MIANN models in medicinal, physical and organic chemistry

[Abstract] Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational techniques that can be used in this sense. In any case, almost all these methods focus on few fundamental aspects including: type (1) methods to quantify the molecular structure, type (2) methods to link the structure with the biological activity, and others. In particular, MARCH-INSIDE (MI), acronym for Markov Chain Invariants for Networks Simulation and Design, is a well-known method for QSAR analysis useful in step (1). In addition, the bio-inspired Artificial-Intelligence (AI) algorithms called Artificial Neural Networks (ANNs) are among the most powerful type (2) methods. We can combine MI with ANNs in order to seek QSAR models, a strategy which is called herein MIANN (MI & ANN models). One of the first applications of the MIANN strategy was in the development of new QSAR models for drug discovery. MIANN strategy has been expanded to the QSAR study of proteins, protein-drug interactions, and protein-protein interaction networks. In this paper, we review for the first time many interesting aspects of the MIANN strategy including theoretical basis, implementation in web servers, and examples of applications in Medicinal and Biological chemistry. We also report new applications of the MIANN strategy in Medicinal chemistry and the first examples in Physical and Organic Chemistry, as well. In so doing, we developed new MIANN models for several self-assembly physicochemical properties of surfactants and large reaction networks in organic synthesis. In some of the new examples we also present experimental results which were not published up to date.Ministerio de Ciencia e Innovación; CTQ2009-07733Universidad del Pais Vasco; UFI11/22Universidad del Pais Vasco; GIU 094

Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Streptococcal collagen-like protein 1, Scl1, modulates group a Streptococcus adhesion, biofilm formation and virulence

Background: The collagens comprise a large family of versatile proteins found in all three domains of life. The streptococcal collagen-like protein 1, scl1, of group A Streptococcus (GAS) binds extracellular matrix components (ECM), cellular fibronectin and laminin, via the surface-exposed globular domain. GAS strains express scl1 and form biofilm in vitro, except for M3-type strains that are particularly invasive to humans. Hypothesis: Lack of scl1 adhesin in M3 GAS results in decreased adherence and biofilm formation, and increased virulence. Results and Discussion : First crystal structure of the globular domain revealed a unique six-helical bundle fold, consisting of three pairs of alpha helices connected by variable loops. ECM binding by Scl1 promotes the formation of stable tissue microcolonies, which was demonstrated in vitro during infection of wounded human skin equivalents. A conserved nonsense mutation was identified in the scl1 allele of the M3-type strains (scl1.3) that truncates the coding sequence, presumably resulting in a secreted Scl1 variant. Absence of Scl1 on the surface of M3-type GAS was demonstrated experimentally, as well as diminished expression of the scl1 transcript in M3 strains relative to other M-types. Therefore, M3-type strains have reduced biofilm capacity on ECM coatings relative to other M-types. Constructed full-length recombinant Scl1.3 protein displayed binding capacity to cellular fibronectin and laminin, and M3 strains complemented with functional Scl1.3 adhesin displayed increased biofilm formation. The isoallelic M3 strain, carrying a rare carrier allele encoding cell-associated Scl1.3 variant, showed decreased pathology in mice, compared to the invasive M3 strain. Similarly, scl1 inactivation in biofilm-capable M28- and M41-type GAS led to increased lesion size during subcutaneous infection. Conclusions: The studies presented here demonstrate the importance of surface Scl1 in modulating biofilm formation and virulence of GAS, and provide insight into the structure and function of Scl proteins

HLA class I supertype and supermotif definition by chemometric approaches.

