R-3 Repository :: Browsing by Author "Kavraki, Lydia E."

Browsing by Author "Kavraki, Lydia E."

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A review of parameters and heuristics for guiding metabolic pathfinding
(Springer International Publishing, 2017-09-15) Kim, Sarah M.; Peña, Matthew I.; Moll, Mark; Bennett, George N.; Kavraki, Lydia E.
Abstract Recent developments in metabolic engineering have led to the successful biosynthesis of valuable products, such as the precursor of the antimalarial compound, artemisinin, and opioid precursor, thebaine. Synthesizing these traditionally plant-derived compounds in genetically modified yeast cells introduces the possibility of significantly reducing the total time and resources required for their production, and in turn, allows these valuable compounds to become cheaper and more readily available. Most biosynthesis pathways used in metabolic engineering applications have been discovered manually, requiring a tedious search of existing literature and metabolic databases. However, the recent rapid development of available metabolic information has enabled the development of automated approaches for identifying novel pathways. Computer-assisted pathfinding has the potential to save biochemists time in the initial discovery steps of metabolic engineering. In this paper, we review the parameters and heuristics used to guide the search in recent pathfinding algorithms. These parameters and heuristics capture information on the metabolic network structure, compound structures, reaction features, and organism-specificity of pathways. No one metabolic pathfinding algorithm or search parameter stands out as the best to use broadly for solving the pathfinding problem, as each method and parameter has its own strengths and shortcomings. As assisted pathfinding approaches continue to become more sophisticated, the development of better methods for visualizing pathway results and integrating these results into existing metabolic engineering practices is also important for encouraging wider use of these pathfinding methods.
An incremental constraint-based framework for task and motion planning
(Sage, 2018) Dantam, Neil T.; Kingston, Zachary K.; Chaudhuri, Swarat; Kavraki, Lydia E.
We present a new constraint-based framework for task and motion planning (TMP). Our approach is extensible, probabilistically complete, and offers improved performance and generality compared with a similar, state-of-the-art planner. The key idea is to leverage incremental constraint solving to efficiently incorporate geometric information at the task level. Using motion feasibility information to guide task planning improves scalability of the overall planner. Our key abstractions address the requirements of manipulation and object rearrangement. We validate our approach on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-of-magnitude gains compared with the benchmark planner and improved scalability from additional geometric guidance. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.
Analysis of molecular motion using non-linear dimensionality reduction
(2007) Stamati, Hernan F.; Kavraki, Lydia E.
Understanding the main stable shapes and transitions of biomolecules is key to solving problems in computational biology. Because simulated molecular samples are high-dimensional, it is important to classify them using few parameters. Traditionally, this requires empirical reaction coordinates to be devised by an expert. This work, in contrast, automates the classification by applying an algorithmic tool for non-linear dimensionality reduction, called ScIMAP, that requires minimal user intervention. A comparison with the most popular linear dimensionality reduction technique, Principal Components Analysis, shows how non-linearity is crucial for capturing the main motion parameters. The contribution is validated by several results on increasingly complex systems, ranging from the motion of small peptides to the folding of large proteins. In all cases considered in this work, only 1--3 parameters are sufficient to characterize the motion landscape and prove as excellent reaction coordinates.
Analysis of probabilistic roadmap methods for motion planning and applications to polygon manipulation
(2003) Ladd, Andrew M.; Kavraki, Lydia E.
Motion planning deals with the problem of finding trajectory between two configurations in some space under some constraints. The science of motion planning deals with solving this problem efficiently in different spaces and under varying sets of constraints. This thesis provides an extended analysis of the PRM algorithm by using tools from measure theory to obtain greater generality. We then apply the results to planning in finite CW complexes and detail an application to planning with a polygonal robot in a polygonal workspace when contact is allowed. The final part of thesis deals with an application of motion planning to untangling mathematical knots. This problem is particularly interesting as it addresses motion planning with large number of degrees of freedom in a reconfigurable space. Planning applications with flexible robots, reconfigurable robots, molecular biology and other instances that share similar characteristics are some of the most challenging in planning today.
