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Keng M, Merz KM. Eliminating the Deadwood: A Machine Learning Model for CCS Knowledge-Based Conformational Focusing for Lipids. J Chem Inf Model 2024. [PMID: 39378407 DOI: 10.1021/acs.jcim.4c01051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2024]
Abstract
Accurate elucidation of gas-phase chemical structures using collision cross section (CCS) values obtained from ion-mobility mass spectrometry benefits from a synergism between experimental and in silico results. We have shown in recent work that for a molecule of modest size with a proscribed conformational space we can successfully capture a conformation(s) that can match experimental CCS values. However, for flexible systems such as fatty acids that have many rotatable bonds and multiple intramolecular London dispersion interactions, it becomes necessary to sample a much greater conformational space. Sampling more conformers, however, accrues significant computational cost downstream in optimization steps involving quantum mechanics. To reduce this computational expense for lipids, we have developed a novel machine learning (ML) model to facilitate conformer filtering according to the estimated gas-phase CCS values. Herein we report that the implementation of our CCS knowledge-based approach for conformational sampling resulted in improved structure prediction agreement with experiment by achieving favorable average CCS prediction errors of ∼2% for lipid systems in both the validation set and the test set. Moreover, most of the gas-phase candidate conformations obtained by using CCS focusing achieved lower energy-minimum geometries than the candidate conformations without focusing. Altogether, the implementation of this ML model into our modeling workflow has proven to be beneficial for both the quality of the results and the turnaround time. Finally, while our approach is limited to lipids, it can be readily extended to other molecules of interest.
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Affiliation(s)
- Mithony Keng
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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2
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Cormanich RA, da Silva GD. Autobench V1.0: Benchmarking Automation for Electronic Structure Calculations. J Chem Inf Model 2024; 64:3322-3331. [PMID: 38536765 DOI: 10.1021/acs.jcim.4c00250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
This work reports on new software for automatic conformer energy benchmarking calculations for flexible molecules. The software workflow consists of four parts: conformational search, preoptimization, optimization, and frequency calculations at a higher level and last calculations using several theoretical levels. The software was written to be user-friendly and versatile to be used by nonexperts in computational chemistry. Any theoretical levels available in either Gaussian 16 or ORCA 5 may be applied in the benchmarking study. The workflow will automatically run conformational search calculations and deal with conformers that converge to the same minimum and those that show a negative frequency. At the end of the workflow, the user will have the mean absolute deviations and the most accurate method/DFT functional and basis set in comparison to the benchmark to be applied for the molecular system of interest. Case examples are given at the end of the paper that may help users to get insight into the software's main features.
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Affiliation(s)
- Rodrigo A Cormanich
- Instituto de Química, Departamento de Química Orgânica, Universidade Estadual de Campinas, PO Box 6154, Campinas 13083-970, São Paulo, Brazil
| | - Gabriel D da Silva
- Instituto de Química, Departamento de Química Orgânica, Universidade Estadual de Campinas, PO Box 6154, Campinas 13083-970, São Paulo, Brazil
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3
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Otaki H, Ishiuchi SI, Fujii M, Sugita Y, Yagi K. Similarity scores of vibrational spectra reveal the atomistic structure of pentapeptides in multiple basins. Phys Chem Chem Phys 2024; 26:9906-9914. [PMID: 38477212 DOI: 10.1039/d4cp00064a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Vibrational spectroscopy combined with theoretical calculations is a powerful tool for analyzing the interaction and conformation of peptides at the atomistic level. Nonetheless, identifying the structure becomes increasingly difficult as the peptide size grows large. One example is acetyl-SIVSF-N-methylamide, a capped pentapeptide, whose atomistic structure has remained unknown since its first observation [T. Sekiguchi, M. Tamura, H. Oba, P. Çarçarbal, R. R. Lozada-Garcia, A. Zehnacker-Rentien, G. Grégoire, S. Ishiuchi and M. Fujii, Angew. Chem., Int. Ed., 2018, 57, 5626-5629]. Here, we propose a novel conformational search method, which exploits the structure-spectrum correlation using a similarity score that measures the agreement of theoretical and experimental spectra. Surprisingly, the two conformers have distinctly different energy and geometry. The second conformer is 25 kJ mol-1 higher in energy than the other, lowest-energy conformer. The result implies that there are multiple pathways in the early stage of the folding process: one to the global minimum and the other to a different basin. Once such a structure is established, the second conformer is unlikely to overcome the barrier to produce the most stable structure due to a vastly different hydrogen bond network of the backbone. Our proposed method can characterize the lowest-energy conformer and kinetically trapped, high-energy conformers of complex biomolecules.
