Molecular Modeling of the NS1/anti-NS1 complex interactions for application in differential diagnosis of Flaviviruses

The development of differential diagnosis methods between Dengue and Zika Flaviviruses, which overcome cross-reactivity, is a challenge that finds in imunosensors a promising alternative due to its greater selectivity and specificity. In this work, molecular modelling methods were used to understand and elucidate, in silico, specific molecular interactions of nonstructural 1 protein (NS1) and antibody anti-NS1 complexes, required by these devices. The MD simulations suggested energetic and structural stability of the anti-NS1 antibody and the NS1 protein, even in the absence of glycosylation, and allowed the statistical sampling of their representative structures in aqueous environment with good stereochemical quality. The NS1 proteins and antibody structures were submitted to the Molecular Docking protocol to predict 3D structure of near-native complexes. The proposed protocol combines rigid body Molecular Docking with different pose prediction methods, CAPRI evaluation criteria, search for epitope amino acids residues and semiempirical approach, and presented better performance for systems studied with crystallographic ligand and receptor input files. From its application, the most representative models of each studied system were predicted by ClusPro webserver and selected considering the higher number of conserved epitopes residues in the PPIs characterized by a higher number of hydrogen bonds, hydrophobic and p-p intermolecular specific interactions. Therefore, these models were described as the most representative models of antigen-antibody specific interaction. The wing and b-leader domains were, respectively, the preferential interaction region of the antibody with NS1-DENV2 and NS1-ZIKV proteins. Furthermore, binding free energy values ranges from -6.5 to -13.0 kcal mol-1, which is usual for this kind of complexes, and dissociation constants (Kd) in different magnitude orders (10-8 to 10-10 M) suggested selectivity and specificity for the studied systems, with potential application in sensoring devices, in particular AFM-type sensors, for differential and early diagnosis of Dengue and Zika.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFAPEMIG - Fundação de Amparo a Pesquisa do Estado de Minas GeraisRQMG - Rede Mineira de QuímicaTese (Doutorado)O desenvolvimento de métodos de diagnóstico diferencial das flaviroses Dengue e Zika, que superem as limitações de reatividade cruzada, é um desafio que encontra nos imunossensores uma alternativa promissora pela sua maior seletividade e especificidade. Neste trabalho, métodos de modelagem molecular foram utilizados para compreender e elucidar, in silico, as interações moleculares específicas de complexos formados pela proteína não-estrutural 1 (NS1) e um anticorpo anti-NS1 requeridas por esses dispositivos. As simulações de dinâmica molecular sugeriram a estabilidade energética e estrutural do anticorpo anti-NS1 e das proteínas NS1, mesmo na ausência de glicosilações, além de possibilitarem a amostragem estatística de suas estruturas representativas em meio aquoso com boa qualidade estereoquímica. As estruturas das proteínas NS1 e do anticorpo foram submetidas ao protocolo de Docking Molecular para a predição da estrutura 3D de complexos com caráter nativo. O protocolo proposto combina Docking Molecular de corpo rígido com diferentes métodos de busca, critérios de avaliação CAPRI, busca por resíduos de aminoácidos pertencentes a epítopos descritos na literatura e cálculos quânticos semi-empíricos, e apresentou melhor desempenho para sistemas estudados com arquivos input cristalográficos do receptor e ligante. A partir da sua aplicação, os modelos mais representativos de cada um dos sistemas estudados foram preditos pelo webserver ClusPro e selecionados considerando o maior número de resíduos pertencentes a epítopos conservados nas PPIs caracterizadas por um maior número de ligações de hidrogênio, hidrofóbicas e interações específicas p-p intermoleculares. Portanto, esses foram descritos como mais representativos da interação específica entre antígeno e anticorpo. Os domínios distintos wing e b-ladder foram, respectivamente, as regiões de interação preferencial do anticorpo com as proteínas NS1-DENV2 e NS1-ZIKV. Ademais, valores de energia livre de ligação na faixa de -6,5 a -13,0 kcal mol-1, característica de complexos dessa natureza, e constantes de dissociação (kd) em ordens de grandeza diferentes (10-8 a 10-10 M) sugerem seletividade e especificidade para os sistemas estudados, com potencial aplicabilidade em dispositivos de sensoriamento, em particular os sensores do tipo AFM, no diagnóstico diferencial e precoce de Dengue e Zika

A shape descriptor for fast complementarity matching in molecular docking

This paper presents a novel approach for fast rigid docking of proteins based on geometric complementarity. After extraction of the 3D molecular surface, a set of local surface patches is generated based on the local surface curvature. The shape complementarity between a pair of patches is calculated using an efficient shape descriptor, the Shape Impact Descriptor. The key property of the Shape Impact Descriptor is its rotation invariance, which obviates the need for taking an exhaustive set of rotations for each pair of patches. Thus, complementarity matching between two patches is reduced to a simple histogram matching. Finally, a condensed set of almost complementary pairs of surface patches is supplied as input to the final scoring step, where each pose is evaluated using a 3D distance grid. The experimental results prove that the proposed method demonstrates superior performance over other well-known geometry-based, rigid-docking approaches. © 2011 IEEE

Protein contour modelling and computation for complementarity detection and docking

