Stochastic roadmap simulation for the study of ligand-protein interactions

Abstract

Understanding the dynamics of ligand-protein interactions is indispensable in the design of novel therapeutic agents. In this paper, we establish the use of Stochastic Roadmap Simulation (SRS) for the study of ligand-protein interactions through two studies. In our first study, we measure the effects of mutations on the catalytic site of a protein, a process called computational mutagenesis. In our second study, we focus on distinguishing the catalytic site from other putative binding sites. SRS compactly represents many Monte Carlo (MC) simulation paths in a compact graph structure, or roadmap. Furthermore, SRS allows us to analyze all the paths in this roadmap simultaneously. In our application of SRS to the domain of ligand-protein interactions, we consider a new parameter called escape time, the expected number of MC simulation steps required for the ligand to escape from the 'funnel of attraction' of the binding site, as a metric for analyzing such interactions. Although computing escape times would probably be infeasible with MC simulation, these computations can be performed very efficiently with SRS. Our results for six mutant complexes for the first study and seven ligand-protein complexes for the second study, are very promising: In particular, the first results agree well with the biological interpretation of the mutations, while the second results show that escape time is a good metric to distinguish the catalytic site for five out of seven complexes.

Stochastic Conformational Roadmaps for Computing Ensemble Properties of Molecular Motion

“…This paper combines and extends results presented in [3,4]. It provides additional experimental results and give new ideas on how efficient sampling strategies can be eventually designed to extend SRS to very high dimensional conformation spaces.…”

Chaintweak: Sampling From the Neighbourhood of a Protein Conformation

“…Using ChainTweak, conformational propensities of disordered regions in proteins 25 and conformational variation across sets of re-engineered 28,29 or homologous 30 proteins could be studied. It could be used in conjunction with existing methods 26,27 to analyze the ligand-protein docking process. We are also considering extending the algorithm to handle sidechain rotamer preferences and covalently-modified resiudues (e.g., phosphorylation).…”

Exploring the Landscape of Protein-Ligand Interaction Energy Using Probabilistic Approach

“…The structures of rings are assumed constant. Internal interactions between atoms are divided into Coulombic and van der Waals interactions (Apaydin et al, 2002). …”

“…These solutions are based on the most often used docking software AutoDock (Morris et al, 1998). We propose a different framework, based on the Stochastic Roadmap Simulation (SRS) (Apaydin et al, 2002(Apaydin et al, , 2003, a Monte Carlo (MC) type method derived from planning methodology of robotic motion. The method consists of effective sampling of the combined transformational and conformational space of the ligand.…”

“…The original SRS approach is capable of detecting putative binding sites or even distinguishing the catalytic binding site. Apaydin et al (2002Apaydin et al ( , 2003 propose the time to escape from the so-called funnel of attraction as a measure of binding affinity. However, SRS does not provide information about the nature of interactions between the ligand and the binding site (e.g., hydrogen bonds), and it should be combined with a more direct model in order to analyze the bound state.…”