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Affiliation(s)
- Hiroki Otaki
- Center for Bioinformatics and Molecular Medicine, Graduate School of Biomedical Sciences, Nagasaki University, 1-14 Bunkyo, Nagasaki, Nagasaki 852-8521, Japan
| | - Shun-Ichi Ishiuchi
- Department of Chemistry, School of Science, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Masaaki Fujii
- School of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Yuji Sugita
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
- Computational Biophysics Research Team, RIKEN Center for Computational Science, 7-1-26 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, 1-6-5 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Kiyoshi Yagi
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
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4
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Das S, Merz KM. Molecular Gas-Phase Conformational Ensembles. J Chem Inf Model 2024; 64:749-760. [PMID: 38206321 DOI: 10.1021/acs.jcim.3c01309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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5
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Han F, Hao D, He X, Wang L, Niu T, Wang J. Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential. J Chem Inf Model 2023; 63:6608-6618. [PMID: 37899502 PMCID: PMC10647024 DOI: 10.1021/acs.jcim.3c01350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 10/31/2023]
Abstract
In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). We first evaluated the combination of these methods (ANI-2X/CG-BS) using two molecule sets. For the 231-molecule set, ab initio calculations were performed at both the ωB97X/6-31G(d) and B3LYP-D3BJ/DZVP levels for accuracy comparison, while for the 8,992-molecule set, ab initio calculations were carried out at the B3LYP-D3BJ/DZVP level. For each molecule in the two molecular sets, up to 10 conformations were generated, which diminish the influence of individual outliers on the performance evaluation. Encouraged by the performance of ANI-2x/CG-BS in these evaluations, we calculated the energy distributions using ANI-2x/CG-BS for more than 27,000 ligands in the protein data bank (PDB). Each ligand has at least one conformation bound to a biological molecule, and this ligand conformation is labeled as a bound conformation. Besides the bound conformations, up to 200 conformations were generated using OpenEye's Omega2 software (https://docs.eyesopen.com/applications/ omega/) for each conformation. We performed a statistical analysis of how the bound conformation energies are distributed in the ensembles for 17,197 PDB ligands that have their bound conformation energies within the energy ranges of the Omega2-generated conformation ensembles. We found that half of the ligands have their relative conformation energy lower than 2.91 kcal/mol for the bound conformations in comparison with the global conformations, and about 90% of the bound conformations are within 10 kcal/mol above the global conformation energies. This information is useful to guide the construction of libraries for shape-based virtual screening and to improve the docking algorithm to efficiently sample bound conformations.
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Affiliation(s)
- Fengyang Han
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, School of Pharmacy, University of
Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Dongxiao Hao
- School
of Electronics and Information Engineering, Ankang University, Ankang 725000, China
| | - Xibing He
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, School of Pharmacy, University of
Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Luxuan Wang
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, School of Pharmacy, University of
Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Taoyu Niu
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, School of Pharmacy, University of
Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Junmei Wang
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, School of Pharmacy, University of
Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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6
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Das S, Dinpazhoh L, Tanemura KA, Merz KM. Rapid and Automated Ab Initio Metabolite Collisional Cross Section Prediction from SMILES Input. J Chem Inf Model 2023; 63:4995-5000. [PMID: 37548575 DOI: 10.1021/acs.jcim.3c00890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
We implemented an ab initio CCS prediction workflow which incrementally refines generated structures using molecular mechanics, a deep learning potential, conformational clustering, and quantum mechanics (QM). Automating intermediate steps for a high performance computing (HPC) environment allows users to input the SMILES structure of small organic molecules and obtain a Boltzmann averaged collisional cross section (CCS) value as output. The CCS of a molecular species is a metric measured by ion mobility spectrometry (IMS) which can improve annotation of untargeted metabolomics experiments. We report only a minor drop in accuracy when we expedite the CCS calculation by replacing the QM geometry refinement step with a single-point energy calculation. Even though the workflow involves stochastic steps (i.e., conformation generation and clustering), the final CCS value was highly reproducible for multiple iterations on L-carnosine. Finally, we illustrate that the gas phase ensembles modeled for the workflow are intermediate files which can be used for the prediction of other properties such as aqueous phase nuclear magnetic resonance chemical shift prediction. The software is available at the following link: https://github.com/DasSusanta/snakemake_ccs.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Laleh Dinpazhoh
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kiyoto Aramis Tanemura
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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7
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Nielson FF, Kay B, Young SJ, Colby SM, Renslow RS, Metz TO. Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics. Metabolites 2023; 13:105. [PMID: 36677030 PMCID: PMC9864474 DOI: 10.3390/metabo13010105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 12/14/2022] [Accepted: 12/30/2022] [Indexed: 01/11/2023] Open
Abstract
Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called "reference-free metabolomics" methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3-7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.
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Affiliation(s)
- Felicity F. Nielson
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA; (F.F.N.); (S.M.C.); (R.S.R.)
| | - Bill Kay
- Pacific Northwest National Laboratory, Advanced Computing, Mathematics, and Data Division, Richland, WA 99354, USA; (B.K.); (S.J.Y.)
| | - Stephen J. Young
- Pacific Northwest National Laboratory, Advanced Computing, Mathematics, and Data Division, Richland, WA 99354, USA; (B.K.); (S.J.Y.)
| | - Sean M. Colby
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA; (F.F.N.); (S.M.C.); (R.S.R.)
| | - Ryan S. Renslow
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA; (F.F.N.); (S.M.C.); (R.S.R.)
| | - Thomas O. Metz
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA; (F.F.N.); (S.M.C.); (R.S.R.)
