Welcome to IGC

The group for computer-aided chemistry (Informatikgestützte Chemie, igc) was established at the Department of Chemistry of the ETH in 1990 by Wilfred van Gunsteren. The creation of this chair was meant to stimulate the development of computational methodology and its applications for molecular systems in the department and at the ETH, and to integrate this methodology in the educational program. The major research interest of the igc group is to develop methodology to simulate the behavior of biomolecular systems and thus to investigate their function at the atomic level. Currently the major areas of research are:

The three classical computational chemistry groups in the Laboratory of Physical Chemistry are :


The group for computer-aided chemistry has as major research interest the development of methodology to simulate the behaviour of biomolecular systems [06.16]. By testing the developed methodology to biomolecular systems of practical interest, for which ample experimental data are available, deficiencies of current methodology can be identified and new ideas emerge. Simulation of biomolecular systems per se leads to enhanced insight into biomolecular processes at the atomic level, which is often inaccessible to experimental probes. The applications carried out in the group thus serve a dual goal: method development and understanding of biomolecular processes at the atomic level. Below we briefly sketch the past and ongoing research of the group.
The research group has a long record of methodological contributions and applications in the field of biomolecular simulations, in particular molecular dynamics (MD) simulation. We distinguish four aspects and mention only major contributions and broad areas of applications with limited references. The references to publications are coded according to the IGC MD publication list.

A Algorithm developments

- Use of constraints in biomolecular simulation [77.01]
- Constant temperature and pressure MD [84.09]
- Use of MD for protein structure refinement based on NMR data [85.01]
- Leap-frog algorithm for stochastic dynamics (SD) simulation [88.02]
- Use of time-averaged distance restraints in protein structure determination based on NMR data [89.04]
- Calculation of free energy via indirect, unphysical pathways [91.02]
- Potential energy annealing conformational search (PEACS) [92.04]
- Conformational search enhancement through MD simulation in four dimensions[93.26]
- Use of time-averaged restraints in protein structure determination based on X-ray data [93.18, 95.02, 99.15]
- Use of soft-core atoms to enhance conformational sampling [94.12]
- Local elevation (LE), a powerful method to improve the searching properties of MD simulation [94.38]
- Force-field parametrisation using the weak-coupling technique [95.14]
- A generalised reaction-field method for MD [95.15]
- Umbrella sampling along linear combinations of generalised coordinates [95.16]
- Calculating electrostatic interactions using the particle-particle particle-mesh method with non-periodic long-range forces [96.04]
- One-step perturbation technique to compute relative free energies [96.15]
- Conformational a sampling using a Boltzmann-weighted mean-field technique [96.26]
- Force-field parameterisation using quasi-Newtonian dynamics for the parameters [97.05]
- Stochastic dynamical treatment of the dielectric continuum in MD [97.09]
- Improved diffusion-equation conformational search [97.20]
- SWARM-MD: a method for searching conformational space by cooperative MD [98.10]
- A fast SHAKE algorithm to impose constraints [01.08]
- Calculation of dielectric permittivity via constrained dipole-moment simulation [01.28]
- Improved one-step perturbation method to compute free energies [01.42]
- A method to simulate proteins at constant pH [02.20]
- A technique to compute entropic contributions to co-solvent binding to solutes [04.04]
- A technique to impose flexible distance constraints [05.35]
- A method for mixed fine-grained/coarse-grained simulation [06.28]
- A method to obtain free-energy differences using hidden restraints [06.25]
- A method for biomolecular structure refinement based on adaptive restraints using local-elevation simulation [07.29, 09.17]
- Improved one-simulation method, EDS, to compute free energies [07.10, 08.08, 09.01, 09.15, 11.20, 11.32, 12.06, 12.18, 13.08, 13.09, 13.16, 13.17, 13.22, 14.01]
- A technique to obtain free energies using flexible constraints [07.13]
- Improved leap-frog kinetic energy expression [07.27]
- An improved conformational clustering algorithm [10.04]
- A method to compute dielectric properties of liquids using an external field [11.13]
- A method for enhanced conformational sampling based on adiabatic decoupling and force scaling [11.23, 11.09, 11.26, 12.10]
- A method to enforce bond-angle constraints [A656]
- A method to probe ligand-binding sites [12.35]
- A method for biomolecular structure refinement based on NMR order parameters [14.12]

B Software development

The developed algorithms have, together with other simulation algorithms, found their way into the biomolecular simulation software package GROMOS (Groningen Molecular Simulation), which has been and is developed in the group [95.35, 96.40, 99.10, 05.32, 10.06, 11.25, 11.27, 11.30, 12.04, 12.05, 12.23, 14.11]. This simulation software is used in hundreds of laboratories in 60 countries on all continents.

