One of the major challenges in structural biology is to determine the structures of macromolecular complexes and to understand their function and mechanism of action. However, structural characterization of macromolecular assemblies is very difficult. To begin addressing this problem, we have developed PyRy3D. In a nutshell, PyRy3D employs a hybrid computational approach that incorporates spatial information from a variety of experimental methods into the modelling procedure to build low-resolution models of large macromolecular complexes. The PyRy3D model building procedure uses a Monte Carlo approach to sample the solutions-space. It uses spatial restraints to define components interacting with each other, and a simple scoring function is applied to pack them tightly into contours of the entire complex (e.g. cryoEM density maps).
Comparative genomics between species and between types of data facilitates the understanding of what these data really reflect about the underlying processes. Comparative genomics therewith relies on a solid understanding of basic elements of Bioinformatics like homology and orthology. Moreover it is important to know the assumptions and heuristics of bioinformatic methods for comparative genomics and hence we aim to let participants develop an understanding of why they fail (or misdetect) as a consequence of a variety of biological /evolutionary causes.
The first two days of the course provide a basis for the course with the aim to move “beyond blast” in terms of more sensitive homology searches, domain level analysis of protein evolution as well as more fine grained definitions of relatedness as can be obtained from the proper interpretation of gene trees (i.e. various levels of orthology).
This foundation is used to discuss in the three following days in more detail three topics:
(1) the study of functional and evolutionary consequence of (genome) duplications,
(2) the evolution of interactions and complexes and
(3) the prediction and evolution of genomic regulatory elements.
In the third edition of this course, we will first give a brief overview of molecular biology, the advent of high-throughput measurement techniques and large databases containing biological knowledge, and the importance of networks to model all this. We will highlight a number of peculiar features of biological networks. Next, a number of basic network models (linear, Boolean, Bayesian) will be discussed, as well as methods of inferring networks from observed measurement data and of integrating various data sources and databases to refine networks. Once networks are derived they often serve as the cornerstone in the visualization, analysis and interpretation of high-throughput data; we will discuss a number of methods in this area.
As an alternative to static networks, a number of alternative dynamic network models more suited for high-level simulation of cellular behaviour for will be introduced. Finally, we will give some examples of algorithms exploiting the networks found to learn about biology, specifically for inspecting protein interaction networks and for finding active sub networks.
The Sloan Consortium, an influential champion of online learning that grew out of the Alfred P. Sloan Foundation’s early interest in the topic, is changing its name and will now be known as the Online Learning Consortium.
This course, developed together with Schering-Plough/MSD, hosted by the Radboud University Nijmegen and supported by The Netherlands Bioinformatics Centre and The Netherlands eScience Center, is part of a series of bioinformatics courses offered to advanced students in (bio)chemistry, medical chemistry or related sciences at the University of Nijmegen. Computational discovery has become a well-established scientific discipline in pharmaceutical and food research and has created numerous opportunities to speed up and rationalize the compound design and discovery process. Compound discovery is intrinsically multidisciplinary. The miniaturization and robotisation of chemical and biological experiments have resulted in a huge increase of data volumes. Today, in silico chemists have to bridge several disciplines ranging from molecular biology to chemistry and physics in order to translate these huge amounts of structural and sequence information from protein targets, ligands and their complexes, into useful knowledge. The scientific discipline of compound discovery informatics covers an increasingly large number of subjects, including molecular modelling, 3D-QSAR, pharmacophore and target based molecular design. The course covers some of the recent advances in discovery informatics, with a focus on the application of e-science to real life problems. Scientists from academia and industry will give lectures on real-life-examples and describe amongst others the process of in silico gene hunting, virtual molecular screening and structure-based design. They will also introduce and discuss tools and scientific concepts that are part of the modern drug, food supplement and ingredient discovery pipeline from target discovery & validation to lead discovery and optimization. CMBI lecturers will explain underlying expertise and techniques. The practical component of the course will provide participants the opportunity to work with the different in silico tools and databases available to the in silico chemist such as 3D protein-ligand modelling, virtual docking, and molecular superposition techniques.
You can expect lectures and practicals on the following topics:
· Introduction to Computational Discovery & Design.
· Comparative Protein Modelling.
· Sequence Retrieval, Analysis and Alignment Techniques.