1
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Braun R, Tfirn M, Ford RM. Listening to life: Sonification for enhancing discovery in biological research. Biotechnol Bioeng 2024. [PMID: 38678506 DOI: 10.1002/bit.28729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 04/05/2024] [Accepted: 04/16/2024] [Indexed: 05/01/2024]
Abstract
Sonification, or the practice of generating sound from data, is a promising alternative or complement to data visualization for exploring research questions in the life sciences. Expressing or communicating data in the form of sound rather than graphs, tables, or renderings can provide a secondary information source for multitasking or remote monitoring purposes or make data accessible when visualizations cannot be used. While popular in astronomy, neuroscience, and geophysics as a technique for data exploration and communication, its potential in the biological and biotechnological sciences has not been fully explored. In this review, we introduce sonification as a concept, some examples of how sonification has been used to address areas of interest in biology, and the history of the technique. We then highlight a selection of biology-related publications that involve sonifications of DNA datasets and protein datasets, sonifications for data collection and interpretation, and sonifications aimed to improve science communication and accessibility. Through this review, we aim to show how sonification has been used both as a discovery tool and a communication tool and to inspire more life-science researchers to incorporate sonification into their own studies.
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Affiliation(s)
- Rhea Braun
- Department of Chemical Engineering, School of Engineering and Applied Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Maxwell Tfirn
- Department of Music, Christopher Newport University, Newport News, Virginia, USA
| | - Roseanne M Ford
- Department of Chemical Engineering, School of Engineering and Applied Sciences, University of Virginia, Charlottesville, Virginia, USA
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2
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Buehler MJ. Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design. ACS ENGINEERING AU 2024; 4:241-277. [PMID: 38646516 PMCID: PMC11027160 DOI: 10.1021/acsengineeringau.3c00058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 04/23/2024]
Abstract
Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design, and manufacturing, including their capacity to work effectively with human language, symbols, code, and numerical data. Here, we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. Moreover, when used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem-solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how fine-tuning endows LLMs with a reasonable understanding of subject area knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty recalling correct information and may hallucinate. We show how this can be addressed using retrieval-augmented Ontological Knowledge Graph strategies. The graph-based strategy helps us not only to discern how the model understands what concepts are important but also how they are related, which significantly improves generative performance and also naturally allows for injection of new and augmented data sources into generative AI algorithms. We find that the additional feature of relatedness provides advantages over regular retrieval augmentation approaches and not only improves LLM performance but also provides mechanistic insights for exploration of a material design process. Illustrated for a use case of relating distinct areas of knowledge, here, music and proteins, such strategies can also provide an interpretable graph structure with rich information at the node, edge, and subgraph level that provides specific insights into mechanisms and relationships. We discuss other approaches to improve generative qualities, including nonlinear sampling strategies and agent-based modeling that offer enhancements over single-shot generations, whereby LLMs are used to both generate content and assess content against an objective target. Examples provided include complex question answering, code generation, and execution in the context of automated force-field development from actively learned density functional theory (DFT) modeling and data analysis.
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Affiliation(s)
- Markus J. Buehler
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Department
of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Department
of Mechanical Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Center
for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
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3
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Singh S, Sahani H. Current Advancement and Future Prospects: Biomedical Nanoengineering. Curr Radiopharm 2024; 17:120-137. [PMID: 38058099 DOI: 10.2174/0118744710274376231123063135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/19/2023] [Accepted: 10/27/2023] [Indexed: 12/08/2023]
Abstract
Recent advancements in biomedicine have seen a significant reliance on nanoengineering, as traditional methods often fall short in harnessing the unique attributes of biomaterials. Nanoengineering has emerged as a valuable approach to enhance and enrich the performance and functionalities of biomaterials, driving research and development in the field. This review emphasizes the most prevalent biomaterials used in biomedicine, including polymers, nanocomposites, and metallic materials, and explores the pivotal role of nanoengineering in developing biomedical treatments and processes. Particularly, the review highlights research focused on gaining an in-depth understanding of material properties and effectively enhancing material performance through molecular dynamics simulations, all from a nanoengineering perspective.
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Affiliation(s)
- Sonia Singh
- Institute of Pharmaceutical Research, GLA University, 17 km Stone, NH-2, Mathura-Delhi Road Mathura, Chaumuhan, Uttar Pradesh, 281406, India
| | - Hrishika Sahani
- Lifecell International Pvt. Ltd., NSP Office, Pearls Business Park, 8th Floor Office No-804, Netaji Subhash Palace Delhi, 110034, India
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4
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Goh T, Song Y, Yonekura T, Obushi N, Den Z, Imizu K, Tomizawa Y, Kondo Y, Miyashima S, Iwamoto Y, Inami M, Chen YW, Nakajima K. In-Depth Quantification of Cell Division and Elongation Dynamics at the Tip of Growing Arabidopsis Roots Using 4D Microscopy, AI-Assisted Image Processing and Data Sonification. PLANT & CELL PHYSIOLOGY 2023; 64:1262-1278. [PMID: 37861079 DOI: 10.1093/pcp/pcad105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 09/05/2023] [Accepted: 09/13/2023] [Indexed: 10/21/2023]
Abstract
One of the fundamental questions in plant developmental biology is how cell proliferation and cell expansion coordinately determine organ growth and morphology. An amenable system to address this question is the Arabidopsis root tip, where cell proliferation and elongation occur in spatially separated domains, and cell morphologies can easily be observed using a confocal microscope. While past studies revealed numerous elements of root growth regulation including gene regulatory networks, hormone transport and signaling, cell mechanics and environmental perception, how cells divide and elongate under possible constraints from cell lineages and neighboring cell files has not been analyzed quantitatively. This is mainly due to the technical difficulties in capturing cell division and elongation dynamics at the tip of growing roots, as well as an extremely labor-intensive task of tracing the lineages of frequently dividing cells. Here, we developed a motion-tracking confocal microscope and an Artificial Intelligence (AI)-assisted image-processing pipeline that enables semi-automated quantification of cell division and elongation dynamics at the tip of vertically growing Arabidopsis roots. We also implemented a data sonification tool that facilitates human recognition of cell division synchrony. Using these tools, we revealed previously unnoted lineage-constrained dynamics of cell division and elongation, and their contribution to the root zonation boundaries.
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Affiliation(s)
| | - Yu Song
- College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577 Japan
| | - Takaaki Yonekura
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192 Japan
- Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Tokyo, 113-0033 Japan
| | - Noriyasu Obushi
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Tokyo, 153-8904 Japan
| | - Zeping Den
- College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577 Japan
| | - Katsutoshi Imizu
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192 Japan
| | - Yoko Tomizawa
- The Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji, Okazaki, Aichi, 444-8787 Japan
| | - Yohei Kondo
- The Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji, Okazaki, Aichi, 444-8787 Japan
| | - Shunsuke Miyashima
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192 Japan
| | - Yutaro Iwamoto
- College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577 Japan
- Faculty of Information and Communication Engineering, Osaka Electro-Communication University, 18-8 Hatsucho, Neyagawa, Osaka, 572-8530 Japan
| | - Masahiko Inami
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Tokyo, 153-8904 Japan
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192 Japan
| | - Yen-Wei Chen
- College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577 Japan
| | - Keiji Nakajima
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192 Japan
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5
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Nakajima K, Higaki T, Ueda T, Inami M. Gaining New Insights in Plant Biology through Human-Machine Collaboration. PLANT & CELL PHYSIOLOGY 2023; 64:1257-1261. [PMID: 37952100 DOI: 10.1093/pcp/pcad144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/14/2023]
Affiliation(s)
- Keiji Nakajima
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192 Japan
| | - Takumi Higaki
- Department of Biological Sciences, Graduate School of Science and Technology, Kumamoto University, Kurokami 2-39-1, Chuo-ku, Kumamoto, 860-8555 Japan
- International Research Organization for Advanced Science and Technology, Kumamoto University, Kurokami 2-39-1, Chuo-ku, Kumamoto, 860-8555 Japan
| | - Takashi Ueda
- Division of Cellular Dynamics, National Institute for Basic Biology, Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
- Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Studies), Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
| | - Masahiko Inami
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Tokyo, 153-8904 Japan
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6
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Liu Y, Li Y, Wang Q, Ren J, Ye C, Li F, Ling S, Liu Y, Ling D. Biomimetic Silk Architectures Outperform Animal Horns in Strength and Toughness. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2303058. [PMID: 37596721 PMCID: PMC10582412 DOI: 10.1002/advs.202303058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/16/2023] [Indexed: 08/20/2023]
Abstract
Structural biomimicry is an intelligent approach for developing lightweight, strong, and tough materials (LSTMs). Current fabrication technologies, such as 3D printing and two-photon lithography often face challenges in constructing complex interlaced structures, such as the sinusoidal crossed herringbone structure that contributes to the ultrahigh strength and fracture toughness of the dactyl club of peacock mantis shrimps. Herein, bioinspired LSTMs with laminated or herringbone structures is reported, by combining textile processing and silk fiber "welding" techniques. The resulting biomimetic silk LSTMs (BS-LSTMs) exhibit a remarkable combination of lightweight with a density of 0.6-0.9 g cm-3 , while also being 1.5 times stronger and 16 times more durable than animal horns. These findings demonstrate that BS-LSTMs are among the toughest natural materials made from silk proteins. Finite element simulations further reveal that the fortification and hardening of BS-LSTMs arise primarily from the hierarchical organization of silk fibers and mechanically transferable meso-interfaces. This study highlights the rational, cost-effective, controllable mesostructure, and transferable strategy of integrating textile processing and fiber "welding" techniques for the fabrication of BS-LSTMs with advantageous structural and mechanical properties. These findings have significant implications for a wide range of applications in biomedicine, mechanical engineering, intelligent textiles, aerospace industries, and beyond.
