@article {80848, title = {Protein energy landscape exploration with structure-based models.}, journal = {Curr Opin Struct Biol}, volume = {64}, year = {2020}, month = {2020 Aug 11}, pages = {145-151}, abstract = {

Exploring the multi-dimensional energy landscape of a large protein in detail is a computational challenge. Such investigations may include analysis of multiple folding pathways, rate constants for important conformational transitions, locating intermediate states populated during folding, estimating energetic and entropic barriers that separate populated basins, and visualising a high-dimensional surface. The complexity of the landscape can be simplified through coarse-grained structure-based models (SBMs). These widely used coarse-grained representations of proteins provide a minimalist approximation to the free energy landscape, which subsumes the folding behaviour of many single-domain proteins. Here we describe the combination of SBMs with discrete path sampling (DPS), and show how this approach can provide details of the landscape and folding pathways. Combining SBMs and DPS provides an efficient framework for sampling the protein free energy landscape and for calculating various kinetic and thermodynamic quantities.

}, issn = {1879-033X}, doi = {10.1016/j.sbi.2020.07.003}, author = {Neelamraju, Sridhar and Wales, David J and Gosavi, Shachi} } @article {55790, title = {Go-Kit: A Tool To Enable Energy Landscape Exploration of Proteins.}, journal = {J Chem Inf Model}, year = {2019}, month = {2019 Apr 12}, abstract = {

Coarse-grained Go̅-like models, based on the principle of minimal frustration, provide valuable insight into fundamental questions in the field of protein folding and dynamics. In conjunction with commonly used molecular dynamics (MD) simulations, energy landscape exploration methods like discrete path sampling (DPS) with Go̅-like models can provide quantitative details of the thermodynamics and kinetics of proteins. Here we present Go-kit, a software that facilitates the setup of MD and DPS simulations of several flavors of Go̅-like models. Go-kit is designed for use with MD (GROMACS) and DPS (PATHSAMPLE) simulation engines that are open source. The Go-kit code is written in python2.7 and is also open source. A case study for the ribosomal protein S6 is discussed to illustrate the utility of the software, which is available at https://github.com/gokit1/gokit .

}, issn = {1549-960X}, doi = {10.1021/acs.jcim.9b00007}, author = {Neelamraju, Sridhar and Wales, David J and Gosavi, Shachi} } @article {49277, title = {Energy Landscape of the Designed Protein Top7.}, journal = {J Phys Chem B}, year = {2018}, month = {2018 Nov 29}, abstract = {

To fold on biologically relevant timescales, proteins have evolved funnelled energy landscapes with minimal energetic trapping. However, the polymeric nature of proteins and the spatial arrangement of secondary structural elements can create topological traps and slow folding. It is challenging to identify, visualise and quantify such topological trapping. Designed proteins have not had the benefit of evolution and it has been hypothesised that de novo designed protein topologies may therefore feature more topological trapping. Structure-based models (SBMs) are inherently funnelled and remove most energetic trapping and can thus be used to isolate the effect of protein topology on the folding energy landscape. Here, we compare properties of the potential energy landscapes (PEL) of Top7, a designed protein with a topology unknown in nature, with those of S6, a naturally occurring ribosomal protein of similar size and topology. Possible kinetic traps and the energetic barriers separating them from the native state are elucidated. We find that even with an SBM, the PEL of the designed protein is more frustrated than that of the natural protein. We then quantify the effect of adding non-native hydrophobic interactions and coarse-grained side-chains through a frustration density parameter. A clear increase in frustration is observed on including side-chains, whereas adding hydrophobic interactions leads to a narrowing of the funnel and a decrease in complexity. The most likely (un)folding routes for all models are derived through the construction of "probability contact maps". The ability to quantitatively understand and optimise the organisation of the PEL for designed proteins may enable us to sculpt structure-seeking landscapes, mimicking the effect of evolution.

}, issn = {1520-5207}, doi = {10.1021/acs.jpcb.8b08499}, author = {Neelamraju, Sridhar and Gosavi, Shachi and Wales, David J} } @article {24894, title = {The threshold algorithm: Description of the methodology and new developments.}, journal = {J Chem Phys}, volume = {147}, year = {2017}, month = {2017 Oct 21}, pages = {152713}, abstract = {

Understanding the dynamics of complex systems requires the investigation of their energy landscape. In particular, the flow of probability on such landscapes is a central feature in visualizing the time evolution of complex systems. To obtain such flows, and the concomitant stable states of the systems and the generalized barriers among them, the threshold algorithm has been developed. Here, we describe the methodology of this approach starting from the fundamental concepts in complex energy landscapes and present recent new developments, the threshold-minimization algorithm and the molecular dynamics threshold algorithm. For applications of these new algorithms, we draw on landscape studies of three disaccharide molecules: lactose, maltose, and sucrose.

}, issn = {1089-7690}, doi = {10.1063/1.4985912}, author = {Neelamraju, Sridhar and Oligschleger, Christina and Sch{\"o}n, J Christian} }