APE-Gen: A Fast Method for Generating Ensembles of Bound Peptide-MHC Conformations
(MDPI, 2019) Abella, Jayvee R.; Antunes, Dinler A.; Clementi, Cecilia; Kavraki, Lydia E.
The Class I Major Histocompatibility Complex (MHC) is a central protein in immunology as it binds to intracellular peptides and displays them at the cell surface for recognition by T-cells. The structural analysis of bound peptide-MHC complexes (pMHCs) holds the promise of interpretable and general binding prediction (i.e., testing whether a given peptide binds to a given MHC). However, structural analysis is limited in part by the difficulty in modelling pMHCs given the size and flexibility of the peptides that can be presented by MHCs. This article describes APE-Gen (Anchored Peptide-MHC Ensemble Generator), a fast method for generating ensembles of bound pMHC conformations. APE-Gen generates an ensemble of bound conformations by iterated rounds of (i) anchoring the ends of a given peptide near known pockets in the binding site of the MHC, (ii) sampling peptide backbone conformations with loop modelling, and then (iii) performing energy minimization to fix steric clashes, accumulating conformations at each round. APE-Gen takes only minutes on a standard desktop to generate tens of bound conformations, and we show the ability of APE-Gen to sample conformations found in X-ray crystallography even when only sequence information is used as input. APE-Gen has the potential to be useful for its scalability (i.e., modelling thousands of pMHCs or even non-canonical longer peptides) and for its use as a flexible search tool. We demonstrate an example for studying cross-reactivity.
Atlas + X: Sampling-based Planners on Constraint Manifolds
(2017-06-14) Voss, Caleb; Moll, Mark; Kavraki, Lydia E.
Sampling-based planners struggle when the valid configurations are constrained to an implicit manifold. Special planners have been proposed for this problem recently. Our new framework is decoupled from any particular planner and augments existing algorithms not explicitly designed for constraint planning. We demonstrate the advantages of our generalized approach.
AutoDock-based incremental docking protocol improves docking of large ligands
(2012-10-07) Dhanik, Ankur; Kavraki, Lydia E.; McMurray, John S.
It is well known that computer-aided docking of large ligands, with many rotatable bonds, is extremely difficult. AutoDock is a widely used docking program that can dock small ligands, with up to 5 or 6 rotatable bonds, accurately and quickly. Docking of larger ligands, however, is not very accurate and is computationally expensive. In this paper we present an AutoDock-based incremental docking protocol which docks a large ligand to its target protein in increments. A fragment of the large ligand is first chosen and then docked. Best docked conformations are incrementally grown and docked again, and this process is repeated until all the atoms of the ligand are docked. Each docking operation is performed using AutoDock. However, in each docking operation only a small number of rotatable bonds are allowed to rotate. We did a systematic docking study on a dataset of 73 protein-ligand complexes derived from the core set of PDBbind database. The number of rotatable bonds in the ligands vary from 7 to 30. Multiple docking experiments were done to evaluate the docking performance of the incremental protocol in comparison to AutoDock’s standard protocol. Results from the study show that, on average over the dataset, docking of large ligands using our incremental protocol is upto 23-fold computationally faster than docking using AutoDock’s standard protocol and also has better or comparable accuracy. We propose that, for docking large ligands, our incremental protocol can be used as an alternative to AutoDock’s standard protocol.
Automated Abstraction of Manipulation Domains for Cost-Based Reactive Synthesis
(IEEE, 2019) He, Keliang; Lahijanian, Morteza; Kavraki, Lydia E.; Vardi, Moshe Y.
