Web-based toolkits for topology prediction of transmembrane helical proteins, fold recognition, structure and binding scoring, folding-kinetics analysis and comparative analysis of domain combinations

We have developed the following web servers for protein structural modeling and analysis at http:// theory.med.buffalo.edu: THUMBUP, UMDHMMTMHP and TUPS, predictors of trans-membrane helical protein topology based on a mean-burial-propensity scale of amino acid residues (THUMBUP), hidden Markov model (UMDHMMTMHP) and their combinations (TUPS); SPARKS 2.0 and SP3, two profile– profile alignment methods, that match input query sequence(s) to structural templates by integrating sequence profile with knowledge-based structural score (SPARKS 2.0) and structure-derived profile (SP3); DFIRE, a knowledge-based potential for scoring free energy of monomers (DMONOMER), loop conformations (DLOOP), mutant stability (DMUTANT) and binding affinity of protein–protein/ peptide/DNA complexes (DCOMPLEX & DDNA); TCD, a program for protein-folding rate and transition-state analysis of small globular proteins; and DOGMA, a web-server that allows comparative analysis of domain combinations between plant and other 55 organisms. These servers provide tools for prediction and/or analysis of proteins on the secondary structure, tertiary structure and interaction levels, respectively

Web-based toolkits for topology prediction of transmembrane helical proteins, fold recognition, structure and binding scoring, folding-kinetics analysis and comparative analysis of domain combinations

We have developed the following web servers for protein structural modeling and analysis at : THUMBUP, UMDHMM(TMHP) and TUPS, predictors of transmembrane helical protein topology based on a mean-burial-propensity scale of amino acid residues (THUMBUP), hidden Markov model (UMDHMM(TMHP)) and their combinations (TUPS); SPARKS 2.0 and SP(3), two profile–profile alignment methods, that match input query sequence(s) to structural templates by integrating sequence profile with knowledge-based structural score (SPARKS 2.0) and structure-derived profile (SP(3)); DFIRE, a knowledge-based potential for scoring free energy of monomers (DMONOMER), loop conformations (DLOOP), mutant stability (DMUTANT) and binding affinity of protein–protein/peptide/DNA complexes (DCOMPLEX & DDNA); TCD, a program for protein-folding rate and transition-state analysis of small globular proteins; and DOGMA, a web-server that allows comparative analysis of domain combinations between plant and other 55 organisms. These servers provide tools for prediction and/or analysis of proteins on the secondary structure, tertiary structure and interaction levels, respectively

CoBaltDB: Complete bacterial and archaeal orfeomes subcellular localization database and associated resources

International audienceBACKGROUND: The functions of proteins are strongly related to their localization in cell compartments (for example the cytoplasm or membranes) but the experimental determination of the sub-cellular localization of proteomes is laborious and expensive. A fast and low-cost alternative approach is in silico prediction, based on features of the protein primary sequences. However, biologists are confronted with a very large number of computational tools that use different methods that address various localization features with diverse specificities and sensitivities. As a result, exploiting these computer resources to predict protein localization accurately involves querying all tools and comparing every prediction output; this is a painstaking task. Therefore, we developed a comprehensive database, called CoBaltDB, that gathers all prediction outputs concerning complete prokaryotic proteomes. DESCRIPTION: The current version of CoBaltDB integrates the results of 43 localization predictors for 784 complete bacterial and archaeal proteomes (2.548.292 proteins in total). CoBaltDB supplies a simple user-friendly interface for retrieving and exploring relevant information about predicted features (such as signal peptide cleavage sites and transmembrane segments). Data are organized into three work-sets ("specialized tools", "meta-tools" and "additional tools"). The database can be queried using the organism name, a locus tag or a list of locus tags and may be browsed using numerous graphical and text displays. CONCLUSIONS: With its new functionalities, CoBaltDB is a novel powerful platform that provides easy access to the results of multiple localization tools and support for predicting prokaryotic protein localizations with higher confidence than previously possible. CoBaltDB is available at http://www.umr6026.univ-rennes1.fr/english/home/research/basic/software/cobalten

Current state-of-the-art of the research conducted in mapping protein cavities – binding sites of bioactive compounds, peptides or other proteins