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Affiliation(s)
- Yawen Liu
- Frontiers Science Center for Transformative MoleculesSchool of Chemistry and Chemical EngineeringState Key Laboratory of Oncogenes and Related GenesNational Center for Translational MedicineShanghai Jiao Tong UniversityShanghai200240China
- School of Physical Science and TechnologyShanghaiTech University393 Middle Huaxia RoadShanghai201210China
| | - Yushu Li
- Laboratory for Multiscale Mechanics and Medical ScienceSV LABSchool of AerospaceXi'an Jiaotong UniversityXi'an710049China
| | - Qiyue Wang
- Frontiers Science Center for Transformative MoleculesSchool of Chemistry and Chemical EngineeringState Key Laboratory of Oncogenes and Related GenesNational Center for Translational MedicineShanghai Jiao Tong UniversityShanghai200240China
| | - Jing Ren
- School of Physical Science and TechnologyShanghaiTech University393 Middle Huaxia RoadShanghai201210China
| | - Chao Ye
- School of Physical Science and TechnologyShanghaiTech University393 Middle Huaxia RoadShanghai201210China
| | - Fangyuan Li
- Frontiers Science Center for Transformative MoleculesSchool of Chemistry and Chemical EngineeringState Key Laboratory of Oncogenes and Related GenesNational Center for Translational MedicineShanghai Jiao Tong UniversityShanghai200240China
| | - Shengjie Ling
- School of Physical Science and TechnologyShanghaiTech University393 Middle Huaxia RoadShanghai201210China
- Shanghai Clinical Research and Trial CenterShanghai201210China
| | - Yilun Liu
- Laboratory for Multiscale Mechanics and Medical ScienceSV LABSchool of AerospaceXi'an Jiaotong UniversityXi'an710049China
| | - Daishun Ling
- Frontiers Science Center for Transformative MoleculesSchool of Chemistry and Chemical EngineeringState Key Laboratory of Oncogenes and Related GenesNational Center for Translational MedicineShanghai Jiao Tong UniversityShanghai200240China
- World Laureates Association (WLA) LaboratoriesShanghai201203China
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7
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Álvarez-Meza AM, Torres-Cardona HF, Orozco-Alzate M, Pérez-Nastar HD, Castellanos-Dominguez G. Affective Neural Responses Sonified through Labeled Correlation Alignment. SENSORS (BASEL, SWITZERLAND) 2023; 23:5574. [PMID: 37420740 DOI: 10.3390/s23125574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/10/2023] [Accepted: 06/11/2023] [Indexed: 07/09/2023]
Abstract
Sound synthesis refers to the creation of original acoustic signals with broad applications in artistic innovation, such as music creation for games and videos. Nonetheless, machine learning architectures face numerous challenges when learning musical structures from arbitrary corpora. This issue involves adapting patterns borrowed from other contexts to a concrete composition objective. Using Labeled Correlation Alignment (LCA), we propose an approach to sonify neural responses to affective music-listening data, identifying the brain features that are most congruent with the simultaneously extracted auditory features. For dealing with inter/intra-subject variability, a combination of Phase Locking Value and Gaussian Functional Connectivity is employed. The proposed two-step LCA approach embraces a separate coupling stage of input features to a set of emotion label sets using Centered Kernel Alignment. This step is followed by canonical correlation analysis to select multimodal representations with higher relationships. LCA enables physiological explanation by adding a backward transformation to estimate the matching contribution of each extracted brain neural feature set. Correlation estimates and partition quality represent performance measures. The evaluation uses a Vector Quantized Variational AutoEncoder to create an acoustic envelope from the tested Affective Music-Listening database. Validation results demonstrate the ability of the developed LCA approach to generate low-level music based on neural activity elicited by emotions while maintaining the ability to distinguish between the acoustic outputs.
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Affiliation(s)
| | | | - Mauricio Orozco-Alzate
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia
| | - Hernán Darío Pérez-Nastar
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia
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8
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Buehler MJ. Unsupervised cross-domain translation via deep learning and adversarial attention neural networks and application to music-inspired protein designs. PATTERNS (NEW YORK, N.Y.) 2023; 4:100692. [PMID: 36960446 PMCID: PMC10028431 DOI: 10.1016/j.patter.2023.100692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/02/2023] [Accepted: 01/24/2023] [Indexed: 02/16/2023]
Abstract
Taking inspiration from nature about how to design materials has been a fruitful approach, used by humans for millennia. In this paper we report a method that allows us to discover how patterns in disparate domains can be reversibly related using a computationally rigorous approach, the AttentionCrossTranslation model. The algorithm discovers cycle- and self-consistent relationships and offers a bidirectional translation of information across disparate knowledge domains. The approach is validated with a set of known translation problems, and then used to discover a mapping between musical data-based on the corpus of note sequences in J.S. Bach's Goldberg Variations created in 1741-and protein sequence data-information sampled more recently. Using protein folding algorithms, 3D structures of the predicted protein sequences are generated, and their stability is validated using explicit solvent molecular dynamics. Musical scores generated from protein sequences are sonified and rendered into audible sound.
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Affiliation(s)
- Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
- Corresponding author
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9
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Nepal D, Kang S, Adstedt KM, Kanhaiya K, Bockstaller MR, Brinson LC, Buehler MJ, Coveney PV, Dayal K, El-Awady JA, Henderson LC, Kaplan DL, Keten S, Kotov NA, Schatz GC, Vignolini S, Vollrath F, Wang Y, Yakobson BI, Tsukruk VV, Heinz H. Hierarchically structured bioinspired nanocomposites. NATURE MATERIALS 2023; 22:18-35. [PMID: 36446962 DOI: 10.1038/s41563-022-01384-1] [Citation(s) in RCA: 80] [Impact Index Per Article: 80.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 09/17/2022] [Indexed: 06/16/2023]
Abstract
Next-generation structural materials are expected to be lightweight, high-strength and tough composites with embedded functionalities to sense, adapt, self-repair, morph and restore. This Review highlights recent developments and concepts in bioinspired nanocomposites, emphasizing tailoring of the architecture, interphases and confinement to achieve dynamic and synergetic responses. We highlight cornerstone examples from natural materials with unique mechanical property combinations based on relatively simple building blocks produced in aqueous environments under ambient conditions. A particular focus is on structural hierarchies across multiple length scales to achieve multifunctionality and robustness. We further discuss recent advances, trends and emerging opportunities for combining biological and synthetic components, state-of-the-art characterization and modelling approaches to assess the physical principles underlying nature-inspired design and mechanical responses at multiple length scales. These multidisciplinary approaches promote the synergetic enhancement of individual materials properties and an improved predictive and prescriptive design of the next era of structural materials at multilength scales for a wide range of applications.
