In computational chemistry, conformational ensembles, also known as structural ensembles, are experimentally constrained computational models describing the structure of intrinsically unstructured proteins. Such proteins are flexible in nature, lacking a stable tertiary structure, and therefore cannot be described with a single structural representation. The techniques of ensemble calculation are relatively new on the field of structural biology, and are still facing certain limitations that need to be addressed before it will become comparable to classical structural description methods such as biological macromolecular crystallography.
Ensembles are models consisting of a set of conformations that together attempt to describe the structure of a flexible protein. Even though the degree of conformational freedom is extremely high, flexible/disordered protein generally differ from fully random coil structures. The main purpose of these models is to gain insights regarding the function of the flexible protein, extending the structure-function paradigm from folded proteins to intrinsically disordered proteins.
The calculation of ensembles rely on experimental measurements, mostly by Nuclear Magnetic Resonance spectroscopy and Small-angle X-ray scattering. These measurements yield short and long-range structural information.
Chemical Shifts (CS)
Residual Dipolar Couplings (RDCs)
J-couplings
Hydrogen-exchange
Solvent-accessibility.
Paramagnetic Relaxation Enhancements (PREs)
Nuclear Overhauser effects (NOEs)
SAXS topological restraints.
The structure of disordered proteins may be approximated by running constrained molecular dynamics (MD) simulations where the conformational sampling is being influenced by experimentally derived constraints.
Another approach uses selection algorithms such as ENSEMBLE and ASTEROIDS. Calculation procedures first generate a pool of random conformers (initial pool) so that they sufficiently sample the conformation space. The selection algorithms start by choosing a smaller set of conformers (an ensemble) from the initial pool.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
This course covers the basic biophysical principles governing the thermodynamic and kinetic properties of biomacromolecules involved in chemical processes of life.
The course is held in English.
Biochemistry is a key discipline for the Life Sciences. Biological Chemistry I and II are two tightly interconnected courses that aim to describe and understand in molecular terms the processes that m
This advanced Bachelor/Master level course will cover fundamentals and approaches at the interface of biology, chemistry, engineering and computer science for diverse fields of synthetic biology. This
Protein tertiary structure is the three dimensional shape of a protein. The tertiary structure will have a single polypeptide chain "backbone" with one or more protein secondary structures, the protein domains. Amino acid side chains may interact and bond in a number of ways. The interactions and bonds of side chains within a particular protein determine its tertiary structure. The protein tertiary structure is defined by its atomic coordinates. These coordinates may refer either to a protein domain or to the entire tertiary structure.
Covers challenges in protein design, energy functions, search algorithms, and applications in synthetic biology, emphasizing the importance of protein design and the methods used for designing proteins.
The true understanding of most cellular functions is only really achievable through the structural determination of their underlying macromolecular assemblies. However, their size, large number of individual components and metastable states make their eluc ...
Native reactive electrophilic species (RES) are long-recognized regulators of pathophysiology; yet, knowledge surrounding how RES regulate context-specific biology remains limited. The latest technological advances in profiling and precision decoding of RE ...
WILEY-V C H VERLAG GMBH2023
,
Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel str ...