Ο σκοπός της διπλωματικής εργασίας είναι η διερεύνηση και αποτύπωση των ερευνητικών μελετών που αφορούν στον χαρακτηρισμό μιας πρωτεϊνικής κοιλότητας – κέντρου πρόσδεσης βιοδραστικών ενώσεων, πεπτιδίων ή άλλων πρωτεϊνών. Στην παρούσα εργασία χρησιμοποιήθηκε η μέθοδος της βιβλιογραφικής επισκόπησης. Παρουσιάζονται τα κυριότερα ευρήματα προηγούμενων ερευνών που σχετίζονται με τη διαδικασία σχεδιασμού φαρμάκων και τον εντοπισμό φαρμακοφόρων με βάση ένα σύνολο προσδετών. Στη συνέχεια συγκρίνονται διαδικασίες επεξεργασίας και ανάλυσης της πρωτεϊνικής κοιλότητας προγενέστερων ερευνών με τη προσέγγιση που προτάθηκε από τους Παπαθανασίου και Φωτόπουλου το 2015. Αναδεικνύονται βασικά πλεονεκτήματα της προσέγγισης αυτής, όπως η εφαρμογή του αλγορίθμου πολυδιάστατη k-means ομαδοποίηση (multidimensional k-means clustering). Η εύρεση βιβλιογραφίας βασίστηκε σε αναζήτηση επιστημονικών άρθρων σε ξενόγλωσσα επιστημονικά περιοδικά, σε κεφάλαια βιβλίων και σε διάφορα άρθρα σε ηλεκτρονικούς ιστότοπους σχετικά με τον σχεδιασμό φαρμάκων και τις κοιλότητες που απαντώνται στις πρωτεΐνες. Στην παρούσα εργασία παρουσιάζονται εν συντομία εργαλεία που εντοπίστηκαν χρησιμοποιώντας λέξεις κλειδιά όπως για παράδειγμα δυναμική πρωτεϊνικής κοιλότητας, καταλυτικό κέντρο ενός ενζύμου, πρόσδεση, πρωτεϊνική θήκη κλπ. Στη συνέχεια συγκροτήθηκε κατάλογος με τα εργαλεία βιοπληροφορικής ανάλυσης που βρέθηκαν και ακολούθησε εκτενής αναφορά επιλεκτικά σε κάποια από αυτά. Κριτήριο επιλογής αυτών των εργαλείων αποτέλεσε η ημερομηνία δημοσίευσής τους, οι αλγόριθμοι και η μεθοδολογία που χρησιμοποιούν. Τα εργαλεία αυτά κατηγοριοποιήθηκαν με βάση τις λέξεις κλειδιά που χρησιμοποιήθηκαν για την εξόρυξη των δεδομένων από την βιβλιογραφία. Τέλος πραγματοποιήθηκε συγκριτική μελέτη αυτών αναδεικνύοντας τα πλεονεκτήματα και εστιάζοντας στην περαιτέρω αξιοποίησή τους.The aim of this thesis was to report on the current state-of-the-art of the research conducted concerning mapping of protein cavities with a potential function role as binding sites of bioactive compounds, peptides or other proteins. A literature review was performed with emphasis on the relevant tools developed during the last decade. In addition, the main research findings regarding drug design and druggable targets based on binding sites are presented. Processes performed in protein cavity detection and analysis, of previous research articles, are compared with the approach described by Anaxagoras Fotopoulos and Athanasios Papathanasiou (2015). The results showed that a competitive advantage of their approach is the multidimensional k-means algorithm for clustering. For the bibliographic review the scientific knowledgebase has been used, which includes international articles and journals, book chapters, as well as online articles regarding drug design and protein cavity. Search keywords such as protein cavity dynamics, catalytic sites of enzymes, protein pocket etc. were used to identify bioinformatics tools with text mining. A catalogue of the most recently developed tools is presented followed by a brief description of selected tools. The selection criteria imposed for preparing the catalogue and the detailed description included the publication date, as well as the algorithms and the methods they use. The tools were then classified according to the search keywords. The findings of this research are discussed, and the algorithms and methods they use are compared, highlighting the advantages of protein cavity detection

In-vivo and in silico studies of the receptor binding specificity of human rhinoviruses