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Affiliation(s)
- Dhriti Nepal
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, OH, USA.
| | - Saewon Kang
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Katarina M Adstedt
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Krishan Kanhaiya
- Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, CO, USA
| | - Michael R Bockstaller
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - L Catherine Brinson
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA
| | - Markus J Buehler
- Department of Civil and Environmental Engineering, MIT, Cambridge, MA, USA
| | - Peter V Coveney
- Department of Chemistry, University College London, London, UK
| | - Kaushik Dayal
- Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jaafar A El-Awady
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Luke C Henderson
- Institute for Frontier Materials, Deakin University, Waurn Ponds, Victoria, Australia
| | - David L Kaplan
- Department of Biomedical Engineering, Tufts University, Medford, MA, USA
| | - Sinan Keten
- Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA
| | - Nicholas A Kotov
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - George C Schatz
- Department of Chemistry, Northwestern University, Evanston, IL, USA
| | - Silvia Vignolini
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | | | - Yusu Wang
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Boris I Yakobson
- Department of Materials Science and Nanoengineering, Rice University, Houston, TX, USA
- Department of Chemistry, Rice University, Houston, TX, USA
| | - Vladimir V Tsukruk
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Hendrik Heinz
- Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, CO, USA.
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10
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Hu Y, Buehler MJ. End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification. ACS NANO 2022; 16:20656-20670. [PMID: 36416536 DOI: 10.1021/acsnano.2c07681] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The prediction of mechanical and dynamical properties of proteins is an important frontier, especially given the greater availability of proteins structures. Here we report a series of models that provide end-to-end predictions of nanodynamical properties of proteins, focused on high-throughput normal mode predictions directly from the amino acid sequence. Using neural network models within the family of Natural Language Processing and graph-based methods, we offer atomistically based mechanistic predictions of key protein mechanical features. The models include an end-to-end long short-term memory (LSTM) model, an end-to-end transformer model, a graph-based transformer model, and an equivariant graph neural network. All four models show exceptional performance, with the graph-based transformer architecture offering the best results but at the cost of requiring a graph structure as input. Conversely, the LSTM and transformer models offer end-to-end sequence-to-property prediction capabilities, providing efficient avenues for protein engineering, analysis, and design. We compare our results against published data based on a Principal Neighborhood Aggregation graph neural network, revealing that the transformer model offers better performance while also being able to predict a large set of the first 64 normal mode frequencies, simultaneously. The use of the end-to-end transformer model may facilitate other downstream applications through the use of transfer learning, and it offers a comprehensive prediction of dynamical properties without any structural knowledge, directly from the amino acid sequence. We demonstrate a potential application in scientific sonification, where the normal mode frequencies are transposed to generate audible signals for a detailed analysis of subtle changes of protein sequences.
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Affiliation(s)
- Yiwen Hu
- Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
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11
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Buehler MJ. Multiscale Modeling at the Interface of Molecular Mechanics and Natural Language through Attention Neural Networks. Acc Chem Res 2022; 55:3387-3403. [PMID: 36378952 DOI: 10.1021/acs.accounts.2c00330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Humans are continually bombarded with massive amounts of data. To deal with this influx of information, we use the concept of attention in order to perceive the most relevant input from vision, hearing, touch, and others. Thereby, the complex ensemble of signals is used to generate output by querying the processed data in appropriate ways. Attention is also the hallmark of the development of scientific theories, where we elucidate which parts of a problem are critical, often expressed through differential equations. In this Account we review the emergence of attention-based neural networks as a class of approaches that offer many opportunities to describe materials across scales and modalities, including how universal building blocks interact to yield a set of material properties. In fact, the self-assembly of hierarchical, structurally complex, and multifunctional biomaterials remains a grand challenge in modeling, theory, and experiment. Expanding from the process by which material building blocks physically interact to form a type of material, in this Account we view self-assembly as both the functional emergence of properties from interacting building blocks as well as the physical process by which elementary building blocks interact and yield structure and, thereby, functions. This perspective, integrated through the theory of materiomics, allows us to solve multiscale problems with a first-principles-based computational approach based on attention-based neural networks that transform information to feature to property while providing a flexible modeling approach that can integrate theory, simulation, and experiment. Since these models are based on a natural language framework, they offer various benefits including incorporation of general domain knowledge via general-purpose pretraining, which can be accomplished without labeled data or large amounts of lower-quality data. Pretrained models then offer a general-purpose platform that can be fine-tuned to adapt these models to make specific predictions, often with relatively little labeled data. The transferrable power of the language-based modeling approach realizes a neural olog description, where mathematical categorization is learned by multiheaded attention, without domain knowledge in its formulation. It can hence be applied to a range of complex modeling tasks─such as physical field predictions, molecular properties, or structure predictions, all using an identical formulation. This offers a complementary modeling approach that is already finding numerous applications, with great potential to solve complex assembly problems, enabling us to learn, build, and utilize functional categorization of how building blocks yield a range of material functions. In this Account, we demonstrate the approach in various application areas, including protein secondary structure prediction and prediction of normal-mode frequencies as well as predicting mechanical fields near cracks. Unifying these diverse problem areas is the building block approach, where the models are based on a universally applicable platform that offers benefits ranging from transferability, interpretability, and cross-domain pollination of knowledge as exemplified through a transformer model applied to predict how musical compositions infer de novo protein structures. We discuss future potentialities of this approach for a variety of material phenomena across scales, including the use in multiparadigm modeling schemes.
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Affiliation(s)
- Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States.,Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
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12
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Zhang W, Huang W, Tan J, Guo Q, Wu B. Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects. CHEMOSPHERE 2022; 308:136447. [PMID: 36116627 DOI: 10.1016/j.chemosphere.2022.136447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/08/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Qingwei Guo
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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13
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Algorithmically-guided discovery of viral epitopes via linguistic parsing: Problem formulation and solving by soft computing. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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14
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Qing R, Hao S, Smorodina E, Jin D, Zalevsky A, Zhang S. Protein Design: From the Aspect of Water Solubility and Stability. Chem Rev 2022; 122:14085-14179. [PMID: 35921495 PMCID: PMC9523718 DOI: 10.1021/acs.chemrev.1c00757] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Indexed: 12/13/2022]
Abstract
Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.
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Affiliation(s)
- Rui Qing
- State
Key Laboratory of Microbial Metabolism, School of Life Sciences and
Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- The
David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Shilei Hao
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Key
Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Eva Smorodina
- Department
of Immunology, University of Oslo and Oslo
University Hospital, Oslo 0424, Norway
| | - David Jin
- Avalon GloboCare
Corp., Freehold, New Jersey 07728, United States
| | - Arthur Zalevsky
- Laboratory
of Bioinformatics Approaches in Combinatorial Chemistry and Biology, Shemyakin−Ovchinnikov Institute of Bioorganic
Chemistry RAS, Moscow 117997, Russia
| | - Shuguang Zhang
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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15
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Yang Z, Buehler MJ. High-Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning. SMALL METHODS 2022; 6:e2200537. [PMID: 35905488 DOI: 10.1002/smtd.202200537] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/07/2022] [Indexed: 06/15/2023]
Abstract
3D graphene assemblies are proposed as solutions to meet the goal toward efficient utilization of 2D graphene sheets, showing excellent performances in applications such as mechanical support, energy storage, and electrochemical catalysis. However, given the diversity and complexity of possible graphene 3D structures, there does not yet exist a systematic approach that can generate target 3D shapes and also, evaluate their performance. Here high-throughput data generation is combined with artificial intelligence approaches to realize rapid structure formation and property quantification of 3D graphene foams with mathematically controlled topologies, driven by molecular dynamics simulations. More than 4000 different foam structures are created, which feature diverse topologies that contain potential pathways for small molecules and auxetic structures with negative Poisson's ratio. Empowered by machine learning (ML) algorithms including graph neural networks, not only global properties such as elastic moduli, but also local behaviors such as atomic stress can be predicted and optimized based on their atomic structure, bypassing expensive atomistic simulations. The key findings of the research reported in this paper include a high-throughput virtual framework of generating diverse 3D graphene assemblies with mechanical performances quantification, and highly efficient methods of evaluating physical properties based on ML.
