1
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Gaidhani PM, Chakraborty S, Ramesh K, Velayudhaperumal Chellam P, van Hullebusch ED. Molecular interactions of paraben family of pollutants with embryonic neuronal proteins of Danio rerio: A step ahead in computational toxicity towards adverse outcome pathway. CHEMOSPHERE 2024; 351:141155. [PMID: 38211790 DOI: 10.1016/j.chemosphere.2024.141155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/28/2023] [Accepted: 01/07/2024] [Indexed: 01/13/2024]
Abstract
The paraben family of endocrine disruptors exhibit persistent behaviours in aquatic matrices, having bio-accumulative effects and necessitating toxicity analysis and safe use, as well as prevention of food web penetration. In this study, the toxicity effects of 9 different parabens (Methyl, Ethyl, Propyl, Butyl, Heptyl, Isopropyl, Isobutyl, benzyl parabens and p-hydroxybenzoic acid) were studied against 17 neuronal proteins (Neurog1, Ascl1a, DLA, Syn2a, Ntn1a, Pitx2, and SoxB1, Her/Hes, Zic family) expressed during the early embryonic developmental stage of Danio rerio. The neuronal genes were selected as a biomarker to study the inhibitory effects on the cascade of genes expressed in the early developmental stage. The study uses trRossetta software to predict protein structures of neuronal genes, followed by structural refinement, energy minimisation, and active site prediction, evaluated using energy value, RC plot and ERRAT scores of PROCHECK and ERRAT programs. Compared to raw structures, highly confident predicted structures and quality scores were observed for refined protein with few exceptions. Based on the polarity and charge of the aminoacids, the probable pockets were identified using active site prediction, which were then used for molecular docking analysis. Further, the ADMET analysis, ligand likeliness and toxicological test revealed the paraben family of compounds as one of the most susceptible toxic and mutagenic compounds. The molecular docking results showed an interesting pattern of increasing binding affinity with increase in the carbon chains of paraben molecules. Benzyl Paraben showed higher binding affinities across all 17 neuronal proteins. Finally, gene co-occurrence/co-expression and protein-protein interaction studies using the STRING database depict that all proteins are functionally related and play essential roles in standard biological processes or pathways, conserved and expressed in diverse organisms. The interaction between paraben compounds and neuronal genes indicates high risks of inhibiting reactions in embryonic stages, emphasising the need for effective treatment measures and strict regulations.
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Affiliation(s)
- Prerna Mahesh Gaidhani
- Water Research Group, Department of Bioengineering, National Institute of Technology Agartala, India
| | - Swastik Chakraborty
- Water Research Group, Department of Bioengineering, National Institute of Technology Agartala, India
| | - Kheerthana Ramesh
- Water Research Group, Department of Bioengineering, National Institute of Technology Agartala, India
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2
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Degn K, Beltrame L, Tiberti M, Papaleo E. PDBminer to Find and Annotate Protein Structures for Computational Analysis. J Chem Inf Model 2023; 63:7274-7281. [PMID: 37977136 DOI: 10.1021/acs.jcim.3c00884] [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: 11/19/2023]
Abstract
Computational methods relying on protein structure strongly depend on the structure selected for investigation. Typical sources of protein structures include experimental structures available at the Protein Data Bank (PDB) and high-quality in silico model structures, such as those available at the AlphaFold Protein Structure Database. Either option has significant advantages and drawbacks, and exploring the wealth of available structures to identify the most suitable ones for specific applications can be a daunting task. We provide an open-source software package, PDBminer, with the purpose of making structure identification and selection easier, faster, and less error prone. PDBminer searches the AlphaFold Database and the PDB for available structures of interest and provides an up-to-date, quality-ranked table of structures applicable for further use. PDBminer provides an overview of the available protein structures to one or more input proteins, parallelizing the runs if multiple cores are specified. The output table reports the coverage of the protein structures aligned to the UniProt sequence, overcoming numbering differences in PDB structures and providing information regarding model quality, protein complexes, ligands, and nucleic acid chain binding. The PDBminer2coverage and PDBminer2network tools assist in visualizing the results. PDBminer can be applied to overcome the tedious task of choosing a PDB structure without losing the wealth of additional information available in the PDB. Here, we showcase the main functionalities of the package on the p53 tumor suppressor protein. The package is available at http://github.com/ELELAB/PDBminer.