Humane Rhinoviren (HRV) sind verantwortlich für rund die Hälfte aller Erkältungen beim Menschen. Die zur Familie der Picornaviren gehörigen Rhinoviren besitzen ein ikosaedrisches Kapsid, das aus den 4 viralen Strukturproteinen (VP1, VP2, VP3 und VP4) aufgebaut ist. Der Durchmesser dieses Kapsids beträgt 30 nm. Die mehr als hundert verschiedenen Virustypen können anhand ihrer Fähigkeit an zelluläre Rezeptoren zu binden eingeteilt werden. Die weitaus größere Gruppe der beschriebenen Rhinoviren binden an den ICAM-1 Rezeptor um in die Wirtszelle zu gelangen. Eine kleine Gruppe von Rhinoviren, genannt „minor group“ Viren binden an LDL-Rezeptoren, wie LDLR, LRP und VLDLR. Ein Ziel dieser Arbeit war es neue Einblicke in die Details der Interaktion von Virus und Rezeptor zu erhalten. Vorhergehende Untersuchungen dieser Interaktion zeigten, dass nur ein virales Protein in diese Wechselwirkung involviert ist. Dieses virale Protein 1 (VP1 genannt), und im speziellen die Oberflächen-„loops“ sind an der Wechselwirkung mit den LDL Rezeptoren beteiligt. Sequenzanalysen der LDLR bindenden Rhinoviren („minor group“ viruses) zeigten, dass lediglich ein Aminosäurerest, ein Lysin, strikt konserviert ist. Dieses Lysin befindet sich in der Mitte des HI-„loop“. Man nahm an, dass dieses Lysin verantwortlich sei für die Bindung an LDL Rezeptoren. Es wurden jedoch einige Rhinovirus Typen gefunden, die an der gleichen Stelle ebenfalls ein Lysin aufweisen. Diese Gruppe von Viren (K-Typen) können anhand ihrer Sequenzmerkmale nicht von „minor group“ Viren unterschieden werden, jedoch können sie den LDL Rezeptor nicht für die Infektion verwenden. Im ersten Teil meiner Diplomarbeit ging es um die Entwicklung eines Bioinformatischen Klassifizierungsverfahrens. Mit diesem sollte es möglich sein alle bekannten Rhinovirus Typen gemäß ihrer Rezeptor Spezifikation einzuteilen. Die Methode beruht auf der Hypothese, dass bindende Typen eine zum Rezeptor komplementäre Ladungsverteilung an ihrer Oberfläche aufweisen, die es ihnen ermöglicht den Rezeptor zu binden. Gemäß dieser Hypothese sollten die Bindungsaffinitäten der bindenden Typen höher sein als die der nicht bindenden Typen. Die 3D Strukturen des VP1 Proteins wurden durch „homology modelling“ erhalten. 3D Koordinaten, die der determinierten Röntgenstruktur von HRV2 mit gebundenen V3 entnommen wurden, dienten als Matrize für alle die Anordnung aller anderen Rhinoviren-Rezeptor Komplexe. Nach einem Energie Minimisierungsschritt wurden die Modelle aller Rhinoviren mit gebundenem Rezeptor hinsichtlich ihrer Bindungsaffinität analysiert. Eine der verwendeten Methoden, war tatsächlich in der Lage alle Rhinoviren korrekt zu klassifizieren. Die Gruppe der K-Typen hatten generell höhere Affinitäten zu dem verwendeten Rezeptor (V3), als andere „major group“ Viren. Im anschließenden Teil der Arbeit wurde ein „major group“ Virus (HRV14) so mutiert, das er einen gänzlich veränderten HI-loop aufweist. Die Sequenz des am stärksten in der Virus-Rezeptor Interaktion involvierten Oberflächen“loop“ (HI-loop) wurde gegen die Sequenz eines „minor group“ Virus mittels Zielgerichteter Mutagenese ausgetauscht. RNA Klone der mutierten Sequenz waren jedoch nicht infektiös. Es gelang lediglich einen infektiösen Klon der nur eine Punktmutation im HI-„loop“ aufwies herzustellen.Major group HRVs bind intercellular adhesion molecule 1 (ICAM1) and minor group HRVs bind members of the low-density lipoprotein receptor (LDLR) family for cell entry. Whereas the former share common sequence motives in their capsid proteins, in the latter only a lysine residue within the binding epitope in VP1 is conserved; this lysine is also present in ten "K-type" major group HRVs which fail to bind LDLR. A bioinformatic approach based on the available VP1 sequences three-dimensional models of VP1 of all HRVs were built and binding energies, with respect to module 3 of the very-low density lipoprotein receptor, were calculated. Based on the predicted affinities, K-type HRVs and minor group HRVs were correctly classified. With the intention to find conserved binding patterns the energy tables that indicate the interacting binding partners were transformed into heatmaps. In addition to the heatmaps a bar diagram that shows the interaction energy of the different receptor residues of all minor group and K-type viruses was made. In further improvements the module 3 of VLDLR was replaced by the ligand binding repeat 5 of human and mouse LDLR. To examine the predictive power of the in silico application two non-classified field isolates were analyzed. In a site directed mutagenesis experiment the HI-loop of HRV14 (major group) was changed into the sequence of the HI-loop of HRV2 (minor group). The newly created chimeric virus (HRV14_HI2) was not infective. Also a revision of the experiments under optimized conditions could not create an infective virus. The only chimeric virus that could be produced was HRV14_K. In this virus a histidine to lysine mutation at position 232 was successfully accomplished. The properties of this artificial K-Type virus to bind LDLR were checked in infection assays using RD cells

Applications and Development of the MMPBSA Method for Rational Drug Design

The growing cost of new drugs has become a concern for the biopharmaceutical industry, which depends on innovation to sustain itself. As a result, computational methods have been increasingly implemented into the drug design workflow in an effort to reduce the cost of finding new lead candidates. Here, we focus on several applications and the development of the Molecular Mechanics Poisson-Boltzmann (MMPBSA) method for its use in rational drug design efforts. Chapter 1 demonstrates the use of computational methods in the analysis and design of anti-A?? antibodies for their prospective use in the treatment of Alzheimer’s Disease. Chapter 2 applies our single dielectric implicit membrane model to MMPBSA calculations of the membrane-bound human purinergic platelet receptor, a prominent target for treating myocardial infarction and stroke. Chapter 3 documents the development, implementation, and application of a new heterogeneous dielectric implicit membrane model for MMPBSA calculations. Chapter 4 shows the validity of our method to parameterize the non-polar terms in a depth dependent manner within our implicit membrane model. This work as a whole demonstrates both the present utility and ongoing improvement of the MMPBSA method for its use in the rational design of new drugs

27th Fungal Genetics Conference

Program and abstracts from the 27th Fungal Genetics Conference Asilomar, March 12-17, 2013

27th Fungal Genetics Conference

Program and abstracts from the 27th Fungal Genetics Conference Asilomar, March 12-17, 2013