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Affiliation(s)
- Zhenze Yang
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Center for Materials Science and Engineering, Cambridge, MA, 02139, USA
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16
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Yu C, Tseng B, Yang Z, Tung C, Zhao E, Ren Z, Yu S, Chen P, Chen C, Buehler MJ. Hierarchical Multiresolution Design of Bioinspired Structural Composites Using Progressive Reinforcement Learning. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202200459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Chi‐Hua Yu
- Department of Engineering Science National Cheng Kung University No. 1, University Rd. Tainan 701 Taiwan
- Laboratory for Atomistic and Molecular Mechanics Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 USA
| | - Bor‐Yann Tseng
- Department of Engineering Science National Cheng Kung University No. 1, University Rd. Tainan 701 Taiwan
| | - Zhenze Yang
- Laboratory for Atomistic and Molecular Mechanics Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 USA
- Department of Materials Science and Engineering Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 USA
| | - Cheng‐Che Tung
- Department of Materials Science and Engineering National Tsing Hua University No.101, Section 2, Kuang‐Fu Road Hsinchu 300044 Taiwan
| | - Elena Zhao
- Deerfield Academy 7 Boyden Ln Deerfield MA 01342 USA
| | - Zhi‐Fan Ren
- Department of Chemical Engineering National Cheng Kung University No. 1, University Rd. Tainan 701 Taiwan
| | - Sheng‐Sheng Yu
- Department of Chemical Engineering National Cheng Kung University No. 1, University Rd. Tainan 701 Taiwan
| | - Po‐Yu Chen
- Department of Materials Science and Engineering National Tsing Hua University No.101, Section 2, Kuang‐Fu Road Hsinchu 300044 Taiwan
| | - Chuin‐Shan Chen
- Department of Civil Engineering National Taiwan University No. 1, Sec. 4, Roosevelt Rd. Taipei 10617 Taiwan
- Department of Materials Science and Engineering National Taiwan University No. 1, Sec. 4, Roosevelt Rd. Taipei 10617 Taiwan
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 USA
- Department of Materials Science and Engineering Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 USA
- Center for Computational Science and Engineering, Schwarzman College of Computing Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge MA 02139 USA
- Center for Materials Science and Engineering 77 Massachusetts Ave Cambridge MA 02139 USA
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17
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Villalobos-Alva J, Ochoa-Toledo L, Villalobos-Alva MJ, Aliseda A, Pérez-Escamirosa F, Altamirano-Bustamante NF, Ochoa-Fernández F, Zamora-Solís R, Villalobos-Alva S, Revilla-Monsalve C, Kemper-Valverde N, Altamirano-Bustamante MM. Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field. Front Bioeng Biotechnol 2022; 10:788300. [PMID: 35875501 PMCID: PMC9301016 DOI: 10.3389/fbioe.2022.788300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 05/25/2022] [Indexed: 11/23/2022] Open
Abstract
Proteins are some of the most fascinating and challenging molecules in the universe, and they pose a big challenge for artificial intelligence. The implementation of machine learning/AI in protein science gives rise to a world of knowledge adventures in the workhorse of the cell and proteome homeostasis, which are essential for making life possible. This opens up epistemic horizons thanks to a coupling of human tacit-explicit knowledge with machine learning power, the benefits of which are already tangible, such as important advances in protein structure prediction. Moreover, the driving force behind the protein processes of self-organization, adjustment, and fitness requires a space corresponding to gigabytes of life data in its order of magnitude. There are many tasks such as novel protein design, protein folding pathways, and synthetic metabolic routes, as well as protein-aggregation mechanisms, pathogenesis of protein misfolding and disease, and proteostasis networks that are currently unexplored or unrevealed. In this systematic review and biochemical meta-analysis, we aim to contribute to bridging the gap between what we call binomial artificial intelligence (AI) and protein science (PS), a growing research enterprise with exciting and promising biotechnological and biomedical applications. We undertake our task by exploring "the state of the art" in AI and machine learning (ML) applications to protein science in the scientific literature to address some critical research questions in this domain, including What kind of tasks are already explored by ML approaches to protein sciences? What are the most common ML algorithms and databases used? What is the situational diagnostic of the AI-PS inter-field? What do ML processing steps have in common? We also formulate novel questions such as Is it possible to discover what the rules of protein evolution are with the binomial AI-PS? How do protein folding pathways evolve? What are the rules that dictate the folds? What are the minimal nuclear protein structures? How do protein aggregates form and why do they exhibit different toxicities? What are the structural properties of amyloid proteins? How can we design an effective proteostasis network to deal with misfolded proteins? We are a cross-functional group of scientists from several academic disciplines, and we have conducted the systematic review using a variant of the PICO and PRISMA approaches. The search was carried out in four databases (PubMed, Bireme, OVID, and EBSCO Web of Science), resulting in 144 research articles. After three rounds of quality screening, 93 articles were finally selected for further analysis. A summary of our findings is as follows: regarding AI applications, there are mainly four types: 1) genomics, 2) protein structure and function, 3) protein design and evolution, and 4) drug design. In terms of the ML algorithms and databases used, supervised learning was the most common approach (85%). As for the databases used for the ML models, PDB and UniprotKB/Swissprot were the most common ones (21 and 8%, respectively). Moreover, we identified that approximately 63% of the articles organized their results into three steps, which we labeled pre-process, process, and post-process. A few studies combined data from several databases or created their own databases after the pre-process. Our main finding is that, as of today, there are no research road maps serving as guides to address gaps in our knowledge of the AI-PS binomial. All research efforts to collect, integrate multidimensional data features, and then analyze and validate them are, so far, uncoordinated and scattered throughout the scientific literature without a clear epistemic goal or connection between the studies. Therefore, our main contribution to the scientific literature is to offer a road map to help solve problems in drug design, protein structures, design, and function prediction while also presenting the "state of the art" on research in the AI-PS binomial until February 2021. Thus, we pave the way toward future advances in the synthetic redesign of novel proteins and protein networks and artificial metabolic pathways, learning lessons from nature for the welfare of humankind. Many of the novel proteins and metabolic pathways are currently non-existent in nature, nor are they used in the chemical industry or biomedical field.
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Affiliation(s)
- Jalil Villalobos-Alva
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Luis Ochoa-Toledo
- Instituto de Ciencias Aplicadas y Tecnología (ICAT), Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | - Mario Javier Villalobos-Alva
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Atocha Aliseda
- Instituto de Investigaciones Filosóficas, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | - Fernando Pérez-Escamirosa
- Instituto de Ciencias Aplicadas y Tecnología (ICAT), Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | | | - Francine Ochoa-Fernández
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Ricardo Zamora-Solís
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Sebastián Villalobos-Alva
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Cristina Revilla-Monsalve
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Nicolás Kemper-Valverde
- Instituto de Ciencias Aplicadas y Tecnología (ICAT), Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | - Myriam M. Altamirano-Bustamante
- Unidad de Investigación en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
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18
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Construction of Music Intelligent Creation Model Based on Convolutional Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2854066. [PMID: 35837219 PMCID: PMC9276503 DOI: 10.1155/2022/2854066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/05/2022] [Accepted: 06/11/2022] [Indexed: 11/18/2022]
Abstract
The application of machine learning technology to intelligent music creation has become a very important field in music creation. The main current research on music intelligent creation methods uses fixed coding steps in audio data, which lead to weak feature expression ability. Based on convolutional neural network theory, this paper proposes a deep music intelligent creation method. The model uses a convolutional recurrent neural network to generate an effective hash code, first preprocesses the music signal to obtain a Mel spectrogram, and then inputs it into a pretrained CNN to extract from its convolutional layers. The network space details and the semantic information of musical symbols are used to construct the feature map sequence using selection strategy for the feature map of each convolutional layer, so as to solve the problem of high data feature dimension and poor recognition performance. In the simulation process, the Mel cepstral coefficient method (MFCC) was used to extract the features of four different music signals, and the features that could represent each signal were extracted through the convolutional neural network, and the continuous signals were discretized and reduced. The experimental results show that the high-dimensional music data are dimensionally reduced at the data level. After the data are compressed, the correct rate of intelligent creation is as high as 98%, and the characteristic signal distortion rate is reduced to 5% below, effectively improving the algorithm performance and the ability to create music intelligently.
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19
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Guo K, Buehler MJ. Rapid prediction of protein natural frequencies using graph neural networks. DIGITAL DISCOVERY 2022; 1:277-285. [PMID: 35769204 PMCID: PMC9189858 DOI: 10.1039/d1dd00007a] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 03/28/2022] [Indexed: 11/21/2022]
Abstract
Natural vibrational frequencies of proteins help to correlate functional shifts with sequence or geometric variations that lead to negligible changes in protein structures, such as point mutations related to disease lethality or medication effectiveness. Normal mode analysis is a well-known approach to accurately obtain protein natural frequencies. However, it is not feasible when high-resolution protein structures are not available or time consuming to obtain. Here we provide a machine learning model to directly predict protein frequencies from primary amino acid sequences and low-resolution structural features such as contact or distance maps. We utilize a graph neural network called principal neighborhood aggregation, trained with the structural graphs and normal mode frequencies of more than 34 000 proteins from the protein data bank. combining with existing contact/distance map prediction tools, this approach enables an end-to-end prediction of the frequency spectrum of a protein given its primary sequence.
