Classic molecular motion simulation techniques, such as Monte Carlo (MC) simulation, generate motion pathways one at a time and spend most of their time in the local minima of the energy landscape defined over a molecular conformation space. Their high computational cost prevents them from being used to compute ensemble properties (properties requiring the analysis of many pathways). This paper introduces stochastic roadmap simulation (SRS) as a new computational approach for exploring the kinetics of molecular motion by simultaneously examining multiple pathways. These pathways are compactly encoded in a graph, which is constructed by sampling a molecular conformation space at random. This computation, which does not trace any particular pathway explicitly, circumvents the local-minima problem. Each edge in the graph represents a potential transition of the molecule and is associated with a probability indicating the likelihood of this transition. By viewing the graph as a Markov chain, ensemble properties can be efficiently computed over the entire molecular energy landscape. Furthermore, SRS converges to the same distribution as MC simulation. SRS is applied to two biological problems: computing the probability of folding, an important order parameter that measures the "kinetic distance" of a protein's conformation from its native state; and estimating the expected time to escape from a ligand-protein binding site. Comparison with MC simulations on protein folding shows that SRS produces arguably more accurate results, while reducing computation time by several orders of magnitude. Computational studies on ligand-protein binding also demonstrate SRS as a promising approach to study ligand-protein interactions.
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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.
The problems of protein folding and ligand docking have been explored largely using molecular dynamics or Monte Carlo methods. These methods are very compute intensive because they often explore a much wider range of energies, conformations and time than necessary. In addition, Monte Carlo methods often get trapped in local minima. We initially showed that robotic motion planning permitted one to determine the energy of binding and dissociation of ligands from protein binding sites (Singh et al., 1999). The robotic motion planning method maps complicated three-dimensional conformational states into a much simpler, but higher dimensional space in which conformational rearrangements can be represented as linear paths. The dimensionality of the conformation space is of the same order as the number of degrees of conformational freedom in three-dimensional space. We were able to determine the relative energy of association and dissociation of a ligand to a protein by calculating the energetics of interaction for a few thousand conformational states in the vicinity of the protein and choosing the best path from the roadmap. More recently, we have applied roadmap planning to the problem of protein folding (Apaydin et al., 2002a). We represented multiple conformations of a protein as nodes in a compact graph with the edges representing the probability of moving between neighboring states. Instead of using Monte Carlo simulation to simulate thousands of possible paths through various conformational states, we were able to use Markov methods to calculate the steady state occupancy of each conformation, needing to calculate the energy of each conformation only once. We referred to this Markov method of representing multiple conformations and transitions as stochastic roadmap simulation or SRS. We demonstrated that the distribution of conformational states calculated with exhaustive Monte Carlo simulations asymptotically approached the Markov steady state if the same Boltzman energy distribution was used in both methods. SRS permits one to calculate contributions from all possible paths simultaneously with far fewer energy calculations than Monte Carlo or molecular dynamics methods. The SRS method also permits one to represent multiple unfolded starting states and multiple, near-native, folded states and all possible paths between them simultaneously. The SRS method is also independent of the function used to calculate the energy of the various conformational states. In a paper to be presented at this conference (Apaydin et al., 2002b) we have also applied SRS to ligand docking in which we calculate the dynamics of ligand-protein association and dissociation in the region of various binding sites on a number of proteins. SRS permits us to determine the relative times of association to and dissociation from various catalytic and non-catalytic binding sites on protein surfaces. Instead of just following the best path in a roadmap, we can calculate the contribution of all the possible binding or dissociation paths and their rel...
P e r s u a s i v e T e c h n o l o g y Lab, C o r d u r a Hall, S t a n f o r d U n i v e r s i t y Stanford, C A 9 4 3 0 9 U S A bj f o g g @ s t a n f o r d . e d u , w w w . w e b c r e d i b i l i t y . o r g ABSTRACTWe conducted an online survey about Web credibility, which included over 1400 participants. People reported that Web site credibility increases when the site conveys a real-world presence, is easy to use, and is updated often. People reported that a Web site loses credibility when it has errors, technical problems, or distracting advertisements. Our study is an early effort to identify Web credibility elements and empirically investigate the effect of each.
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a) Many cancer drivers are considered “undruggable” and without targeted treatments because they lack binding sites for conventional small molecules. Here, we introduce The FRONTIER™ Platform applying machine learning (ML), chemoproteomics and covalent chemistry to identify binding sites and cell-active covalent fragments across the human proteome, including against most cancer drivers and previously “undruggable” targets. Molecules tested in functional assays are active and can serve as starting points for new drug discovery initiatives. b) We are using mass spectrometry and data analysis workflows to perform high-throughput chemoproteomic profiling experiments. These experiments identify hits across the proteome using different cancer-relevant cell backgrounds and characterize binding sites for drug discovery. Customized ML algorithms using chemoproteomic, genomic and structural data to characterize and prioritize identified binding sites for covalent drug discovery. The performance of the platform allows the profiling of thousands of compounds from a custom-built library that has been optimized by ML for covalent fragment-based drug discovery. The nature of the fragment hits and the ability to map them to and focus on preferred binding sites for covalent drugs enables accelerated lead generation. c) We show details of the platform highlighting library design concepts and the hotspot map with binding site prioritization algorithms. We show coverage and applicability across important cancer target classes and signaling pathways. Covalent fragment hits have been identified for multiple difficult cancer targets, including KRAS, p53, STAT3, KEAP1, PTPN11 and others. The platform also identifies ligands for novel allosteric binding sites in established oncology targets such as CDK4, PI3KCA and BTK. We will highlight how discovered ligands show functional activity in orthogonal assays, demonstrating their fitness for drug discovery campaigns and target validation experiments. d) Undruggable targets across a variety of cancer target classes have become druggable. Citation Format: Johannes C. Hermann, Robert Everley, Laura Marholz, Matthew Berberich, Tzu-Yi Yang, Yu-Hsin Chao, Abduselam K. Awol, Michael Shaghafi, Han Yoon, Rohan Varma, Reed Stein, Karsten Krug, Emily Lachtara, Daniel Erlanson, Chris Varma, Kevin R. Webster. Combining chemoproteomics with machine learning identifies functionally active covalent fragments for hard-to-drug cancer drivers. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5333.
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