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Ghamari D, Covino R, Faccioli P. Sampling a Rare Protein Transition Using Quantum Annealing. J Chem Theory Comput 2024; 20:3322-3334. [PMID: 38587482 DOI: 10.1021/acs.jctc.3c01174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Simulating spontaneous structural rearrangements in macromolecules with classical molecular dynamics is an outstanding challenge. Conventional supercomputers can access time intervals of up to tens of μs, while many key events occur on exponentially longer time scales. Path sampling techniques have the advantage of focusing the computational power on barrier-crossing trajectories, but generating uncorrelated transition paths that explore diverse conformational regions remains a problem. We employ a hybrid path-sampling paradigm that addresses this issue by generating trial transition paths using a quantum annealing (QA) machine. We first employ a classical computer to perform an uncharted exploration of the conformational space. The data set generated in this exploration is then postprocessed using a path integral-based method to yield a coarse-grained network representation of the reactive kinetics. By resorting to a quantum annealer, quantum superposition can be exploited to encode all of the transition pathways in the initial quantum state, thus potentially solving the path exploration problem. Furthermore, each QA cycle yields a completely uncorrelated trial trajectory. We previously validated this scheme on a prototypically simple transition, which could be extensively characterized on a desktop computer. Here, we scale up in complexity and perform an all-atom simulation of a protein conformational transition that occurs on the millisecond time scale, obtaining results that match those of the Anton special-purpose supercomputer. Despite limitations due to the available quantum annealers, our study highlights how realistic biomolecular simulations provide potentially impactful new ground for applying, testing, and advancing quantum technologies.
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Affiliation(s)
- Danial Ghamari
- Physics Department, Trento University, Via Sommarive 14, Povo 38123, Trento, Italy
- INFN-TIFPA, Via Sommarive 14, Povo 38123, Trento, Italy
| | - Roberto Covino
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1 Frankfurt am Main, Frankfurt D-60438, Germany
- Department of Biochemistry, University of Bayreuth, Universitätsstraße 30, Bayreuth 95447, Germany
| | - Pietro Faccioli
- INFN-TIFPA, Via Sommarive 14, Povo 38123, Trento, Italy
- Bicocca Quantum Technology Center and Physics Department, University of Milan Bicocca, Piazza della Scienza 2/A, Milan 20126, Italy
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Slongo F, Hauke P, Faccioli P, Micheletti C. Quantum-inspired encoding enhances stochastic sampling of soft matter systems. SCIENCE ADVANCES 2023; 9:eadi0204. [PMID: 37878707 PMCID: PMC10599611 DOI: 10.1126/sciadv.adi0204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/21/2023] [Indexed: 10/27/2023]
Abstract
Quantum advantage in solving physical problems is still hard to assess due to hardware limitations. However, algorithms designed for quantum computers may engender transformative frameworks for modeling and simulating paradigmatically hard systems. Here, we show that the quadratic unconstrained binary optimization encoding enables tackling classical many-body systems that are challenging for conventional Monte Carlo. Specifically, in self-assembled melts of rigid lattice ring polymers, the combination of high density, chain stiffness, and topological constraints results in divergent autocorrelation times for real-space Monte Carlo. Our quantum-inspired encoding overcomes this problem and enables sampling melts of lattice rings with fixed curvature and compactness, unveiling counterintuitive topological effects. Tackling the same problems with the D-Wave quantum annealer leads to substantial performance improvements and advantageous scaling of sampling computational cost with the size of the self-assembled ring melts.
