Estimating the Conformational Energy of Biological Macromolecules
European Molecular Biology Laboratory (EMBL) –
The conformational energy of a molecule in solution consists of
two components: the intramolecular potential energy of the molecule and
the solvation free energy of the molecule, the latter describing its
interaction with the solvent. Although the intramolecular potential
energy of small organic molecules can be calculated to a high degree of
accuracy with modern Quantum Mechanical methods, biological
macromolecules such as proteins and nucleic acids are far too large for
the application of these methods. Alternative strategies for estimating
the intramolecular potential energy have had to be devised for these
molecules. These can be broadly classifed as physical effective energy
function (PEEF) methods and statistical effective energy function (SEEF)
methods. PEEFs attempt to reproduce the true Born-Oppenheimer surface of
the molecule. This class contains the various Molecular Mechanics force
fields. SEEFs are derived from the statistical analysis of the known 3D
structures of biological macromolecules and attempt to reproduce the
observed frequencies of given features in these structures. This class
contains, among many others, the protein threading potentials. As for
solvation, the most direct strategy consisting of modelling solvent
molecules explicitly is usually too slow for most applications.
Therefore, several implicit solvent solvation models have been developed
to estimate the solvation free energy of a molecule. In this talk, I
describe the general features of PEEF and SEEF methods, as well as the
directions current research on the development of these methods is
taking. I will also give an overview of the solvation methods most
commonly applied to biological macromolecules. These will include, among
others, the Poisson-Boltzmann method and the Langevin Dipoles method.
Date: 2004-Jun-29 Time: 14:00:00 Room: 336
For more information:
Mathematics, Physics & Machine Learning Seminar Series (Online)
The Mathematics, Physics & Machine Learning seminar series has started on October 2020 and runs until March 2021.
The seminars aim to bring together mathematicians and physicists interested in machine learning (ML) with ML and AI experts interested in mathematics and physics, with the goal of introducing innovative Mathematics and Physics-inspired techniques in Machine Learning and, reciprocally, applying Machine Learning to problems in Mathematics and Physics.
Attendance is free but registration is required.
More information is available here.
International European Conference on Parallel and Distributed Computing
The 27th International European Conference on Parallel and Distributed Computing (Euro-Par 2021) will take from August 30 to September 3 2021 in Lisbon.
Euro-Par is the prime European conference covering all aspects of parallel and distributed processing, ranging from theory to practice, from small to the largest parallel and distributed systems and infrastructures, from fundamental computational problems to full-fledged applications, from architecture, compiler, language and interface design and implementation, to tools, support infrastructures, and application performance aspects.
The 2021 edition of Euro-Par will be organized as a collaboration between INESC-ID and Instituto Superior Técnico (IST).
– Abstract Submission: February 5, 2021
– Paper Submission Deadline: February 12, 2021
– Author Notification: April 30, 2021
– Camera-Ready Papers: June 6, 2021
More information is available here.