incomplete coarse-grained to all atomistic models. Typical reconstruction schemes can be divided into four major steps. Usually, the first step is reconstruction of the protein backbone chain starting from the C-alpha trace. This is followed by side-chains rebuilding based on protein backbone geometry. Subsequently, hydrogen atoms can be reconstructed. Finally, the resulting all-atom models may require structure optimization. Many methods are available to perform each of these tasks. We discuss the available tools and their potential applications in integrative modeling pipelines that can transfer coarse-grained information from computational predictions, or experiment, to all atomistic structures.

ProteinsPlus: a web portal for structure analysis of macromolecules

With currently more than 126 000 publicly available structures and an increasing growth rate, the Protein Data Bank constitutes a rich data source for structure-driven research in fields like drug discovery, crop science and biotechnology in general. Typical workflows in these areas involve manifold computational tools for the analysis and prediction of molecular functions. Here, we present the ProteinsPlus web server that offers a unified easy-to-use interface to a broad range of tools for the early phase of structure-based molecular modeling. This includes solutions for commonly required pre-processing tasks like structure quality assessment (EDIA), hydrogen placement (Protoss) and the search for alternative conformations (SIENA). Beyond that, it also addresses frequent problems as the generation of 2D-interaction diagrams (PoseView), protein-protein interface classification (HyPPI) as well as automatic pocket detection and druggablity assessment (DoGSiteScorer). The unified ProteinsPlus interface covering all featured approaches provides various facilities for intuitive input and result visualization, case-specific parameterization and download options for further processing. Moreover, its generalized workflow allows the user a quick familiarization with the different tools. ProteinsPlus also stores the calculated results temporarily for future request and thus facilitates convenient result communication and re-access. The server is freely available at http://proteins.plus.

DynaDom: structure-based prediction of T cell receptor inter-domain and T cell receptor-peptide-MHC (class I) association angles

Background

T cell receptor (TCR) molecules are involved in the adaptive immune response as they distinguish between self- and foreign-peptides, presented in major histocompatibility complex molecules (pMHC). Former studies showed that the association angles of the TCR variable domains (Vα/Vβ) can differ significantly and change upon binding to the pMHC complex. These changes can be described as a rotation of the domains around a general Center of Rotation, characterized by the interaction of two highly conserved glutamine residues.

Methods

We developed a computational method, DynaDom, for the prediction of TCR Vα/Vβ inter-domain and TCR/pMHC orientations in TCRpMHC complexes, which allows predicting the orientation of multiple protein-domains. In addition, we implemented a new approach to predict the correct orientation of the carboxamide endgroups in glutamine and asparagine residues, which can also be used as an external, independent tool.

Results

The approach was evaluated for the remodeling of 75 and 53 experimental structures of TCR and TCRpMHC (class I) complexes, respectively. We show that the DynaDom method predicts the correct orientation of the TCR Vα/Vβ angles in 96 and 89% of the cases, for the poses with the best RMSD and best interaction energy, respectively. For the concurrent prediction of the TCR Vα/Vβ and pMHC orientations, the respective rates reached 74 and 72%. Through an exhaustive analysis, we could show that the pMHC placement can be further improved by a straightforward, yet very time intensive extension of the current approach.

Conclusions

The results obtained in the present remodeling study prove the suitability of our approach for interdomain-angle optimization. In addition, the high prediction rate obtained specifically for the energetically highest ranked poses further demonstrates that our method is a powerful candidate for blind prediction. Therefore it should be well suited as part of any accurate atomistic modeling pipeline for TCRpMHC complexes and potentially other large molecular assemblies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-016-0071-7) contains supplementary material, which is available to authorized users.

Molecular Docking at a Glance

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

The Art of Compiling Protein Binding Site Ensembles

Structure-based drug design starts with the collection, preparation, and initial analysis of protein structures. With more than 115,000 structures publically available in the Protein Data Bank (PDB), fully automated processes reliably performing these important preprocessing steps are needed. Several tools are available for these tasks, however, most of them do not address the special needs of scientists interested in protein-ligand interactions. In this paper, we summarize our research activities towards an automated processing pipeline from raw PDB data towards ready-to-use protein binding site ensembles. Starting from a single protein structure, the pipeline covers the following phases: Extracting structurally related binding sites from the PDB, aligning disconnected binding site sequences, resolving tautomeric forms and protonation, orienting hydrogens and flippable side-chains, structurally aligning the multitude of binding sites, and performing a reasonable reduction of ensemble structures. The pipeline, named SIENA, creates protein-structural ensembles for the analysis of protein flexibility, molecular design efforts like docking or de novo design within seconds. For the first time, we are able to process the whole PDB in order to create a large collection of protein binding site ensembles. SIENA is available as part of the ZBH ProteinsPlus webserver under http://proteinsplus.zbh.uni-hamburg.de.