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Affiliation(s)
- Francesco Slongo
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, I-34136 Trieste, Italy
| | - Philipp Hauke
- Pitaevskii BEC Center, Department of Physics, University of Trento, Via Sommarive 14, I-38123 Povo, Trento, Italy
- INFN-TIFPA, Via Sommarive 14, I-38123 Povo, Trento, Italy
| | - Pietro Faccioli
- Department of Physics and BiQuTe Center, University of Milano-Bicocca, Piazza della Scienza 3, I-20126 Milan, Italy
- Department of Physics, University of Trento, Via Sommarive 14, I-38123 Povo, Trento, Italy
| | - Cristian Micheletti
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, I-34136 Trieste, Italy
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Sheka EF. Virtual Free-Radical Polymerization of Vinyl Monomers in View of Digital Twins. Polymers (Basel) 2023; 15:2999. [PMID: 37514389 PMCID: PMC10383948 DOI: 10.3390/polym15142999] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/02/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
The first case of virtual polymerization based on the concept of digital twins (DTs) is presented. The free-radical polymerization of vinyl monomers is considered to be a chain reaction consisting of a set of elementary ones. Those three types, related to the polymerization initiation and propagation as well as to the termination of polymer chain growth, are discussed. Special sets of DTs, whose total number approaches 60, distinguish each reaction type. The calculations are carried out using a semi-empirical version of the unrestricted Hartree-Fock approximation. The main energy and spin-density parameters of the ground state of the DTs are determined. The barrier profiles of two pairs of DTs are calculated, based on which two Evans-Polanyi-Semenov relations, attributed to elementary reactions of type (1) and (2), are constructed. These provide a quite reliable evaluation of the activation energy for the initiation and propagation of the free-radical polymerization of vinyl monomers in all the cases. The decisive role of spins in the formation of the elementary reaction transition states is established.
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Affiliation(s)
- Elena F Sheka
- Institute of Physical Researches and Technology, Peoples' Friendship University of Russia (RUDN University), 117198 Moscow, Russia
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Marchetti L, Nifosì R, Martelli PL, Da Pozzo E, Cappello V, Banterle F, Trincavelli ML, Martini C, D’Elia M. Quantum computing algorithms: getting closer to critical problems in computational biology. Brief Bioinform 2022; 23:6758194. [PMID: 36220772 PMCID: PMC9677474 DOI: 10.1093/bib/bbac437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/15/2022] [Accepted: 09/08/2022] [Indexed: 12/14/2022] Open
Abstract
The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.
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Affiliation(s)
| | | | - Pier Luigi Martelli
- Corresponding authors: Pier Luigi Martelli. Tel.: +39 0512094005; Fax: +39 0512094005; E-mail: ; Claudia Martini. Tel.: +39 0502219522; Fax: +39 050 2210680; E-mail:
| | - Eleonora Da Pozzo
- University of Pisa, Department of Pharmacy, via Bonanno 6, 56126 Pisa Italy
| | - Valentina Cappello
- Italian Institute of Technology, Center for Materials Interfaces, Viale Rinaldo Piaggio 34, 56025 Pontedera (PI), Italy
| | | | | | - Claudia Martini
- Corresponding authors: Pier Luigi Martelli. Tel.: +39 0512094005; Fax: +39 0512094005; E-mail: ; Claudia Martini. Tel.: +39 0502219522; Fax: +39 050 2210680; E-mail:
| | - Massimo D’Elia
- University of Pisa, Department of Physics, Largo Bruno Pontecorvo 3, 56127, Pisa Italy
- INFN, Sezione di Pisa, Largo Bruno Pontecorvo 3, I-56127 Pisa, Italy
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Ghamari D, Hauke P, Covino R, Faccioli P. Sampling rare conformational transitions with a quantum computer. Sci Rep 2022; 12:16336. [PMID: 36175529 PMCID: PMC9522734 DOI: 10.1038/s41598-022-20032-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 09/07/2022] [Indexed: 11/09/2022] Open
Abstract
Structural rearrangements play a central role in the organization and function of complex biomolecular systems. In principle, Molecular Dynamics (MD) simulations enable us to investigate these thermally activated processes with an atomic level of resolution. In practice, an exponentially large fraction of computational resources must be invested to simulate thermal fluctuations in metastable states. Path sampling methods focus the computational power on sampling the rare transitions between states. One of their outstanding limitations is to efficiently generate paths that visit significantly different regions of the conformational space. To overcome this issue, we introduce a new algorithm for MD simulations that integrates machine learning and quantum computing. First, using functional integral methods, we derive a rigorous low-resolution spatially coarse-grained representation of the system's dynamics, based on a small set of molecular configurations explored with machine learning. Then, we use a quantum annealer to sample the transition paths of this low-resolution theory. We provide a proof-of-concept application by simulating a benchmark conformational transition with all-atom resolution on the D-Wave quantum computer. By exploiting the unique features of quantum annealing, we generate uncorrelated trajectories at every iteration, thus addressing one of the challenges of path sampling. Once larger quantum machines will be available, the interplay between quantum and classical resources may emerge as a new paradigm of high-performance scientific computing. In this work, we provide a platform to implement this integrated scheme in the field of molecular simulations.
