Enhanced fold recognition using efficient short fragment clustering

The main structure aligner in the CCP4 Software Suite, SSM (Secondary Structure Matching) has a limited applicability on the intermediate stages of the structure solution process, when the secondary structure cannot be reliably computed due to structural incompleteness or a fragmented mainchain. In this study, we describe a new algorithm for the alignment and comparison of protein structures in CCP4, which was designed to overcome SSM's limitations but retain its quality and speed. The new algorithm, named GESAMT (General Efficient Structural Alignment of Macromolecular Targets), employs the old idea of deriving the global structure similarity from a promising set of locally similar short fragments, but uses a few technical solutions that make it considerably faster. A comparative sensitivity and selectivity analysis revealed an unexpected significant improvement in the fold recognition properties of the new algorithm, which also makes it useful for applications in the structural bioinformatics domain. The new tool is included in the CCP4 Software Suite starting from version 6.3

Interactions between large molecules pose a puzzle for reference quantum mechanical methods

Quantum-mechanical methods are used for understanding molecular interactions throughout the natural sciences. Quantum diffusion Monte Carlo (DMC) and coupled cluster with single, double, and perturbative triple excitations [CCSD(T)] are state-of-the-art trusted wavefunction methods that have been shown to yield accurate interaction energies for small organic molecules. These methods provide valuable reference information for widely-used semi-empirical and machine learning potentials, especially where experimental information is scarce. However, agreement for systems beyond small molecules is a crucial remaining milestone for cementing the benchmark accuracy of these methods. We show that CCSD(T) and DMC interaction energies are not consistent for a set of polarizable supramolecules. Whilst there is agreement for some of the complexes, in a few key systems disagreements of up to 8 kcal mol−1 remain. These findings thus indicate that more caution is required when aiming at reproducible non-covalent interactions between extended molecules

Computational methods for molecular docking

This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Recently, it has been demonstrated that the knowledge of the three-dimensional structure of the protein can be used to derive new protein ligands with improved binding properties. This tutorial focuses on the following questions: What is its binding affinity toward a particular receptor? What are putative conformations of a ligand at the binding site? What are the similarities of different ligands in terms of their recognition capabilities? Where and in which orientation will a ligand bind to the active site? How is a new putative protein ligand selected? An overview is presented of the algorithms which are presently used to handle and predict protein-ligand interactions and to dock small molecule ligands into proteins

Locating Guest Molecules inside Metal–Organic Framework Pores with a Multilevel Computational Approach

Molecular docking has traditionally mostly been employed in the field of protein–ligand binding. Here, we extend this method, in combination with DFT-level geometry optimizations, to locate guest molecules inside the pores of metal–organic frameworks. The position and nature of the guest molecules tune the physicochemical properties of the host–guest systems. Therefore, it is essential to be able to reliably locate them to rationally enhance the performance of the known metal–organic frameworks and facilitate new material discovery. The results obtained with this approach are compared to experimental data. We show that the presented method can, in general, accurately locate adsorption sites and structures of the host–guest complexes. We therefore propose our approach as a computational alternative when no experimental structures of guest-loaded MOFs are available. Additional information on the adsorption strength in the studied host–guest systems emerges from the computed interaction energies. Our findings provide the basis for other computational studies on MOF–guest systems and contribute to a better understanding of the structure–interaction–property interplay associated with them

Crystal Structure Analysis Reveals Functional Flexibility in the Selenocysteine-Specific tRNA from Mouse