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8
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Das S, Tanemura KA, Dinpazhoh L, Keng M, Schumm C, Leahy L, Asef CK, Rainey M, Edison AS, Fernández FM, Merz KM. In Silico Collision Cross Section Calculations to Aid Metabolite Annotation. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:750-759. [PMID: 35378036 PMCID: PMC9277703 DOI: 10.1021/jasms.1c00315] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kiyoto Aramis Tanemura
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Laleh Dinpazhoh
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Mithony Keng
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Christina Schumm
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Lydia Leahy
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Carter K Asef
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Markace Rainey
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Arthur S Edison
- Departments of Genetics and Biochemistry, Institute of Bioinformatics and Complex Carbohydrate Center, University of Georgia, 315 Riverbend Road, Athens, Georgia 30602, United States
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
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9
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Platero-Rochart D, González-Alemán R, Hernández-Rodríguez EW, Leclerc F, Caballero J, Montero-Cabrera L. RCDPeaks: memory-efficient density peaks clustering of long molecular dynamics. Bioinformatics 2022; 38:1863-1869. [PMID: 35020783 DOI: 10.1093/bioinformatics/btac021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/06/2021] [Accepted: 01/07/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Density Peaks is a widely spread clustering algorithm that has been previously applied to Molecular Dynamics (MD) simulations. Its conception of cluster centers as elements displaying both a high density of neighbors and a large distance to other elements of high density, particularly fits the nature of a geometrical converged MD simulation. Despite its theoretical convenience, implementations of Density Peaks carry a quadratic memory complexity that only permits the analysis of relatively short trajectories. RESULTS Here, we describe DP+, an exact novel implementation of Density Peaks that drastically reduces the RAM consumption in comparison to the scarcely available alternatives designed for MD. Based on DP+, we developed RCDPeaks, a refined variant of the original Density Peaks algorithm. Through the use of DP+, RCDPeaks was able to cluster a one-million frames trajectory using less than 4.5 GB of RAM, a task that would have taken more than 2 TB and about 3× more time with the fastest and less memory-hunger alternative currently available. Other key features of RCDPeaks include the automatic selection of parameters, the screening of center candidates and the geometrical refining of returned clusters. AVAILABILITY AND IMPLEMENTATION The source code and documentation of RCDPeaks are free and publicly available on GitHub (https://github.com/LQCT/RCDPeaks.git). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daniel Platero-Rochart
- Departamento de Química-Física, Laboratorio de Química Computacional y Teórica (LQCT), Facultad de Química, Universidad de La Habana, La Habana 10400, Cuba
| | - Roy González-Alemán
- Departamento de Química-Física, Laboratorio de Química Computacional y Teórica (LQCT), Facultad de Química, Universidad de La Habana, La Habana 10400, Cuba.,Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Saclay, Gif-sur-Yvette F-91198, France
| | - Erix W Hernández-Rodríguez
- Laboratorio de Bioinformática y Química Computacional (LBQC), Facultad de Medicina, Universidad Católica del Maule, Talca 3460000, Chile.,Escuela de Química y Farmacia, Facultad de Medicina, Universidad Católica del Maule, Talca 3460000, Chile
| | - Fabrice Leclerc
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Saclay, Gif-sur-Yvette F-91198, France
| | - Julio Caballero
- Departamento de Bioinformática, Facultad de Ingeniería, Centro de Bioinformática, Simulación y Modelado (CBSM), Universidad de Talca, Talca 3460000, Chile
| | - Luis Montero-Cabrera
- Departamento de Química-Física, Laboratorio de Química Computacional y Teórica (LQCT), Facultad de Química, Universidad de La Habana, La Habana 10400, Cuba
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10
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Sharma S, Arya A, Cruz R, Cleaves II HJ. Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives. Life (Basel) 2021; 11:1140. [PMID: 34833016 PMCID: PMC8624352 DOI: 10.3390/life11111140] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 12/12/2022] Open
Abstract
Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models.
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Affiliation(s)
- Siddhant Sharma
- Blue Marble Space Institute of Science, Seattle, WA 98154, USA; (S.S.); (A.A.); (R.C.)
- Department of Biochemistry, Deshbandhu College, University of Delhi, New Delhi 110019, India
- Department of Chemistry and Chemical Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Aayush Arya
- Blue Marble Space Institute of Science, Seattle, WA 98154, USA; (S.S.); (A.A.); (R.C.)
- Department of Physics, Lovely Professional University, Jalandhar-Delhi GT Road, Phagwara 144001, India
| | - Romulo Cruz
- Blue Marble Space Institute of Science, Seattle, WA 98154, USA; (S.S.); (A.A.); (R.C.)
- Big Data Laboratory, Information and Communications Technology Center (CTIC), National University of Engineering, Amaru 210, Lima 15333, Peru
| | - Henderson James Cleaves II
- Blue Marble Space Institute of Science, Seattle, WA 98154, USA; (S.S.); (A.A.); (R.C.)
- Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo 152-8550, Japan
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