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Affiliation(s)
- Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Ludovica Beltrame
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800 Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
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3
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Roy RS, Liu J, Giri N, Guo Z, Cheng J. Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15. Proteins 2023; 91:1889-1902. [PMID: 37357816 PMCID: PMC10749984 DOI: 10.1002/prot.26542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/27/2023]
Abstract
Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter-chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and performed very well in estimating the global structure accuracy of assembly models. The average per-target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per-target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analyzed. The results demonstrate that combining the multi-model method (PSS) with the complementary single-model method (ICPS) is a promising approach to EMA.
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Affiliation(s)
- Raj S. Roy
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Nabin Giri
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Zhiye Guo
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
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4
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Yue T, Wang Y, Zhang L, Gu C, Xue H, Wang W, Lyu Q, Dun Y. Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models. Int J Mol Sci 2023; 24:15858. [PMID: 37958843 PMCID: PMC10649223 DOI: 10.3390/ijms242115858] [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: 09/30/2023] [Revised: 10/24/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023] Open
Abstract
The data explosion driven by advancements in genomic research, such as high-throughput sequencing techniques, is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in various fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning, since we expect a superhuman intelligence that explores beyond our knowledge to interpret the genome from deep learning. A powerful deep learning model should rely on the insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with proper deep learning-based architecture, and we remark on practical considerations of developing deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research and point out current challenges and potential research directions for future genomics applications. We believe the collaborative use of ever-growing diverse data and the fast iteration of deep learning models will continue to contribute to the future of genomics.
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Affiliation(s)
- Tianwei Yue
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (Y.W.); (L.Z.); (W.W.)
| | - Yuanxin Wang
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (Y.W.); (L.Z.); (W.W.)
| | - Longxiang Zhang
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (Y.W.); (L.Z.); (W.W.)
| | - Chunming Gu
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA;
| | - Haoru Xue
- The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Wenping Wang
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (Y.W.); (L.Z.); (W.W.)
| | - Qi Lyu
- Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI 48824, USA;
| | - Yujie Dun
- School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China;
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Stuart DD, Guzman-Perez A, Brooijmans N, Jackson EL, Kryukov GV, Friedman AA, Hoos A. Precision Oncology Comes of Age: Designing Best-in-Class Small Molecules by Integrating Two Decades of Advances in Chemistry, Target Biology, and Data Science. Cancer Discov 2023; 13:2131-2149. [PMID: 37712571 PMCID: PMC10551669 DOI: 10.1158/2159-8290.cd-23-0280] [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: 03/06/2023] [Revised: 04/27/2023] [Accepted: 07/28/2023] [Indexed: 09/16/2023]
Abstract
Small-molecule drugs have enabled the practice of precision oncology for genetically defined patient populations since the first approval of imatinib in 2001. Scientific and technology advances over this 20-year period have driven the evolution of cancer biology, medicinal chemistry, and data science. Collectively, these advances provide tools to more consistently design best-in-class small-molecule drugs against known, previously undruggable, and novel cancer targets. The integration of these tools and their customization in the hands of skilled drug hunters will be necessary to enable the discovery of transformational therapies for patients across a wider spectrum of cancers. SIGNIFICANCE Target-centric small-molecule drug discovery necessitates the consideration of multiple approaches to identify chemical matter that can be optimized into drug candidates. To do this successfully and consistently, drug hunters require a comprehensive toolbox to avoid following the "law of instrument" or Maslow's hammer concept where only one tool is applied regardless of the requirements of the task. Combining our ever-increasing understanding of cancer and cancer targets with the technological advances in drug discovery described below will accelerate the next generation of small-molecule drugs in oncology.
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Affiliation(s)
| | | | | | | | | | | | - Axel Hoos
- Scorpion Therapeutics, Boston, Massachusetts
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6
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Liu J, Guo Z, Wu T, Roy RS, Chen C, Cheng J. Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15. Commun Chem 2023; 6:188. [PMID: 37679431 PMCID: PMC10484931 DOI: 10.1038/s42004-023-00991-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/21/2023] [Indexed: 09/09/2023] Open
Abstract
Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure predictions to account for tertiary structural changes induced by protein-protein interaction. The system participated in the tertiary structure prediction in 2022 CASP15 experiment. Our server predictor MULTICOM_refine ranked 3rd among 47 CASP15 server predictors and our human predictor MULTICOM ranked 7th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are ~0.80 and ~0.92, 9.6% and 8.2% higher than ~0.73 and 0.85 of the standard AlphaFold2 predictor respectively.
