Geometric algorithms for cavity detection on protein surfaces

Macromolecular structures such as proteins heavily empower cellular processes or functions. These biological functions result from interactions between proteins and peptides, catalytic substrates, nucleotides or even human-made chemicals. Thus, several interactions can be distinguished: protein-ligand, protein-protein, protein-DNA, and so on. Furthermore, those interactions only happen under chemical- and shapecomplementarity conditions, and usually take place in regions known as binding sites. Typically, a protein consists of four structural levels. The primary structure of a protein is made up of its amino acid sequences (or chains). Its secondary structure essentially comprises -helices and -sheets, which are sub-sequences (or sub-domains) of amino acids of the primary structure. Its tertiary structure results from the composition of sub-domains into domains, which represent the geometric shape of the protein. Finally, the quaternary structure of a protein results from the aggregate of two or more tertiary structures, usually known as a protein complex. This thesis fits in the scope of structure-based drug design and protein docking. Specifically, one addresses the fundamental problem of detecting and identifying protein cavities, which are often seen as tentative binding sites for ligands in protein-ligand interactions. In general, cavity prediction algorithms split into three main categories: energy-based, geometry-based, and evolution-based. Evolutionary methods build upon evolutionary sequence conservation estimates; that is, these methods allow us to detect functional sites through the computation of the evolutionary conservation of the positions of amino acids in proteins. Energy-based methods build upon the computation of interaction energies between protein and ligand atoms. In turn, geometry-based algorithms build upon the analysis of the geometric shape of the protein (i.e., its tertiary structure) to identify cavities. This thesis focuses on geometric methods. We introduce here three new geometric-based algorithms for protein cavity detection. The main contribution of this thesis lies in the use of computer graphics techniques in the analysis and recognition of cavities in proteins, much in the spirit of molecular graphics and modeling. As seen further ahead, these techniques include field-of-view (FoV), voxel ray casting, back-face culling, shape diameter functions, Morse theory, and critical points. The leading idea is to come up with protein shape segmentation, much like we commonly do in mesh segmentation in computer graphics. In practice, protein cavity algorithms are nothing more than segmentation algorithms designed for proteins.Estruturas macromoleculares tais como as proteínas potencializam processos ou funções celulares. Estas funções resultam das interações entre proteínas e peptídeos, substratos catalíticos, nucleótideos, ou até mesmo substâncias químicas produzidas pelo homem. Assim, há vários tipos de interacções: proteína-ligante, proteína-proteína, proteína-DNA e assim por diante. Além disso, estas interações geralmente ocorrem em regiões conhecidas como locais de ligação (binding sites, do inglês) e só acontecem sob condições de complementaridade química e de forma. É também importante referir que uma proteína pode ser estruturada em quatro níveis. A estrutura primária que consiste em sequências de aminoácidos (ou cadeias), a estrutura secundária que compreende essencialmente por hélices e folhas , que são subsequências (ou subdomínios) dos aminoácidos da estrutura primária, a estrutura terciária que resulta da composição de subdomínios em domínios, que por sua vez representa a forma geométrica da proteína, e por fim a estrutura quaternária que é o resultado da agregação de duas ou mais estruturas terciárias. Este último nível estrutural é frequentemente conhecido por um complexo proteico. Esta tese enquadra-se no âmbito da conceção de fármacos baseados em estrutura e no acoplamento de proteínas. Mais especificamente, aborda-se o problema fundamental da deteção e identificação de cavidades que são frequentemente vistos como possíveis locais de ligação (putative binding sites, do inglês) para os seus ligantes (ligands, do inglês). De forma geral, os algoritmos de identificação de cavidades dividem-se em três categorias principais: baseados em energia, geometria ou evolução. Os métodos evolutivos baseiam-se em estimativas de conservação das sequências evolucionárias. Isto é, estes métodos permitem detectar locais funcionais através do cálculo da conservação evolutiva das posições dos aminoácidos das proteínas. Em relação aos métodos baseados em energia estes baseiam-se no cálculo das energias de interação entre átomos da proteína e do ligante. Por fim, os algoritmos geométricos baseiam-se na análise da forma geométrica da proteína para identificar cavidades. Esta tese foca-se nos métodos geométricos. Apresentamos nesta tese três novos algoritmos geométricos para detecção de cavidades em proteínas. A principal contribuição desta tese está no uso de técnicas de computação gráfica na análise e reconhecimento de cavidades em proteínas, muito no espírito da modelação e visualização molecular. Como pode ser visto mais à frente, estas técnicas incluem o field-of-view (FoV), voxel ray casting, back-face culling, funções de diâmetro de forma, a teoria de Morse, e os pontos críticos. A ideia principal é segmentar a proteína, à semelhança do que acontece na segmentação de malhas em computação gráfica. Na prática, os algoritmos de detecção de cavidades não são nada mais que algoritmos de segmentação de proteínas

PocketPicker: analysis of ligand binding-sites with shape descriptors

Background Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding. Results We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITEcs, PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITEcs and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites. Conclusions The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections