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Affiliation(s)
- Kai Guo
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology 77 Massachusetts Ave. 1-165 Cambridge Massachusetts 02139 USA +1 617 452 2750
- Institute of High Performance Computing, ASTAR Singapore 138632 Singapore
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology 77 Massachusetts Ave. 1-165 Cambridge Massachusetts 02139 USA +1 617 452 2750
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge Massachusetts 02139 USA
- Center for Materials Science and Engineering 77 Massachusetts Ave Cambridge Massachusetts 02139 USA
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20
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Analysing bio-art’s epistemic landscape: from metaphoric to post-metaphoric structure. BIOSOCIETIES 2022. [DOI: 10.1057/s41292-022-00270-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
AbstractSince its emergence, bio-art has developed numerous metaphors central to the transfer of concepts of modern biology, genetics, and genomics to the public domain that reveal several cultural, ethical, and social variations in their related themes. This article assumes that a general typology of metaphors developed by practices related to bio-art can be categorised into two categories: pictorial and operational metaphors. Through these, information regarding several biological issues is transferred to the public arena. Based on the analysis, this article attempts to answer the following questions: How does bio-art develop metaphors to advance epistemic and discursive agendas that constitute public understanding of a set of deeply problematic assumptions regarding how today’s biology operates? Under the influence of today’s synthetic biology, could bio-media operationally reframe these epistemic agendas by reframing complex and multi-layered metaphors towards post-metaphoric structures? Finally, what are the scientific, cultural, and social implications of reframing?
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21
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Milazzo M, Anderson GI, Buehler MJ. Bioinspired translation of classical music into de novoprotein structures using deep learning and molecular modeling. BIOINSPIRATION & BIOMIMETICS 2021; 17:015001. [PMID: 34700310 DOI: 10.1088/1748-3190/ac338a] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/26/2021] [Indexed: 05/25/2023]
Abstract
Architected biomaterials, as well as sound and music, are constructed from small building blocks that are assembled across time- and length-scales. Here we present a novel deep learning-enabled integrated algorithmic workflow to merge the two concepts for radical discovery ofde novoprotein materials, exploiting musical creativity as the foundation, and extrapolating through a recursive method to increase protein complexity by successively injecting protein chemistry into the process. Indeed, music is one of the few universal expressions that can create bridges between cultures, find associations between seemingly unrelated concepts, and can be used as a novel way to generate bio-inspired designs that derive functions from the imaginations of the creative mind. Earlier work has offered a pathway to convert proteins into sound, and sound into proteins. Here we build on this paradigm and translate a piece of classical music into matter. Based on Bach's Goldberg variations, we offer a series of case studies to convert the musical data imagined by the composer into protein design, and folded into a 3D structure using deep learning. The quest we seek to address is to identify semblances, or memories, or information content in such musical creation, that offers new insights into pattern relationships between distinct manifestations of information. Using basic local alignment search tool analysis, we find that several fragments of the new proteins display similarities to existing protein sequences found in proteobacteria among other organisms, especially in regions of low complexity and repetitive motifs. The resulting protein forms the basis for iterative musical composition, and an evolutionary paradigm that defines a variational pathway for melodic development, complementing conventional creative or mathematical methods. This paper broadens the concept of what is understood as bio-inspiration to include a broad array of systems created by humans, animals, or other natural mechanisms.
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Affiliation(s)
- Mario Milazzo
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, United States of America
| | - Grace I Anderson
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, United States of America
- Center for Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, United States of America
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, United States of America
- Center for Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, United States of America
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, United States of America
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22
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ColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences. J Mech Behav Biomed Mater 2021; 125:104921. [PMID: 34758444 DOI: 10.1016/j.jmbbm.2021.104921] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/21/2021] [Indexed: 11/22/2022]
Abstract
Collagen is the most abundant structural protein in humans, with dozens of sequence variants accounting for over 30% of the protein in an animal body. The fibrillar and hierarchical arrangements of collagen are critical in providing mechanical properties with high strength and toughness. Due to this ubiquitous role in human tissues, collagen-based biomaterials are commonly used for tissue repairs and regeneration, requiring chemical and thermal stability over a range of temperatures during materials preparation ex vivo and subsequent utility in vivo. Collagen unfolds from a triple helix to a random coil structure during a temperature interval in which the midpoint or Tm is used as a measure to evaluate the thermal stability of the molecules. However, finding a robust framework to facilitate the design of a specific collagen sequence to yield a specific Tm remains a challenge, including using conventional molecular dynamics modeling. Here we propose a de novo framework to provide a model that outputs the Tm values of input collagen sequences by incorporating deep learning trained on a large data set of collagen sequences and corresponding Tm values. By using this framework, we are able to quickly evaluate how mutations and order in the primary sequence affect the stability of collagen triple helices. Specifically, we confirm that mutations to glycines, mutations in the middle of a sequence, and short sequence lengths cause the greatest drop in Tm values.
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23
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Wang N, Xu H, Xu F, Cheng L. The algorithmic composition for music copyright protection under deep learning and blockchain. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107763] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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24
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Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model. FORCES IN MECHANICS 2021. [DOI: 10.1016/j.finmec.2021.100054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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25
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Tay NW, Liu F, Wang C, Zhang H, Zhang P, Chen YZ. Protein music of enhanced musicality by music style guided exploration of diverse amino acid properties. Heliyon 2021; 7:e07933. [PMID: 34632134 PMCID: PMC8488493 DOI: 10.1016/j.heliyon.2021.e07933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/19/2021] [Accepted: 09/02/2021] [Indexed: 11/27/2022] Open
Abstract
Inspired by the traceable analogies between protein sequences and music notes, protein music has been composed from amino acid sequences for popularizing science and sourcing melodies. Despite the continuous development of protein-to-music algorithms, the musicality of protein music lags far behind human music. Musicality may be enhanced by fine-tuned protein-to-music mapping to the features of a specific music style. We analyzed the features of a music style (Fantasy-Impromptu style), and used the quantized musical features to guide broad exploration of diverse amino acid properties (104 properties, sequence patterns and variations) for developing a novel protein-to-music algorithm of enhanced musicality. This algorithm was applied to 18 proteins of various biological functions. The derived music pieces consistently exhibited enhanced musicality with respect to existing protein music. Music style guided exploration of diverse amino acid properties enable protein music composition of enhanced musicality, which may be further developed and applied to a wider variety of music styles.
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Affiliation(s)
- Nicole WanNi Tay
- Raffles Institution, 1 Raffles Institution Ln, 575954, Singapore
| | - Fanxi Liu
- Raffles Institution, 1 Raffles Institution Ln, 575954, Singapore
| | - Chaoxin Wang
- Department of Computer Science, Kansas State University, Manhattan, KS, 66506, USA
| | - Hui Zhang
- School of Arts, Minnan Normal University, Zhengzhou, 363000, China
| | - Peng Zhang
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543, Singapore
| | - Yu Zong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543, Singapore
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo, 315211, China
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26
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Milazzo M, Buehler MJ. Designing and fabricating materials from fire using sonification and deep learning. iScience 2021; 24:102873. [PMID: 34409268 PMCID: PMC8361214 DOI: 10.1016/j.isci.2021.102873] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/28/2021] [Accepted: 07/14/2021] [Indexed: 11/26/2022] Open
Abstract
Fire has fascinated humankind since the prehistoric era. Rooted in the interactions between sound and flames, here we report a method to use fire for a variety of purposes, including sonification, art, and the design and manufacturing nature-inspired materials. We present a method to sonify fire, thereby offering a translation from the silent nature of flames, to represent audible information and to generate de novo flame images. To realize material specimen derived from fire, we use the autoencoder to generate image stacks to yield continuous 3D geometries that are manufactured using 3D printing. This represents the first generation of nature-inspired materials from fire and can be a platform to be used for other natural phenomena in the quest for de novo architectures, geometries, and design ideas, thus creating additional directions in artistic and scientific research through the creative manipulation of data with structural similarities across fields.
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Affiliation(s)
- Mario Milazzo
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue, Rm. 1-165, Cambridge, MA 02139, USA
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue, Rm. 1-165, Cambridge, MA 02139, USA
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
- Center for Materials Science and Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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Zhou S, Jin K, Buehler MJ. Understanding Plant Biomass via Computational Modeling. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2003206. [PMID: 32945027 DOI: 10.1002/adma.202003206] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/13/2020] [Indexed: 06/11/2023]
Abstract
Plant biomass, especially wood, has been used for structural materials since ancient times. It is also showing great potential for new structural materials and it is the major feedstock for the emerging biorefineries for building a sustainable society. The plant cell wall is a hierarchical matrix of mainly cellulose, hemicellulose, and lignin. Herein, the structure, properties, and reactions of cellulose, lignin, and wood cell walls, studied using density functional theory (DFT) and molecular dynamics (MD), which are the widely used computational modeling approaches, are reviewed. Computational modeling, which has played a crucial role in understanding the structure and properties of plant biomass and its nanomaterials, may serve a leading role on developing new hierarchical materials from biomass in the future.