Selenocysteine tRNAs (tRNA(Sec)) exhibit a number of unique identity elements that are recognized specifically by proteins of the selenocysteine biosynthetic pathways and decoding machineries. Presently, these identity elements and the mechanisms by which they are interpreted by tRNA(Sec)-interacting factors are incompletely understood.We applied rational mutagenesis to obtain well diffracting crystals of murine tRNA(Sec). tRNA(Sec) lacking the single-stranded 3'-acceptor end ((ΔGCCA)RNA(Sec)) yielded a crystal structure at 2.0 Å resolution. The global structure of (ΔGCCA)RNA(Sec) resembles the structure of human tRNA(Sec) determined at 3.1 Å resolution. Structural comparisons revealed flexible regions in tRNA(Sec) used for induced fit binding to selenophosphate synthetase. Water molecules located in the present structure were involved in the stabilization of two alternative conformations of the anticodon stem-loop. Modeling of a 2'-O-methylated ribose at position U34 of the anticodon loop as found in a sub-population of tRNA(Sec)in vivo showed how this modification favors an anticodon loop conformation that is functional during decoding on the ribosome. Soaking of crystals in Mn(2+)-containing buffer revealed eight potential divalent metal ion binding sites but the located metal ions did not significantly stabilize specific structural features of tRNA(Sec).We provide the most highly resolved structure of a tRNA(Sec) molecule to date and assessed the influence of water molecules and metal ions on the molecule's conformation and dynamics. Our results suggest how conformational changes of tRNA(Sec) support its interaction with proteins

Interaction between Gold Nanoparticles and Blood Proteins to define Disease states

One of the most studied subjects in Bionanotechnology is the application of Gold Nanoparticles (AuNPs). These have unique optical and chemical properties and interact with proteins and other biomolecules forming dynamic (Protein-Corona) layers at the surface. These protein coronas are responsible for increased in vivo biocompatibility, and can be studied by multiple techniques, tracking for disease-specific protein profiles. In this work, 15 nm AuNPs were synthesized by the Turkevich method, and 40 nm AuNPs were provided. Sample concentration and size were determined by UV-Vis spectroscopy, exploiting the Surface Plasmon Resonance (SPR) effect. Successful surface functionalization was performed with the alkanethiol 11-mercaptoundecanoic acid (MUA) or a pentapeptide (CALNN), maintaining a negative global net charge and increasing overall stability. Bionanoconjugation with Bovine Serum Albumin (BSA) and Fibrinogen (Fib), with molecular weights of 66 and 340 kDa respectively, was performed and characterized by Agarose Gel Electrophoresis (AGE). Electrophoretic mobility was determined using image and video analysis performed by the eReuss software. Adsorption affinity constant were determined using the conjugation curves obtained in the AGE results, fitted using the Langmuir Isotherm, and resulted in (1.5 ± 0.1) x 10-2 (AuNP-MUA) for BSA conjugation, and (51.2 ± 4.7) x 10-2 (AuNP-CALNN) and (34.3 ± 1.2) x 10-2 (AuNP-MUA) for Fib conjugation. Bioconjugation of AuNP-CALNN with BSA was inconclusive. Competitive scenarios of a protein mixture favored Fib adsorption over BSA. Fib conjugation of 40 nm AuNPs showed multiple adsorption constants of (3 ± 0.7) x 10-2 and (9.7 ± 2.2) x 10-4 respectively. The eReuss software proved to be a powerful tool to analyze image results from electrophoretic runs, and the video analysis feature gives way to an innovative way of analyzing these experiments and extract further information on the Protein Corona stability. Fergusson Plot analysis and Light scattering techniques (DLS, NTA and ELS) were performed to determine hydrodynamic sizes and Zeta-Potential of bionanoconjugated samples.Uma das mais estudadas áreas em Bionanotecnologia é a aplicação de Nanopartículas de Ouro (AuNPs). Estas possuem propriedades óticas e químicas únicas e interagem com proteínas e outras biomoléculas, formando camadas dinâmicas a superfícies (Coroa Proteica). Estas coroas são responsáveis pelo aumento da biocompatibilidade in vivo, e podem ser estudadas com múltiplas técnicas, podendo identificar perfis de doença específicos. Neste trabalho, AuNPs de 15 nm foram sintetizadas pelo método de Turkevich, e AuNPs de 40 nm foram fornecidas. Concentração e tamanho das nanopartículas foram determinadas por espectroscopia UV-Vis, usando o efeito de Ressonância Plasmónica de Superfície (SPR). Funcionalização da superfície foi executada com adição de ácido 11-mercaptoundecanoico (MUA) e um penta-péptido (CALNN), mantendo a carga global negative e aumentando a estabilidade. Bioconjugação com Albumina (BSA) e Fibrinogénio (Fib) de soro bovino, com pesos moleculares de 66 e 340 kDa, respetivamente, foi executada e caracterizada por Eletroforese em Gel de Agarose (AGE). Mobilidade eletroforética foi determinada usando análise de imagem e vídeo com o programa eReuss. As constantes de afinidade de adsorção foram determinadas usando as curvas de conjugação pelos resultados de AGE, com a equação do modelo de adsorção de Langmuir, e resultou em (1.5 ± 0.1)x 10-2 (AuNP-MUA) para a conjugação com BSA, e (51.2 ± 4.7)x 10-2 (AuNP-CALNN) e (34.3 ± 1.2) x 10-2 (AuNP-MUA) para a conjugação com Fib. Bioconjugação de AuNP-CALNN com BSA foi inconclusiva. Cenários de competição numa mistura de proteínas favoreceu o Fib sobre a BSA. A conjugação de AuNPs de 40 nm mostrou múltiplas constantes de adsorção de (3 ± 0.7) x 10-2 e (9.7 ± 2.2) x 10-4 respetivamente. O programa eReuss provou ser uma poderosa ferramenta de análise de imagens das corridas eletroforéticas, e a componente de análise de vídeo sugere uma forma inovadora de analisar estas experiências e extrair informação adicional sobre a estabilidade da Coroa Proteica. A análise de Fergusson e técnicas de dispersão de luz (DLS, NTA e ELS) foram executadas para determinar o tamanho hidrodinâmico e o Potencial-Zeta de bionanoconjugados

