1
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Gong X, Zhang J, Gan Q, Teng Y, Hou J, Lyu Y, Liu Z, Wu Z, Dai R, Zou Y, Wang X, Zhu D, Zhu H, Liu T, Yan Y. Advancing microbial production through artificial intelligence-aided biology. Biotechnol Adv 2024; 74:108399. [PMID: 38925317 DOI: 10.1016/j.biotechadv.2024.108399] [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: 01/03/2024] [Revised: 05/20/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024]
Abstract
Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.
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Affiliation(s)
- Xinyu Gong
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Jianli Zhang
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Qi Gan
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Yuxi Teng
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Jixin Hou
- School of ECAM, College of Engineering, University of Georgia, Athens, GA 30602, USA
| | - Yanjun Lyu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington 76019, USA
| | - Zhengliang Liu
- School of Computing, The University of Georgia, Athens, GA 30602, USA
| | - Zihao Wu
- School of Computing, The University of Georgia, Athens, GA 30602, USA
| | - Runpeng Dai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yusong Zou
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Xianqiao Wang
- School of ECAM, College of Engineering, University of Georgia, Athens, GA 30602, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington 76019, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tianming Liu
- School of Computing, The University of Georgia, Athens, GA 30602, USA
| | - Yajun Yan
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA.
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2
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Wang Q, Liu X, Zhang H, Chu H, Shi C, Zhang L, Bai J, Liu P, Li J, Zhu X, Liu Y, Chen Z, Huang R, Chang H, Liu T, Chang Z, Cheng J, Jiang H. Cytochrome P450 Enzyme Design by Constraining the Catalytic Pocket in a Diffusion Model. RESEARCH (WASHINGTON, D.C.) 2024; 7:0413. [PMID: 38979516 PMCID: PMC11227911 DOI: 10.34133/research.0413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/27/2024] [Indexed: 07/10/2024]
Abstract
Although cytochrome P450 enzymes are the most versatile biocatalysts in nature, there is insufficient comprehension of the molecular mechanism underlying their functional innovation process. Here, by combining ancestral sequence reconstruction, reverse mutation assay, and progressive forward accumulation, we identified 5 founder residues in the catalytic pocket of flavone 6-hydroxylase (F6H) and proposed a "3-point fixation" model to elucidate the functional innovation mechanisms of P450s in nature. According to this design principle of catalytic pocket, we further developed a de novo diffusion model (P450Diffusion) to generate artificial P450s. Ultimately, among the 17 non-natural P450s we generated, 10 designs exhibited significant F6H activity and 6 exhibited a 1.3- to 3.5-fold increase in catalytic capacity compared to the natural CYP706X1. This work not only explores the design principle of catalytic pockets of P450s, but also provides an insight into the artificial design of P450 enzymes with desired functions.
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Affiliation(s)
- Qian Wang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Xiaonan Liu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Hejian Zhang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
- College of Biotechnology,
Tianjin University of Science and Technology, Tianjin 300457, China
| | - Huanyu Chu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chao Shi
- Department of Biochemistry and Biophysics, School of Basic Medical Sciences,
Peking University, Beijing 100191, China
| | - Lei Zhang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- College of Life Science and Technology,
Wuhan Polytechnic University, Wuhan, Hubei 430023, China
| | - Jie Bai
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Pi Liu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Jing Li
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
- State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry,
Nankai University, Tianjin 300071, China
- College of Life Science,
Nankai University, Tianjin 300071, China
| | - Xiaoxi Zhu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Yuwan Liu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Zhangxin Chen
- Department of Biochemistry and Biophysics, School of Basic Medical Sciences,
Peking University, Beijing 100191, China
| | - Rong Huang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Hong Chang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Tian Liu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Zhenzhan Chang
- Department of Biochemistry and Biophysics, School of Basic Medical Sciences,
Peking University, Beijing 100191, China
| | - Jian Cheng
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Huifeng Jiang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
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3
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Reveguk I, Simonson T. Classifying protein kinase conformations with machine learning. Protein Sci 2024; 33:e4918. [PMID: 38501429 PMCID: PMC10962494 DOI: 10.1002/pro.4918] [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: 07/26/2023] [Revised: 01/02/2024] [Accepted: 01/22/2024] [Indexed: 03/20/2024]
Abstract