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Affiliation(s)
- Shengfei Zhou
- Laboratory for Atomistic and Molecular Mechanics, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Mass. Ave 1-290, Cambridge, MA, 02139, USA
| | - Kai Jin
- Laboratory for Atomistic and Molecular Mechanics, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Mass. Ave 1-290, Cambridge, MA, 02139, USA
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Mass. Ave 1-290, Cambridge, MA, 02139, USA
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28
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Kolel-Veetil M, Sen A, Buehler MJ. Surface adhesion of viruses and bacteria: Defend only and/or vibrationally extinguish also?! A perspective. MRS ADVANCES 2021; 6:355-361. [PMID: 34150335 PMCID: PMC8204927 DOI: 10.1557/s43580-021-00079-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/28/2021] [Indexed: 12/22/2022]
Abstract
Coronaviruses COVID-19, SARS-CoV and NL63 use spikes in their corona to bind to angiotensin converting enzyme 2 (ACE2) sites on cytoskeletal membranes of host cells to deliver their viral payload. While groups such as disulfides in ACE2's zinc metallopeptidase, and also in COVID-19's spikes, facilitate such binding, it is worth exploring how similar complementary sites on materials such as polymers, metals, ceramics, fabrics, and biomaterials promote binding of viruses and bacteria and how they could be further engineered to prevent bioactivity, or to act as agents to collect viral payloads in filters or similar devices. In that vein, this article offers a perspective on novel tools and approaches for chemically and topologically modifying most utilitarian surfaces via defensive topological vibrational engineering to either prevent such adhesion or to enhance adhesion and elicit vibrational characteristics/'musical signatures' from the surfaces so that the structure of the binding sites of viruses and bacteria is permanently altered and/or their cellular machinery is permanently disabled by targeted chemical transformations. Graphic abstract Supplementary Information The online version contains supplementary material available at 10.1557/s43580-021-00079-0.
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Affiliation(s)
- Manoj Kolel-Veetil
- Chemistry Division, US Naval Research Laboratory, Washington, DC 20375 USA
| | - Ayusman Sen
- Departments of Chemistry and Chemical Engineering, Pennsylvania State University, University Park, PA 16802 USA
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA USA
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29
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Moreira-Filho JT, Silva AC, Dantas RF, Gomes BF, Souza Neto LR, Brandao-Neto J, Owens RJ, Furnham N, Neves BJ, Silva-Junior FP, Andrade CH. Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence. Front Immunol 2021; 12:642383. [PMID: 34135888 PMCID: PMC8203334 DOI: 10.3389/fimmu.2021.642383] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 04/30/2021] [Indexed: 12/20/2022] Open
Abstract
Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.
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Affiliation(s)
- José T. Moreira-Filho
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Arthur C. Silva
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Rafael F. Dantas
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Barbara F. Gomes
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Lauro R. Souza Neto
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Jose Brandao-Neto
- Diamond Light Source Ltd., Didcot, United Kingdom
- Research Complex at Harwell, Didcot, United Kingdom
| | - Raymond J. Owens
- The Rosalind Franklin Institute, Harwell, United Kingdom
- Division of Structural Biology, The Wellcome Centre for Human Genetic, University of Oxford, Oxford, United Kingdom
| | - Nicholas Furnham
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Bruno J. Neves
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Floriano P. Silva-Junior
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Carolina H. Andrade
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
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Yang Z, Yu CH, Buehler MJ. Deep learning model to predict complex stress and strain fields in hierarchical composites. SCIENCE ADVANCES 2021; 7:eabd7416. [PMID: 33837076 PMCID: PMC8034856 DOI: 10.1126/sciadv.abd7416] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 02/24/2021] [Indexed: 06/06/2023]
Abstract
Materials-by-design is a paradigm to develop previously unknown high-performance materials. However, finding materials with superior properties is often computationally or experimentally intractable because of the astronomical number of combinations in design space. Here we report an AI-based approach, implemented in a game theory-based conditional generative adversarial neural network (cGAN), to bridge the gap between a material's microstructure-the design space-and physical performance. Our end-to-end deep learning model predicts physical fields like stress or strain directly from the material microstructure geometry, and reaches an astonishing accuracy not only for predicted field data but also for derivative material property predictions. Furthermore, the proposed approach offers extensibility by predicting complex materials behavior regardless of component shapes, boundary conditions, and geometrical hierarchy, providing perspectives of performing physical modeling and simulations. The method vastly improves the efficiency of evaluating physical properties of hierarchical materials directly from the geometry of its structural makeup.
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Affiliation(s)
- Zhenze Yang
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - Chi-Hua Yu
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
- Department of Engineering Science, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
- Center for Materials Science and Engineering, 77 Massachusetts Ave., Cambridge, MA 02139, USA
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31
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Guo K, Yang Z, Yu CH, Buehler MJ. Artificial intelligence and machine learning in design of mechanical materials. MATERIALS HORIZONS 2021; 8:1153-1172. [PMID: 34821909 DOI: 10.1039/d0mh01451f] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials and mechanical engineering, attributed to its power to predict materials properties, design de novo materials and discover new mechanisms beyond intuitions. As the structural complexity of novel materials soars, the material design problem to optimize mechanical behaviors can involve massive design spaces that are intractable for conventional methods. Addressing this challenge, ML models trained from large material datasets that relate structure, properties and function at multiple hierarchical levels have offered new avenues for fast exploration of the design spaces. The performance of a ML-based materials design approach relies on the collection or generation of a large dataset that is properly preprocessed using the domain knowledge of materials science underlying chemical and physical concepts, and a suitable selection of the applied ML model. Recent breakthroughs in ML techniques have created vast opportunities for not only overcoming long-standing mechanics problems but also for developing unprecedented materials design strategies. In this review, we first present a brief introduction of state-of-the-art ML models, algorithms and structures. Then, we discuss the importance of data collection, generation and preprocessing. The applications in mechanical property prediction, materials design and computational methods using ML-based approaches are summarized, followed by perspectives on opportunities and open challenges in this emerging and exciting field.
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Affiliation(s)
- Kai Guo
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave. 1-290, Cambridge, Massachusetts 02139, USA.
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Mu X, Agostinacchio F, Xiang N, Pei Y, Khan Y, Guo C, Cebe P, Motta A, Kaplan DL. Recent Advances in 3D Printing with Protein-Based Inks. Prog Polym Sci 2021; 115:101375. [PMID: 33776158 PMCID: PMC7996313 DOI: 10.1016/j.progpolymsci.2021.101375] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Three-dimensional (3D) printing is a transformative manufacturing strategy, allowing rapid prototyping, customization, and flexible manipulation of structure-property relationships. Proteins are particularly appealing to formulate inks for 3D printing as they serve as essential structural components of living systems, provide a support presence in and around cells and for tissue functions, and also provide the basis for many essential ex vivo secreted structures in nature. Protein-based inks are beneficial in vivo due to their mechanics, chemical and physical match to the specific tissue, and full degradability, while also to promoting implant-host integration and serving as an interface between technology and biology. Exploiting the biological, chemical, and physical features of protein-based inks can provide key opportunities to meet the needs of tissue engineering and regenerative medicine. Despite these benefits, protein-based inks impose nontrivial challenges to 3D printing such as concentration and rheological features and reconstitution of the structural hierarchy observed in nature that is a source of the robust mechanics and functions of these materials. This review introduces photo-crosslinking mechanisms and rheological principles that underpins a variety of 3D printing techniques. The review also highlights recent advances in the design, development, and biomedical utility of monolithic and composite inks from a range of proteins, including collagen, silk, fibrinogen, and others. One particular focus throughout the review is to introduce unique material characteristics of proteins, including amino acid sequences, molecular assembly, and secondary conformations, which are useful for designing printing inks and for controlling the printed structures. Future perspectives of 3D printing with protein-based inks are also provided to support the promising spectrum of biomedical research accessible to these materials.