Activation of cytotoxic T cells in human requires specific binding of antigenic peptides to human leukocyte antigen (HLA) molecules. HLA is the most polymorphic protein in the human body, currently 1814 different alleles collected in the HLA sequence database at the European Bioinformatics Institute. Most of the HLA molecules recognise different peptides. Also, some peptides can be recognised by several of HLA molecules. In the present project, all available class I HLA alleles are classified into supertypes. Super - binding motifs for peptides binding to some supertypes are defined where binding data are available. A variety of chemometric techniques are used in the project, including 2D and 3D QSAR techniques and different variable selection methods like SIMCA, GOLPE and genetic algorithm. Principal component analysis combined with molecular interaction fields calculation by the program GRID is used in the class I HLA classification. This thesis defines an HLA-A3 supermotif using two QSAR methods: the 3D-QSAR method CoMSIA, and a recently developed 2D-QSAR method, which is named the additive method. Four alleles with high phenotype frequency were included in the study: HLA-A*0301, HLA-A*1101, HLA-A*3101 and HLA- A*6801. An A*020T binding motif is also defined using amino acid descriptors and variable selection methods. Novel peptides have been designed according to the motifs and the binding affinity is tested experimentally. The results of the additive method are used in the online server, MHCPred, to predict binding affinity of unknown peptides. In HLA classification, the HLA-A, B and C molecules are classified into supertypes separately. A total of eight supertypes are observed for class I HLA, including A2, A3, A24, B7, B27, B44, CI and C4 supertype. Using the HLA classification, any newly discovered class I HLA molecule can be grouped into a supertype easily, thus simplifying the experimental function characterisation process

2018 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

Excerpt: As an academic institution, we strive to meet and exceed the expectations for graduate programs and laud our values and contributions to the academic community. At the same time, we must recognize, appreciate, and promote the unique non-academic values and accomplishments that our faculty team brings to the national defense, which is a priority of the Federal Government. In this respect, through our diverse and multi-faceted contributions, our faculty, as a whole, excel, not only along the metrics of civilian academic expectations, but also along the metrics of military requirements, and national priorities