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Affiliation(s)
- Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Zhiye Guo
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Tianqi Wu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Raj S Roy
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Chen Chen
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
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7
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Wodak SJ, Vajda S, Lensink MF, Kozakov D, Bates PA. Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes. Annu Rev Biophys 2023; 52:183-206. [PMID: 36626764 PMCID: PMC10885158 DOI: 10.1146/annurev-biophys-102622-084607] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.
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Affiliation(s)
- Shoshana J Wodak
- VIB-VUB Center for Structural Biology, Vrije Universiteit Brussel, Brussels, Belgium;
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA;
- Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Marc F Lensink
- Univ. Lille, CNRS, UMR 8576-UGSF-Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France;
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA;
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, United Kingdom;
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8
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Roy RS, Liu J, Giri N, Guo Z, Cheng J. Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.08.531814. [PMID: 36945536 PMCID: PMC10028888 DOI: 10.1101/2023.03.08.531814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter-chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and ranked first out of 24 predictors in estimating the global accuracy of assembly models. The average per-target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per-target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analayzed. The results demonstrate that combining the multi-model method (PSS) with the complementary single-model method (ICPS) is a promising approach to EMA. The source code of MULTICOM_qa is available at https://github.com/BioinfoMachineLearning/MULTICOM_qa .
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Affiliation(s)
- Raj S. Roy
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Nabin Giri
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Zhiye Guo
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
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Khare N, Maheshwari SK, Rizvi SMD, Albadrani HM, Alsagaby SA, Alturaiki W, Iqbal D, Zia Q, Villa C, Jha SK, Jha NK, Jha AK. Homology Modelling, Molecular Docking and Molecular Dynamics Simulation Studies of CALMH1 against Secondary Metabolites of Bauhinia variegata to Treat Alzheimer's Disease. Brain Sci 2022; 12:brainsci12060770. [PMID: 35741655 PMCID: PMC9220886 DOI: 10.3390/brainsci12060770] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/05/2022] [Accepted: 05/31/2022] [Indexed: 02/06/2023] Open
Abstract
Calcium homeostasis modulator 1 (CALHM1) is a protein responsible for causing Alzheimer’s disease. In the absence of an experimentally designed protein molecule, homology modelling was performed. Through homology modelling, different CALHM1 models were generated and validated through Rampage. To carry out further in silico studies, through molecular docking and molecular dynamics simulation experiments, various flavonoids and alkaloids from Bauhinia variegata were utilised as inhibitors to target the protein (CALHM1). The sequence of CALHM1 was retrieved from UniProt and the secondary structure prediction of CALHM1 was done through CFSSP, GOR4, and SOPMA methods. The structure was identified through LOMETS, MUSTER, and MODELLER and finally, the structures were validated through Rampage. Bauhinia variegata plant was used to check the interaction of alkaloids and flavonoids against CALHM1. The protein and protein–ligand complex were also validated through molecular dynamics simulations studies. The model generated through MODELLER software with 6VAM A was used because this model predicted the best results in the Ramachandran plot. Further molecular docking was performed, quercetin was found to be the most appropriate candidate for the protein molecule with the minimum binding energy of −12.45 kcal/mol and their ADME properties were analysed through Molsoft and Molinspiration. Molecular dynamics simulations showed that CALHM1 and CALHM1–quercetin complex became stable at 2500 ps. It may be seen through the study that quercetin may act as a good inhibitor for treatment. With the help of an in silico study, it was easier to analyse the 3D structure of the protein, which may be scrutinized for the best-predicted model. Quercetin may work as a good inhibitor for treating Alzheimer’s disease, according to in silico research using molecular docking and molecular dynamics simulations, and future in vitro and in vivo analysis may confirm its effectiveness.
