Designing Peptides on a Quantum Computer

Mulligan, Vikram Khipple; Melo, Hans; Merritt, Haley I.; Slocum, Stewart; Weitzner, Brian D.; Watkins, Andrew; Renfrew, P. Douglas; Pelissier, Craig; Arora, Paramjit S.; Bonneau, Richard

doi:10.1101/752485

Cited by 49 publications

(43 citation statements)

References 43 publications

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“…Although most of the literature has focused on the protein lattice model, a recent article [210] has attempted to use quantum annealing to perform rotamer sampling in the Rosetta energy function [211]. The authors used the D‐Wave 2000Q processor finding a scaling that seemed almost constant in comparison with classical simulated annealing.…”

Section: Optimization Problemsmentioning

confidence: 99%

The prospects of quantum computing in computational molecular biology

Outeiral

Strahm

Shi

et al. 2020

WIREs Comput Mol Sci

158

110

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Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to "hype," and it is also important to recognize the caveats and challenges in this new technology.Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Computer Algorithms and Programming Electronic Structure Theory > Ab Initio Electronic Structure Methods K E Y W O R D S ab initio simulations, machine learning, optimization, protein folding, quantum computingand X-ray diffraction data processing [10,11]. Despite such progress, many challenges in biology remain computationally infeasible. The best algorithms for problems like predicting the folding of a protein, calculating the binding affinity of a ligand for a macromolecule, or finding optimal large-scale genomic alignments require computational resources that are beyond even the most powerful supercomputers of our era.The solution to these challenges may lie in a paradigm shift in computing. In the 1980s, Richard Feynman [12] and, independently, Yuri Manin [13] proposed using quantum mechanical effects to build a new, more powerful generation of computers. Quantum theory has proved to be a highly successful description of physical reality, and has led, since its introduction in the early 20th century, to advances such as lasers, transistors and semiconductor microprocessors. A quantum computer would enable more effective algorithms by introducing operations that are not possible in classical machines. Quantum processors do not work faster than classical computers, but operate in a fundamentally different way, achieving unprecedented speedups by avoiding unnecessary computation. For example, computing the full electronic wavefunction of an average drug molecule numerically is expected to take longer than the age of the universe on any current supercomputer using conventional algorithms [14], while even a modest-sized quantum computer may be able to solve this in a timescale of days. Motivated by this promise of quantum advantage, the quest to build a quantum processor is ongoing. Unfortunately, the technical difficulties in manufacturi...

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Section: Optimization Problemsmentioning

confidence: 99%

The prospects of quantum computing in computational molecular biology

Outeiral

Strahm

Shi

et al. 2020

WIREs Comput Mol Sci

158

110

View full text Add to dashboard Cite

show abstract

“…Moreover, their parallel modeling capacity scales exponentially with the number of quantum bits, or qubits [157]. Although today's quantum computers are hindered by thermal noise, limited inter-qubit connectivity, small qubit counts [158], early and ongoing work on the D-Wave quantum annealer has produced quantum algorithms for conformational sampling in a simplified peptide-like system [159] and for designing real peptide sequences without simplification [160]. Applying this technology practically to drug design, and demonstrating an advantage over classical approaches, is the subject of ongoing research.…”

Section: Ongoing Research: Enhancing Peptide Macrocycle Design With Ementioning

confidence: 99%

The emerging role of computational design in peptide macrocycle drug discovery

Mulligan

2020

Expert Opinion on Drug Discovery

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Drug discovery is a laborious process with rising cost per new drug. Peptide macrocycles are promising therapeutics, though conformational flexibility can reduce target affinity and specificity. Recent computational advancements address this problem by enabling rational design of rigidly folded peptide macrocycles. Areas Covered: This review summarizes currently approved peptide macrocycle therapeutics and discusses advantages of mesoscale drugs over small molecules or protein therapeutics. It describes the history, rationale, and state of the art of computational tools, such as Rosetta, that allow the design of rigidly structured peptide macrocycles. The emerging pipeline for designing peptide macrocycle drugs is described, including current challenges in designing permeable molecules that can emulate the chameleonic behavior of natural macrocycles. Prospects for reducing computational cost and improving accuracy with emerging computational technologies are also discussed. Expert opinion: To embrace computational design of peptide macrocycle drugs, we must shift current attitudes regarding the role of computation in drug discovery, and move beyond Lipinski's rules. This technology has the potential to shift failures to earlier in silico stages of the drug discovery process, improving success rates in costly clinical trials. Given the available tools, now is the time for drug developers to incorporate peptide macrocycle design into drug discovery pipelines.

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“…Recently, there has been a growing amount of work focused on finding the minimal and maximal points of protein models through the analysis of protein shapes, or topology, before adding electrostatically-or electrodynamically-based equations for full pre-binding analysis in order to screen for top sites prior to compute-intensive force calculation. The completion of this task on classical computers with clever, third-degree polynomial topological algorithms [6] or by quantum annealing devices that straddle the line between highly optimized classical computing devices and fully quantum computational devices within a first-degree polynomial complexity class [25] has led to a new paradigm within biological modeling that is focused more on topological feature distinction.…”

Section: Topological Analysis Of Protein Surfaces For Binding Site Anmentioning

confidence: 99%

Quantum Approximated Graph Cutting: A Rapid Replacement for T-REMD?

Sandeep

Aramyan

et al. 2020

Preprint

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Determining an optimal protein configuration for the employment of protein binding analysis as completed by Temperature based Replica Exchange Molecular Dynamics (T-REMD) is an important process in the accurate depiction of a protein’s behavior in different solvent environments, especially when determining a protein’s top binding sites for use in protein-ligand and protein-protein docking studies. However, the completion of this analysis, which pushes out top binding sites through configurational changes, is an polynomial-state computational problem that can take multiple hours to compute, even on the fastest supercomputers. In this study, we aim to determine if graph cutting provide approximated solutions the MaxCut problem can be used as a method to provide similar results to T-REMD in the determination of top binding sites of Surfactant Protein A (SP-A) for binding analysis. Additionally, we utilize a quantum-hybrid algorithm within Iff Technology’s Polar+ package using an actual quantum processor unit (QPU), an implementation of Polar+ using an emulated QPU, or Quantum Abstract Machine (QAM), on a large scale classical computing device, and an implementation of a classical MaxCut algorithm on a supercomputer in order to determine the types of advantages that can be gained through using a quantum computing device for this problem, or even using quantum algorithms on a classical device. This study found that Polar+ provides a dramatic speedup over a classical implementation of a MaxCut approximation algorithm or the use of GROMACS T-REMD, and produces viable results, in both its QPU and QAM implementations. However, the lack of direct configurational changes carried out onto the structure of SP-A after the use of graph cutting methods produces different final binding results than those produced by GROMACS T-REMD. Thus, further work needs to be completed into translating quantum-based probabilities into configurational changes based on a variety of noise conditions to better determine the accuracy advantage that quantum algorithms and quantum devices can provide in the near future.

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Designing Peptides on a Quantum Computer

Cited by 49 publications

References 43 publications

The prospects of quantum computing in computational molecular biology

The prospects of quantum computing in computational molecular biology

The emerging role of computational design in peptide macrocycle drug discovery

Quantum Approximated Graph Cutting: A Rapid Replacement for T-REMD?

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