TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.Comment: 20 pages, 8 figures, 5 table

Metodologías innovadoras basadas en 19F-RMN de ligando y técnicas computacionales para el estudio de procesos de reconocimiento molecular azúcar-lectina

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Farmacia, leída el 08-06-2021Carbohydrates play a central role in a large myriad of biological processes. They are found in all living organisms in nature, participating in different functions ranging from their use as energy source or as structural fragments, to infection-related processes in complex organisms. In vertebrates, they are located both in the cell surface and in the extracellular space, forming very diverse and intricate structures, but they are also present in the nucleus and cytoplasm of eukaryotic cells bound to proteins (glycoproteins). Their location almost ubiquitous in the organism confers them the capacity of mediate in a large number of ‘communication’ processes with other entities, for instance, in cell-cell, cell-molecule and cell-matrix interactions. In addition, carbohydrates intervene in molecular recognition processes between different organisms, such as the pathogen and parasite recognition by host cells...Los carbohidratos juegan un papel fundamental en una enorme variedad de procesos biológicos. Se encuentran en todos los organismos vivos en la naturaleza, donde intervienen en funciones que abarcan desde su uso como fuente de energía o como fragmentos estructurales, hasta procesos de infección en organismos superiores. En vertebrados, se localizan tanto en la superficie celular como en el espacio extracelular, formando estructuras muy diversas y complejas, pero también están presentes en el núcleo y citoplasma de células eucariotas unidos a proteínas (glicoproteínas). Su localización casi universal en el organismo les confiere la capacidad de intervenir en un gran número de procesos de ‘comunicación’ con otras entidades, por ejemplo, interacciones intercelulares, célula-molécula y célula-matriz extracelular. Además, los carbohidratos median procesos de reconocimiento molecular entre distintos organismos, como el reconocimiento de patógenos y parásitos por la célula de un huésped...Fac. de FarmaciaTRUEunpu

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

An enhanced-sampling MD-based protocol for molecular docking

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g. from molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the unbound (apo) protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables able to sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. In this work, we assessed the method on re-docking and cross-docking calculations. In first case, we selected three different protein targets undergoing different extent of conformational changes upon binding and, for each of them, we docked the experimental ligand conformation into an ensemble of receptor structures generated by EDES. In the second case, in the contest of a blind docking challenge, we generated the 3D structures of a set of different ligands of the same receptor and docked them into a set of EDES-generated conformations of that receptor. In all cases, for both re-docking and cross-docking experiments, our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry of the receptor. Moreover, ensemble docking calculations using those conformations yielded in almost all cases to native-like poses among the top-ranked ones. Finally, we also tested an improved EDES recipe on a further target, known to be extremely challenging due to its extended binding region and the large extent of conformational changes accompanying the binding of its ligands

Information Extraction from Text for Improving Research on Small Molecules and Histone Modifications

The cumulative number of publications, in particular in the life sciences, requires efficient methods for the automated extraction of information and semantic information retrieval. The recognition and identification of information-carrying units in text – concept denominations and named entities – relevant to a certain domain is a fundamental step. The focus of this thesis lies on the recognition of chemical entities and the new biological named entity type histone modifications, which are both important in the field of drug discovery. As the emergence of new research fields as well as the discovery and generation of novel entities goes along with the coinage of new terms, the perpetual adaptation of respective named entity recognition approaches to new domains is an important step for information extraction. Two methodologies have been investigated in this concern: the state-of-the-art machine learning method, Conditional Random Fields (CRF), and an approximate string search method based on dictionaries. Recognition methods that rely on dictionaries are strongly dependent on the availability of entity terminology collections as well as on its quality. In the case of chemical entities the terminology is distributed over more than 7 publicly available data sources. The join of entries and accompanied terminology from selected resources enables the generation of a new dictionary comprising chemical named entities. Combined with the automatic processing of respective terminology – the dictionary curation – the recognition performance reached an F1 measure of 0.54. That is an improvement by 29 % in comparison to the raw dictionary. The highest recall was achieved for the class of TRIVIAL-names with 0.79. The recognition and identification of chemical named entities provides a prerequisite for the extraction of related pharmacological relevant information from literature data. Therefore, lexico-syntactic patterns were defined that support the automated extraction of hypernymic phrases comprising pharmacological function terminology related to chemical compounds. It was shown that 29-50 % of the automatically extracted terms can be proposed for novel functional annotation of chemical entities provided by the reference database DrugBank. Furthermore, they are a basis for building up concept hierarchies and ontologies or for extending existing ones. Successively, the pharmacological function and biological activity concepts obtained from text were included into a novel descriptor for chemical compounds. Its successful application for the prediction of pharmacological function of molecules and the extension of chemical classification schemes, such as the the Anatomical Therapeutic Chemical (ATC), is demonstrated. In contrast to chemical entities, no comprehensive terminology resource has been available for histone modifications. Thus, histone modification concept terminology was primary recognized in text via CRFs with a F1 measure of 0.86. Subsequent, linguistic variants of extracted histone modification terms were mapped to standard representations that were organized into a newly assembled histone modification hierarchy. The mapping was accomplished by a novel developed term mapping approach described in the thesis. The combination of term recognition and term variant resolution builds up a new procedure for the assembly of novel terminology collections. It supports the generation of a term list that is applicable in dictionary-based methods. For the recognition of histone modification in text it could be shown that the named entity recognition method based on dictionaries is superior to the used machine learning approach. In conclusion, the present thesis provides techniques which enable an enhanced utilization of textual data, hence, supporting research in epigenomics and drug discovery

A REVIEW ON SUPRAMOLECULAR CHEMISTRY IN DRUG DESIGN AND FORMULATION RESEARCH

Supramolecular chemistry, other way called as intermolecular chemistry disclose the relationship of molecules with environment. It exploits while exposing the physicochemical phenomina that happens when two like or unlike molecules/ions/systems contact each other. Drug action involve the target recognition process and response triggered by the intermolecular complex of drug and target. Drug design therefore require in depth study of intermolecular forces that exist between drug and target. Formulation of the drug or Active Pharmaceutical Ingredient (API) is also regulated by these forces. Compatibility and incompatibility in formulations are nothing but of the effect of the intermolecular forces on physical behavior of systems. Therefore review of intermolecular chemistry in general and its role particularly in pharmaceutical research is presented here for the benefit of the students and research scholars who aspire to work on interdisciplinary projects in the field of pharmacy. Key words: intermolecular forces, hydrogen bond, drug design, active pharmaceutical ingredient (API), crystal