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Affiliation(s)
- Xuan Mu
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
| | - Francesca Agostinacchio
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
- Department of Industrial Engineering, University of Trento, via Sommarive 9, Trento 38123, Italy
| | - Ning Xiang
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
| | - Ying Pei
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
- School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Yousef Khan
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
| | - Chengchen Guo
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
| | - Peggy Cebe
- Department of Physics and Astronomy, Tufts University, Medford, MA 02155, USA
| | - Antonella Motta
- Department of Industrial Engineering, University of Trento, via Sommarive 9, Trento 38123, Italy
| | - David L. Kaplan
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
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Comparative Genomics and Integrated Network Approach Unveiled Undirected Phylogeny Patterns, Co-mutational Hot Spots, Functional Cross Talk, and Regulatory Interactions in SARS-CoV-2. mSystems 2021; 6:6/1/e00030-21. [PMID: 33622851 PMCID: PMC8573956 DOI: 10.1128/msystems.00030-21] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has resulted in 92 million cases in a span of 1 year. The study focuses on understanding population-specific variations attributing its high rate of infections in specific geographical regions particularly in the United States. Rigorous phylogenomic network analysis of complete SARS-CoV-2 genomes (245) inferred five central clades named a (ancestral), b, c, d, and e (subtypes e1 and e2). Clade d and subclade e2 were found exclusively comprised of U.S. strains. Clades were distinguished by 10 co-mutational combinations in Nsp3, ORF8, Nsp13, S, Nsp12, Nsp2, and Nsp6. Our analysis revealed that only 67.46% of single nucleotide polymorphism (SNP) mutations were at the amino acid level. T1103P mutation in Nsp3 was predicted to increase protein stability in 238 strains except for 6 strains which were marked as ancestral type, whereas co-mutation (P409L and Y446C) in Nsp13 were found in 64 genomes from the United States highlighting its 100% co-occurrence. Docking highlighted mutation (D614G) caused reduction in binding of spike proteins with angiotensin-converting enzyme 2 (ACE2), but it also showed better interaction with the TMPRSS2 receptor contributing to high transmissibility among U.S. strains. We also found host proteins, MYO5A, MYO5B, and MYO5C, that had maximum interaction with viral proteins (nucleocapsid [N], spike [S], and membrane [M] proteins). Thus, blocking the internalization pathway by inhibiting MYO5 proteins which could be an effective target for coronavirus disease 2019 (COVID-19) treatment. The functional annotations of the host-pathogen interaction (HPI) network were found to be closely associated with hypoxia and thrombotic conditions, confirming the vulnerability and severity of infection. We also screened CpG islands in Nsp1 and N conferring the ability of SARS-CoV-2 to enter and trigger zinc antiviral protein (ZAP) activity inside the host cell. IMPORTANCE In the current study, we presented a global view of mutational pattern observed in SARS-CoV-2 virus transmission. This provided a who-infect-whom geographical model since the early pandemic. This is hitherto the most comprehensive comparative genomics analysis of full-length genomes for co-mutations at different geographical regions especially in U.S. strains. Compositional structural biology results suggested that mutations have a balance of opposing forces affecting pathogenicity suggesting that only a few mutations are effective at the translation level. Novel HPI analysis and CpG predictions elucidate the proof of concept of hypoxia and thrombotic conditions in several patients. Thus, the current study focuses the understanding of population-specific variations attributing a high rate of SARS-CoV-2 infections in specific geographical regions which may eventually be vital for the most severely affected countries and regions for sharp development of custom-made vindication strategies.
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Huang C, Zhu X, Li N, Ma X, Li Z, Fan J. Simultaneous Sensing of Force and Current Signals to Recognize Proteinogenic Amino Acids at a Single-Molecule Level. J Phys Chem Lett 2021; 12:793-799. [PMID: 33411544 DOI: 10.1021/acs.jpclett.0c02989] [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: 06/12/2023]
Abstract
The identification ability of nanopore sequencing is severely hindered by the diversity of amino acids in a protein. To tackle this problem, a graphene nanoslit sensor is adopted to collect force and current signals to distinguish 20 residues. Extensive molecular dynamics simulations are performed on sequencing peptides under pulling force and applied electric field. Results show that the signals of force and current can be simultaneously collected. Tailoring the geometry of the nanoslit sensor optimizes signal differences between tyrosine and alanine residues. Using the tailored geometry, the characteristic signals of 20 types of residues are detected, enabling excellent distinguishability so that the residues are well-grouped by their properties and signals. The signals reveal a trend in which the larger amino acids have larger pulling forces and lower ionic currents. Generally, the graphene nanoslit sensor can be employed to simultaneously sense two signals, thereby enhancing the identification ability and providing an effective mode of nanopore protein sequencing.
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Affiliation(s)
- Changxiong Huang
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong, China
| | - Xiaohong Zhu
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong, China
| | - Na Li
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong, China
| | - Xinyao Ma
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong, China
| | - Zhen Li
- School of Materials Science and Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Jun Fan
- Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong, China
- Center for Advanced Nuclear Safety and Sustainable Development, City University of Hong Kong, Kowloon 999077, Hong Kong, China
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35
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Gao W, Mahajan SP, Sulam J, Gray JJ. Deep Learning in Protein Structural Modeling and Design. PATTERNS (NEW YORK, N.Y.) 2020; 1:100142. [PMID: 33336200 PMCID: PMC7733882 DOI: 10.1016/j.patter.2020.100142] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the "sequence → structure → function" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.
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Affiliation(s)
- Wenhao Gao
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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36
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Su I, Jung GS, Narayanan N, Buehler MJ. Perspectives on three-dimensional printing of self-assembling materials and structures. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2020. [DOI: 10.1016/j.cobme.2020.01.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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37
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d'Ischia M, Napolitano A, Pezzella A, Meredith P, Buehler M. Melanin Biopolymers: Tailoring Chemical Complexity for Materials Design. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.201914276] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Marco d'Ischia
- Department of Chemical Sciences University of Naples “Federico II” Via Cintia 4 80126 Naples Italy
| | - Alessandra Napolitano
- Department of Chemical Sciences University of Naples “Federico II” Via Cintia 4 80126 Naples Italy
| | - Alessandro Pezzella
- Department of Chemical Sciences University of Naples “Federico II” Via Cintia 4 80126 Naples Italy
| | - Paul Meredith
- Department of Physics Swansea University Vivian Building, Singleton Campus SA2 8PP Swansea UK
| | - Markus Buehler
- Laboratory for Atomistic and Molecular Mechanics School of Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
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38
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Melanin Biopolymers: Tailoring Chemical Complexity for Materials Design. Angew Chem Int Ed Engl 2020; 59:11196-11205. [DOI: 10.1002/anie.201914276] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Indexed: 12/17/2022]
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39
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Affiliation(s)
- Si-Min Lu
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
| | - Yue-Yi Peng
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
| | - Yi-Lun Ying
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
| | - Yi-Tao Long
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
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40
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Qi Y, Zhang JZH. DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet. J Chem Inf Model 2020; 60:1245-1252. [DOI: 10.1021/acs.jcim.0c00043] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Yifei Qi
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU−ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
| | - John Z. H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU−ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Department of Chemistry, New York University, New York, New York 10003, United States
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41
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Chen CT, Gu GX. Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2020; 7:1902607. [PMID: 32154072 PMCID: PMC7055566 DOI: 10.1002/advs.201902607] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 11/11/2019] [Indexed: 05/19/2023]
Abstract
In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck. A general-purpose inverse design approach is presented using generative inverse design networks. This ML-based inverse design approach uses backpropagation to calculate the analytical gradients of an objective function with respect to design variables. This inverse design approach is capable of overcoming local minima traps by using backpropagation to provide rapid calculations of gradient information and running millions of optimizations with different initial values. Furthermore, an active learning strategy is adopted in the inverse design approach to improve the performance of candidate materials and reduce the amount of training data needed to do so. Compared to passive learning, the active learning strategy is capable of generating better designs and reducing the amount of training data by at least an order-of-magnitude in the case study on composite materials. The inverse design approach is compared with conventional gradient-based topology optimization and gradient-free genetic algorithms and the pros and cons of each method are discussed when applied to materials discovery and design problems.