Protein kinases are key actors of signaling networks and important drug targets. They cycle between active and inactive conformations, distinguished by a few elements within the catalytic domain. One is the activation loop, whose conserved DFG motif can occupy DFG-in, DFG-out, and some rarer conformations. Annotation and classification of the structural kinome are important, as different conformations can be targeted by different inhibitors and activators. Valuable resources exist; however, large-scale applications will benefit from increased automation and interpretability of structural annotation. Interpretable machine learning models are described for this purpose, based on ensembles of decision trees. To train them, a set of catalytic domain sequences and structures was collected, somewhat larger and more diverse than existing resources. The structures were clustered based on the DFG conformation and manually annotated. They were then used as training input. Two main models were constructed, which distinguished active/inactive and in/out/other DFG conformations. They considered initially 1692 structural variables, spanning the whole catalytic domain, then identified ("learned") a small subset that sufficed for accurate classification. The first model correctly labeled all but 3 of 3289 structures as active or inactive, while the second assigned the correct DFG label to all but 17 of 8826 structures. The most potent classifying variables were all related to well-known structural elements in or near the activation loop and their ranking gives insights into the conformational preferences. The models were used to automatically annotate 3850 kinase structures predicted recently with the Alphafold2 tool, showing that Alphafold2 reproduced the active/inactive but not the DFG-in proportions seen in the Protein Data Bank. We expect the models will be useful for understanding and engineering kinases.
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Affiliation(s)
- Ivan Reveguk
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654)Ecole PolytechniquePalaiseauFrance
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654)Ecole PolytechniquePalaiseauFrance
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4
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Notin P, Rollins N, Gal Y, Sander C, Marks D. Machine learning for functional protein design. Nat Biotechnol 2024; 42:216-228. [PMID: 38361074 DOI: 10.1038/s41587-024-02127-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
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Affiliation(s)
- Pascal Notin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Computer Science, University of Oxford, Oxford, UK.
| | | | - Yarin Gal
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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5
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Fannjiang C, Listgarten J. Is Novelty Predictable? Cold Spring Harb Perspect Biol 2024; 16:a041469. [PMID: 38052497 PMCID: PMC10835614 DOI: 10.1101/cshperspect.a041469] [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: 12/07/2023]
Abstract
Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal applications ranging from drug development and plastic degradation to carbon sequestration. When designing objects to achieve novel property values with machine learning, one faces a fundamental challenge: how to push past the frontier of current knowledge, distilled from the training data into the model, in a manner that rationally controls the risk of failure. If one trusts learned models too much in extrapolation, one is likely to design rubbish. In contrast, if one does not extrapolate, one cannot find novelty. Herein, we ponder how one might strike a useful balance between these two extremes. We focus in particular on designing proteins with novel property values, although much of our discussion is relevant to machine learning-based design more broadly.
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Affiliation(s)
- Clara Fannjiang
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
| | - Jennifer Listgarten
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
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6
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Rahban M, Ahmad F, Piatyszek MA, Haertlé T, Saso L, Saboury AA. Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry. RSC Adv 2023; 13:35947-35963. [PMID: 38090079 PMCID: PMC10711991 DOI: 10.1039/d3ra06476j] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 11/30/2023] [Indexed: 04/26/2024] Open
Abstract
Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.
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Affiliation(s)
- Mahdie Rahban
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences Kerman Iran
| | - Faizan Ahmad
- Department of Biochemistry, School of Chemical & Life Sciences, Jamia Hamdard New Delhi-110062 India
| | | | | | - Luciano Saso
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University Rome Italy
| | - Ali Akbar Saboury
- Institute of Biochemistry and Biophysics, University of Tehran Tehran 1417614335 Iran +9821 66404680 +9821 66956984
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7
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Xie WJ, Warshel A. Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering. Natl Sci Rev 2023; 10:nwad331. [PMID: 38299119 PMCID: PMC10829072 DOI: 10.1093/nsr/nwad331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/27/2023] [Accepted: 10/13/2023] [Indexed: 02/02/2024] Open
Abstract
Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novo enzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.