When robotic manipulators perform high-level tasks in the presence of another agent, e.g., a human, they must have a strategy that considers possible interferences in order to guarantee task completion and efficient resource usage. One approach to generate such strategies is called reactive synthesis. Reactive synthesis requires an abstraction, which is a discrete structure that captures the domain in which the robot and other agents operate. Existing works discuss the construction of abstractions for mobile robots through space decomposition; however, they cannot be applied to manipulation domains due to the curse of dimensionality caused by the manipulator and the objects. In this work, we present the first algorithm for automatic abstraction construction for reactive synthesis of manipulation tasks. We focus on tasks that involve picking and placing objects with possible extensions to other types of actions. The abstraction also provides an upper bound on path-based costs for robot actions. We combine this abstraction algorithm with our reactive synthesis planner to construct correct-by-construction plans. We demonstrate the power of the framework on a UR5 robot, completing complex tasks in face of interferences by a human.
Bayesian graphical models for biological network inference
(2013-11-20) Peterson, Christine; Vannucci, Marina; Ensor, Katherine B.; Kavraki, Lydia E.; Maletic-Savatic, Mirjana; Stingo, Francesco C.
In this work, we propose approaches for the inference of graphical models in the Bayesian framework. Graphical models, which use a network structure to represent conditional dependencies among random variables, provide a valuable tool for visualizing and understanding the relationships among many variables. However, since these networks are complex systems, they can be difficult to infer given a limited number of observations. Our research is focused on development of methods which allow incorporation of prior information on particular edges or on the model structure to improve the reliability of inference given small to moderate sample sizes. First, we propose an approach to graphical model inference using the Bayesian graphical lasso. Our method incorporates informative priors on the shrinkage parameters specific to each edge. We demonstrate through simulations that this method allows improved learning of the network structure when relevant prior information is available, and illustrate the approach on inference of the cellular metabolic network under neuroinflammation. This application highlights the strength of our method since the number of samples available is fairly small, but we are able to draw on rich reference information from publicly available databases describing known metabolic interactions to construct informative priors. Next, we propose a modeling approach for settings where we would like to estimate networks for a collection of possibly related sample groups, where the sample size for each subgroup may be limited. We use a Markov random field prior to link the graphs within each group, and a selection prior to infer which groups have shared network structure. This allows us to encourage common edges across sample groups, when supported by the data. We provide simulation studies to illustrate the properties of our method and compare its performance to competing approaches. We conclude by demonstrating use of the proposed method to infer protein networks for various subtypes of acute myeloid leukemia and to infer signaling networks under different experimental perturbations.
Binding Modes of Peptidomimetics Designed to Inhibit STAT3
(Public Library of Science, 2012) Dhanik, Ankur; McMurray, John S.; Kavraki, Lydia E.
STAT3 is a transcription factor that has been found to be constitutively activated in a number of human cancers. Dimerization of STAT3 via its SH2 domain and the subsequent translocation of the dimer to the nucleus leads to transcription of anti-apoptotic genes. Prevention of the dimerization is thus an attractive strategy for inhibiting the activity of STAT3. Phosphotyrosine-based peptidomimetic inhibitors, which mimic pTyr-Xaa-Yaa-Gln motif and have strong to weak binding affinities, have been previously investigated. It is well-known that structures of protein-inhibitor complexes are important for understanding the binding interactions and designing stronger inhibitors. Experimental structures of inhibitors bound to the SH2 domain of STAT3 are, however, unavailable. In this paper we describe a computational study that combined molecular docking and molecular dynamics to model structures of 12 peptidomimetic inhibitors bound to the SH2 domain of STAT3. A detailed analysis of the modeled structures was performed to evaluate the characteristics of the binding interactions. We also estimated the binding affinities of the inhibitors by combining MMPB/GBSA-based energies and entropic cost of binding. The estimated affinities correlate strongly with the experimentally obtained affinities. Modeling results show binding modes that are consistent with limited previous modeling studies on binding interactions involving the SH2 domain and phosphotyrosine(pTyr)-based inhibitors. We also discovered a stable novel binding mode that involves deformation of two loops of the SH2 domain that subsequently bury the C-terminal end of one of the stronger inhibitors. The novel binding mode could prove useful for developing more potent inhibitors aimed at preventing dimerization of cancer target protein STAT3.