Fragment based Drug Discovery; Design and Validation of a Fragment Library; Computer-based Fragment Screening and Fragment-to-Lead Expansion

In recent years, fragment screening has become a popular approach to identify new lead structures. Fragments are usually defined by the Astex ‘rule of three’ (RO3). Surface Plasmon Resonance (SPR), Nuclear Magnetic Resonance spectroscopy (NMR), biochemical assays and X-ray crystallography are efficient screening techniques to discover prospective fragments as binders. However, these methods need an assembled fragment library. We designed an in-house fragment library, starting from approx. 380,000 commercially available fragments. During library design, we modified the RO3 and we did no strict filtering of physico-chemical properties during fragment enumeration (e.g. twice the number of H-bond acceptors was allowed). The fragments were stepwise reduced to 4,000 compounds. The last step was a visual inspection of the candidates, which lead to a final fragment library of 364 fragments. To validate the quality of the library, we screened it against endothiapepsin. The biochemical screening suggested 55 hits, which were entered into a crystallographic screen. Eleven complex crystal structures were determined, pointing out the remarkably high hit rate of the designed library. HotspotsX is a program which predicts (based on knowledge-based potentials) the probability of a certain atom type at a certain position in the binding pocket of a target enzyme. The eleven crystal structures obtained before were used to validate the program HotspotsX. Due to chemical diversity and the different binding modes of the fragments observed for the library examples we obtained binding through aromatic- , H-bond donor- , acceptor- , doneptor- and hydrophobic interactions. The calculated HotspotsX maps coincide remarkably well with the crystallographically determined fragment positions inside the binding pocket. The program HotspotsX has also been validated with crystal structures of molecular probes like phenol, urea and methylurea. Crystal structures of these molecular probes were determined with different targets. Overall, the experimental hotspot analysis coincided well with the computed contour maps. Thus, the calculated maps by HotspotsX have an excellent predictive power. Based on the binding modes of the molecular probe phenol to the cAMP-dependent protein kinase A (PKA), we started a fragment growing approach. In the latter complex, three phenol molecules are bound. Two are occupying the ATP binding site and one is sitting on top of the glycine-rich loop (G-loop). A virtual screening, using the hinge binding phenol as constraint, suggested a phenol derivative for which a crystal structure could be determined. Starting from this hit, a hotspot analysis was performed. This analysis indicates that growth in the direction of the G-loop, placing an aromatic portion under the G-loop and an acceptor functionality capable to address Lys72 is desired. The first compound of this de novo design had an affinity of 70 µM. In the following first design cycle, we were able to enhance the affinity to 6.5 µM. In the second design cycle an additional amino function was introduced, which did not improve affinity dramatically, but enhanced ligand efficiency to 0.38. In the last cycle, a spacer of one and two methylene groups was introduced and the affinity could be increased to about 110 nM for a diastereomeric mixture of four compounds. The phenol-PKA complex provides a putative allosteric site of PKA. The G-loop in this structure is in a closed state which is stabilized by two H-bonds. This G-loop conformation is probably induced by the phenol molecule sitting on top of the G-loop. Therefore, several molecular dynamics (MD) studies were performed, lacking different phenol molecules, to get insights into the G-loop opening. The MD studies suggest that after removal of the phenol sitting on top of the G-loop some first side chain movements are initiated that can indicate the first steps of the G-loop opening cascade. In a different project, a virtual screening approach was used to find new inhibitors for aldose reductase. A pre-filtered subset of the ZINC database was used as ligand dataset. For the best hit, a series of five compounds was synthesized. Among them one compound displayed an inhibition of 920 nM. The available assays to detect fragment hits are currently not sufficient. The challenges are the low affinity of the fragments and their poor solubility. Therefore, the known thermal shift assay was applied and adapted to detect fragment hits. To validate the method, it was used to characterize variant mutations of EctD. Lastly, a modeling study was used to get ideas about possible binding modes of arachidonic acid derivatives in a K+ ion channel. One predominant binding pose could not be suggested. The study proposes, however, that one arachidonic acid molecule can occupy the inner pore cavity, which is consistent with experimental data