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Affiliation(s)
- Danial Ghamari
- Department of Physics, University of Trento, Via Sommarive 14, Trento, 38123, Italy.,INFN-TIFPA, Via Sommarive 14, Trento, 38123, Italy
| | - Philipp Hauke
- INO-CNR BEC Center & Department of Physics, University of Trento, Via Sommarive 14, Trento, 38123, Italy
| | - Roberto Covino
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, Frankfurt am Main, 60438, Germany.
| | - Pietro Faccioli
- Department of Physics, University of Trento, Via Sommarive 14, Trento, 38123, Italy. .,INFN-TIFPA, Via Sommarive 14, Trento, 38123, Italy.
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Yarkoni S, Raponi E, Bäck T, Schmitt S. Quantum annealing for industry applications: introduction and review. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2022; 85:104001. [PMID: 36001953 DOI: 10.1088/1361-6633/ac8c54] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 08/24/2022] [Indexed: 06/15/2023]
Abstract
Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the QA algorithm for programmable use. Specifically, QA processors produced by D-Wave systems have been studied and tested extensively in both research and industrial settings across different disciplines. In this paper we provide a literature review of the theoretical motivations for QA as a heuristic quantum optimization algorithm, the software and hardware that is required to use such quantum processors, and the state-of-the-art applications and proofs-of-concepts that have been demonstrated using them. The goal of our review is to provide a centralized and condensed source regarding applications of QA technology. We identify the advantages, limitations, and potential of QA for both researchers and practitioners from various fields.
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Gianti E, Percec S. Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go? Biomacromolecules 2022; 23:576-591. [PMID: 35133143 DOI: 10.1021/acs.biomac.1c01436] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of biomacromolecules. The achievement of these tasks requires the navigation of vast and complex chemical and biological spaces, difficult to accomplish with reasonable speed. Using modern algorithms and supercomputers, quantum physics methods are able to examine systems containing a few hundred interacting species and determine the probability of finding them in a particular region of phase space, thereby anticipating their properties. Likewise, modern approaches in chemistry and biomolecular simulation, supported by high performance computing, have culminated in producing data sets of escalating size and intrinsically high complexity. Hence, using ML to extract relevant information from these fields is of paramount importance to advance our understanding of chemical and biomolecular systems. At the heart of ML approaches lie statistical algorithms, which by evaluating a portion of a given data set, identify, learn, and manipulate the underlying rules that govern the whole data set. The assembly of a quality model to represent the data followed by the predictions and elimination of error sources are the key steps in ML. In addition to a growing infrastructure of ML tools to address complex problems, an increasing number of aspects related to our understanding of the fundamental properties of biomacromolecules are exposed to ML. These fields, including those residing at the interface of polymer science and biology (i.e., structure determination, de novo design, folding, and dynamics), strive to adopt and take advantage of the transformative power offered by approaches in the ML domain, which clearly has the potential of accelerating research in the field of biomacromolecules.
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Affiliation(s)
- Eleonora Gianti
- Institute for Computational Molecular Science (ICMS), Temple University, Philadelphia, Pennsylvania 19122, United States.,Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Simona Percec
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
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