Bounded Policy Synthesis for POMDPs with Safe-Reachability and Quantitative Objectives
(2018-10-05) Wang, Yue; Chaudhuri, Swarat; Kavraki, Lydia E.
Robots are being deployed for many real-world applications like autonomous driving, disaster rescue, and personal assistance. Effectively planning robust executions under uncertainty is critical for building these autonomous robots. Partially Observable Markov Decision Processes (POMDPs) provide a standard approach to model many robot applications under uncertainty. A key algorithmic problem for POMDPs is the synthesis of policies that specify the actions to take contingent on all possible events. Policy synthesis for POMDPs with two kinds of objectives is considered in this thesis: (1) boolean objectives for a correctness guarantee of accomplishing tasks and (2) quantitative objectives for optimal behaviors. For boolean objectives, this thesis focuses on a common safe-reachability objective: with a probability above a threshold, a goal state is eventually reached while keeping the probability of visiting unsafe states below a different threshold. Previous results have shown that policy synthesis for POMDPs over infinite horizon is generally undecidable. For decidability, this thesis focuses on POMDPs over a bounded horizon. Solving POMDPs requires reasoning over a vast space of beliefs (probability distributions). To address this, this thesis introduces the notion of a goal-constrained belief space that only contains beliefs reachable under desired executions that can achieve the safe-reachability objectives. Based on this notion, this thesis presents an offline approach that constructs policies over the goal-constrained belief space instead of the entire belief space. Simulation experiments show that this offline approach can scale to large belief spaces by focusing on the goal-constrained belief space. A full policy is generally costly to compute. To improve efficiency, this thesis presents an online approach that interleaves the computation of partial policies and execution. A partial policy is parameterized by a replanning probability and only contain a sampled subset of all possible events. This online approach allows users to specify an appropriate bound on the replanning probability to balance efficiency and correctness. Finally, this thesis presents an approximate policy synthesis approach that combines the safe-reachability objectives with the quantitative objectives. The results demonstrate that the constructed policies not only achieve the safe-reachability objective but also are of high quality concerning the quantitative objective.
Coarse-Grained Conformational Sampling of Protein Structure Improves the Fit to Experimental Hydrogen-Exchange Data
(Frontiers Media S.A., 2017) Devaurs, Didier; Antunes, Dinler A.; Papanastasiou, Malvina; Moll, Mark; Ricklin, Daniel; Lambris, John D.; Kavraki, Lydia E.
Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution produces experimental data that translates into valuable information about the protein's structure. Data produced by HDX experiments is often interpreted using a crystal structure of the protein, when available. However, it has been shown that the correspondence between experimental HDX data and crystal structures is often not satisfactory. This creates difficulties when trying to perform a structural analysis of the HDX data. In this paper, we evaluate several strategies to obtain a conformation providing a good fit to the experimental HDX data, which is a premise of an accurate structural analysis. We show that performing molecular dynamics simulations can be inadequate to obtain such conformations, and we propose a novel methodology involving a coarse-grained conformational sampling approach instead. By extensively exploring the intrinsic flexibility of a protein with this approach, we produce a conformational ensemble from which we extract aﾠsingleﾠconformation providing a good fit to the experimental HDX data. We successfully demonstrate the applicability of our method to four small and medium-sized proteins.
Combinatorial Clustering of Residue Position Subsets Predicts Inhibitor Affinity across the Human Kinome
(Public Library of Science, 2013) Bryant, Drew H.; Moll, Mark; Finn, Paul W.; Kavraki, Lydia E.
The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (CCORPS) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, CCORPS is applied to the problem of identifying structural features of the kinase ATP binding site that are informative of inhibitor binding. CCORPS is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, CCORPS is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors.