Computational Methods for Protein Inference in Shotgun Proteomics Experiments

In den letzten Jahrzehnten kam es zu einem signifikanten Anstiegs des Einsatzes von Hochdurchsatzmethoden in verschiedensten Bereichen der Naturwissenschaften, welche zu einem regelrechten Paradigmenwechsel führte. Eine große Anzahl an neuen Technologien wurde entwickelt um die Quantifizierung von Molekülen, die in verschiedenste biologische Prozesse involviert sind, voranzutreiben und zu beschleunigen. Damit einhergehend konnte eine beträchtliche Steigerung an Daten festgestellt werden, die durch diese verbesserten Methoden generiert wurden. Durch die Bereitstellung von computergestützten Verfahren zur Analyse eben dieser Masse an Rohdaten, spielt der Forschungsbereich der Bioinformatik eine immer größere Rolle bei der Extraktion biologischer Erkenntnisse. Im Speziellen hilft die computergestützte Massenspektrometrie bei der Prozessierung, Analyse und Visualisierung von Daten aus massenspektrometrischen Hochdursatzexperimenten. Bei der Erforschung der Gesamtheit aller Proteine einer Zelle oder einer anderweitigen Probe biologischen Materials, kommen selbst neueste Methoden an ihre Grenzen. Deswegen greifen viele Labore zu einer, dem Massenspektrometer vorgeschalteten, Verdauung der Probe um die Komplexität der zu messenden Moleküle zu verringern. Diese sogenannten "Bottom-up"-Proteomikexperimente mit Massenspektrometern führen allerdings zu einer erhöhten Schwierigkeit bei der anschließenden computergestützen Analyse. Durch die Verdauung von Proteinen zu Peptiden müssen komplexe Mehrdeutigkeiten während Proteininferenz, Proteingruppierung und Proteinquantifizierung berücksichtigt und/oder aufgelöst werden. Im Rahmen dieser Dissertation stellen wir mehrere Entwicklungen vor, die dabei helfen sollen eine effiziente und vollständig automatisierte Analyse von komplexen und umfangreichen \glqq Bottom-up\grqq{}-Proteomikexperimenten zu ermöglichen. Um die hinderliche Komplexität diskreter, Bayes'scher Proteininferenzmethoden zu verringern, wird neuerdings von sogenannten Faltungsbäumen (engl. "convolution trees") Gebrauch gemacht. Diese bieten bis jetzt jedoch keine genaue und gleichzeitig numerisch stabile Möglichkeit um "max-product"-Inferenz zu betreiben. Deswegen wird in dieser Dissertation zunächst eine neue Methode beschrieben die das mithilfe eines stückweisen bzw. extrapolierendem Verfahren ermöglicht. Basierend auf der Integration dieser Methode in eine mitentwickelte Bibliothek für Bayes'sche Inferenz, wird dann ein OpenMS-Tool für Proteininferenz präsentiert. Dieses Tool ermöglicht effiziente Proteininferenz auf Basis eines diskreten Bayes'schen Netzwerks mithilfe eines "loopy belief propagation" Algorithmus'. Trotz der streng probabilistischen Formulierung des Problems übertrifft unser Verfahren die meisten etablierten Methoden in Recheneffizienz. Das Interface des Algorithmus' bietet außerdem einzigartige Eingabe- und Ausgabeoptionen, wie z.B. das Regularisieren der Anzahl von Proteinen in einer Gruppe, proteinspezifische "Priors", oder rekalibrierte "Posteriors" der Peptide. Schließlich zeigt diese Arbeit einen kompletten, einfach zu benutzenden, aber trotzdem skalierenden Workflow für Proteininferenz und -quantifizierung, welcher um das neue Tool entwickelt wurde. Die Pipeline wurde in nextflow implementiert und ist Teil einer Gruppe von standardisierten, regelmäßig getesteten und von einer Community gepflegten Standardworkflows gebündelt unter dem Projekt nf-core. Unser Workflow ist in der Lage selbst große Datensätze mit komplizierten experimentellen Designs zu prozessieren. Mit einem einzigen Befehl erlaubt er eine (Re-)Analyse von lokalen oder öffentlich verfügbaren Datensätzen mit kompetetiver Genauigkeit und ausgezeichneter Performance auf verschiedensten Hochleistungsrechenumgebungen oder der Cloud.Since the beginning of this millennium, the advent of high-throughput methods in numerous fields of the life sciences led to a shift in paradigms. A broad variety of technologies emerged that allow comprehensive quantification of molecules involved in biological processes. Simultaneously, a major increase in data volume has been recorded with these techniques through enhanced instrumentation and other technical advances. By supplying computational methods that automatically process raw data to obtain biological information, the field of bioinformatics plays an increasingly important role in the analysis of the ever-growing mass of data. Computational mass spectrometry in particular, is a bioinformatics field of research which provides means to gather, analyze and visualize data from high-throughput mass spectrometric experiments. For the study of the entirety of proteins in a cell or an environmental sample, even current techniques reach limitations that need to be circumvented by simplifying the samples subjected to the mass spectrometer. These pre-digested (so-called bottom-up) proteomics experiments then pose an even bigger computational burden during analysis since complex ambiguities need to be resolved during protein inference, grouping and quantification. In this thesis, we present several developments in the pursuit of our goal to provide means for a fully automated analysis of complex and large-scale bottom-up proteomics experiments. Firstly, due to prohibitive computational complexities in state-of-the-art Bayesian protein inference techniques, a refined, more stable technique for performing inference on sums of random variables was developed to enable a variation of standard Bayesian inference for the problem. nextflow and part of a set of standardized, well-tested, and community-maintained workflows by the nf-core collective. Our workflow runs on large-scale data with complex experimental designs and allows a one-command analysis of local and publicly available data sets with state-of-the-art accuracy on various high-performance computing environments or the cloud

Air Force Institute of Technology Research Report 2015

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Air Force Institute of Technology Research Report 2015

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Cancer proteomics of mouse serum and liver tissue samples to discover candidate biomarkers for hepatocellular carcinoma (HCC) in c-Myc transgenic mice

[no abstract