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Affiliation(s)
- Wen Jun Xie
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, Genetics Institute, University of Florida, Gainesville, FL 32610, USA
| | - Arieh Warshel
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA
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8
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Malbranke C, Rostain W, Depardieu F, Cocco S, Monasson R, Bikard D. Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment. PLoS Comput Biol 2023; 19:e1011621. [PMID: 37976326 PMCID: PMC10729993 DOI: 10.1371/journal.pcbi.1011621] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 12/19/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023] Open
Abstract
We present here an approach to protein design that combines (i) scarce functional information such as experimental data (ii) evolutionary information learned from a natural sequence variants and (iii) physics-grounded modeling. Using a Restricted Boltzmann Machine (RBM), we learn a sequence model of a protein family. We use semi-supervision to leverage available functional information during the RBM training. We then propose a strategy to explore the protein representation space that can be informed by external models such as an empirical force-field method (FoldX). Our approach is applied to a domain of the Cas9 protein responsible for recognition of a short DNA motif. We experimentally assess the functionality of 71 variants generated to explore a range of RBM and FoldX energies. Sequences with as many as 50 differences (20% of the protein domain) to the wild-type retained functionality. Overall, 21/71 sequences designed with our method were functional. Interestingly, 6/71 sequences showed an improved activity in comparison with the original wild-type protein sequence. These results demonstrate the interest in further exploring the synergies between machine-learning of protein sequence representations and physics grounded modeling strategies informed by structural information.
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Affiliation(s)
- Cyril Malbranke
- Laboratory of Physics of the Ecole Normale Superieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Paris, France
- Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, Paris, France
| | - William Rostain
- Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, Paris, France
| | - Florence Depardieu
- Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, Paris, France
| | - Simona Cocco
- Laboratory of Physics of the Ecole Normale Superieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Paris, France
| | - Rémi Monasson
- Laboratory of Physics of the Ecole Normale Superieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Paris, France
| | - David Bikard
- Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, Paris, France
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9
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Xie WJ, Warshel A. Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561808. [PMID: 37873334 PMCID: PMC10592750 DOI: 10.1101/2023.10.10.561808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. By applying generative models, we could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, activity, and stability, rationalizing the laboratory evolution of de novo enzymes, decoding protein sequence semantics, and its applications in enzyme engineering. Notably, the prediction of enzyme activity and stability using natural enzyme sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.
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Affiliation(s)
- Wen Jun Xie
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA
- Departmet of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development (CNPD3), Genetics Institute, University of Florida, Gainesville, FL, USA
| | - Arieh Warshel
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA
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10
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Braghetto A, Orlandini E, Baiesi M. Interpretable Machine Learning of Amino Acid Patterns in Proteins: A Statistical Ensemble Approach. J Chem Theory Comput 2023; 19:6011-6022. [PMID: 37552831 PMCID: PMC10500975 DOI: 10.1021/acs.jctc.3c00383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Indexed: 08/10/2023]
Abstract
Explainable and interpretable unsupervised machine learning helps one to understand the underlying structure of data. We introduce an ensemble analysis of machine learning models to consolidate their interpretation. Its application shows that restricted Boltzmann machines compress consistently into a few bits the information stored in a sequence of five amino acids at the start or end of α-helices or β-sheets. The weights learned by the machines reveal unexpected properties of the amino acids and the secondary structure of proteins: (i) His and Thr have a negligible contribution to the amphiphilic pattern of α-helices; (ii) there is a class of α-helices particularly rich in Ala at their end; (iii) Pro occupies most often slots otherwise occupied by polar or charged amino acids, and its presence at the start of helices is relevant; (iv) Glu and especially Asp on one side and Val, Leu, Iso, and Phe on the other display the strongest tendency to mark amphiphilic patterns, i.e., extreme values of an effective hydrophobicity, though they are not the most powerful (non)hydrophobic amino acids.
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Affiliation(s)
- Anna Braghetto
- Department
of Physics and Astronomy, University of
Padova, Via Marzolo 8, 35131 Padua, Italy
- INFN,
Sezione di Padova, Via
Marzolo 8, 35131 Padua, Italy
| | - Enzo Orlandini
- Department
of Physics and Astronomy, University of
Padova, Via Marzolo 8, 35131 Padua, Italy
- INFN,
Sezione di Padova, Via
Marzolo 8, 35131 Padua, Italy
| | - Marco Baiesi
- Department
of Physics and Astronomy, University of
Padova, Via Marzolo 8, 35131 Padua, Italy
- INFN,
Sezione di Padova, Via
Marzolo 8, 35131 Padua, Italy
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