C Force field development

The quality of the interaction function or force field that describes the forces between the atoms of a biomolecular system is of decisive importance for the predictive power of MD simulations. Therefore, we have over the past decade spent much effort to gradually improve the GROMOS force field whenever results of simulation applications pointed at force field deficiencies. The first set of (non-bonded) GROMOS force field parameters dates from 1984 [84.01]. Since then, the force field has continuously been improved and refined [95.23, 98.02, 00.22, 01.26, 03.04, 04.28, 05.07, 05.27, 09.11, 09.24, 11.05, 11.08, 11.19]. The most widely used versions of the GROMOS force field are the GROMOS 37C4 force field of 1985, the GROMOS 43A1 force field of 1996 [96.40, 98.02] and the GROMOS 45A3 force field of 2001 [01.26]. The currently used versions are the 45A4 parameter set [03.04, 05.07, 05.27], the 53A5/6 [04.28], and the 54A7 one [09.24, 11.19, 13.24]. In parallel to the development of force field parameters for biomolecules, solvent models that are consistent with the GROMOS biomolecular force field were developed for much used (co-)solvents [06.06]: water [81.04, 02.18], methanol [00.09], DMSO [04.06], chloroform [94.36], carbontetrachloride [96.33], urea [04.05], acetonitrile [06.02], dimethyl sulfone [11.29]. Polarisable (solvent) models are available for water [14.14], methanol [06.22], DMSO [14.07], chloroform [10.20], carbotetrachloride [11.02], urea [15.05], acetone [A104], n-alkanes [14.04]. Supra molecular polarisable coarse-grained solvent models are available for water [11.03], methanol [12.07, 14.05], DMSO [12.07], chloroform [12.07], and for n-alkanes [15.14] and cyclohexane [15.07].

D Application of simulation

MD Simulation has been applied to a variety of biomolecular systems with the aim of understanding biomolecular phenomena or processes, or in order to help interpret experimental observations. Here we only mention as examples a few recent applications in different areas of research.
- MD simulation of conformational ensembles may help to interpret X-ray, CD or NMR data, see e.g. [99.15, 02.40, 03.21, 04.35, 05.42, 06.38, 07.09, 08.02, 09.07, 09.09, 09.17, 09.23, 10.05, 10.14, 10.41, 12.01, 12.14, 12.15, 13.02, 13.11, 13.19, 13.21, 14.03, 14.12]
- Antibody, polysaccharide, and protein stability has been studied as function of solute or solvent composition [00.17, 04.25, 05.03, 07.11, 07.14, 07.31, 08.01, 08.02, 08.09, 08.19, 08.20, 09.10, 09.16, 10.01, 10.03, 10.13, 10.15, 10.37, 10.44, 12.22, 13.04, 13.16,13.17,14.01]
- Reversible polypeptide folding under different thermodynamic conditions and solute/solute composition has been extensively studied [02.40, 04.35, 01.01, 01.10, 02.42, 04.01, 04.30, 04.32, 04.34, 04.36, 05.33, 05.42, 06.01, 06.04, 06.08, 07.05, 07.24, 08.03, 08.06, 09.08, 09.12, 10.09, 10.11, 10.40, 11.01, 11.14, 11.28, 12.18, 13.15]
- Ligand-binding free energies and conformational preferences have been calculated for various proteins [03.17, 04.03, 05.22, 05.04, 05.15, 07.04, 07.09, 08.17, 10.17, 10.43, 11.12, 11.24, 12.12]
- An enzyme and chemical reactions have been analyzed using simulation [01.07, 07.28, 10.12, 12.03, 12.29, 13.12]
- Membrane simulations are carried out to study stability, pore formation, transport, peptide binding, etc. [04.27, 05.26, 05.34, 06.03, 06.18, 10.38]
All four aspects of computer simulation of biomolecular systems are to be further investigated and developed in the next years.

E Reviews

[90.06] Biomolecular simulation
[93.29] Computation of free energy
[94.14] Biomolecular solvation
[94.46] Fundamentals of drug design
[95.25] Search and sampling techniques
[95.29] Computer simulation of protein motion
[98.03] Validation of simulation
[99.01] Force fields
[99.14] Biomolecular structure refinement
[01.23] Biomolecular simulation
[01.34] MD of biomolecules
[02.08] MD simulation
[02.38] Computation of free energy
[03.01] Structure refinement of biomolecules
[04.09] Computation of entropy
[05.31] Polarisability in molecular simulation
[06.16] Biomolecular modelling
[06.48] Computation of configurational entropy
[07.02] Molecular dynamics and drug design
[07.19] Search and sampling methods
[07.12] Simulation of folding equilibria
[07.22] Driving forces of biomolecular solvation and association
[08.04] Biomolecular simulation: history and perspectives
[08.05] Molecular simulation as an aid to experimentalists
[08.14] Computer simulation of biomolecular systems: Where do we stand?
[09.28] Basic ingredients of free energy calculations: a review
[12.21] On developing coarse-grained models for biomolecular simulation: a review
[12.33] Thirty-five years of biomolecular simulation: development of methodolgy, force fields, and software
[13.01] Academic behavior
[13.06] Multi-resolution simulation in chemistry: methodological issues
[14.10] Practical aspects of free-energy calculations
[15.16] Peer reviews



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© 2016 ETH Zurich | Imprint | 25 February 2016