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Affiliation(s)
- Noopur Khare
- Institute of Biosciences and Technology, Shri Ramswaroop Memorial University, Barabanki 225003, Uttar Pradesh, India; (N.K.); (S.K.M.)
- Department of Biotechnology, Dr. A.P.J. Abdul Kalam Technical University, Lucknow 226021, Uttar Pradesh, India
| | - Sanjiv Kumar Maheshwari
- Institute of Biosciences and Technology, Shri Ramswaroop Memorial University, Barabanki 225003, Uttar Pradesh, India; (N.K.); (S.K.M.)
| | - Syed Mohd Danish Rizvi
- Department of Pharmaceutics, College of Pharmacy, University of Hail, Hail 2240, Saudi Arabia;
| | - Hind Muteb Albadrani
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah 11952, Saudi Arabia; (H.M.A.); (S.A.A.); (W.A.); (Q.Z.)
| | - Suliman A. Alsagaby
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah 11952, Saudi Arabia; (H.M.A.); (S.A.A.); (W.A.); (Q.Z.)
| | - Wael Alturaiki
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah 11952, Saudi Arabia; (H.M.A.); (S.A.A.); (W.A.); (Q.Z.)
| | - Danish Iqbal
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah 11952, Saudi Arabia; (H.M.A.); (S.A.A.); (W.A.); (Q.Z.)
- Health and Basic Sciences Research Center, Majmaah University, Al Majmaah 15341, Saudi Arabia
- Correspondence: (D.I.); (A.K.J.)
| | - Qamar Zia
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah 11952, Saudi Arabia; (H.M.A.); (S.A.A.); (W.A.); (Q.Z.)
- Health and Basic Sciences Research Center, Majmaah University, Al Majmaah 15341, Saudi Arabia
| | - Chiara Villa
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy;
| | - Saurabh Kumar Jha
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida 201310, Uttar Pradesh, India; (S.K.J.); (N.K.J.)
- Department of Biotechnology, School of Applied & Life Sciences (SALS), Uttaranchal University, Dehradun 248007, Uttarakhand, India
- Department of Biotechnology Engineering and Food Technology, Chandigarh University, Mohali 140413, Punjab, India
| | - Niraj Kumar Jha
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida 201310, Uttar Pradesh, India; (S.K.J.); (N.K.J.)
- Department of Biotechnology, School of Applied & Life Sciences (SALS), Uttaranchal University, Dehradun 248007, Uttarakhand, India
- Department of Biotechnology Engineering and Food Technology, Chandigarh University, Mohali 140413, Punjab, India
| | - Abhimanyu Kumar Jha
- Department of Biotechnology, School of Engineering and Technology, Sharda University, Greater Noida 201310, Uttar Pradesh, India; (S.K.J.); (N.K.J.)
- Correspondence: (D.I.); (A.K.J.)
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10
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Hou Q, Pucci F, Pan F, Xue F, Rooman M, Feng Q. Using metagenomic data to boost protein structure prediction and discovery. Comput Struct Biotechnol J 2022; 20:434-442. [PMID: 35070166 PMCID: PMC8760478 DOI: 10.1016/j.csbj.2021.12.030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/17/2021] [Accepted: 12/21/2021] [Indexed: 11/19/2022] Open
Abstract
Over the past decade, metagenomic sequencing approaches have been providing an ever-increasing amount of protein sequence data at an astonishing rate. These constitute an invaluable source of information which has been exploited in various research fields such as the study of the role of the gut microbiota in human diseases and aging. However, only a small fraction of all metagenomic sequences collected have been functionally or structurally characterized, leaving much of them completely unexplored. Here, we review how this information has been used in protein structure prediction and protein discovery. We begin by presenting some widely used metagenomic databases and analyze in detail how metagenomic data has contributed to the impressive improvement in the accuracy of structure prediction methods in recent years. We then examine how metagenomic information can be exploited to annotate protein sequences. More specifically, we focus on the role of metagenomes in the discovery of enzymes and new CRISPR-Cas systems, and in the identification of antibiotic resistance genes. With this review, we provide an overview of how metagenomic data is currently revolutionizing our understanding of protein science.