MolAxis: a server for identification of channels in macromolecules

MolAxis is a freely available, easy-to-use web server for identification of channels that connect buried cavities to the outside of macromolecules and for transmembrane (TM) channels in proteins. Biological channels are essential for physiological processes such as electrolyte and metabolite transport across membranes and enzyme catalysis, and can play a role in substrate specificity. Motivated by the importance of channel identification in macromolecules, we developed the MolAxis server. MolAxis implements state-of-the-art, accurate computational-geometry techniques that reduce the dimensions of the channel finding problem, rendering the algorithm extremely efficient. Given a protein or nucleic acid structure in the PDB format, the server outputs all possible channels that connect buried cavities to the outside of the protein or points to the main channel in TM proteins. For each channel, the gating residues and the narrowest radius termed ‘bottleneck’ are also given along with a full list of the lining residues and the channel surface in a 3D graphical representation. The users can manipulate advanced parameters and direct the channel search according to their needs. MolAxis is available as a web server or as a stand-alone program at http://bioinfo3d.cs.tau.ac.il/MolAxis

MolAxis: a server for identification of channels in macromolecules

MolAxis is a freely available, easy-to-use web server for identification of channels that connect buried cavities to the outside of macromolecules and for transmembrane (TM) channels in proteins. Biological channels are essential for physiological processes such as electrolyte and metabolite transport across membranes and enzyme catalysis, and can play a role in substrate specificity. Motivated by the importance of channel identification in macromolecules, we developed the MolAxis server. MolAxis implements state-of-the-art, accurate computational-geometry techniques that reduce the dimensions of the channel finding problem, rendering the algorithm extremely efficient. Given a protein or nucleic acid structure in the PDB format, the server outputs all possible channels that connect buried cavities to the outside of the protein or points to the main channel in TM proteins. For each channel, the gating residues and the narrowest radius termed ‘bottleneck’ are also given along with a full list of the lining residues and the channel surface in a 3D graphical representation. The users can manipulate advanced parameters and direct the channel search according to their needs. MolAxis is available as a web server or as a stand-alone program at http://bioinfo3d.cs.tau.ac.il/MolAxis

Caver Web 1.0: identification of tunnels and channels in proteins and analysis of ligand transport

Caver Web 1.0 is a web server for comprehensive analysis of protein tunnels and channels, and study of the ligands’ transport through these transport pathways. Caver Web is the first interactive tool allowing both the analyses within a single graphical user interface. The server is built on top of the abundantly used tunnel detection tool Caver 3.02 and CaverDock 1.0 enabling the study of the ligand transport. The program is easy-to-use as the only required inputs are a protein structure for a tunnel identification and a list of ligands for the transport analysis. The automated guidance procedures assist the users to set up the calculation in a way to obtain biologically relevant results. The identified tunnels, their properties, energy profiles and trajectories for ligands’ passages can be calculated and visualized. The tool is very fast (2–20 min per job) and is applicable even for virtual screening purposes. Its simple setup and comprehensive graphical user interface make the tool accessible for a broad scientific community. The server is freely available at https://loschmidt.chemi.muni.cz/caverweb.Caver Web 1.0 je webový server pro komplexní analýzu tunelů a kanálů v proteinech a pro studium transportu ligandu přes tyto transportní cesty. Caver Web je první interaktivní nástroj umožňující obě analýzy v jednom grafickém uživatelském rozhraní. Server je vybudován nad hojně užívaným nástrojem pro detekci tunelů Caver 3.02 a nad CaverDock 1.0 umožňujícím studium transportu ligandů. Program se snadno ovládá, jelikož vyžaduje pouze strukturu proteinu pro identifikaci tunelů a seznam ligandů pro analýzu transportu. Procedury pro automatické nastavení výpočtů asistují uživatelům tak, aby získali biologicky relevantní výsledky. Identifikované tunely, jejich vlastnosti, energetické profily a trajektorie průchodů ligandů mohou být spočítány a vizualizovány. Nástroj je velmi rychlý (2-20 minut na úlohu) a je použitelný dokonce pro virtuální screening. Jeho snadné nastavení a ucelené grafické rozhraní dělá nástroj přístupným pro širokou vědeckou komunitu. Server je volně k dispozici na https://loschmidt.chemi.muni.cz/caverweb

Molecular Dynamics Simulations of the Bacterial Outer Membrane Channels TolC and OprM & dxTuber, a Biomolecular Cavity Detection Tool based on Protein and Solvent Dynamics