Kinetic barriers in the isomerization of substituted ureas: implications for computer-aided drug design

Urea derivatives are ubiquitously found in many chemical disciplines. N,N'-substituted ureas may show different conformational preferences depending on their substitution pattern. The high energetic barrier for isomerization of the cis and trans state poses additional challenges on computational simulation techniques aiming at a reproduction of the biological properties of urea derivatives. Herein, we investigate energetics of urea conformations and their interconversion using a broad spectrum of methodologies ranging from data mining, via quantum chemistry to molecular dynamics simulation and free energy calculations. We find that the inversion of urea conformations is inherently slow and beyond the time scale of typical simulation protocols. Therefore, extra care needs to be taken by computational chemists to work with appropriate model systems. We find that both knowledge-driven approaches as well as physics-based methods may guide molecular modelers towards accurate starting structures for expensive calculations to ensure that conformations of urea derivatives are modeled as adequately as possible.

Protoss: a holistic approach to predict tautomers and protonation states in protein-ligand complexes

The calculation of hydrogen positions is a common preprocessing step when working with crystal structures of protein-ligand complexes. An explicit description of hydrogen atoms is generally needed in order to analyze the binding mode of particular ligands or to calculate the associated binding energies. Due to the large number of degrees of freedom resulting from different chemical moieties and the high degree of mutual dependence this problem is anything but trivial. In addition to an efficient algorithm to take care of the complexity resulting from complicated hydrogen bonding networks, a robust chemical model is needed to describe effects such as tautomerism and ionization consistently. We present a novel method for the placement of hydrogen coordinates in protein-ligand complexes which takes tautomers and protonation states of both protein and ligand into account. Our method generates the most probable hydrogen positions on the basis of an optimal hydrogen bonding network using an empirical scoring function. The high quality of our results could be verified by comparison to the manually adjusted Astex diverse set and a remarkably low rate of undesirable hydrogen contacts compared to other tools.

Intuitive, but not simple: including explicit water molecules in protein-protein docking simulations improves model quality

Characterizing the nature of interaction between proteins that have not been experimentally cocrystallized requires a computational docking approach that can successfully predict the spatial conformation adopted in the complex. In this work, the Hydropathic INTeractions (HINT) force field model was used for scoring docked models in a data set of 30 high-resolution crystallographically characterized "dry" protein-protein complexes and was shown to reliably identify native-like models. However, most current protein-protein docking algorithms fail to explicitly account for water molecules involved in bridging interactions that mediate and stabilize the association of the protein partners, so we used HINT to illuminate the physical and chemical properties of bridging waters and account for their energetic stabilizing contributions. The HINT water Relevance metric identified the "truly" bridging waters at the 30 protein-protein interfaces and we utilized them in "solvated" docking by manually inserting them into the input files for the rigid body ZDOCK program. By accounting for these interfacial waters, a statistically significant improvement of ∼24% in the average hit-count within the top-10 predictions the protein-protein dataset was seen, compared to standard "dry" docking. The results also show scoring improvement, with medium and high accuracy models ranking much better than incorrect ones. These improvements can be attributed to the physical presence of water molecules that alter surface properties and better represent native shape and hydropathic complementarity between interacting partners, with concomitantly more accurate native-like structure predictions.

Bound water at protein-protein interfaces: partners, roles and hydrophobic bubbles as a conserved motif

Background

There is a great interest in understanding and exploiting protein-protein associations as new routes for treating human disease. However, these associations are difficult to structurally characterize or model although the number of X-ray structures for protein-protein complexes is expanding. One feature of these complexes that has received little attention is the role of water molecules in the interfacial region.

Methodology

A data set of 4741 water molecules abstracted from 179 high-resolution (≤ 2.30 Å) X-ray crystal structures of protein-protein complexes was analyzed with a suite of modeling tools based on the HINT forcefield and hydrogen-bonding geometry. A metric termed Relevance was used to classify the general roles of the water molecules.