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Affiliation(s)
- Chun-Teh Chen
- Department of Materials Science and Engineering University of California Berkeley CA 94720 USA
| | - Grace X Gu
- Department of Mechanical Engineering University of California Berkeley CA 94720 USA
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Yu CH, Buehler MJ. Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling. APL Bioeng 2020; 4:016108. [PMID: 32206742 PMCID: PMC7078008 DOI: 10.1063/1.5133026] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 01/29/2020] [Indexed: 11/14/2022] Open
Abstract
We report the use of a deep learning model to design de novo proteins, based on the interplay of elementary building blocks via hierarchical patterns. The deep neural network model is based on translating protein sequences and structural information into a musical score that features different pitches for each of the amino acids, and variations in note length and note volume reflecting secondary structure information and information about the chain length and distinct protein molecules. We train a deep learning model whose architecture is composed of several long short-term memory units from data consisting of musical representations of proteins classified by certain features, focused here on alpha-helix rich proteins. Using the deep learning model, we then generate de novo musical scores and translate the pitch information and chain lengths into sequences of amino acids. We use a Basic Local Alignment Search Tool to compare the predicted amino acid sequences against known proteins, and estimate folded protein structures using the Optimized protein fold RecognitION method (ORION) and MODELLER. We find that the method proposed here can be used to design de novo proteins that do not exist yet, and that the designed proteins fold into specified secondary structures. We validate the newly predicted protein by molecular dynamics equilibration in explicit water and subsequent characterization using a normal mode analysis. The method provides a tool to design novel protein materials that could find useful applications as materials in biology, medicine, and engineering.
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Affiliation(s)
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM),
Department of Civil and Environmental Engineering, Massachusetts Institute of
Technology, 77 Massachusetts Ave. 1-290, Cambridge, Massachusetts 02139,
USA
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43
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The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation. Sci Rep 2020; 10:2068. [PMID: 32034199 PMCID: PMC7005769 DOI: 10.1038/s41598-020-58868-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 10/16/2019] [Indexed: 12/11/2022] Open
Abstract
Intrinsically disordered proteins (IDPs) and intrinsically disordered regions within proteins (IDRs) serve an increasingly expansive list of biological functions, including regulation of transcription and translation, protein phosphorylation, cellular signal transduction, as well as mechanical roles. The strong link between protein function and disorder motivates a deeper fundamental characterization of IDPs and IDRs for discovering new functions and relevant mechanisms. We review recent advances in experimental techniques that have improved identification of disordered regions in proteins. Yet, experimentally curated disorder information still does not currently scale to the level of experimentally determined structural information in folded protein databases, and disorder predictors rely on several different binary definitions of disorder. To link secondary structure prediction algorithms developed for folded proteins and protein disorder predictors, we conduct molecular dynamics simulations on representative proteins from the Protein Data Bank, comparing secondary structure and disorder predictions with simulation results. We find that structure predictor performance from neural networks can be leveraged for the identification of highly dynamic regions within molecules, linked to disorder. Low accuracy structure predictions suggest a lack of static structure for regions that disorder predictors fail to identify. While disorder databases continue to expand, secondary structure predictors and molecular simulations can improve disorder predictor performance, which aids discovery of novel functions of IDPs and IDRs. These observations provide a platform for the development of new, integrated structural databases and fusion of prediction tools toward protein disorder characterization in health and disease.
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44
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Milazzo M, Jung GS, Danti S, Buehler MJ. Wave Propagation and Energy Dissipation in Collagen Molecules. ACS Biomater Sci Eng 2020; 6:1367-1374. [PMID: 33455394 DOI: 10.1021/acsbiomaterials.9b01742] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Collagen is the key protein of connective tissue (i.e., skin, tendons and ligaments, and cartilage, among others), accounting for 25-35% of the whole-body protein content and conferring mechanical stability. This protein is also a fundamental building block of bone because of its excellent mechanical properties together with carbonated hydroxyapatite minerals. Although the mechanical resilience and viscoelasticity have been studied both in vitro and in vivo from the molecular to tissue level, wave propagation properties and energy dissipation have not yet been deeply explored, in spite of being crucial to understanding the vibration dynamics of collagenous structures (e.g., eardrum, cochlear membranes) upon impulsive loads. By using a bottom-up atomistic modeling approach, here we study a collagen peptide under two distinct impulsive displacement loads, including longitudinal and transversal inputs. Using a one-dimensional string model as a model system, we investigate the roles of hydration and load direction on wave propagation along the collagen peptide and the related energy dissipation. We find that wave transmission and energy-dissipation strongly depend on the loading direction. Also, the hydrated collagen peptide can dissipate five times more energy than dehydrated one. Our work suggests a distinct role of collagen in term of wave transmission of different tissues such as tendon and eardrum. This study can step toward understanding the mechanical behavior of collagen upon transient loads, impact loading and fatigue, and designing biomimetic and bioinspired materials to replace specific native tissues such as the tympanic membrane.
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Affiliation(s)
- Mario Milazzo
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa 56127, Italy
| | - Gang Seob Jung
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Serena Danti
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa 56127, Italy.,Department of Civil and Industrial Engineering, University of Pisa, Pisa 56126, Italy
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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45
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Qin Z, Yu Q, Buehler MJ. Machine learning model for fast prediction of the natural frequencies of protein molecules. RSC Adv 2020; 10:16607-16615. [PMID: 35498827 PMCID: PMC9053087 DOI: 10.1039/c9ra04186a] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 04/03/2020] [Indexed: 11/21/2022] Open
Abstract
Natural vibrations and resonances are intrinsic features of protein structures and enable differentiation of one structure from another. These nanoscale features are important to help to understand the dynamics of a protein molecule and identify the effects of small sequence or other geometric alterations that may not cause significant visible structural changes, such as point mutations associated with disease or drug design. Although normal mode analysis provides a powerful way to accurately extract the natural frequencies of a protein, it must meet several critical conditions, including availability of high-resolution structures, availability of good chemical force fields and memory-intensive large-scale computing resources. Here, we study the natural frequency of over 100 000 known protein molecular structures from the Protein Data Bank and use this dataset to carefully investigate the correlation between their structural features and these natural frequencies by using a machine learning model composed of a Feedforward Neural Network made of four hidden layers that predicts the natural frequencies in excellent agreement with full-atomistic normal mode calculations, but is significantly more computationally efficient. In addition to the computational advance, we demonstrate that this model can be used to directly obtain the natural frequencies by merely using five structural features of protein molecules as predictor variables, including the largest and smallest diameter, and the ratio of amino acid residues with alpha-helix, beta strand and 3–10 helix domains. These structural features can be either experimentally or computationally obtained, and do not require a full-atomistic model of a protein of interest. This method is helpful in predicting the absorption and resonance functions of an unknown protein molecule without solving its full atomic structure. Natural vibrations and resonances are intrinsic features of protein structures and can be learnt from existing structures.![]()
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Affiliation(s)
- Zhao Qin
- Laboratory for Atomistic and Molecular Mechanics (LAMM)
- Department of Civil and Environmental Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
| | - Qingyi Yu
- Dereck Bok Center for Teaching and Learning
- Harvard University
- Cambridge
- USA
- Department of Educational Psychology, Counseling and Special Education
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM)
- Department of Civil and Environmental Engineering
- Massachusetts Institute of Technology
- Cambridge
- USA
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46
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Ren J, Wang Y, Yao Y, Wang Y, Fei X, Qi P, Lin S, Kaplan DL, Buehler MJ, Ling S. Biological Material Interfaces as Inspiration for Mechanical and Optical Material Designs. Chem Rev 2019; 119:12279-12336. [DOI: 10.1021/acs.chemrev.9b00416] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Jing Ren
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Yu Wang
- Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Yuan Yao
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Yang Wang
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Xiang Fei
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, International Joint Laboratory for Advanced Fiber and Low-Dimension Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, China
| | - Ping Qi
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Shihui Lin
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - David L. Kaplan
- Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Markus J. Buehler
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Shengjie Ling
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
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47
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Franjou SL, Milazzo M, Yu CH, Buehler MJ. Sounds interesting: can sonification help us design new proteins? Expert Rev Proteomics 2019; 16:875-879. [PMID: 31756126 DOI: 10.1080/14789450.2019.1697236] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Introduction: The practice of turning scientific data into music, a practice known as sonification, is a growing field. Driven by analogies between the hierarchical structures of proteins and many forms of music, multiple attempts of mapping proteins to music have been made. Previous works have either worked at a low level, mapping amino acid to notes, or at a higher level, using the overall structure as a basis for composition.Areas covered: We report a comprehensive mapping strategy that encompasses the encoding of the geometry of proteins, in addition to the amino acid sequence and secondary structure information. This leads to a piece of music that is both more complete and closely linked to the original protein. By using this mapping, we can invert the process and map music to proteins, retrieving not only the amino acid sequence but also the secondary structure and folding from musical data.Expert opinion: We can train a machine learning model on 'protein music' to generate new music that can be translated to new proteins. By selecting proper datasets and conditioning parameters on the generative model, we could tune de novo proteins with high level parameters to achieve certain protein design features.
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Affiliation(s)
- Sebastian L Franjou
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA, USA.,Music and Theater Arts, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mario Milazzo
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA, USA.,The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Chi-Hua Yu
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA, USA
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