Combining Sampling and Optimizing for Robotic Path Planning
(2018-09-12) Willey, Bryce Steven; Kavraki, Lydia E.; Moll, Mark
Robotic path planning is a critical problem in autonomous robotics. Two com- mon approaches to robotic path planning are sampling-based motion planners and continuous optimization methods. Sampling-based motion planners explore the search space effectively, but either return low quality paths or take a long time to ini- tially find a path. Continuous optimization methods quickly find high-quality paths, but often return paths in collision with obstacles. This thesis combines sampling- based and continuous optimization techniques in order to improve the performance of these planning approaches. This thesis shows that the advantages and disad- vantages of these approaches are complementary and proposes combining them into a pipeline. The proposed pipeline results in better path quality than either ap- proach alone, providing a robust, efficient, and high-quality general path planning solution. The use of collision checking techniques introduced by continuous opti- mization methods in sampling-based planners is also analyzed and approximation error rates and timing results are provided.
Computational Analysis of Gene Duplication and Network Evolution
(2014-04-25) Zhu, Yun; Nakhleh, Luay K.; Kavraki, Lydia E.; Kohn, Michael H.; Lin, Zhenguo
Molecular interaction networks have emerged as a powerful data source for answering a plethora of biological questions ranging from how cells make decisions to how species evolve. The availability of such data from multiple organisms allows for their analysis from an evolutionary perspective. Gene duplication plays an important role in the evolution of genomes and interactomes, and elucidating the interplay between how genomes and interactomes evolve in light of gene duplication is of great interest. In order to achieve this goal, it is important to develop models and algorithms for analyzing network evolution, particularly with respect to gene duplication events. The contributions of my thesis are four-fold. First, I developed a new genotype model that combines genomes with regulatory network, and a population genetic framework for simulating the evolution of this genotype. Using the simulator, I established explanations for gene duplicability. Second, I developed novel algorithms for probabilistic inference of ancestral networks from extant taxa, in a phylogenetic setup. Third, I conducted data analyses focusing on whole-genome duplication in yeast, and established a rate of protein-protein interaction networks, and devised a method for generating hypotheses about gene duplicate fates from network data. Fourth, and not least, I investigated the role of networks in defining adaptive models for gene duplication. In summary, my thesis contributes new analytical tools and data analyses that help elucidate and understand the interplay between gene duplication at the genomic and interactomic levels.
Computational discovery and analysis of metabolic pathways
(2010) Heath, Allison Park; Kavraki, Lydia E.
Finding novel or non-standard metabolic pathways, possibly spanning multiple species, has important applications in fields such as metabolic engineering, metabolic network analysis, and metabolic network reconstruction. Traditionally, this has been a manual process, but the large volume of metabolic data now available has created a need for computational tools to automatically identify biologically relevant pathways. This thesis presents new algorithms for automatically finding biologically meaningful linear and branched metabolic pathways in multi-genome scale metabolic networks. These algorithms utilize atom mapping data, which provides the correspondence between atoms in the substrates to atoms in the products of a chemical reaction, to find pathways which conserve a given number of atoms between desired start and target compounds. The first algorithm presented identifies atom conserving linear pathways by explicitly tracking atoms during an exploration of a graph structure constructed from the atom mapping data. The explicit tracking of atoms enables finding branched pathways because it provides automatic identification of the reactions and compounds through which atoms are lost or gained. The thesis then describes two algorithmic approaches for identifying branched metabolic pathways based upon atom conserving linear pathways. One approach takes one linear pathway at a time and attempts to add branches that connect loss and gain compounds. The other approach takes a group of linear pathways and attempts to merge pathways that move mutually exclusive sets of atoms from the start to the target compounds. Comparisons to known metabolic pathways demonstrate that atom tracking causes the algorithms to avoid many unrealistic connections, often found in previous approaches, and return biologically meaningful pathways. While the theoretical complexity of finding even linear atom conserving pathways is high, by choosing the appropriate representations and heuristics, and perhaps due to the structure of the underlying data, the algorithms in this thesis have practical running times on real data. The results also demonstrate the potential of the algorithms to find novel or non-standard pathways that may span multiple organisms.