The multidrug resistance of bacteria is a serious phenomenon in current medical treatment. Beginning with the introduction of antibiotics more and more bacterial strains achieved resistance against these chemical compounds and over the years a competition between antibiotic drug discovery and bacterial drug resistance arose. The well studied Gram-negative bacteria Escherichia coli and Pseudomonas aeruginosa serve in this work as a model organisms for bacterial resistance against antibiotics. Both bacteria evolved multidrug resistant strains through several strategies, including the expelling of harming compounds through efflux systems. The over expression of these efflux systems in the bacterial membranes are responsible for resistance against many antibiotic compounds. The AcrA/B-TolC efflux system induces resistance of E.coli against a broad range of antibiotics. Ranging from the inner membrane towards the outer membrane, the efflux system spans the entire periplasmic space. The system consists of the inner membrane transporter AcrB, the membrane fusion protein AcrA and the outer membrane channel TolC. TolC itself cooperates with several inner membrane transporters and facilitates the export of harming compounds across the outer membrane. Due to this versatility TolC could become a target of drug treatment. A disabled or blocked TolC could prevent drug extrusion via systems that use TolC as an exit gate. At the time of writing the gating functionality of TolC is not known in detail. To gain insights into TolC functionality two series of unbiased molecular dynamics (MD) simulations were performed. Whereas the first series was carried out in absence of AcrB the second one was executed in presence of the AcrB docking domain (AcrB-DD). For the first series unbiased MD simulations between 150-300 ns in a Palmitoyloleoylphosphatidylethanolamine (POPE) / NaCl / water environment were calculated. In most of these simulations TolC opens and closes freely on extracellular side hinting at the absence of a gating functionality on this side. On periplasmic side a double aspartate ring restricts substrate passage in all simulations and grasping-like motions were noticed for the tip loops of helix 7 & 8. A consecutive binding of two sodium ions inside the lower periplasmic part of TolC occured in one simulation, which induced a stabilized closed state on periplasmic side. TolC remained closed on periplasmic side unless all ions were removed from the simulation box indicating a sodium dependent lock on this side. For the second series of MD simulations we added the AcrB-DD to the previously described system setup based on orientations of a previously published data driven modeled structure. Four unbiased 150 ns MD simulations were calculated and in one of these simulations the docking domain spontaneously docks onto TolC. The latter simulation was extended to a simulation time of 1.05 μs resulting in a tighter binding between AcrB and TolC with regards to the modeled structure. A preferred open conformation on extracellular hints analogue to TolC only simulations at the absence of a lock on extracellular side. On the AcrB-facing side TolC's tip loops located at helix 7 & 8 opened up and were stabilized by the AcrB docking domain. However, the double aspartate ring remained closed until the end of the simulation, meaning that either the simulation time is too short to observe an opening of TolC or that another part of the AcrA/B-TolC efflux system is missing to open TolC. In Pseudomonas aeruginosa OprM had been identified as a TolC homologue protein. OprM is part of the multidrug efflux system MexA/B-OprM and acts as an exit duct for several inner membrane transporters. Also for OprM the gating mechanisms are not known in detail at time of writing. To explore OprM's gating mechanisms it has been simulated in a POPE / NaCl / water environment. During all five 200 ns long MD simulations OprM opens and closes freely on extracellular side suggesting also for OprM the absence of a gating mechanism on extracellular side. The tip loops of helix 7 & 8 on periplasmic side open up in a way comparable to TolC simulations and in contrast to TolC no closing motions were noticed for these helices for OprM. In OprM a single aspartate ring limits substrate passage on the inner membrane facing side of OprM. In contrast to TolC simulations a slight opening of this aspartate ring was measured in all five simulations. The absence of heightened sodium densities near the periplasmic entrance regions could mean that either longer simulation time is needed to observe a sodium induced closure of OprM or that the periplasmic access is regulated only by the aspartate ring. Despite the absence of heightened sodium densities in the aspartate ring region, clear peaks of high sodium densities identified sodium pockets between the equatorial region and the aspartate ring region formed by Asp171 and Asp230. Voids inside of proteins can indicate substrate binding sites, ion pockets, pathways through channel proteins, their open and closed states and active sites. Over the years numerous cavity detection tools have been introduced to identify and highlight these voids. All available cavity detection tools were based on static structures and present cavities for single protein conformations only. With dxTuber we developed and introduced a novel cavity detection tool based on an ensemble of protein conformations. It uses averaged protein and solvent density maps, which are derived from MD trajectories, as input. With this technique protein dynamics are taken into account and cavities are detected through the separation of protein external solvent from protein internal solvent. Protein internal solvent can be grouped into cavities and stored in the commonly used PDB file format. Individual cavities can be separated via the atom name field of the PDB file format. dxTuber itself can calculate cavity volume and the cross-sectional area of a single cavity along a principle axis. For convenience a graphical user interface (GUI) and a command line interface (CLI) of dxTuber are released under the GPL v2

CavBench: a benchmark for protein cavity detection methods

Extensive research has been applied to discover new techniques and methods to model protein-ligand interactions. In particular, considerable efforts focused on identifying candidate binding sites, which quite often are active sites that correspond to protein pockets or cavities. Thus, these cavities play an important role in molecular docking. However, there is no established benchmark to assess the accuracy of new cavity detection methods. In practice, each new technique is evaluated using a small set of proteins with known binding sites as ground-truth. However, studies supported by large datasets of known cavities and/or binding sites and statistical classification (i.e., false positives, false negatives, true positives, and true negatives) would yield much stronger and reliable assessments. To this end, we propose CavBench, a generic and extensible benchmark to compare different cavity detection methods relative to diverse ground truth datasets (e.g., PDBsum) using statistical classification methods.info:eu-repo/semantics/publishedVersio