Efficient Exploration of Protein Conformational Pathways using RRT* and MC

Afrasiabi, Fatemeh; Haspel, Nurit

doi:10.1145/3388440.3414705

Cited by 6 publications

(20 citation statements)

References 23 publications

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“…The Rapidly Exploring Random Tree (RRT) algorithm [27] is a robotics-based 90 method that builds a tree rooted at the start configuration by randomly exploring 91 the search space. The algorithm was presented in our previous work, extended by this 92 journal submission [28]. The algorithm is outlined here for clarity.…”

Section: Rrt Rrt* and Adaptive Sampling 89mentioning

confidence: 99%

“…This step makes the path smoother and less jagged looking. three key methods, nearNeighbors, chooseParent, and rewire [28]. The nearNeighbors 158 method will look for a set of nearest neighbors that lie within an RMSD of at most 1Å This contribution is an extension of our conference paper described in [28].…”

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confidence: 99%

“…three key methods, nearNeighbors, chooseParent, and rewire [28]. The nearNeighbors 158 method will look for a set of nearest neighbors that lie within an RMSD of at most 1Å This contribution is an extension of our conference paper described in [28]. Instead 171 of using an optional list of residues for manipulation, our goal is to focus explicitly on 172 flexible residues.…”

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confidence: 99%

“…One denoted by (a) for our previous work [28], one denoted by (b) for the version that faster than our previous work (Table 1, Refer to section 2.4 for details on runtime analysis).…”

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confidence: 99%

“…This algorithm works on a multigraph associated 142 with a mechanical model that is constructed from an arbitrary atom-bond network. In 143 the Kinari software, the authors have developed a faster and more robust variation of 144 the pebble game algorithm tailored to the specificities of biopolymers.In our previous work we implemented a biased Monte Carlo tree search, to explore 147 the conformational space of medium and large proteins[28,34]. In order to decrease the 148 search time and at the same time increase the convergence of the search, we added an…”

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confidence: 99%

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Integrating Rigidity Analysis into the Exploration of Protein Conformational Pathways using RRT* and MC

Afrasiabi

Dehghanpoor

Haspel

2021

Preprint

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To understand how proteins function on a cellular level, it is of paramount importance to understand their structures and dynamics, including the conformational changes they undergo to carry out their function. For the aforementioned reasons, the study of large conformational changes in proteins has been an interest to researchers for years. However, since some proteins experience rapid and transient conformational changes, it is hard to experimentally capture the intermediate structures. Additionally, computational brute force methods are computationally intractable, which makes it impossible to find these pathways which require a search in a high-dimensional, complex space. In our previous work, we implemented a hybrid algorithm that combines Monte-Carlo (MC) sampling and RRT*, a version of the Rapidly Exploring Random Trees (RRT) robotics-based method, to make the conformational exploration more accurate and efficient, and produce smooth conformational pathways. In this work, we integrated the rigidity analysis of proteins into our algorithm to guide the search to explore flexible regions. We demonstrate that rigidity analysis dramatically reduces the run time and accelerates convergence.

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Section: Rrt Rrt* and Adaptive Sampling 89mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…One denoted by (a) for our previous work [28], one denoted by (b) for the version that faster than our previous work (Table 1, Refer to section 2.4 for details on runtime analysis).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Integrating Rigidity Analysis into the Exploration of Protein Conformational Pathways using RRT* and MC

Afrasiabi

Dehghanpoor

Haspel

2021

Preprint

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Using Topological Data Analysis and RRT to Investigate Protein Conformational Spaces

Afrasiabi

Dehghanpoor

Haspel

2021

Preprint

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Investigating the conformational space of proteins is essential in order to associate their structures with their fundamental functions. Nonetheless, it is a challenging task, both experimentally and computationally. Because of the transient nature of these conformational changes and the fact that they are impermanent, empirical methods have fallen short to capture them. In silico methods, on the other hand, have shown great promise in exploring these conformational pathways. In this article, we provide an extensive evaluation of our previously introduced, robotics inspired conformational search algorithm (RRT* with Monte Carlo). We then identify what intermediate conformations appear the most in our generated conformational pathways using TDA Mapper, a topological data analysis algorithm, and examine how close these intermediate conformations are to existing experimental data.

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A New Tool to Study the Binding Behavior of Intrinsically Disordered Proteins

Upadhyay¹,

Ekenna²

2023

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Understanding the binding behavior and conformational dynamics of intrinsically disordered proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes. However, their lack of stable 3D structures poses challenges for analysis. To address this, we propose an algorithm that explores IDP binding behavior with protein complexes by extracting topological and geometric features from the protein surface model. Our algorithm identifies a geometrically favorable binding pose for the IDP and plans a feasible trajectory to evaluate its transition to the docking position. We focus on IDPs from Homo sapiens and Mus-musculus, investigating their interaction with the Plasmodium Falciparum (PF) pathogen associated with malaria-related deaths. We compare our algorithm with HawkDock and HDOCK docking tools for quantitative (computation time) and qualitative (binding affinity) measures. Our results indicate that our method outperforms the compared methods in computation performance and binding affinity of experimental conformations.

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Efficient Exploration of Protein Conformational Pathways using RRT* and MC

Cited by 6 publications

References 23 publications

Integrating Rigidity Analysis into the Exploration of Protein Conformational Pathways using RRT* and MC

Integrating Rigidity Analysis into the Exploration of Protein Conformational Pathways using RRT* and MC

Using Topological Data Analysis and RRT to Investigate Protein Conformational Spaces

A New Tool to Study the Binding Behavior of Intrinsically Disordered Proteins

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