Computing and Updating Molecular Conformations Using the Atomgroup Local Frames Method
(2001-05-11) Kavraki, Lydia E.; Zhang, Ming
Efficiently maintaining molecular conformations is important for molecular modeling and protein engineering. This paper reviews the widely used simple rotations scheme, simple local frames method, and introduces a new atom group local frames method for maintaining the molecular conformation changes due to the changes of torsional angles. The simple rotations scheme applies a sequence of rotations to update all atom positions. The order of the updates is important and some bookkeeping of the atom positions is necessary. Numeric errors can accumulate as rotations around the bonds are repeated. The simple local frames method builds local frames at the bonds, and relational matrices between parents and children frames are constructed. The atom positions are computed by chaining series of such matrices. No bookkeeping is necessary and numeric errors do not accumulate upon rotations. Multiple local frames are needed at a bond if it has more than one child. This paper introduces a new atom group local frames method to efficiently maintain molecular conformations. A single local frame is attached to each atom group. Bookkeeping is not necessary and numeric errors do not accumulate upon rotations. This method also provides lazy evaluations for atom positions. Thus, the conformational maintenance cost is greatly reduced, especially when many conformations are generated and updated such as in a minimization process.
Decomposition-based Motion Planning: Towards Real-time Planning forRobots with Many Degrees of Freedom
(2000-08-25) Brock, Oliver; Kavraki, Lydia E.
Research in motion planning has been striving to develop faster and faster planning algorithms in order to be able to address a wider range of applications. In this paper a novel real-time motion planning framework, called decomposition-based motion planning, is proposed. It is particularly well suited for planning problems that arise in service and field robotics. It decomposes the original planning problem into simpler subproblems, whose successive solution results in a large reduction of the overall complexity. A particular implementation of decomposition-based planning is proposed. It is based on an adaptive wavefront expansion algorithm and reactive motion execution. Using this implementation of decomposition-based planning, real-time motion planning performance for an eleven degree-of-freedom mobile manipulator can be achieved. Some fundamental and preliminary analysis of the decomposition-based motion planning approach is provided.
DINC 2.0: A New Protein–Peptide Docking Webserver Using an Incremental Approach
(AACR, 2017) Antunes, Dinler A.; Moll, Mark; Devaurs, Didier; Jackson, Kyle; Lizée, Gregory; Kavraki, Lydia E.
Molecular docking is a standard computational approach to predict binding modes of protein–ligand complexes by exploring alternative orientations and conformations of the ligand (i.e., by exploring ligand flexibility). Docking tools are largely used for virtual screening of small drug-like molecules, but their accuracy and efficiency greatly decays for ligands with more than 10 flexible bonds. This prevents a broader use of these tools to dock larger ligands, such as peptides, which are molecules of growing interest in cancer research. To overcome this limitation, our group has previously proposed a meta-docking strategy, called DINC, to predict binding modes of large ligands. By incrementally docking overlapping fragments of a ligand, DINC allowed predicting binding modes of peptide-based inhibitors of transcription factors involved in cancer. Here, we describe DINC 2.0, a revamped version of the DINC webserver with enhanced capabilities and a more user-friendly interface. DINC 2.0 allows docking ligands that were previously too challenging for DINC, such as peptides with more than 25 flexible bonds. The webserver is freely accessible at http://dinc.kavrakilab.org, together with additional documentation and video tutorials. Our team will provide continuous support for this tool and is working on extending its applicability to other challenging fields, such as personalized immunotherapy against cancer.
DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins
(Elsevier, 2021) Hall-Swan, Sarah; Devaurs, Didier; Rigo, Mauricio M.; Antunes, Dinler A.; Kavraki, Lydia E.; Zanatta, Geancarlo
An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason might be an often overlooked factor in these computational efforts: receptor flexibility. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimentally-determined binding affinities. Some of the best results have been obtained on a dataset of large ligands resolved via room temperature crystallography, and therefore capturing alternative receptor conformations. In addition, we have shown that the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and that this diversity is useful in finding alternative binding modes of ligands. Overall, our work highlights the importance of accounting for receptor flexibility in docking studies, and provides a platform for the identification of new inhibitors against SARS-CoV-2 proteins.

Browsing by Author "Kavraki, Lydia E."

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