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Affiliation(s)
- Qingzhen Hou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong 250012, China
- National Institute of Health Data Science of China, Shandong University, Shandong 250002, China
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Fengming Pan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong 250012, China
- National Institute of Health Data Science of China, Shandong University, Shandong 250002, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong 250012, China
- National Institute of Health Data Science of China, Shandong University, Shandong 250002, China
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Qiang Feng
- Shandong Provincial Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Department of Human Microbiome, School of Stomatology, Shandong University, Jinan, Shandong Province 250012, China
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong Province 266237, China
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11
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Alshammari M, He J. Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies. Molecules 2021; 26:7049. [PMID: 34834140 PMCID: PMC8624718 DOI: 10.3390/molecules26227049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 11/01/2021] [Accepted: 11/15/2021] [Indexed: 11/23/2022] Open
Abstract
Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set of sequence segments in 1D, a set of amino acid contact pairs in 2D, and a set of traces in 3D at the secondary structure level. A test of fourteen cases shows that the accuracy of predicted secondary structures is critical for deriving topologies. The use of significant long-range contact pairs is most effective at enriching the rank of the maximum-match topology for proteins with a large number of secondary structures, if the secondary structure prediction is fairly accurate. It was observed that the enrichment depends on the quality of initial topology candidates in this approach. We provide detailed analysis in various cases to show the potential and challenge when combining three sources of information.
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Affiliation(s)
| | - Jing He
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA;
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Soltanikazemi E, Quadir F, Roy RS, Guo Z, Cheng J. Distance-based reconstruction of protein quaternary structures from inter-chain contacts. Proteins 2021; 90:720-731. [PMID: 34716620 PMCID: PMC8816881 DOI: 10.1002/prot.26269] [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: 07/16/2021] [Revised: 09/25/2021] [Accepted: 10/12/2021] [Indexed: 12/21/2022]
Abstract
Predicting the quaternary structure of protein complex is an important problem. Inter‐chain residue‐residue contact prediction can provide useful information to guide the ab initio reconstruction of quaternary structures. However, few methods have been developed to build quaternary structures from predicted inter‐chain contacts. Here, we develop the first method based on gradient descent optimization (GD) to build quaternary structures of protein dimers utilizing inter‐chain contacts as distance restraints. We evaluate GD on several datasets of homodimers and heterodimers using true/predicted contacts and monomer structures as input. GD consistently performs better than both simulated annealing and Markov Chain Monte Carlo simulation. Starting from an arbitrarily quaternary structure randomly initialized from the tertiary structures of protein chains and using true inter‐chain contacts as input, GD can reconstruct high‐quality structural models for homodimers and heterodimers with average TM‐score ranging from 0.92 to 0.99 and average interface root mean square distance from 0.72 Å to 1.64 Å. On a dataset of 115 homodimers, using predicted inter‐chain contacts as restraints, the average TM‐score of the structural models built by GD is 0.76. For 46% of the homodimers, high‐quality structural models with TM‐score ≥ 0.9 are reconstructed from predicted contacts. There is a strong correlation between the quality of the reconstructed models and the precision and recall of predicted contacts. Only a moderate precision or recall of inter‐chain contact prediction is needed to build good structural models for most homodimers. Moreover, GD improves the quality of quaternary structures predicted by AlphaFold2 on a Critical Assessment of Techniques for Protein Structure Prediction–Critical Assessments of Predictions of Interactions dataset.
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Affiliation(s)
- Elham Soltanikazemi
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Farhan Quadir
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Raj S Roy
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Zhiye Guo
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
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Skolnick J, Gao M, Zhou H, Singh S. AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function. J Chem Inf Model 2021; 61:4827-4831. [PMID: 34586808 DOI: 10.1021/acs.jcim.1c01114] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
AlphaFold 2 (AF2) was the star of CASP14, the last biannual structure prediction experiment. Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or near experimental resolution. Here, we present our perspective of why AF2 works and show that it is a very sophisticated fold recognition algorithm that exploits the completeness of the library of single domain PDB structures. It has also learned local side chain packing rearrangements that enable it to refine proteins to high resolution. The benefits and limitations of its ability to predict the structures of many more proteins at or close to atomic detail are discussed.
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Affiliation(s)
- Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Mu Gao
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Suresh Singh
- Twilight Design, 4 Adams Road, Kendall Park, New Jersey 08824, United States
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