Predicting multibody assembly of proteins

textThis thesis addresses the multi-body assembly (MBA) problem in the context of protein assemblies. [...] In this thesis, we chose the protein assembly domain because accurate and reliable computational modeling, simulation and prediction of such assemblies would clearly accelerate discoveries in understanding of the complexities of metabolic pathways, identifying the molecular basis for normal health and diseases, and in the designing of new drugs and other therapeutics. [...] [We developed] F²Dock (Fast Fourier Docking) which includes a multi-term function which includes both a statistical thermodynamic approximation of molecular free energy as well as several of knowledge-based terms. Parameters of the scoring model were learned based on a large set of positive/negative examples, and when tested on 176 protein complexes of various types, showed excellent accuracy in ranking correct configurations higher (F² Dock ranks the correcti solution as the top ranked one in 22/176 cases, which is better than other unsupervised prediction software on the same benchmark). Most of the protein-protein interaction scoring terms can be expressed as integrals over the occupied volume, boundary, or a set of discrete points (atom locations), of distance dependent decaying kernels. We developed a dynamic adaptive grid (DAG) data structure which computes smooth surface and volumetric representations of a protein complex in O(m log m) time, where m is the number of atoms assuming that the smallest feature size h is [theta](r[subscript max]) where r[subscript max] is the radius of the largest atom; updates in O(log m) time; and uses O(m)memory. We also developed the dynamic packing grids (DPG) data structure which supports quasi-constant time updates (O(log w)) and spherical neighborhood queries (O(log log w)), where w is the word-size in the RAM. DPG and DAG together results in O(k) time approximation of scoring terms where k << m is the size of the contact region between proteins. [...] [W]e consider the symmetric spherical shell assembly case, where multiple copies of identical proteins tile the surface of a sphere. Though this is a restricted subclass of MBA, it is an important one since it would accelerate development of drugs and antibodies to prevent viruses from forming capsids, which have such spherical symmetry in nature. We proved that it is possible to characterize the space of possible symmetric spherical layouts using a small number of representative local arrangements (called tiles), and their global configurations (tiling). We further show that the tilings, and the mapping of proteins to tilings on arbitrary sized shells is parameterized by 3 discrete parameters and 6 continuous degrees of freedom; and the 3 discrete DOF can be restricted to a constant number of cases if the size of the shell is known (in terms of the number of protein n). We also consider the case where a coarse model of the whole complex of proteins are available. We show that even when such coarse models do not show atomic positions, they can be sufficient to identify a general location for each protein and its neighbors, and thereby restricts the configurational space. We developed an iterative refinement search protocol that leverages such multi-resolution structural data to predict accurate high resolution model of protein complexes, and successfully applied the protocol to model gp120, a protein on the spike of HIV and currently the most feasible target for anti-HIV drug design.Computer Science