Results

The water molecules were found to be involved in: a) (bridging) interactions with both proteins (21%), b) favorable interactions with only one protein (53%), and c) no interactions with either protein (26%). This trend is shown to be independent of the crystallographic resolution. Interactions with residue backbones are consistent for all classes and account for 21.5% of all interactions. Interactions with polar residues are significantly more common for the first group and interactions with non-polar residues dominate the last group. Waters interacting with both proteins stabilize on average the proteins' interaction (−0.46 kcal mol⁻¹), but the overall average contribution of a single water to the protein-protein interaction energy is unfavorable (+0.03 kcal mol⁻¹). Analysis of the waters without favorable interactions with either protein suggests that this is a conserved phenomenon: 42% of these waters have SASA ≤ 10 Ų and are thus largely buried, and 69% of these are within predominantly hydrophobic environments or “hydrophobic bubbles”. Such water molecules may have an important biological purpose in mediating protein-protein interactions.

Factors influencing protein tyrosine nitration--structure-based predictive models

Models for exploring tyrosine nitration in proteins have been created based on 3D structural features of 20 proteins for which high-resolution X-ray crystallographic or NMR data are available and for which nitration of 35 total tyrosines has been experimentally proven under oxidative stress. Factors suggested in previous work to enhance nitration were examined with quantitative structural descriptors. The role of neighboring acidic and basic residues is complex: for the majority of tyrosines that are nitrated the distance to the heteroatom of the closest charged side chain corresponds to the distance needed for suspected nitrating species to form hydrogen bond bridges between the tyrosine and that charged amino acid. This suggests that such bridges play a very important role in tyrosine nitration. Nitration is generally hindered for tyrosines that are buried and for those tyrosines for which there is insufficient space for the nitro group. For in vitro nitration, closed environments with nearby heteroatoms or unsaturated centers that can stabilize radicals are somewhat favored. Four quantitative structure-based models, depending on the conditions of nitration, have been developed for predicting site-specific tyrosine nitration. The best model, relevant for both in vitro and in vivo cases, predicts 30 of 35 tyrosine nitrations (positive predictive value) and has a sensitivity of 60/71 (11 false positives).

Hydrophobicity--shake flasks, protein folding and drug discovery

Hydrophobic interactions are some of the most important interactions in nature. They are the primary driving force in a number of phenomena. This is mostly an entropic effect and can account for a number of biophysical events such as protein-protein or protein-ligand binding that are of immense importance in drug design. The earliest studies on this phenomenon can be dated back to the end of the 19(th) century when Meyer and Overton independently correlated the hydrophobic nature of gases to their anesthetic potency. Since then, significant progress has been made in this realm of science. This review briefly traces the history of hydrophobicity research along with the theoretical estimation of partition coefficients. Finally, the application of hydrophobicity estimation methods in the field of drug design and protein folding is discussed.

Fast automated placement of polar hydrogen atoms in protein-ligand complexes

BACKGROUND

Hydrogen bonds play a major role in the stabilization of protein-ligand complexes. The ability of a functional group to form them depends on the position of its hydrogen atoms. An accurate knowledge of the positions of hydrogen atoms in proteins is therefore important to correctly identify hydrogen bonds and their properties. The high mobility of hydrogen atoms introduces several degrees of freedom: Tautomeric states, where a hydrogen atom alters its binding partner, torsional changes where the position of the hydrogen atom is rotated around the last heavy-atom bond in a residue, and protonation states, where the number of hydrogen atoms at a functional group may change. Also, side-chain flips in glutamine and asparagine and histidine residues, which are common crystallographic ambiguities must be identified before structure-based calculations can be conducted.

RESULTS

We have implemented a method to determine the most probable hydrogen atom positions in a given protein-ligand complex. Optimality of hydrogen bond geometries is determined by an empirical scoring function which is used in molecular docking. This allows to evaluate protein-ligand interactions with an established model. Also, our method allows to resolve common crystallographic ambiguities such as as flipped amide groups and histidine residues. To ensure high speed, we make use of a dynamic programming approach.

CONCLUSION

Our results were checked against selected high-resolution structures from an external dataset, for which the positions of the hydrogen atoms have been validated manually. The quality of our results is comparable to that of other programs, with the advantage of being fast enough to be applied on-the-fly for interactive usage or during score evaluation.