The aim of this thesis is the development and application of a model that effectively and efficiently integrates the evaluation of geometric and electrostatic complementarity for the protein-protein docking problem. Proteins perform their biological roles by interacting with other biomolecules and forming macromolecular complexes. The structural characterization of protein complexes is important to understand the underlying biological processes. Unfortunately, there are several limitations to the available experimental techniques, leaving the vast majority of these complexes to be determined by means of computational methods such as protein-protein docking. The ultimate goal of the protein-protein docking problem is the in silico prediction of the three-dimensional structure of complexes of two or more interacting proteins, as occurring in living organisms, which can later be verified in vitro or in vivo. These interactions are highly specific and take place due to the simultaneous formation of multiple weak bonds: the geometric complementarity of the contours of the interacting molecules is a fundamental requirement in order to enable and maintain these interactions. However, shape complementarity alone cannot guarantee highly accurate docking predictions, as there are several physicochemical factors, such as Coulomb potentials, van der Waals forces and hydrophobicity, affecting the formation of protein complexes. In order to set up correct and efficient methods for the protein-protein docking, it is necessary to provide a unique representation which integrates geometric and physicochemical criteria in the complementarity evaluation. To this end, a novel local surface descriptor, capable of capturing both the shape and electrostatic distribution properties of macromolecular surfaces, has been designed and implemented. The proposed methodology effectively integrates the evaluation of geometrical and electrostatic distribution complementarity of molecular surfaces, while maintaining efficiency in the descriptor comparison phase. The descriptor is based on the 3D Zernike invariants which possess several attractive features, such as a compact representation, rotational and translational invariance and have been shown to adequately capture global and local protein surface shape similarity and naturally represent physicochemical properties on the molecular surface. Locally, the geometric similarity between two portions of protein surface implies a certain degree of complementarity, but the same cannot be stated about electrostatic distributions. Complementarity in electrostatic distributions is more complex to handle, as charges must be matched with opposite ones even if they do not have the same magnitude. The proposed method overcomes this limitation as follows. From a unique electrostatic distribution function, two separate distribution functions are obtained, one for the positive and one for the negative charges, and both functions are normalised in [0, 1]. Descriptors are computed separately for the positive and negative charge distributions, and complementarity evaluation is then done by cross-comparing descriptors of distributions of charges of opposite signs. The proposed descriptor uses a discrete voxel-based representation of the Connolly surface on which the corresponding electrostatic potentials have been mapped. Voxelised surface representations have received a lot of interest in several bioinformatics and computational biology applications as a simple and effective way of jointly representing geometric and physicochemical properties of proteins and other biomolecules by mapping auxiliary information in each voxel. Moreover, the voxel grid can be defined at different resolutions, thus giving the means to effectively control the degree of detail in the discrete representation along with the possibility of producing multiple representations of the same molecule at different resolutions. A specific algorithm has been designed for the efficient computation of voxelised macromolecular surfaces at arbitrary resolutions, starting from experimentally-derived structural data (X-ray crystallography, NMR spectroscopy or cryo-electron microscopy). Fast surface generation is achieved by adapting an approximate Euclidean Distance Transform algorithm in the Connolly surface computation step and by exploiting the geometrical relationship between the latter and the Solvent Accessible surface. This algorithm is at the base of VoxSurf (Voxelised Surface calculation program), a tool which can produce discrete representations of macromolecules at very high resolutions starting from the three-dimensional information of their corresponding PDB files. By employing compact data structures and implementing a spatial slicing protocol, the proposed tool can calculate the three main molecular surfaces at high resolutions with limited memory demands. To reduce the surface computation time without affecting the accuracy of the representation, two parallel algorithms for the computation of voxelised macromolecular surfaces, based on a spatial slicing procedure, have been introduced. The molecule is sliced in a user-defined number of parts and the portions of the overall surface can be calculated for each slice in parallel. The molecule is sliced with planes perpendicular to the abscissa axis of the Cartesian coordinate system defined in the molecule's PDB entry. The first algorithms uses an overlapping margin of one probe-sphere radius length among slices in order to guarantee the correctness of the Euclidean Distance Transform. Because of this margin, the Connolly surface can be computed nearly independently for each slice. Communications among processes are necessary only during the pocket identification procedure which ensures that pockets spanning through more than one slice are correctly identified and discriminated from solvent-excluded cavities inside the molecule. In the second parallel algorithm the size of the overlapping margin between slices has been reduced to a one-voxel length by adapting a multi-step region-growing Euclidean Distance Transform algorithm. At each step, distance values are first calculated independently for every slice, then, a small portion of the borders' information is exchanged between adjacent slices. The proposed methodologies will serve as a basis for a full-fledged protein-protein docking protocol based on local feature matching. Rigorous benchmark tests have shown that the combined geometric and electrostatic descriptor can effectively identify shape and electrostatic distribution complementarity in the binding sites of protein-protein complexes, by efficiently comparing circular surface patches and significantly decreasing the number of false positives obtained when using a purely-geometric descriptor. In the validation experiments, the contours of the two interacting proteins are divided in circular patches: all possible patch pairs from the two proteins are then evaluated in terms of complementarity and a general ranking is produced. Results show that native patch pairs obtain higher ranks when using the newly proposed descriptor, with respect to the ranks obtained when using the purely-geometric one